Bump to 4.1.0

4.1.0

.bowerrc

@@ -1,3 +0,0 @@
-{
-  "directory": "share/jupyter/hub/static/components"
-}

.coveragerc

@@ -0,0 +1,18 @@
+[run]
+parallel = True
+branch = False
+omit =
+    jupyterhub/tests/*
+    jupyterhub/alembic/*
+
+[report]
+exclude_lines =
+    if self.debug:
+    pragma: no cover
+    raise NotImplementedError
+    if __name__ == .__main__.:
+ignore_errors = True
+omit =
+    jupyterhub/tests/*
+    jupyterhub/alembic/*
+    */site-packages/*

.dockerignore

@@ -3,3 +3,8 @@ bench
 jupyterhub_cookie_secret
 jupyterhub.sqlite
 jupyterhub_config.py
+node_modules
+docs
+.git
+dist
+build

.flake8

@@ -0,0 +1,19 @@
+[flake8]
+# Ignore style and complexity
+# E: style errors
+# W: style warnings
+# C: complexity
+# D: docstring warnings (unused pydocstyle extension)
+# F841: local variable assigned but never used
+ignore = E, C, W, D, F841
+builtins = c, get_config
+exclude =
+    .cache,
+    .github,
+    docs,
+    jupyterhub/alembic*,
+    onbuild,
+    scripts,
+    share,
+    tools,
+    setup.py

.github/dependabot.yaml

@@ -0,0 +1,16 @@
+# dependabot.yaml reference: https://docs.github.com/en/code-security/dependabot/dependabot-version-updates/configuration-options-for-the-dependabot.yml-file
+#
+# Notes:
+# - Status and logs from dependabot are provided at
+#   https://github.com/jupyterhub/jupyterhub/network/updates.
+#
+version: 2
+updates:
+  # Maintain dependencies in our GitHub Workflows
+  - package-ecosystem: github-actions
+    directory: /
+    labels: [ci]
+    schedule:
+      interval: monthly
+      time: "05:00"
+      timezone: Etc/UTC

.github/workflows/release.yml

@@ -0,0 +1,230 @@
+# This is a GitHub workflow defining a set of jobs with a set of steps.
+# ref: https://docs.github.com/en/actions/learn-github-actions/workflow-syntax-for-github-actions
+#
+# Test build release artifacts (PyPI package, Docker images) and publish them on
+# pushed git tags.
+#
+name: Release
+
+on:
+  pull_request:
+    paths-ignore:
+      - "docs/**"
+      - "**.md"
+      - "**.rst"
+      - ".github/workflows/*"
+      - "!.github/workflows/release.yml"
+  push:
+    paths-ignore:
+      - "docs/**"
+      - "**.md"
+      - "**.rst"
+      - ".github/workflows/*"
+      - "!.github/workflows/release.yml"
+    branches-ignore:
+      - "dependabot/**"
+      - "pre-commit-ci-update-config"
+    tags:
+      - "**"
+  workflow_dispatch:
+
+jobs:
+  build-release:
+    runs-on: ubuntu-22.04
+    steps:
+      - uses: actions/checkout@v4
+      - uses: actions/setup-python@v5
+        with:
+          python-version: "3.11"
+
+      - uses: actions/setup-node@v4
+        with:
+          node-version: "20"
+
+      - name: install build requirements
+        run: |
+          npm install -g yarn
+          pip install --upgrade pip
+          pip install build
+          pip freeze
+
+      - name: build release
+        run: |
+          python -m build --sdist --wheel .
+          ls -l dist
+
+      - name: verify sdist
+        run: |
+          ./ci/check_sdist.py dist/jupyterhub-*.tar.gz
+
+      - name: verify data-files are installed where they are found
+        run: |
+          pip install dist/*.whl
+          ./ci/check_installed_data.py
+
+      - name: verify sdist can be installed without npm/yarn
+        run: |
+          docker run --rm -v $PWD/dist:/dist:ro docker.io/library/python:3.9-slim-bullseye bash -c 'pip install /dist/jupyterhub-*.tar.gz'
+
+      # ref: https://github.com/actions/upload-artifact#readme
+      - uses: actions/upload-artifact@v4
+        with:
+          name: jupyterhub-${{ github.sha }}
+          path: "dist/*"
+          if-no-files-found: error
+
+      - name: Publish to PyPI
+        if: startsWith(github.ref, 'refs/tags/')
+        env:
+          TWINE_USERNAME: __token__
+          TWINE_PASSWORD: ${{ secrets.PYPI_PASSWORD }}
+        run: |
+          pip install twine
+          twine upload --skip-existing dist/*
+
+  publish-docker:
+    runs-on: ubuntu-22.04
+    timeout-minutes: 20
+
+    services:
+      # So that we can test this in PRs/branches
+      local-registry:
+        image: registry:2
+        ports:
+          - 5000:5000
+
+    steps:
+      - name: Should we push this image to a public registry?
+        run: |
+          if [ "${{ startsWith(github.ref, 'refs/tags/') || (github.ref == 'refs/heads/main') }}" = "true" ]; then
+              echo "REGISTRY=quay.io/" >> $GITHUB_ENV
+          else
+              echo "REGISTRY=localhost:5000/" >> $GITHUB_ENV
+          fi
+
+      - uses: actions/checkout@v4
+
+      # Setup docker to build for multiple platforms, see:
+      # https://github.com/docker/build-push-action/tree/v2.4.0#usage
+      # https://github.com/docker/build-push-action/blob/v2.4.0/docs/advanced/multi-platform.md
+      - name: Set up QEMU (for docker buildx)
+        uses: docker/setup-qemu-action@v3
+
+      - name: Set up Docker Buildx (for multi-arch builds)
+        uses: docker/setup-buildx-action@v3
+        with:
+          # Allows pushing to registry on localhost:5000
+          driver-opts: network=host
+
+      - name: Setup push rights to Docker Hub
+        # This was setup by...
+        # 1. Creating a [Robot Account](https://quay.io/organization/jupyterhub?tab=robots) in the JupyterHub
+        # .  Quay.io org
+        # 2. Giving it enough permissions to push to the jupyterhub and singleuser images
+        # 3. Putting the robot account's username and password in GitHub actions environment
+        if: env.REGISTRY != 'localhost:5000/'
+        run: |
+          docker login -u "${{ secrets.QUAY_USERNAME }}" -p "${{ secrets.QUAY_PASSWORD }}" "${{ env.REGISTRY }}"
+          docker login -u "${{ secrets.DOCKERHUB_USERNAME }}" -p "${{ secrets.DOCKERHUB_TOKEN }}" docker.io
+
+      # image: jupyterhub/jupyterhub
+      #
+      # https://github.com/jupyterhub/action-major-minor-tag-calculator
+      # If this is a tagged build this will return additional parent tags.
+      # E.g. 1.2.3 is expanded to Docker tags
+      # [{prefix}:1.2.3, {prefix}:1.2, {prefix}:1, {prefix}:latest] unless
+      # this is a backported tag in which case the newer tags aren't updated.
+      # For branches this will return the branch name.
+      # If GITHUB_TOKEN isn't available (e.g. in PRs) returns no tags [].
+      - name: Get list of jupyterhub tags
+        id: jupyterhubtags
+        uses: jupyterhub/action-major-minor-tag-calculator@v3
+        with:
+          githubToken: ${{ secrets.GITHUB_TOKEN }}
+          prefix: >-
+            ${{ env.REGISTRY }}jupyterhub/jupyterhub:
+            jupyterhub/jupyterhub:
+          defaultTag: "${{ env.REGISTRY }}jupyterhub/jupyterhub:noref"
+          branchRegex: ^\w[\w-.]*$
+
+      - name: Build and push jupyterhub
+        uses: docker/build-push-action@v5
+        with:
+          context: .
+          platforms: linux/amd64,linux/arm64
+          push: true
+          # tags parameter must be a string input so convert `gettags` JSON
+          # array into a comma separated list of tags
+          tags: ${{ join(fromJson(steps.jupyterhubtags.outputs.tags)) }}
+
+      # image: jupyterhub/jupyterhub-onbuild
+      #
+      - name: Get list of jupyterhub-onbuild tags
+        id: onbuildtags
+        uses: jupyterhub/action-major-minor-tag-calculator@v3
+        with:
+          githubToken: ${{ secrets.GITHUB_TOKEN }}
+          prefix: >-
+            ${{ env.REGISTRY }}jupyterhub/jupyterhub-onbuild:
+            jupyterhub/jupyterhub-onbuild:
+          defaultTag: "${{ env.REGISTRY }}jupyterhub/jupyterhub-onbuild:noref"
+          branchRegex: ^\w[\w-.]*$
+
+      - name: Build and push jupyterhub-onbuild
+        uses: docker/build-push-action@v5
+        with:
+          build-args: |
+            BASE_IMAGE=${{ fromJson(steps.jupyterhubtags.outputs.tags)[0] }}
+          context: onbuild
+          platforms: linux/amd64,linux/arm64
+          push: true
+          tags: ${{ join(fromJson(steps.onbuildtags.outputs.tags)) }}
+
+      # image: jupyterhub/jupyterhub-demo
+      #
+      - name: Get list of jupyterhub-demo tags
+        id: demotags
+        uses: jupyterhub/action-major-minor-tag-calculator@v3
+        with:
+          githubToken: ${{ secrets.GITHUB_TOKEN }}
+          prefix: >-
+            ${{ env.REGISTRY }}jupyterhub/jupyterhub-demo:
+            jupyterhub/jupyterhub-demo:
+          defaultTag: "${{ env.REGISTRY }}jupyterhub/jupyterhub-demo:noref"
+          branchRegex: ^\w[\w-.]*$
+
+      - name: Build and push jupyterhub-demo
+        uses: docker/build-push-action@v5
+        with:
+          build-args: |
+            BASE_IMAGE=${{ fromJson(steps.onbuildtags.outputs.tags)[0] }}
+          context: demo-image
+          # linux/arm64 currently fails:
+          # ERROR: Could not build wheels for argon2-cffi which use PEP 517 and cannot be installed directly
+          # ERROR: executor failed running [/bin/sh -c python3 -m pip install notebook]: exit code: 1
+          platforms: linux/amd64
+          push: true
+          tags: ${{ join(fromJson(steps.demotags.outputs.tags)) }}
+
+      # image: jupyterhub/singleuser
+      #
+      - name: Get list of jupyterhub/singleuser tags
+        id: singleusertags
+        uses: jupyterhub/action-major-minor-tag-calculator@v3
+        with:
+          githubToken: ${{ secrets.GITHUB_TOKEN }}
+          prefix: >-
+            ${{ env.REGISTRY }}jupyterhub/singleuser:
+            jupyterhub/singleuser:
+          defaultTag: "${{ env.REGISTRY }}jupyterhub/singleuser:noref"
+          branchRegex: ^\w[\w-.]*$
+
+      - name: Build and push jupyterhub/singleuser
+        uses: docker/build-push-action@v5
+        with:
+          build-args: |
+            JUPYTERHUB_VERSION=${{ github.ref_type == 'tag' && github.ref_name || format('git:{0}', github.sha) }}
+          context: singleuser
+          platforms: linux/amd64,linux/arm64
+          push: true
+          tags: ${{ join(fromJson(steps.singleusertags.outputs.tags)) }}

.github/workflows/support-bot.yml

@@ -0,0 +1,31 @@
+# https://github.com/dessant/support-requests
+name: "Support Requests"
+
+on:
+  issues:
+    types: [labeled, unlabeled, reopened]
+
+permissions:
+  issues: write
+
+jobs:
+  action:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: dessant/support-requests@v3
+        with:
+          github-token: ${{ github.token }}
+          support-label: "support"
+          issue-comment: |
+            Hi there @{issue-author} :wave:!
+
+            I closed this issue because it was labelled as a support question.
+
+            Please help us organize discussion by posting this on the http://discourse.jupyter.org/ forum.
+
+            Our goal is to sustain a positive experience for both users and developers. We use GitHub issues for specific discussions related to changing a repository's content, and let the forum be where we can more generally help and inspire each other.
+
+            Thank you for being an active member of our community! :heart:
+          close-issue: true
+          lock-issue: false
+          issue-lock-reason: "off-topic"

.github/workflows/test-docs.yml

@@ -0,0 +1,107 @@
+# This is a GitHub workflow defining a set of jobs with a set of steps.
+# ref: https://docs.github.com/en/actions/learn-github-actions/workflow-syntax-for-github-actions
+#
+# This workflow validates the REST API definition and runs the pytest tests in
+# the docs/ folder. This workflow does not build the documentation. That is
+# instead tested via ReadTheDocs (https://readthedocs.org/projects/jupyterhub/).
+#
+name: Test docs
+
+# The tests defined in docs/ are currently influenced by changes to _version.py
+# and scopes.py.
+on:
+  pull_request:
+    paths:
+      - "docs/**"
+      - "jupyterhub/_version.py"
+      - "jupyterhub/scopes.py"
+      - ".github/workflows/test-docs.yml"
+  push:
+    paths:
+      - "docs/**"
+      - "jupyterhub/_version.py"
+      - "jupyterhub/scopes.py"
+      - ".github/workflows/test-docs.yml"
+    branches-ignore:
+      - "dependabot/**"
+      - "pre-commit-ci-update-config"
+    tags:
+      - "**"
+  workflow_dispatch:
+
+env:
+  # UTF-8 content may be interpreted as ascii and causes errors without this.
+  LANG: C.UTF-8
+  PYTEST_ADDOPTS: "--verbose --color=yes"
+
+jobs:
+  validate-rest-api-definition:
+    runs-on: ubuntu-20.04
+    steps:
+      - uses: actions/checkout@v3
+
+      - name: Validate REST API definition
+        uses: char0n/swagger-editor-validate@v1.3.2
+        with:
+          definition-file: docs/source/_static/rest-api.yml
+
+  test-docs:
+    runs-on: ubuntu-20.04
+    steps:
+      - uses: actions/checkout@v3
+        with:
+          # make rediraffecheckdiff requires git history to compare current
+          # commit with the main branch and previous releases.
+          fetch-depth: 0
+
+      - uses: actions/setup-python@v4
+        with:
+          python-version: "3.9"
+
+      - name: Install requirements
+        run: |
+          pip install -r docs/requirements.txt pytest
+
+      - name: pytest docs/
+        run: |
+          pytest docs/
+
+      # readthedocs doesn't halt on warnings,
+      # so raise any warnings here
+      - name: build docs
+        run: |
+          cd docs
+          make html
+
+      - name: check links
+        run: |
+          cd docs
+          make linkcheck
+
+      # make rediraffecheckdiff compares files for different changesets
+      # these diff targets aren't always available
+      # - compare with base ref (usually 'main', always on 'origin') for pull requests
+      # - only compare with tags when running against jupyterhub/jupyterhub
+      #   to avoid errors on forks, which often lack tags
+      - name: check redirects for this PR
+        if: github.event_name == 'pull_request'
+        run: |
+          cd docs
+          export REDIRAFFE_BRANCH=origin/${{ github.base_ref }}
+          make rediraffecheckdiff
+
+      # this should check currently published 'stable' links for redirects
+      - name: check redirects since last release
+        if: github.repository == 'jupyterhub/jupyterhub'
+        run: |
+          cd docs
+          export REDIRAFFE_BRANCH=$(git describe --tags --abbrev=0)
+          make rediraffecheckdiff
+
+      # longer-term redirect check (fixed version) for older links
+      - name: check redirects since 3.0.0
+        if: github.repository == 'jupyterhub/jupyterhub'
+        run: |
+          cd docs
+          export REDIRAFFE_BRANCH=3.0.0
+          make rediraffecheckdiff

.github/workflows/test-jsx.yml

@@ -0,0 +1,52 @@
+# This is a GitHub workflow defining a set of jobs with a set of steps.
+# ref: https://docs.github.com/en/actions/learn-github-actions/workflow-syntax-for-github-actions
+#
+name: Test jsx (admin-react.js)
+
+on:
+  pull_request:
+    paths:
+      - "jsx/**"
+      - ".github/workflows/test-jsx.yml"
+  push:
+    paths:
+      - "jsx/**"
+      - ".github/workflows/test-jsx.yml"
+    branches-ignore:
+      - "dependabot/**"
+      - "pre-commit-ci-update-config"
+    tags:
+      - "**"
+  workflow_dispatch:
+
+permissions:
+  contents: read
+
+jobs:
+  # The ./jsx folder contains React based source code files that are to compile
+  # to share/jupyterhub/static/js/admin-react.js. The ./jsx folder includes
+  # tests also has tests that this job is meant to run with `yarn test`
+  # according to the documentation in jsx/README.md.
+  test-jsx-admin-react:
+    runs-on: ubuntu-20.04
+    timeout-minutes: 5
+
+    steps:
+      - uses: actions/checkout@v3
+      - uses: actions/setup-node@v3
+        with:
+          node-version: "14"
+
+      - name: Install yarn
+        run: |
+          npm install -g yarn
+
+      - name: yarn
+        run: |
+          cd jsx
+          yarn
+
+      - name: yarn test
+        run: |
+          cd jsx
+          yarn test

.github/workflows/test.yml

@@ -0,0 +1,268 @@
+# This is a GitHub workflow defining a set of jobs with a set of steps.
+# ref: https://docs.github.com/en/actions/learn-github-actions/workflow-syntax-for-github-actions
+#
+name: Test
+
+on:
+  pull_request:
+    paths-ignore:
+      - "docs/**"
+      - "**.md"
+      - "**.rst"
+      - ".github/workflows/*"
+      - "!.github/workflows/test.yml"
+  push:
+    paths-ignore:
+      - "docs/**"
+      - "**.md"
+      - "**.rst"
+      - ".github/workflows/*"
+      - "!.github/workflows/test.yml"
+    branches-ignore:
+      - "dependabot/**"
+      - "pre-commit-ci-update-config"
+    tags:
+      - "**"
+  workflow_dispatch:
+
+env:
+  # UTF-8 content may be interpreted as ascii and causes errors without this.
+  LANG: C.UTF-8
+  SQLALCHEMY_WARN_20: "1"
+
+permissions:
+  contents: read
+
+jobs:
+  # Run "pytest jupyterhub/tests" in various configurations
+  pytest:
+    runs-on: ubuntu-20.04
+    timeout-minutes: 15
+
+    strategy:
+      # Keep running even if one variation of the job fail
+      fail-fast: false
+      matrix:
+        # We run this job multiple times with different parameterization
+        # specified below, these parameters have no meaning on their own and
+        # gain meaning on how job steps use them.
+        #
+        # subdomain:
+        #   Tests everything when JupyterHub is configured to add routes for
+        #   users with dedicated subdomains like user1.jupyter.example.com
+        #   rather than jupyter.example.com/user/user1.
+        #
+        # db: [mysql/postgres]
+        #   Tests everything when JupyterHub works against a dedicated mysql or
+        #   postgresql server.
+        #
+        # legacy_notebook:
+        #   Tests everything when the user instances are started with
+        #   the legacy notebook server instead of jupyter_server.
+        #
+        # ssl:
+        #   Tests everything using internal SSL connections instead of
+        #   unencrypted HTTP
+        #
+        # main_dependencies:
+        #   Tests everything when the we use the latest available dependencies
+        #   from: traitlets.
+        #
+        # NOTE: Since only the value of these parameters are presented in the
+        #       GitHub UI when the workflow run, we avoid using true/false as
+        #       values by instead duplicating the name to signal true.
+        # Python versions available at:
+        #   https://github.com/actions/python-versions/blob/HEAD/versions-manifest.json
+        include:
+          - python: "3.7"
+            oldest_dependencies: oldest_dependencies
+            legacy_notebook: legacy_notebook
+          - python: "3.8"
+            jupyter_server: "1.*"
+            subset: singleuser
+          - python: "3.9"
+            db: mysql
+          - python: "3.10"
+            db: postgres
+          - python: "3.11"
+            subdomain: subdomain
+            serverextension: serverextension
+          - python: "3.11"
+            ssl: ssl
+            serverextension: serverextension
+          - python: "3.11"
+            subdomain: subdomain
+            noextension: noextension
+            subset: singleuser
+          - python: "3.11"
+            ssl: ssl
+            noextension: noextension
+            subset: singleuser
+          - python: "3.11"
+            browser: browser
+          - python: "3.11"
+            subdomain: subdomain
+            browser: browser
+          - python: "3.11"
+            main_dependencies: main_dependencies
+
+    steps:
+      # NOTE: In GitHub workflows, environment variables are set by writing
+      #       assignment statements to a file. They will be set in the following
+      #       steps as if would used `export MY_ENV=my-value`.
+      - name: Configure environment variables
+        run: |
+          if [ "${{ matrix.subdomain }}" != "" ]; then
+              echo "JUPYTERHUB_TEST_SUBDOMAIN_HOST=http://localhost.jovyan.org:8000" >> $GITHUB_ENV
+          fi
+          if [ "${{ matrix.db }}" == "mysql" ]; then
+              echo "MYSQL_HOST=127.0.0.1" >> $GITHUB_ENV
+              echo "JUPYTERHUB_TEST_DB_URL=mysql+mysqldb://root@127.0.0.1:3306/jupyterhub" >> $GITHUB_ENV
+          fi
+          if [ "${{ matrix.ssl }}" == "ssl" ]; then
+              echo "SSL_ENABLED=1" >> $GITHUB_ENV
+          fi
+          if [ "${{ matrix.db }}" == "postgres" ]; then
+              echo "PGHOST=127.0.0.1" >> $GITHUB_ENV
+              echo "PGUSER=test_user" >> $GITHUB_ENV
+              echo "PGPASSWORD=hub[test/:?" >> $GITHUB_ENV
+              echo "JUPYTERHUB_TEST_DB_URL=postgresql://test_user:hub%5Btest%2F%3A%3F@127.0.0.1:5432/jupyterhub" >> $GITHUB_ENV
+          fi
+          if [ "${{ matrix.serverextension }}" != "" ]; then
+              echo "JUPYTERHUB_SINGLEUSER_EXTENSION=1" >> $GITHUB_ENV
+          elif [ "${{ matrix.noextension }}" != "" ]; then
+              echo "JUPYTERHUB_SINGLEUSER_EXTENSION=0" >> $GITHUB_ENV
+          fi
+      - uses: actions/checkout@v3
+      # NOTE: actions/setup-node@v3 make use of a cache within the GitHub base
+      #       environment and setup in a fraction of a second.
+      - name: Install Node v14
+        uses: actions/setup-node@v3
+        with:
+          node-version: "14"
+      - name: Install Javascript dependencies
+        run: |
+          npm install
+          npm install -g configurable-http-proxy yarn
+          npm list
+
+      # NOTE: actions/setup-python@v4 make use of a cache within the GitHub base
+      #       environment and setup in a fraction of a second.
+      - name: Install Python ${{ matrix.python }}
+        uses: actions/setup-python@v4
+        with:
+          python-version: "${{ matrix.python }}"
+
+      - name: Install Python dependencies
+        run: |
+          pip install --upgrade pip
+          pip install -e ".[test]"
+
+          if [ "${{ matrix.oldest_dependencies }}" != "" ]; then
+            # take any dependencies in requirements.txt such as tornado>=5.0
+            # and transform them to tornado==5.0 so we can run tests with
+            # the earliest-supported versions
+            cat requirements.txt | grep '>=' | sed -e 's@>=@==@g' > oldest-requirements.txt
+            pip install -r oldest-requirements.txt
+          fi
+
+          if [ "${{ matrix.main_dependencies }}" != "" ]; then
+              # Tests are broken:
+              # https://github.com/jupyterhub/jupyterhub/issues/4418
+              # pip install git+https://github.com/ipython/traitlets#egg=traitlets --force
+              pip install --upgrade --pre sqlalchemy
+          fi
+          if [ "${{ matrix.legacy_notebook }}" != "" ]; then
+              pip uninstall jupyter_server --yes
+              pip install 'notebook<7'
+          fi
+          if [ "${{ matrix.jupyter_server }}" != "" ]; then
+              pip install "jupyter_server==${{ matrix.jupyter_server }}"
+          fi
+          if [ "${{ matrix.db }}" == "mysql" ]; then
+              pip install mysqlclient
+          fi
+          if [ "${{ matrix.db }}" == "postgres" ]; then
+              pip install psycopg2-binary
+          fi
+          if [ "${{ matrix.serverextension }}" != "" ]; then
+            pip install 'jupyter-server>=2'
+          fi
+
+          pip freeze
+
+      # NOTE: If you need to debug this DB setup step, consider the following.
+      #
+      # 1. mysql/postgressql are database servers we start as docker containers,
+      #    and we use clients named mysql/psql.
+      #
+      # 2. When we start a database server we need to pass environment variables
+      #    explicitly as part of the `docker run` command. These environment
+      #    variables are named differently from the similarly named environment
+      #    variables used by the clients.
+      #
+      #    - mysql    server ref: https://hub.docker.com/_/mysql/
+      #    - mysql    client ref: https://dev.mysql.com/doc/refman/5.7/en/environment-variables.html
+      #    - postgres server ref: https://hub.docker.com/_/postgres/
+      #    - psql     client ref: https://www.postgresql.org/docs/9.5/libpq-envars.html
+      #
+      # 3. When we connect, they should use 127.0.0.1 rather than the
+      #    default way of connecting which leads to errors like below both for
+      #    mysql and postgresql unless we set MYSQL_HOST/PGHOST to 127.0.0.1.
+      #
+      #    - ERROR 2002 (HY000): Can't connect to local MySQL server through socket '/var/run/mysqld/mysqld.sock' (2)
+      #
+      - name: Start a database server (${{ matrix.db }})
+        if: ${{ matrix.db }}
+        run: |
+          if [ "${{ matrix.db }}" == "mysql" ]; then
+            if [[ -z "$(which mysql)" ]]; then
+              sudo apt-get update
+              sudo apt-get install -y mysql-client
+            fi
+              DB=mysql bash ci/docker-db.sh
+              DB=mysql bash ci/init-db.sh
+          fi
+          if [ "${{ matrix.db }}" == "postgres" ]; then
+            if [[ -z "$(which psql)" ]]; then
+              sudo apt-get update
+              sudo apt-get install -y postgresql-client
+            fi
+              DB=postgres bash ci/docker-db.sh
+              DB=postgres bash ci/init-db.sh
+          fi
+
+      - name: Configure browser tests
+        if: matrix.browser
+        run: echo "PYTEST_ADDOPTS=$PYTEST_ADDOPTS -m browser" >> "${GITHUB_ENV}"
+
+      - name: Ensure browsers are installed for playwright
+        if: matrix.browser
+        run: python -m playwright install --with-deps
+
+      - name: Run pytest
+        run: |
+          pytest -k "${{ matrix.subset }}" --maxfail=2 --cov=jupyterhub jupyterhub/tests
+
+      - uses: codecov/codecov-action@v3
+
+  docker-build:
+    runs-on: ubuntu-20.04
+    timeout-minutes: 20
+
+    steps:
+      - uses: actions/checkout@v3
+
+      - name: build images
+        run: |
+          DOCKER_BUILDKIT=1 docker build -t jupyterhub/jupyterhub .
+          docker build -t jupyterhub/jupyterhub-onbuild onbuild
+          docker build -t jupyterhub/singleuser singleuser
+
+      - name: smoke test jupyterhub
+        run: |
+          docker run --rm -t jupyterhub/jupyterhub jupyterhub --help
+
+      - name: verify static files
+        run: |
+          docker run --rm -t -v $PWD/dockerfiles:/io jupyterhub/jupyterhub python3 /io/test.py

.gitignore

@@ -1,16 +1,39 @@
 node_modules
 *.py[co]
 *~
+.cache
 .DS_Store
-build
+/build
 dist
+docs/_build
+docs/build
+docs/source/_static/rest-api
+docs/source/rbac/scope-table.md
+docs/source/reference/metrics.md
+
+.ipynb_checkpoints
+jsx/build/
 # ignore config file at the top-level of the repo
 # but not sub-dirs
 /jupyterhub_config.py
 jupyterhub_cookie_secret
 jupyterhub.sqlite
-share/jupyter/hub/static/components
-share/jupyter/hub/static/css/style.min.css
-share/jupyter/hub/static/css/style.min.css.map
+jupyterhub.sqlite*
+package-lock.json
+share/jupyterhub/static/components
+share/jupyterhub/static/css/style.min.css
+share/jupyterhub/static/css/style.min.css.map
+share/jupyterhub/static/js/admin-react.js*
 *.egg-info
 MANIFEST
+.coverage
+.coverage.*
+htmlcov
+.idea/
+.vscode/
+.pytest_cache
+pip-wheel-metadata
+docs/source/reference/metrics.rst
+oldest-requirements.txt
+jupyterhub-proxy.pid
+examples/server-api/service-token

.pre-commit-config.yaml

@@ -0,0 +1,66 @@
+# pre-commit is a tool to perform a predefined set of tasks manually and/or
+# automatically before git commits are made.
+#
+# Config reference: https://pre-commit.com/#pre-commit-configyaml---top-level
+#
+# Common tasks
+#
+# - Run on all files:   pre-commit run --all-files
+# - Register git hooks: pre-commit install --install-hooks
+#
+
+ci:
+  # pre-commit.ci will open PRs updating our hooks once a month
+  autoupdate_schedule: monthly
+
+repos:
+  # Autoformat: Python code, syntax patterns are modernized
+  - repo: https://github.com/asottile/pyupgrade
+    rev: v3.15.0
+    hooks:
+      - id: pyupgrade
+        args:
+          - --py37-plus
+
+  # Autoformat: Python code
+  - repo: https://github.com/PyCQA/autoflake
+    rev: v2.2.1
+    hooks:
+      - id: autoflake
+        # args ref: https://github.com/PyCQA/autoflake#advanced-usage
+        args:
+          - --in-place
+
+  # Autoformat: Python code
+  - repo: https://github.com/pycqa/isort
+    rev: 5.13.2
+    hooks:
+      - id: isort
+
+  # Autoformat: Python code
+  - repo: https://github.com/psf/black
+    rev: 24.1.1
+    hooks:
+      - id: black
+
+  # Autoformat: markdown, yaml, javascript (see the file .prettierignore)
+  - repo: https://github.com/pre-commit/mirrors-prettier
+    rev: v4.0.0-alpha.8
+    hooks:
+      - id: prettier
+
+  # Autoformat and linting, misc. details
+  - repo: https://github.com/pre-commit/pre-commit-hooks
+    rev: v4.5.0
+    hooks:
+      - id: end-of-file-fixer
+        exclude: share/jupyterhub/static/js/admin-react.js
+      - id: requirements-txt-fixer
+      - id: check-case-conflict
+      - id: check-executables-have-shebangs
+
+  # Linting: Python code (see the file .flake8)
+  - repo: https://github.com/PyCQA/flake8
+    rev: "7.0.0"
+    hooks:
+      - id: flake8

.prettierignore

@@ -0,0 +1,3 @@
+share/jupyterhub/templates/
+share/jupyterhub/static/js/admin-react.js
+jupyterhub/singleuser/templates/

.readthedocs.yaml

@@ -0,0 +1,25 @@
+# Configuration on how ReadTheDocs (RTD) builds our documentation
+# ref: https://readthedocs.org/projects/jupyterhub/
+# ref: https://docs.readthedocs.io/en/stable/config-file/v2.html
+#
+version: 2
+
+sphinx:
+  configuration: docs/source/conf.py
+
+build:
+  os: ubuntu-20.04
+  tools:
+    nodejs: "16"
+    python: "3.9"
+
+python:
+  install:
+    - requirements: docs/requirements.txt
+
+formats:
+  # Adding htmlzip enables a Downloads section in the rendered website's RTD
+  # menu where the html build can be downloaded. This doesn't require any
+  # additional configuration in docs/source/conf.py.
+  #
+  - htmlzip

.travis.yml

@@ -1,15 +0,0 @@
-# http://travis-ci.org/#!/jupyter/jupyterhub
-language: python
-sudo: false
-python:
-    - 3.4
-    - 3.3
-before_install:
-    - npm install
-    - npm install -g configurable-http-proxy
-    - git clone --quiet --depth 1 https://github.com/minrk/travis-wheels travis-wheels
-install:
-    - pip install -f travis-wheels/wheelhouse -r dev-requirements.txt .
-    - pip install -f travis-wheels/wheelhouse ipython[notebook]
-script:
-    - py.test jupyterhub

CODE_OF_CONDUCT.md

@@ -0,0 +1 @@
+Please refer to [Project Jupyter's Code of Conduct](https://github.com/jupyter/governance/blob/HEAD/conduct/code_of_conduct.md).

CONTRIBUTING.md

@@ -1,3 +1,14 @@
-# Contributing
+# Contributing to JupyterHub
 
-We mainly follow the [IPython Contributing Guide](https://github.com/ipython/ipython/blob/master/CONTRIBUTING.md).
+Welcome! As a [Jupyter](https://jupyter.org) project,
+you can follow the [Jupyter contributor guide](https://jupyter.readthedocs.io/en/latest/contributing/content-contributor.html).
+
+Make sure to also follow [Project Jupyter's Code of Conduct](https://github.com/jupyter/governance/blob/HEAD/conduct/code_of_conduct.md)
+for a friendly and welcoming collaborative environment.
+
+Please see our documentation on
+
+- [Setting up a development install](https://jupyterhub.readthedocs.io/en/latest/contributing/setup.html)
+- [Testing JupyterHub and linting code](https://jupyterhub.readthedocs.io/en/latest/contributing/tests.html)
+
+If you need some help, feel free to ask on [Gitter](https://gitter.im/jupyterhub/jupyterhub) or [Discourse](https://discourse.jupyter.org/).

COPYING.md

@@ -24,7 +24,7 @@ software without specific prior written permission.
 THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
 ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
 WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
-DISCLAIMED.  IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE
+DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE
 FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
 DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
 SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
@@ -46,8 +46,8 @@ Jupyter uses a shared copyright model. Each contributor maintains copyright
 over their contributions to Jupyter. But, it is important to note that these
 contributions are typically only changes to the repositories. Thus, the Jupyter
 source code, in its entirety is not the copyright of any single person or
-institution.  Instead, it is the collective copyright of the entire Jupyter
-Development Team.  If individual contributors want to maintain a record of what
+institution. Instead, it is the collective copyright of the entire Jupyter
+Development Team. If individual contributors want to maintain a record of what
 changes/contributions they have specific copyright on, they should indicate
 their copyright in the commit message of the change, when they commit the
 change to one of the Jupyter repositories.

Dockerfile

@@ -1,35 +1,136 @@
-# A base docker image that includes juptyerhub and IPython master
+# An incomplete base Docker image for running JupyterHub
 #
-# Build your own derivative images starting with
+# Add your configuration to create a complete derivative Docker image.
 #
-# FROM jupyter/jupyterhub:latest
+# Include your configuration settings by starting with one of two options:
 #
+# Option 1:
+#
+# FROM quay.io/jupyterhub/jupyterhub:latest
+#
+# And put your configuration file jupyterhub_config.py in /srv/jupyterhub/jupyterhub_config.py.
+#
+# Option 2:
+#
+# Or you can create your jupyterhub config and database on the host machine, and mount it with:
+#
+# docker run -v $PWD:/srv/jupyterhub -t quay.io/jupyterhub/jupyterhub
+#
+# NOTE
+# If you base on quay.io/jupyterhub/jupyterhub-onbuild
+# your jupyterhub_config.py will be added automatically
+# from your docker directory.
 
-FROM ipython/ipython
+######################################################################
+# This Dockerfile uses multi-stage builds with optimisations to build
+# the JupyterHub wheel on the native architecture only
+# https://www.docker.com/blog/faster-multi-platform-builds-dockerfile-cross-compilation-guide/
 
-MAINTAINER Jupyter Project <jupyter@googlegroups.com>
+ARG BASE_IMAGE=ubuntu:22.04
 
-# install js dependencies
-RUN npm install -g configurable-http-proxy
 
-RUN mkdir -p /srv/
+######################################################################
+# The JupyterHub wheel is pure Python so can be built for any platform
+# on the native architecture (avoiding QEMU emulation)
+FROM --platform=${BUILDPLATFORM:-linux/amd64} $BASE_IMAGE AS jupyterhub-builder
 
-# install jupyterhub
-ADD requirements.txt /tmp/requirements.txt
-RUN pip3 install -r /tmp/requirements.txt
+ENV DEBIAN_FRONTEND=noninteractive
 
-WORKDIR /srv/
-ADD . /srv/jupyterhub
-WORKDIR /srv/jupyterhub/
+# Don't clear apt cache, and don't combine RUN commands, so that cached layers can
+# be reused in other stages
 
-RUN pip3 install .
+RUN apt-get update -qq \
+ && apt-get install -yqq --no-install-recommends \
+    build-essential \
+    ca-certificates \
+    curl \
+    locales \
+    python3-dev \
+    python3-pip \
+    python3-pycurl \
+    python3-venv \
+ && python3 -m pip install --no-cache-dir --upgrade setuptools pip build wheel
+# Ubuntu 22.04 comes with Nodejs 12 which is too old for building JupyterHub JS
+# It's fine at runtime though (used only by configurable-http-proxy)
+RUN curl -fsSL https://deb.nodesource.com/setup_18.x | bash - \
+ && apt-get install -yqq --no-install-recommends \
+    nodejs \
+ && npm install --global yarn
 
-WORKDIR /srv/jupyterhub/
+WORKDIR /src/jupyterhub
+# copy everything except whats in .dockerignore, its a
+# compromise between needing to rebuild and maintaining
+# what needs to be part of the build
+COPY . .
 
-# Derivative containers should add jupyterhub config,
-# which will be used when starting the application.
+ARG PIP_CACHE_DIR=/tmp/pip-cache
+RUN --mount=type=cache,target=${PIP_CACHE_DIR} \
+    python3 -m build --wheel
+
+
+######################################################################
+# All other wheels required by JupyterHub, some are platform specific
+FROM $BASE_IMAGE AS wheel-builder
+
+ENV DEBIAN_FRONTEND=noninteractive
+
+RUN apt-get update -qq \
+ && apt-get install -yqq --no-install-recommends \
+    build-essential \
+    ca-certificates \
+    curl \
+    locales \
+    python3-dev \
+    python3-pip \
+    python3-pycurl \
+    python3-venv \
+ && python3 -m pip install --no-cache-dir --upgrade setuptools pip build wheel
+
+WORKDIR /src/jupyterhub
+
+COPY --from=jupyterhub-builder /src/jupyterhub/dist/*.whl /src/jupyterhub/dist/
+ARG PIP_CACHE_DIR=/tmp/pip-cache
+RUN --mount=type=cache,target=${PIP_CACHE_DIR} \
+    python3 -m pip wheel --wheel-dir wheelhouse dist/*.whl
+
+
+######################################################################
+# The final JupyterHub image, platform specific
+FROM $BASE_IMAGE AS jupyterhub
+
+ENV DEBIAN_FRONTEND=noninteractive \
+    SHELL=/bin/bash \
+    LC_ALL=en_US.UTF-8 \
+    LANG=en_US.UTF-8 \
+    LANGUAGE=en_US.UTF-8 \
+    PYTHONDONTWRITEBYTECODE=1
 
 EXPOSE 8000
 
-ONBUILD ADD jupyterhub_config.py /srv/jupyterhub/jupyterhub_config.py
-CMD ["jupyterhub", "-f", "/srv/jupyterhub/jupyterhub_config.py"]
+LABEL maintainer="Jupyter Project <jupyter@googlegroups.com>"
+LABEL org.jupyter.service="jupyterhub"
+
+WORKDIR /srv/jupyterhub
+
+RUN apt-get update -qq \
+ && apt-get install -yqq --no-install-recommends \
+    ca-certificates \
+    curl \
+    gnupg \
+    locales \
+    python-is-python3 \
+    python3-pip \
+    python3-pycurl \
+    nodejs \
+    npm \
+ && locale-gen $LC_ALL \
+ && npm install -g configurable-http-proxy@^4.2.0 \
+ # clean cache and logs
+ && rm -rf /var/lib/apt/lists/* /var/log/* /var/tmp/* ~/.npm
+# install the wheels we built in the previous stage
+RUN --mount=type=cache,from=wheel-builder,source=/src/jupyterhub/wheelhouse,target=/tmp/wheelhouse \
+    # always make sure pip is up to date!
+    python3 -m pip install --no-compile --no-cache-dir --upgrade setuptools pip \
+ && python3 -m pip install --no-compile --no-cache-dir /tmp/wheelhouse/*
+
+CMD ["jupyterhub"]

MANIFEST.in

@@ -1,26 +1,38 @@
 include README.md
 include COPYING.md
 include setupegg.py
-include bower.json
+include bower-lite
 include package.json
+include package-lock.json
 include *requirements.txt
+include Dockerfile
 
+graft onbuild
+graft jsx
 graft jupyterhub
 graft scripts
 graft share
+graft singleuser
+graft ci
 
 # Documentation
 graft docs
+prune docs/node_modules
+
+# Intermediate javascript files
+prune jsx/node_modules
+prune jsx/build
 
 # prune some large unused files from components
-prune share/jupyter/hub/static/components/bootstrap/css
-exclude share/jupyter/hub/static/components/components/fonts/*.svg
-exclude share/jupyter/hub/static/components/bootstrap/less/*.js
-exclude share/jupyter/hub/static/components/font-awesome/css
-exclude share/jupyter/hub/static/components/font-awesome/fonts/*.svg
-exclude share/jupyter/hub/static/components/jquery/*migrate*.js
-prune share/jupyter/hub/static/components/moment/lang
-prune share/jupyter/hub/static/components/moment/min
+prune share/jupyterhub/static/components/bootstrap/dist/css
+exclude share/jupyterhub/static/components/bootstrap/dist/fonts/*.svg
+prune share/jupyterhub/static/components/font-awesome/css
+prune share/jupyterhub/static/components/font-awesome/scss
+exclude share/jupyterhub/static/components/font-awesome/fonts/*.svg
+prune share/jupyterhub/static/components/jquery/external
+prune share/jupyterhub/static/components/jquery/src
+prune share/jupyterhub/static/components/moment/lang
+prune share/jupyterhub/static/components/moment/min
 
 # Patterns to exclude from any directory
 global-exclude *~

README.md

@@ -1,93 +1,248 @@
-# JupyterHub: A multi-user server for Jupyter notebooks
+**[Technical Overview](#technical-overview)** |
+**[Installation](#installation)** |
+**[Configuration](#configuration)** |
+**[Docker](#docker)** |
+**[Contributing](#contributing)** |
+**[License](#license)** |
+**[Help and Resources](#help-and-resources)**
 
-JupyterHub is a multi-user server that manages and proxies multiple instances of the single-user <del>IPython</del> Jupyter notebook server.
+---
 
-Three actors:
+# [JupyterHub](https://github.com/jupyterhub/jupyterhub)
 
-- multi-user Hub (tornado process)
-- configurable http proxy (node-http-proxy)
-- multiple single-user IPython notebook servers (Python/IPython/tornado)
+[![Latest PyPI version](https://img.shields.io/pypi/v/jupyterhub?logo=pypi)](https://pypi.python.org/pypi/jupyterhub)
+[![Latest conda-forge version](https://img.shields.io/conda/vn/conda-forge/jupyterhub?logo=conda-forge)](https://anaconda.org/conda-forge/jupyterhub)
+[![Documentation build status](https://img.shields.io/readthedocs/jupyterhub?logo=read-the-docs)](https://jupyterhub.readthedocs.org/en/latest/)
+[![GitHub Workflow Status - Test](https://img.shields.io/github/workflow/status/jupyterhub/jupyterhub/Test?logo=github&label=tests)](https://github.com/jupyterhub/jupyterhub/actions)
+[![Test coverage of code](https://codecov.io/gh/jupyterhub/jupyterhub/branch/main/graph/badge.svg)](https://codecov.io/gh/jupyterhub/jupyterhub)
+[![GitHub](https://img.shields.io/badge/issue_tracking-github-blue?logo=github)](https://github.com/jupyterhub/jupyterhub/issues)
+[![Discourse](https://img.shields.io/badge/help_forum-discourse-blue?logo=discourse)](https://discourse.jupyter.org/c/jupyterhub)
+[![Gitter](https://img.shields.io/badge/social_chat-gitter-blue?logo=gitter)](https://gitter.im/jupyterhub/jupyterhub)
 
-Basic principles:
+With [JupyterHub](https://jupyterhub.readthedocs.io) you can create a
+**multi-user Hub** that spawns, manages, and proxies multiple instances of the
+single-user [Jupyter notebook](https://jupyter-notebook.readthedocs.io)
+server.
 
-- Hub spawns proxy
-- Proxy forwards ~all requests to hub by default
-- Hub handles login, and spawns single-user servers on demand
-- Hub configures proxy to forward url prefixes to single-user servers
+[Project Jupyter](https://jupyter.org) created JupyterHub to support many
+users. The Hub can offer notebook servers to a class of students, a corporate
+data science workgroup, a scientific research project, or a high-performance
+computing group.
 
+## Technical overview
 
-## Dependencies
+Three main actors make up JupyterHub:
 
-JupyterHub requires IPython >= 3.0 (current master) and Python >= 3.3.
+- multi-user **Hub** (tornado process)
+- configurable http **proxy** (node-http-proxy)
+- multiple **single-user Jupyter notebook servers** (Python/Jupyter/tornado)
 
-You will need nodejs/npm, which you can get from your package manager:
+Basic principles for operation are:
 
-    sudo apt-get install npm nodejs-legacy
+- Hub launches a proxy.
+- The Proxy forwards all requests to Hub by default.
+- Hub handles login and spawns single-user servers on demand.
+- Hub configures proxy to forward URL prefixes to the single-user notebook
+  servers.
 
-(The `nodejs-legacy` package installs the `node` executable,
-which is required for npm to work on Debian/Ubuntu at this point)
-
-Then install javascript dependencies:
-
-    sudo npm install -g configurable-http-proxy
+JupyterHub also provides a
+[REST API][]
+for administration of the Hub and its users.
 
+[rest api]: https://jupyterhub.readthedocs.io/en/latest/reference/rest-api.html
 
 ## Installation
 
-Then you can install the Python package by doing:
+### Check prerequisites
 
-    pip install -r requirements.txt
-    pip install .
+- A Linux/Unix based system
+- [Python](https://www.python.org/downloads/) 3.6 or greater
+- [nodejs/npm](https://www.npmjs.com/)
 
-If you plan to run notebook servers locally, you may also need to install the IPython notebook:
+  - If you are using **`conda`**, the nodejs and npm dependencies will be installed for
+    you by conda.
 
-    pip install "ipython[notebook]"
+  - If you are using **`pip`**, install a recent version (at least 12.0) of
+    [nodejs/npm](https://docs.npmjs.com/getting-started/installing-node).
 
+- If using the default PAM Authenticator, a [pluggable authentication module (PAM)](https://en.wikipedia.org/wiki/Pluggable_authentication_module).
+- TLS certificate and key for HTTPS communication
+- Domain name
 
-This will fetch client-side javascript dependencies and compile CSS,
-and install these files to `sys.prefix`/share/jupyter, as well as
-install any Python dependencies.
+### Install packages
 
+#### Using `conda`
 
-### Development install
+To install JupyterHub along with its dependencies including nodejs/npm:
 
-For a development install:
+```bash
+conda install -c conda-forge jupyterhub
+```
 
-    pip install -r dev-requirements.txt
-    pip install -e .
+If you plan to run notebook servers locally, install JupyterLab or Jupyter notebook:
 
-In which case you may need to manually update javascript and css after some updates, with:
+```bash
+conda install jupyterlab
+conda install notebook
+```
 
-    python setup.py js    # fetch updated client-side js (changes rarely)
-    python setup.py css   # recompile CSS from LESS sources
+#### Using `pip`
 
+JupyterHub can be installed with `pip`, and the proxy with `npm`:
 
-## Running the server
+```bash
+npm install -g configurable-http-proxy
+python3 -m pip install jupyterhub
+```
 
-To start the server, run the command:
+If you plan to run notebook servers locally, you will need to install
+[JupyterLab or Jupyter notebook](https://jupyter.readthedocs.io/en/latest/install.html):
+
+    python3 -m pip install --upgrade jupyterlab
+    python3 -m pip install --upgrade notebook
+
+### Run the Hub server
+
+To start the Hub server, run the command:
 
     jupyterhub
 
-and then visit `http://localhost:8000`, and sign in with your unix credentials.
+Visit `http://localhost:8000` in your browser, and sign in with your system username and password.
 
-If you want multiple users to be able to sign into the server, you will need to run the
-`jupyterhub` command as a privileged user, such as root.
-The [wiki](https://github.com/jupyter/jupyterhub/wiki/Using-sudo-to-run-JupyterHub-without-root-privileges) describes how to run the server
-as a less privileged user, which requires more configuration of the system.
+_Note_: To allow multiple users to sign in to the server, you will need to
+run the `jupyterhub` command as a _privileged user_, such as root.
+The [wiki](https://github.com/jupyterhub/jupyterhub/wiki/Using-sudo-to-run-JupyterHub-without-root-privileges)
+describes how to run the server as a _less privileged user_, which requires
+more configuration of the system.
 
-### Some examples
+## Configuration
 
-generate a default config file:
+The [Getting Started](https://jupyterhub.readthedocs.io/en/latest/tutorial/index.html#getting-started) section of the
+documentation explains the common steps in setting up JupyterHub.
+
+The [**JupyterHub tutorial**](https://github.com/jupyterhub/jupyterhub-tutorial)
+provides an in-depth video and sample configurations of JupyterHub.
+
+### Create a configuration file
+
+To generate a default config file with settings and descriptions:
 
     jupyterhub --generate-config
 
-spawn the server on 10.0.1.2:443 with https:
+### Start the Hub
+
+To start the Hub on a specific url and port `10.0.1.2:443` with **https**:
 
     jupyterhub --ip 10.0.1.2 --port 443 --ssl-key my_ssl.key --ssl-cert my_ssl.cert
 
-The authentication and process spawning mechanisms can be replaced,
-which should allow plugging into a variety of authentication or process control environments.
-Some examples, meant as illustration and testing of this concept:
+### Authenticators
 
-- Using GitHub OAuth instead of PAM with [OAuthenticator](https://github.com/jupyter/oauthenticator)
-- Spawning single-user servers with docker, using the [DockerSpawner](https://github.com/jupyter/dockerspawner)
+| Authenticator                                                                | Description                                       |
+| ---------------------------------------------------------------------------- | ------------------------------------------------- |
+| PAMAuthenticator                                                             | Default, built-in authenticator                   |
+| [OAuthenticator](https://github.com/jupyterhub/oauthenticator)               | OAuth + JupyterHub Authenticator = OAuthenticator |
+| [ldapauthenticator](https://github.com/jupyterhub/ldapauthenticator)         | Simple LDAP Authenticator Plugin for JupyterHub   |
+| [kerberosauthenticator](https://github.com/jupyterhub/kerberosauthenticator) | Kerberos Authenticator Plugin for JupyterHub      |
+
+### Spawners
+
+| Spawner                                                        | Description                                                                |
+| -------------------------------------------------------------- | -------------------------------------------------------------------------- |
+| LocalProcessSpawner                                            | Default, built-in spawner starts single-user servers as local processes    |
+| [dockerspawner](https://github.com/jupyterhub/dockerspawner)   | Spawn single-user servers in Docker containers                             |
+| [kubespawner](https://github.com/jupyterhub/kubespawner)       | Kubernetes spawner for JupyterHub                                          |
+| [sudospawner](https://github.com/jupyterhub/sudospawner)       | Spawn single-user servers without being root                               |
+| [systemdspawner](https://github.com/jupyterhub/systemdspawner) | Spawn single-user notebook servers using systemd                           |
+| [batchspawner](https://github.com/jupyterhub/batchspawner)     | Designed for clusters using batch scheduling software                      |
+| [yarnspawner](https://github.com/jupyterhub/yarnspawner)       | Spawn single-user notebook servers distributed on a Hadoop cluster         |
+| [wrapspawner](https://github.com/jupyterhub/wrapspawner)       | WrapSpawner and ProfilesSpawner enabling runtime configuration of spawners |
+
+## Docker
+
+A starter [**docker image for JupyterHub**](https://quay.io/repository/jupyterhub/jupyterhub)
+gives a baseline deployment of JupyterHub using Docker.
+
+**Important:** This `quay.io/jupyterhub/jupyterhub` image contains only the Hub itself,
+with no configuration. In general, one needs to make a derivative image, with
+at least a `jupyterhub_config.py` setting up an Authenticator and/or a Spawner.
+To run the single-user servers, which may be on the same system as the Hub or
+not, Jupyter Notebook version 4 or greater must be installed.
+
+The JupyterHub docker image can be started with the following command:
+
+    docker run -p 8000:8000 -d --name jupyterhub quay.io/jupyterhub/jupyterhub jupyterhub
+
+This command will create a container named `jupyterhub` that you can
+**stop and resume** with `docker stop/start`.
+
+The Hub service will be listening on all interfaces at port 8000, which makes
+this a good choice for **testing JupyterHub on your desktop or laptop**.
+
+If you want to run docker on a computer that has a public IP then you should
+(as in MUST) **secure it with ssl** by adding ssl options to your docker
+configuration or by using an ssl enabled proxy.
+
+[Mounting volumes](https://docs.docker.com/engine/admin/volumes/volumes/) will
+allow you to **store data outside the docker image (host system) so it will be persistent**, even when you start
+a new image.
+
+The command `docker exec -it jupyterhub bash` will spawn a root shell in your docker
+container. You can **use the root shell to create system users in the container**.
+These accounts will be used for authentication in JupyterHub's default configuration.
+
+## Contributing
+
+If you would like to contribute to the project, please read our
+[contributor documentation](https://jupyter.readthedocs.io/en/latest/contributing/content-contributor.html)
+and the [`CONTRIBUTING.md`](CONTRIBUTING.md). The `CONTRIBUTING.md` file
+explains how to set up a development installation, how to run the test suite,
+and how to contribute to documentation.
+
+For a high-level view of the vision and next directions of the project, see the
+[JupyterHub community roadmap](docs/source/contributing/roadmap.md).
+
+### A note about platform support
+
+JupyterHub is supported on Linux/Unix based systems.
+
+JupyterHub officially **does not** support Windows. You may be able to use
+JupyterHub on Windows if you use a Spawner and Authenticator that work on
+Windows, but the JupyterHub defaults will not. Bugs reported on Windows will not
+be accepted, and the test suite will not run on Windows. Small patches that fix
+minor Windows compatibility issues (such as basic installation) **may** be accepted,
+however. For Windows-based systems, we would recommend running JupyterHub in a
+docker container or Linux VM.
+
+[Additional Reference:](http://www.tornadoweb.org/en/stable/#installation) Tornado's documentation on Windows platform support
+
+## License
+
+We use a shared copyright model that enables all contributors to maintain the
+copyright on their contributions.
+
+All code is licensed under the terms of the [revised BSD license](./COPYING.md).
+
+## Help and resources
+
+We encourage you to ask questions and share ideas on the [Jupyter community forum](https://discourse.jupyter.org/).
+You can also talk with us on our JupyterHub [Gitter](https://gitter.im/jupyterhub/jupyterhub) channel.
+
+- [Reporting Issues](https://github.com/jupyterhub/jupyterhub/issues)
+- [JupyterHub tutorial](https://github.com/jupyterhub/jupyterhub-tutorial)
+- [Documentation for JupyterHub](https://jupyterhub.readthedocs.io/en/latest/)
+- [Documentation for JupyterHub's REST API][rest api]
+- [Documentation for Project Jupyter](http://jupyter.readthedocs.io/en/latest/index.html)
+- [Project Jupyter website](https://jupyter.org)
+- [Project Jupyter community](https://jupyter.org/community)
+
+JupyterHub follows the Jupyter [Community Guides](https://jupyter.readthedocs.io/en/latest/community/content-community.html).
+
+---
+
+**[Technical Overview](#technical-overview)** |
+**[Installation](#installation)** |
+**[Configuration](#configuration)** |
+**[Docker](#docker)** |
+**[Contributing](#contributing)** |
+**[License](#license)** |
+**[Help and Resources](#help-and-resources)**

RELEASE.md

@@ -0,0 +1,55 @@
+# How to make a release
+
+`jupyterhub` is a package available on [PyPI][] and [conda-forge][].
+These are instructions on how to make a release.
+
+## Pre-requisites
+
+- Push rights to [jupyterhub/jupyterhub][]
+- Push rights to [conda-forge/jupyterhub-feedstock][]
+
+## Steps to make a release
+
+1. Create a PR updating `docs/source/changelog.md` with [github-activity][] and
+   continue only when its merged.
+
+   ```shell
+   pip install github-activity
+
+   github-activity --heading-level=3 jupyterhub/jupyterhub
+   ```
+
+1. Checkout main and make sure it is up to date.
+
+   ```shell
+   git checkout main
+   git fetch origin main
+   git reset --hard origin/main
+   ```
+
+1. Update the version, make commits, and push a git tag with `tbump`.
+
+   ```shell
+   pip install tbump
+   tbump --dry-run ${VERSION}
+
+   tbump ${VERSION}
+   ```
+
+   Following this, the [CI system][] will build and publish a release.
+
+1. Reset the version back to dev, e.g. `2.1.0.dev` after releasing `2.0.0`
+
+   ```shell
+   tbump --no-tag ${NEXT_VERSION}.dev
+   ```
+
+1. Following the release to PyPI, an automated PR should arrive to
+   [conda-forge/jupyterhub-feedstock][] with instructions.
+
+[pypi]: https://pypi.org/project/jupyterhub/
+[conda-forge]: https://anaconda.org/conda-forge/jupyterhub
+[jupyterhub/jupyterhub]: https://github.com/jupyterhub/jupyterhub
+[conda-forge/jupyterhub-feedstock]: https://github.com/conda-forge/jupyterhub-feedstock
+[github-activity]: https://github.com/executablebooks/github-activity
+[ci system]: https://github.com/jupyterhub/jupyterhub/actions/workflows/release.yml

SECURITY.md

@@ -0,0 +1,5 @@
+# Reporting a Vulnerability
+
+If you believe you’ve found a security vulnerability in a Jupyter
+project, please report it to security@ipython.org. If you prefer to
+encrypt your security reports, you can use [this PGP public key](https://jupyter-notebook.readthedocs.io/en/stable/_downloads/1d303a645f2505a8fd283826fafc9908/ipython_security.asc).

bower-lite

@@ -0,0 +1,33 @@
+#!/usr/bin/env python
+# Copyright (c) Jupyter Development Team.
+# Distributed under the terms of the Modified BSD License.
+"""
+bower-lite
+
+Since Bower's on its way out,
+stage frontend dependencies from node_modules into components
+"""
+import json
+import os
+import shutil
+from os.path import join
+
+HERE = os.path.abspath(os.path.dirname(__file__))
+
+
+components = join(HERE, "share", "jupyterhub", "static", "components")
+node_modules = join(HERE, "node_modules")
+
+if os.path.exists(components):
+    shutil.rmtree(components)
+os.mkdir(components)
+
+with open(join(HERE, 'package.json')) as f:
+    package_json = json.load(f)
+
+dependencies = package_json['dependencies']
+for dep in dependencies:
+    src = join(node_modules, dep)
+    dest = join(components, dep)
+    print(f"{src} -> {dest}")
+    shutil.copytree(src, dest)

bower.json

@@ -1,11 +0,0 @@
-{
-  "name": "jupyterhub-deps",
-  "version": "0.0.0",
-  "dependencies": {
-    "bootstrap": "components/bootstrap#~3.1",
-    "font-awesome": "components/font-awesome#~4.1",
-    "jquery": "components/jquery#~2.0",
-    "moment": "~2.7",
-    "requirejs": "~2.1"
-  }
-}

ci/check_installed_data.py

@@ -0,0 +1,36 @@
+#!/usr/bin/env python
+# Check that installed package contains everything we expect
+
+
+from pathlib import Path
+
+import jupyterhub
+from jupyterhub._data import DATA_FILES_PATH
+
+print("Checking jupyterhub._data", end=" ")
+print(f"DATA_FILES_PATH={DATA_FILES_PATH}", end=" ")
+DATA_FILES_PATH = Path(DATA_FILES_PATH)
+assert DATA_FILES_PATH.is_dir(), DATA_FILES_PATH
+for subpath in (
+    "templates/spawn.html",
+    "static/css/style.min.css",
+    "static/components/jquery/dist/jquery.js",
+    "static/js/admin-react.js",
+):
+    path = DATA_FILES_PATH / subpath
+    assert path.is_file(), path
+
+print("OK")
+
+print("Checking package_data", end=" ")
+jupyterhub_path = Path(jupyterhub.__file__).parent.resolve()
+for subpath in (
+    "alembic.ini",
+    "alembic/versions/833da8570507_rbac.py",
+    "event-schemas/server-actions/v1.yaml",
+    "singleuser/templates/page.html",
+):
+    path = jupyterhub_path / subpath
+    assert path.is_file(), path
+
+print("OK")

ci/check_sdist.py

@@ -0,0 +1,27 @@
+#!/usr/bin/env python
+# Check that sdist contains everything we expect
+
+import sys
+import tarfile
+
+expected_files = [
+    "docs/requirements.txt",
+    "jsx/package.json",
+    "package.json",
+    "README.md",
+]
+
+assert len(sys.argv) == 2, "Expected one file"
+print(f"Checking {sys.argv[1]}")
+
+tar = tarfile.open(name=sys.argv[1], mode="r:gz")
+try:
+    # Remove leading jupyterhub-VERSION/
+    filelist = {f.partition('/')[2] for f in tar.getnames()}
+finally:
+    tar.close()
+
+for e in expected_files:
+    assert e in filelist, f"{e} not found"
+
+print("OK")

ci/docker-db.sh

@@ -0,0 +1,60 @@
+#!/usr/bin/env bash
+# The goal of this script is to start a database server as a docker container.
+#
+# Required environment variables:
+# - DB: The database server to start, either "postgres" or "mysql".
+#
+# - PGUSER/PGPASSWORD: For the creation of a postgresql user with associated
+#   password.
+
+set -eu
+
+# Stop and remove any existing database container
+DOCKER_CONTAINER="hub-test-$DB"
+docker rm -f "$DOCKER_CONTAINER" 2>/dev/null || true
+
+# Prepare environment variables to startup and await readiness of either a mysql
+# or postgresql server.
+if [[ "$DB" == "mysql" ]]; then
+    # Environment variables can influence both the mysql server in the docker
+    # container and the mysql client.
+    #
+    # ref server: https://hub.docker.com/_/mysql/
+    # ref client: https://dev.mysql.com/doc/refman/5.7/en/setting-environment-variables.html
+    #
+    DOCKER_RUN_ARGS="-p 3306:3306 --env MYSQL_ALLOW_EMPTY_PASSWORD=1 mysql:8.0"
+    READINESS_CHECK="mysql --user root --execute \q"
+elif [[ "$DB" == "postgres" ]]; then
+    # Environment variables can influence both the postgresql server in the
+    # docker container and the postgresql client (psql).
+    #
+    # ref server: https://hub.docker.com/_/postgres/
+    # ref client: https://www.postgresql.org/docs/9.5/libpq-envars.html
+    #
+    # POSTGRES_USER / POSTGRES_PASSWORD will create a user on startup of the
+    # postgres server, but PGUSER and PGPASSWORD are the environment variables
+    # used by the postgresql client psql, so we configure the user based on how
+    # we want to connect.
+    #
+    DOCKER_RUN_ARGS="-p 5432:5432 --env "POSTGRES_USER=${PGUSER}" --env "POSTGRES_PASSWORD=${PGPASSWORD}" postgres:15.1"
+    READINESS_CHECK="psql --command \q"
+else
+    echo '$DB must be mysql or postgres'
+    exit 1
+fi
+
+# Start the database server
+docker run --detach --name "$DOCKER_CONTAINER" $DOCKER_RUN_ARGS
+
+# Wait for the database server to start
+echo -n "waiting for $DB "
+for i in {1..60}; do
+    if $READINESS_CHECK; then
+        echo 'done'
+        break
+    else
+        echo -n '.'
+        sleep 1
+    fi
+done
+$READINESS_CHECK

ci/init-db.sh

@@ -0,0 +1,27 @@
+#!/usr/bin/env bash
+# The goal of this script is to initialize a running database server with clean
+# databases for use during tests.
+#
+# Required environment variables:
+# - DB: The database server to start, either "postgres" or "mysql".
+
+set -eu
+
+# Prepare env vars SQL_CLIENT and EXTRA_CREATE_DATABASE_ARGS
+if [[ "$DB" == "mysql" ]]; then
+    SQL_CLIENT="mysql --user root --execute "
+    EXTRA_CREATE_DATABASE_ARGS='CHARACTER SET utf8 COLLATE utf8_general_ci'
+elif [[ "$DB" == "postgres" ]]; then
+    SQL_CLIENT="psql --command "
+else
+    echo '$DB must be mysql or postgres'
+    exit 1
+fi
+
+# Configure a set of databases in the database server for upgrade tests
+# this list must be in sync with versions in test_db.py:test_upgrade
+set -x
+for SUFFIX in '' _upgrade_110 _upgrade_122 _upgrade_130 _upgrade_150 _upgrade_211; do
+    $SQL_CLIENT "DROP DATABASE jupyterhub${SUFFIX};" 2>/dev/null || true
+    $SQL_CLIENT "CREATE DATABASE jupyterhub${SUFFIX} ${EXTRA_CREATE_DATABASE_ARGS:-};"
+done

demo-image/Dockerfile

@@ -0,0 +1,16 @@
+# Demo JupyterHub Docker image
+#
+# This should only be used for demo or testing and not as a base image to build on.
+#
+# It includes the notebook package and it uses the DummyAuthenticator and the SimpleLocalProcessSpawner.
+ARG BASE_IMAGE=quay.io/jupyterhub/jupyterhub-onbuild
+FROM ${BASE_IMAGE}
+
+# Install the notebook package
+RUN python3 -m pip install notebook
+
+# Create a demo user
+RUN useradd --create-home demo
+RUN chown demo .
+
+USER demo

demo-image/README.md

@@ -0,0 +1,26 @@
+## Demo Dockerfile
+
+This is a demo JupyterHub Docker image to help you get a quick overview of what
+JupyterHub is and how it works.
+
+It uses the SimpleLocalProcessSpawner to spawn new user servers and
+DummyAuthenticator for authentication.
+The DummyAuthenticator allows you to log in with any username & password and the
+SimpleLocalProcessSpawner allows starting servers without having to create a
+local user for each JupyterHub user.
+
+### Important!
+
+This should only be used for demo or testing purposes!
+It shouldn't be used as a base image to build on.
+
+### Try it
+
+1. `cd` to the root of your jupyterhub repo.
+
+2. Build the demo image with `docker build -t jupyterhub-demo demo-image`.
+
+3. Run the demo image with `docker run -d -p 8000:8000 jupyterhub-demo`.
+
+4. Visit http://localhost:8000 and login with any username and password
+5. Happy demo-ing :tada:!

demo-image/jupyterhub_config.py

@@ -0,0 +1,7 @@
+# Configuration file for jupyterhub-demo
+
+c = get_config()
+
+# Use DummyAuthenticator and SimpleSpawner
+c.JupyterHub.spawner_class = "simple"
+c.JupyterHub.authenticator_class = "dummy"

dev-requirements.txt

@@ -1,2 +0,0 @@
--r requirements.txt
-pytest

dockerfiles/test.py

@@ -0,0 +1,14 @@
+import os
+
+from jupyterhub._data import DATA_FILES_PATH
+
+print(f"DATA_FILES_PATH={DATA_FILES_PATH}")
+
+for sub_path in (
+    "templates",
+    "static/components",
+    "static/css/style.min.css",
+    "static/js/admin-react.js",
+):
+    path = os.path.join(DATA_FILES_PATH, sub_path)
+    assert os.path.exists(path), path

docs/Makefile

@@ -0,0 +1,62 @@
+# Makefile for Sphinx documentation generated by sphinx-quickstart
+# ----------------------------------------------------------------------------
+
+# You can set these variables from the command line, and also
+# from the environment for the first two.
+SPHINXOPTS    ?= --color -W --keep-going
+SPHINXBUILD   ?= sphinx-build
+SOURCEDIR     = source
+BUILDDIR      = _build
+
+# Put it first so that "make" without argument is like "make help".
+help:
+	@$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS)
+
+.PHONY: help Makefile metrics scopes
+
+# Catch-all target: route all unknown targets to Sphinx using the new
+# "make mode" option.
+#
+# Several sphinx-build commands can be used through this, for example:
+#
+# - make clean
+# - make linkcheck
+# - make spelling
+#
+%: Makefile
+	@$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS)
+
+
+# Manually added targets - related to code generation
+# ----------------------------------------------------------------------------
+
+# For local development:
+# - builds the html
+# - NOTE: If the pre-requisites for the html target is updated, also update the
+#         Read The Docs section in docs/source/conf.py.
+#
+html: metrics scopes
+	$(SPHINXBUILD) -b html "$(SOURCEDIR)" "$(BUILDDIR)/html" $(SPHINXOPTS)
+	@echo
+	@echo "Build finished. The HTML pages are in $(BUILDDIR)/html."
+
+metrics: source/reference/metrics.md
+source/reference/metrics.md:
+	python3 generate-metrics.py
+
+scopes: source/rbac/scope-table.md
+source/rbac/scope-table.md:
+	python3 source/rbac/generate-scope-table.py
+
+
+# Manually added targets - related to development
+# ----------------------------------------------------------------------------
+
+# For local development:
+# - requires sphinx-autobuild, see
+#   https://sphinxcontrib-spelling.readthedocs.io/en/latest/
+# - builds and rebuilds html on changes to source, but does not re-generate
+#   metrics/scopes files
+# - starts a livereload enabled webserver and opens up a browser
+devenv: html
+	sphinx-autobuild -b html --open-browser "$(SOURCEDIR)" "$(BUILDDIR)/html"

docs/authenticators.md

@@ -1,78 +0,0 @@
-# Writing a custom Authenticator
-
-The [Authenticator][] is the mechanism for authorizing users.
-Basic authenticators use simple username and password authentication.
-JupyterHub ships only with a [PAM][]-based Authenticator,
-for logging in with local user accounts.
-
-You can use custom Authenticator subclasses to enable authentication via other systems.
-One such example is using [GitHub OAuth][].
-
-Because the username is passed from the Authenticator to the Spawner,
-a custom Authenticator and Spawner are often used together.
-
-
-## Basics of Authenticators
-
-A basic Authenticator has one central method:
-
-
-### Authenticator.authenticate
-
-    Authenticator.authenticate(handler, data)
-
-This method is passed the tornado RequestHandler and the POST data from the login form.
-Unless the login form has been customized, `data` will have two keys:
-
-- `username` (self-explanatory)
-- `password` (also self-explanatory)
-
-`authenticate`'s job is simple:
-
-- return a username (non-empty str)
-  of the authenticated user if authentication is successful
-- return `None` otherwise
-
-Writing an Authenticator that looks up passwords in a dictionary
-requires only overriding this one method:
-
-```python
-from tornado import gen
-from IPython.utils.traitlets import Dict
-from jupyterhub.auth import Authenticator
-
-class DictionaryAuthenticator(Authenticator):
-
-    passwords = Dict(config=True,
-        help="""dict of username:password for authentication"""
-    )
-    
-    @gen.coroutine
-    def authenticate(self, handler, data):
-        if self.passwords.get(data['username']) == data['password']:
-            return data['username']
-```
-
-
-### Authenticator.whitelist
-
-Authenticators can specify a whitelist of usernames to allow authentication.
-For local user authentication (e.g. PAM), this lets you limit which users
-can login.
-
-
-## OAuth and other non-password logins
-
-Some login mechanisms, such as [OAuth][], don't map onto username+password.
-For these, you can override the login handlers.
-
-You can see an example implementation of an Authenticator that uses [GitHub OAuth][]
-at [OAuthenticator][].
-
-
-[Authenticator]: ../jupyterhub/auth.py
-[PAM]: http://en.wikipedia.org/wiki/Pluggable_authentication_module
-[OAuth]: http://en.wikipedia.org/wiki/OAuth 
-[GitHub OAuth]: https://developer.github.com/v3/oauth/
-[OAuthenticator]: https://github.com/jupyter/oauthenticator
-

docs/generate-metrics.py

@@ -0,0 +1,53 @@
+import os
+
+from pytablewriter import MarkdownTableWriter
+
+import jupyterhub.metrics
+
+HERE = os.path.abspath(os.path.dirname(__file__))
+
+
+class Generator:
+    @classmethod
+    def create_writer(cls, table_name, headers, values):
+        writer = MarkdownTableWriter()
+        writer.table_name = table_name
+        writer.headers = headers
+        writer.value_matrix = values
+        writer.margin = 1
+        return writer
+
+    def _parse_metrics(self):
+        table_rows = []
+        for name in dir(jupyterhub.metrics):
+            obj = getattr(jupyterhub.metrics, name)
+            if obj.__class__.__module__.startswith('prometheus_client.'):
+                for metric in obj.describe():
+                    table_rows.append([metric.type, metric.name, metric.documentation])
+        return table_rows
+
+    def prometheus_metrics(self):
+        generated_directory = f"{HERE}/source/reference"
+        if not os.path.exists(generated_directory):
+            os.makedirs(generated_directory)
+
+        filename = f"{generated_directory}/metrics.md"
+        table_name = ""
+        headers = ["Type", "Name", "Description"]
+        values = self._parse_metrics()
+        writer = self.create_writer(table_name, headers, values)
+
+        with open(filename, 'w') as f:
+            f.write("# List of Prometheus Metrics\n\n")
+            f.write(writer.dumps())
+            f.write("\n")
+        print(f"Generated {filename}")
+
+
+def main():
+    doc_generator = Generator()
+    doc_generator.prometheus_metrics()
+
+
+if __name__ == "__main__":
+    main()

docs/howitworks.md

@@ -1,75 +0,0 @@
-# How JupyterHub works
-
-JupyterHub is a multi-user server that manages and proxies multiple instances of the single-user <del>IPython</del> Jupyter notebook server.
-
-There are three basic processes involved:
-
-- multi-user Hub (Python/Tornado)
-- configurable http proxy (nodejs)
-- multiple single-user IPython notebook servers (Python/IPython/Tornado)
-
-The proxy is the only process that listens on a public interface.
-The Hub sits behind the proxy at `/hub`.
-Single-user servers sit behind the proxy at `/user/[username]`.
-
-
-## Logging in
-
-When a new browser logs in to JupyterHub, the following events take place:
-
-- Login data is handed to the [Authenticator](#authentication) instance for validation
-- The Authenticator returns the username, if login information is valid
-- A single-user server instance is [Spawned](#spawning) for the logged-in user
-- When the server starts, the proxy is notified to forward `/user/[username]/*` to the single-user server
-- Two cookies are set, one for `/hub/` and another for `/user/[username]`,
-  containing an encrypted token.
-- The browser is redirected to `/user/[username]`, which is handled by the single-user server
-
-Logging into a single-user server is authenticated via the Hub:
-
-- On request, the single-user server forwards the encrypted cookie to the Hub for verification
-- The Hub replies with the username if it is a valid cookie
-- If the user is the owner of the server, access is allowed
-- If it is the wrong user or an invalid cookie, the browser is redirected to `/hub/login`
-
-
-## Customizing  JupyterHub
-
-There are two basic extension points for JupyterHub: How users are authenticated,
-and how their server processes are started.
-Each is governed by a customizable class,
-and JupyterHub ships with just the most basic version of each.
-
-To enable custom authentication and/or spawning,
-subclass Authenticator or Spawner,
-and override the relevant methods.
-
-
-### Authentication
-
-Authentication is customizable via the Authenticator class.
-Authentication can be replaced by any mechanism,
-such as OAuth, Kerberos, etc.
-
-JupyterHub only ships with [PAM](http://en.wikipedia.org/wiki/Pluggable_authentication_module) authentication,
-which requires the server to be run as root,
-or at least with access to the PAM service,
-which regular users typically do not have
-(on Ubuntu, this requires being added to the `shadow` group).
-
-[More info on custom Authenticators](authenticators.md).
-
-
-### Spawning
-
-Each single-user server is started by a Spawner.
-The Spawner represents an abstract interface to a process,
-and needs to be able to take three actions:
-
-1. start the process
-2. poll whether the process is still running
-3. stop the process
-
-[More info on custom Spawners](spawners.md).
-
-[An example using Docker](https://github.com/jupyter/dockerspawner).

docs/make.bat

@@ -0,0 +1,49 @@
+@ECHO OFF
+
+pushd %~dp0
+
+REM Command file for Sphinx documentation
+
+if "%SPHINXBUILD%" == "" (
+	set SPHINXBUILD=--color -W --keep-going
+)
+if "%SPHINXBUILD%" == "" (
+	set SPHINXBUILD=sphinx-build
+)
+set SOURCEDIR=source
+set BUILDDIR=_build
+
+if "%1" == "" goto help
+if "%1" == "devenv" goto devenv
+goto default
+
+
+:default
+%SPHINXBUILD% >NUL 2>NUL
+if errorlevel 9009 (
+	echo.
+	echo.The 'sphinx-build' command was not found. Open and read README.md!
+	exit /b 1
+)
+%SPHINXBUILD% -M %1 "%SOURCEDIR%" "%BUILDDIR%" %SPHINXOPTS%
+goto end
+
+
+:help
+%SPHINXBUILD% -M help "%SOURCEDIR%" "%BUILDDIR%" %SPHINXOPTS%
+goto end
+
+
+:devenv
+sphinx-autobuild >NUL 2>NUL
+if errorlevel 9009 (
+	echo.
+	echo.The 'sphinx-autobuild' command was not found. Open and read README.md!
+	exit /b 1
+)
+sphinx-autobuild -b html --open-browser "%SOURCEDIR%" "%BUILDDIR%/html"
+goto end
+
+
+:end
+popd

docs/requirements.txt

@@ -0,0 +1,21 @@
+# We install the jupyterhub package to help autodoc-traits inspect it and
+# generate documentation.
+#
+# FIXME: If there is a way for this requirements.txt file to pass a flag that
+#        the build system can intercept to not build the javascript artifacts,
+#        then do so so. That would mean that installing the documentation can
+#        avoid needing node/npm installed.
+#
+--editable .
+
+autodoc-traits
+jupyterhub-sphinx-theme
+myst-parser>=0.19
+pre-commit
+pytablewriter>=0.56
+ruamel.yaml
+sphinx>=4
+sphinx-copybutton
+sphinx-jsonschema
+sphinxext-opengraph
+sphinxext-rediraffe

docs/source/_static/custom.css

@@ -0,0 +1,10 @@
+/* Added to avoid logo being too squeezed */
+.navbar-brand {
+  height: 4rem !important;
+}
+
+/* hide redundant funky-formatted swagger-ui version */
+
+.swagger-ui .info .title small {
+  display: none !important;
+}

docs/source/conf.py

@@ -0,0 +1,249 @@
+# Configuration file for Sphinx to build our documentation to HTML.
+#
+# Configuration reference: https://www.sphinx-doc.org/en/master/usage/configuration.html
+#
+import contextlib
+import datetime
+import io
+import os
+import subprocess
+
+from docutils import nodes
+from sphinx.directives.other import SphinxDirective
+
+import jupyterhub
+from jupyterhub.app import JupyterHub
+
+# -- Project information -----------------------------------------------------
+# ref: https://www.sphinx-doc.org/en/master/usage/configuration.html#project-information
+#
+project = "JupyterHub"
+author = "Project Jupyter Contributors"
+copyright = f"{datetime.date.today().year}, {author}"
+
+
+# -- General Sphinx configuration --------------------------------------------
+# ref: https://www.sphinx-doc.org/en/master/usage/configuration.html#general-configuration
+#
+extensions = [
+    "sphinx.ext.autodoc",
+    "sphinx.ext.intersphinx",
+    "sphinx.ext.napoleon",
+    "autodoc_traits",
+    "sphinx_copybutton",
+    "sphinx-jsonschema",
+    "sphinxext.opengraph",
+    "sphinxext.rediraffe",
+    "jupyterhub_sphinx_theme",
+    "myst_parser",
+]
+root_doc = "index"
+source_suffix = [".md"]
+# default_role let's use use `foo` instead of ``foo`` in rST
+default_role = "literal"
+
+
+# -- MyST configuration ------------------------------------------------------
+# ref: https://myst-parser.readthedocs.io/en/latest/configuration.html
+#
+myst_heading_anchors = 2
+
+myst_enable_extensions = [
+    # available extensions: https://myst-parser.readthedocs.io/en/latest/syntax/optional.html
+    "attrs_inline",
+    "colon_fence",
+    "deflist",
+    "fieldlist",
+    "substitution",
+]
+
+myst_substitutions = {
+    # date example: Dev 07, 2022
+    "date": datetime.date.today().strftime("%b %d, %Y").title(),
+    "version": jupyterhub.__version__,
+}
+
+
+# -- Custom directives to generate documentation -----------------------------
+# ref: https://myst-parser.readthedocs.io/en/latest/syntax/roles-and-directives.html
+#
+# We define custom directives to help us generate documentation using Python on
+# demand when referenced from our documentation files.
+#
+
+# Create a temp instance of JupyterHub for use by two separate directive classes
+# to get the output from using the "--generate-config" and "--help-all" CLI
+# flags respectively.
+#
+jupyterhub_app = JupyterHub()
+
+
+class ConfigDirective(SphinxDirective):
+    """Generate the configuration file output for use in the documentation."""
+
+    has_content = False
+    required_arguments = 0
+    optional_arguments = 0
+    final_argument_whitespace = False
+    option_spec = {}
+
+    def run(self):
+        # The generated configuration file for this version
+        generated_config = jupyterhub_app.generate_config_file()
+        # post-process output
+        home_dir = os.environ["HOME"]
+        generated_config = generated_config.replace(home_dir, "$HOME", 1)
+        par = nodes.literal_block(text=generated_config)
+        return [par]
+
+
+class HelpAllDirective(SphinxDirective):
+    """Print the output of jupyterhub help --all for use in the documentation."""
+
+    has_content = False
+    required_arguments = 0
+    optional_arguments = 0
+    final_argument_whitespace = False
+    option_spec = {}
+
+    def run(self):
+        # The output of the help command for this version
+        buffer = io.StringIO()
+        with contextlib.redirect_stdout(buffer):
+            jupyterhub_app.print_help("--help-all")
+        all_help = buffer.getvalue()
+        # post-process output
+        home_dir = os.environ["HOME"]
+        all_help = all_help.replace(home_dir, "$HOME", 1)
+        par = nodes.literal_block(text=all_help)
+        return [par]
+
+
+def setup(app):
+    app.add_css_file("custom.css")
+    app.add_directive("jupyterhub-generate-config", ConfigDirective)
+    app.add_directive("jupyterhub-help-all", HelpAllDirective)
+
+
+# -- Read The Docs -----------------------------------------------------------
+#
+# Since RTD runs sphinx-build directly without running "make html", we run the
+# pre-requisite steps for "make html" from here if needed.
+#
+if os.environ.get("READTHEDOCS"):
+    docs = os.path.dirname(os.path.dirname(__file__))
+    subprocess.check_call(["make", "metrics", "scopes"], cwd=docs)
+
+
+# -- Spell checking ----------------------------------------------------------
+# ref: https://sphinxcontrib-spelling.readthedocs.io/en/latest/customize.html#configuration-options
+#
+# The "sphinxcontrib.spelling" extension is optionally enabled if its available.
+#
+try:
+    import sphinxcontrib.spelling  # noqa
+except ImportError:
+    pass
+else:
+    extensions.append("sphinxcontrib.spelling")
+spelling_word_list_filename = "spelling_wordlist.txt"
+
+
+# -- Options for HTML output -------------------------------------------------
+# ref: https://www.sphinx-doc.org/en/master/usage/configuration.html#options-for-html-output
+#
+html_logo = "_static/images/logo/logo.png"
+html_favicon = "_static/images/logo/favicon.ico"
+html_static_path = ["_static"]
+
+html_theme = "jupyterhub_sphinx_theme"
+html_theme_options = {
+    "icon_links": [
+        {
+            "name": "GitHub",
+            "url": "https://github.com/jupyterhub/jupyterhub",
+            "icon": "fa-brands fa-github",
+        },
+    ],
+    "use_edit_page_button": True,
+    "navbar_align": "left",
+}
+html_context = {
+    "github_user": "jupyterhub",
+    "github_repo": "jupyterhub",
+    "github_version": "main",
+    "doc_path": "docs/source",
+}
+
+
+# -- Options for linkcheck builder -------------------------------------------
+# ref: https://www.sphinx-doc.org/en/master/usage/configuration.html#options-for-the-linkcheck-builder
+#
+linkcheck_ignore = [
+    r"(.*)github\.com(.*)#",  # javascript based anchors
+    r"(.*)/#%21(.*)/(.*)",  # /#!forum/jupyter - encoded anchor edge case
+    r"https://github.com/[^/]*$",  # too many github usernames / searches in changelog
+    "https://github.com/jupyterhub/jupyterhub/pull/",  # too many PRs in changelog
+    "https://github.com/jupyterhub/jupyterhub/compare/",  # too many comparisons in changelog
+    r"https?://(localhost|127.0.0.1).*",  # ignore localhost references in auto-links
+    r".*/rest-api.html#.*",  # ignore javascript-resolved internal rest-api links
+    r"https://linux.die.net/.*",  # linux.die.net seems to block requests from CI with 403 sometimes
+    # don't check links to unpublished advisories
+    r"https://github.com/jupyterhub/jupyterhub/security/advisories/.*",
+]
+linkcheck_anchors_ignore = [
+    "/#!",
+    "/#%21",
+]
+
+# -- Intersphinx -------------------------------------------------------------
+# ref: https://www.sphinx-doc.org/en/master/usage/extensions/intersphinx.html#configuration
+#
+intersphinx_mapping = {
+    "python": ("https://docs.python.org/3/", None),
+    "tornado": ("https://www.tornadoweb.org/en/stable/", None),
+    "jupyter-server": ("https://jupyter-server.readthedocs.io/en/stable/", None),
+    "nbgitpuller": ("https://nbgitpuller.readthedocs.io/en/latest", None),
+}
+
+# -- Options for the opengraph extension -------------------------------------
+# ref: https://github.com/wpilibsuite/sphinxext-opengraph#options
+#
+# ogp_site_url is set automatically by RTD
+ogp_image = "_static/logo.png"
+ogp_use_first_image = True
+
+
+# -- Options for the rediraffe extension -------------------------------------
+# ref: https://github.com/wpilibsuite/sphinxext-rediraffe#readme
+#
+# This extension helps us relocate content without breaking links. If a
+# document is moved internally, a redirect link should be configured as below to
+# help us not break links.
+#
+# The workflow for adding redirects can be as follows:
+# 1. Change "rediraffe_branch" below to point to the commit/ branch you
+#    want to base off the changes.
+# 2. Option 1: run "make rediraffecheckdiff"
+#       a. Analyze the output of this command.
+#       b. Manually add the redirect entries to the "redirects.txt" file.
+#    Option 2: run "make rediraffewritediff"
+#       a. rediraffe will then automatically add the obvious redirects to redirects.txt.
+#       b. Analyze the output of the command for broken links.
+#       c. Check the "redirects.txt" file for any files that were moved/ renamed but are not listed.
+#       d. Manually add the redirects that have been mised by the automatic builder to "redirects.txt".
+#    Option 3: Do not use the commands above and, instead, do everything manually - by taking
+#    note of the files you have moved or renamed and adding them to the "redirects.txt" file.
+#
+# If you are basing changes off another branch/ commit, always change back
+# rediraffe_branch to main before pushing your changes upstream.
+#
+rediraffe_branch = os.environ.get("REDIRAFFE_BRANCH", "main")
+rediraffe_redirects = "redirects.txt"
+
+# allow 80% match for autogenerated redirects
+rediraffe_auto_redirect_perc = 80
+
+# rediraffe_redirects = {
+# "old-file": "new-folder/new-file-name",
+# }

docs/source/contributing/community.md

@@ -0,0 +1,27 @@
+# Community communication channels
+
+We use different channels of communication for different purposes. Whichever one you use will depend on what kind of communication you want to engage in.
+
+## Discourse (recommended)
+
+We use [Discourse](https://discourse.jupyter.org) for online discussions and support questions.
+You can ask questions here if you are a first-time contributor to the JupyterHub project.
+Everyone in the Jupyter community is welcome to bring ideas and questions there.
+
+We recommend that you first use our Discourse as all past and current discussions on it are archived and searchable. Thus, all discussions remain useful and accessible to the whole community.
+
+## Gitter
+
+We use [our Gitter channel](https://gitter.im/jupyterhub/jupyterhub) for online, real-time text chat; a place for more ephemeral discussions. When you're not on Discourse, you can stop here to have other discussions on the fly.
+
+## Github Issues
+
+[Github issues](https://docs.github.com/en/issues/tracking-your-work-with-issues/about-issues) are used for most long-form project discussions, bug reports and feature requests.
+
+- Issues related to a specific authenticator or spawner should be opened in the appropriate repository for the authenticator or spawner.
+- If you are using a specific JupyterHub distribution (such as [Zero to JupyterHub on Kubernetes](https://github.com/jupyterhub/zero-to-jupyterhub-k8s) or [The Littlest JupyterHub](https://github.com/jupyterhub/the-littlest-jupyterhub/)), you should open issues directly in their repository.
+- If you cannot find a repository to open your issue in, do not worry! Open the issue in the [main JupyterHub repository](https://github.com/jupyterhub/jupyterhub/) and our community will help you figure it out.
+
+```{note}
+Our community is distributed across the world in various timezones, so please be patient if you do not get a response immediately!
+```

docs/source/contributing/contributor-list.md

@@ -0,0 +1,123 @@
+# Contributors
+
+Project Jupyter thanks the following people for their help and
+contribution on JupyterHub:
+
+- adelcast
+- Analect
+- anderbubble
+- anikitml
+- ankitksharma
+- apetresc
+- athornton
+- barrachri
+- BerserkerTroll
+- betatim
+- Carreau
+- cfournie
+- charnpreetsingh
+- chicovenancio
+- cikao
+- ckald
+- cmoscardi
+- consideRatio
+- cqzlxl
+- CRegenschein
+- cwaldbieser
+- danielballen
+- danoventa
+- daradib
+- darky2004
+- datapolitan
+- dblockow-d2dcrc
+- DeepHorizons
+- DerekHeldtWerle
+- dhirschfeld
+- dietmarw
+- dingc3
+- dmartzol
+- DominicFollettSmith
+- dsblank
+- dtaniwaki
+- echarles
+- ellisonbg
+- emmanuel
+- evanlinde
+- Fokko
+- fperez
+- franga2000
+- GladysNalvarte
+- glenak1911
+- gweis
+- iamed18
+- jamescurtin
+- JamiesHQ
+- JasonJWilliamsNY
+- jbweston
+- jdavidheiser
+- jencabral
+- jhamrick
+- jkinkead
+- johnkpark
+- josephtate
+- jzf2101
+- karfai
+- kinuax
+- KrishnaPG
+- kroq-gar78
+- ksolan
+- mbmilligan
+- mgeplf
+- minrk
+- mistercrunch
+- Mistobaan
+- mpacer
+- mwmarkland
+- ndly
+- nthiery
+- nxg
+- ObiWahn
+- ozancaglayan
+- paccorsi
+- parente
+- PeterDaveHello
+- peterruppel
+- phill84
+- pjamason
+- prasadkatti
+- rafael-ladislau
+- rcthomas
+- rgbkrk
+- rkdarst
+- robnagler
+- rschroll
+- ryanlovett
+- sangramga
+- Scrypy
+- schon
+- shreddd
+- Siecje
+- smiller5678
+- spoorthyv
+- ssanderson
+- summerswallow
+- syutbai
+- takluyver
+- temogen
+- ThomasMChen
+- Thoralf Gutierrez
+- timfreund
+- TimShawver
+- tklever
+- Todd-Z-Li
+- toobaz
+- tsaeger
+- tschaume
+- vilhelmen
+- whitead
+- willingc
+- YannBrrd
+- yuvipanda
+- zoltan-fedor
+- zonca
+- Neeraj Natu

docs/source/contributing/docs.md

@@ -0,0 +1,76 @@
+(contributing-docs)=
+
+# Contributing Documentation
+
+Documentation is often more important than code. This page helps
+you get set up on how to contribute to JupyterHub's documentation.
+
+## Building documentation locally
+
+We use [sphinx](https://www.sphinx-doc.org) to build our documentation. It takes
+our documentation source files (written in [markdown](https://daringfireball.net/projects/markdown/) or [reStructuredText](https://www.sphinx-doc.org/en/master/usage/restructuredtext/basics.html) &
+stored under the `docs/source` directory) and converts it into various
+formats for people to read. To make sure the documentation you write or
+change renders correctly, it is good practice to test it locally.
+
+1. Make sure you have successfully completed {ref}`contributing/setup`.
+
+2. Install the packages required to build the docs.
+
+   ```bash
+   python3 -m pip install -r docs/requirements.txt
+   ```
+
+3. Build the html version of the docs. This is the most commonly used
+   output format, so verifying it renders correctly is usually good
+   enough.
+
+   ```bash
+   cd docs
+   make html
+   ```
+
+   This step will display any syntax or formatting errors in the documentation,
+   along with the filename / line number in which they occurred. Fix them,
+   and re-run the `make html` command to re-render the documentation.
+
+4. View the rendered documentation by opening `_build/html/index.html` in
+   a web browser.
+
+   :::{tip}
+   **On Windows**, you can open a file from the terminal with `start <path-to-file>`.
+
+   **On macOS**, you can do the same with `open <path-to-file>`.
+
+   **On Linux**, you can do the same with `xdg-open <path-to-file>`.
+
+   After opening index.html in your browser you can just refresh the page whenever
+   you rebuild the docs via `make html`
+   :::
+
+(contributing-docs-conventions)=
+
+## Documentation conventions
+
+This section lists various conventions we use in our documentation. This is a
+living document that grows over time, so feel free to add to it / change it!
+
+Our entire documentation does not yet fully conform to these conventions yet,
+so help in making it so would be appreciated!
+
+### `pip` invocation
+
+There are many ways to invoke a `pip` command, we recommend the following
+approach:
+
+```bash
+python3 -m pip
+```
+
+This invokes pip explicitly using the python3 binary that you are
+currently using. This is the **recommended way** to invoke pip
+in our documentation, since it is least likely to cause problems
+with python3 and pip being from different environments.
+
+For more information on how to invoke `pip` commands, see
+[the pip documentation](https://pip.pypa.io/en/stable/).

docs/source/contributing/index.md

@@ -0,0 +1,22 @@
+# Contributing
+
+We want you to contribute to JupyterHub in ways that are most exciting
+and useful to you. We value documentation, testing, bug reporting & code equally,
+and are glad to have your contributions in whatever form you wish.
+
+Be sure to first check our [Code of Conduct](https://github.com/jupyter/governance/blob/HEAD/conduct/code_of_conduct.md)
+([reporting guidelines](https://github.com/jupyter/governance/blob/HEAD/conduct/reporting_online.md)), which help keep our community welcoming to as many people as possible.
+
+This section covers information about our community, as well as ways that you can connect and get involved.
+
+```{toctree}
+:maxdepth: 2
+
+contributor-list
+community
+setup
+docs
+tests
+roadmap
+security
+```

docs/source/contributing/roadmap.md

@@ -0,0 +1,95 @@
+# The JupyterHub roadmap
+
+This roadmap collects "next steps" for JupyterHub. It is about creating a
+shared understanding of the project's vision and direction amongst
+the community of users, contributors, and maintainers.
+The goal is to communicate priorities and upcoming release plans.
+It is not aimed at limiting contributions to what is listed here.
+
+## Using the roadmap
+
+### Sharing Feedback on the Roadmap
+
+All of the community is encouraged to provide feedback as well as share new
+ideas with the community. Please do so by submitting an issue. If you want to
+have an informal conversation first use one of the other communication channels.
+After submitting the issue, others from the community will probably
+respond with questions or comments they have to clarify the issue. The
+maintainers will help identify what a good next step is for the issue.
+
+### What do we mean by "next step"?
+
+When submitting an issue, think about what "next step" category best describes
+your issue:
+
+- **now**, concrete/actionable step that is ready for someone to start work on.
+  These might be items that have a link to an issue or more abstract like
+  "decrease typos and dead links in the documentation"
+- **soon**, less concrete/actionable step that is going to happen soon,
+  discussions around the topic are coming close to an end at which point it can
+  move into the "now" category
+- **later**, abstract ideas or tasks, need a lot of discussion or
+  experimentation to shape the idea so that it can be executed. Can also
+  contain concrete/actionable steps that have been postponed on purpose
+  (these are steps that could be in "now" but the decision was taken to work on
+  them later)
+
+### Reviewing and Updating the Roadmap
+
+The roadmap will get updated as time passes (next review by 1st December) based
+on discussions and ideas captured as issues.
+This means this list should not be exhaustive, it should only represent
+the "top of the stack" of ideas. It should
+not function as a wish list, collection of feature requests or todo list.
+For those please create a
+[new issue](https://github.com/jupyterhub/jupyterhub/issues/new).
+
+The roadmap should give the reader an idea of what is happening next, what needs
+input and discussion before it can happen and what has been postponed.
+
+## The roadmap proper
+
+### Project vision
+
+JupyterHub is a dependable tool used by humans that reduces the complexity of
+creating the environment in which a piece of software can be executed.
+
+### Now
+
+These "Now" items are considered active areas of focus for the project:
+
+- HubShare - a sharing service for use with JupyterHub.
+  - Users should be able to:
+    - Push a project to other users.
+    - Get a checkout of a project from other users.
+    - Push updates to a published project.
+    - Pull updates from a published project.
+    - Manage conflicts/merges by simply picking a version (our/theirs)
+    - Get a checkout of a project from the internet. These steps are completely different from saving notebooks/files.
+    - Have directories that are managed by git completely separately from our stuff.
+    - Look at pushed content that they have access to without an explicit pull.
+    - Define and manage teams of users.
+      - Adding/removing a user to/from a team gives/removes them access to all projects that team has access to.
+    - Build other services, such as static HTML publishing and dashboarding on top of these things.
+
+### Soon
+
+These "Soon" items are under discussion. Once an item reaches the point of an
+actionable plan, the item will be moved to the "Now" section. Typically,
+these will be moved at a future review of the roadmap.
+
+- resource monitoring and management:
+  - (prometheus?) API for resource monitoring
+  - tracking activity on single-user servers instead of the proxy
+  - notes and activity tracking per API token
+
+### Later
+
+The "Later" items are things that are at the back of the project's mind. At this
+time there is no active plan for an item. The project would like to find the
+resources and time to discuss these ideas.
+
+- real-time collaboration
+  - Enter into real-time collaboration mode for a project that starts a shared execution context.
+  - Once the single-user notebook package supports realtime collaboration,
+    implement sharing mechanism integrated into the Hub.

docs/source/contributing/security.md

@@ -0,0 +1,9 @@
+# Reporting security issues in Jupyter or JupyterHub
+
+If you find a security vulnerability in Jupyter or JupyterHub,
+whether it is a failure of the security model described in [Security Overview](web-security)
+or a failure in implementation,
+please report it to <mailto:security@ipython.org>.
+
+If you prefer to encrypt your security reports,
+you can use {download}`this PGP public key </ipython_security.asc>`.

docs/source/contributing/setup.md

@@ -0,0 +1,175 @@
+(contributing/setup)=
+
+# Setting up a development install
+
+## System requirements
+
+JupyterHub can only run on macOS or Linux operating systems. If you are
+using Windows, we recommend using [VirtualBox](https://virtualbox.org)
+or a similar system to run [Ubuntu Linux](https://ubuntu.com) for
+development.
+
+### Install Python
+
+JupyterHub is written in the [Python](https://python.org) programming language and
+requires you have at least version 3.6 installed locally. If you haven’t
+installed Python before, the recommended way to install it is to use
+[Miniforge](https://github.com/conda-forge/miniforge#download).
+
+### Install nodejs
+
+[NodeJS 12+](https://nodejs.org/en/) is required for building some JavaScript components.
+`configurable-http-proxy`, the default proxy implementation for JupyterHub, is written in Javascript.
+If you have not installed NodeJS before, we recommend installing it in the `miniconda` environment you set up for Python.
+You can do so with `conda install nodejs`.
+
+Many in the Jupyter community use \[`nvm`\](<https://github.com/nvm-sh/nvm>) to
+managing node dependencies.
+
+### Install git
+
+JupyterHub uses [Git](https://git-scm.com) & [GitHub](https://github.com)
+for development & collaboration. You need to [install git](https://git-scm.com/book/en/v2/Getting-Started-Installing-Git) to work on
+JupyterHub. We also recommend getting a free account on GitHub.com.
+
+## Setting up a development install
+
+When developing JupyterHub, you would need to make changes and be able to instantly view the results of the changes. To achieve that, a developer install is required.
+
+:::{note}
+This guide does not attempt to dictate _how_ development
+environments should be isolated since that is a personal preference and can
+be achieved in many ways, for example, `tox`, `conda`, `docker`, etc. See this
+[forum thread](https://discourse.jupyter.org/t/thoughts-on-using-tox/3497) for
+a more detailed discussion.
+:::
+
+1. Clone the [JupyterHub git repository](https://github.com/jupyterhub/jupyterhub)
+   to your computer.
+
+   ```bash
+   git clone https://github.com/jupyterhub/jupyterhub
+   cd jupyterhub
+   ```
+
+2. Make sure the `python` you installed and the `npm` you installed
+   are available to you on the command line.
+
+   ```bash
+   python -V
+   ```
+
+   This should return a version number greater than or equal to 3.6.
+
+   ```bash
+   npm -v
+   ```
+
+   This should return a version number greater than or equal to 5.0.
+
+3. Install `configurable-http-proxy` (required to run and test the default JupyterHub configuration) and `yarn` (required to build some components):
+
+   ```bash
+   npm install -g configurable-http-proxy yarn
+   ```
+
+   If you get an error that says `Error: EACCES: permission denied`, you might need to prefix the command with `sudo`.
+   `sudo` may be required to perform a system-wide install.
+   If you do not have access to sudo, you may instead run the following commands:
+
+   ```bash
+   npm install configurable-http-proxy yarn
+   export PATH=$PATH:$(pwd)/node_modules/.bin
+   ```
+
+   The second line needs to be run every time you open a new terminal.
+
+   If you are using conda you can instead run:
+
+   ```bash
+   conda install configurable-http-proxy yarn
+   ```
+
+4. Install an editable version of JupyterHub and its requirements for
+   development and testing. This lets you edit JupyterHub code in a text editor
+   & restart the JupyterHub process to see your code changes immediately.
+
+   ```bash
+   python3 -m pip install --editable ".[test]"
+   ```
+
+5. Set up a database.
+
+   The default database engine is `sqlite` so if you are just trying
+   to get up and running quickly for local development that should be
+   available via [Python](https://docs.python.org/3.5/library/sqlite3.html).
+   See [The Hub's Database](hub-database) for details on other supported databases.
+
+6. You are now ready to start JupyterHub!
+
+   ```bash
+   jupyterhub
+   ```
+
+7. You can access JupyterHub from your browser at
+   `http://localhost:8000` now.
+
+Happy developing!
+
+## Using DummyAuthenticator & SimpleLocalProcessSpawner
+
+To simplify testing of JupyterHub, it is helpful to use
+{class}`~jupyterhub.auth.DummyAuthenticator` instead of the default JupyterHub
+authenticator and SimpleLocalProcessSpawner instead of the default spawner.
+
+There is a sample configuration file that does this in
+`testing/jupyterhub_config.py`. To launch JupyterHub with this
+configuration:
+
+```bash
+jupyterhub -f testing/jupyterhub_config.py
+```
+
+The default JupyterHub [authenticator](PAMAuthenticator)
+& [spawner](LocalProcessSpawner)
+require your system to have user accounts for each user you want to log in to
+JupyterHub as.
+
+DummyAuthenticator allows you to log in with any username & password,
+while SimpleLocalProcessSpawner allows you to start servers without having to
+create a Unix user for each JupyterHub user. Together, these make it
+much easier to test JupyterHub.
+
+Tip: If you are working on parts of JupyterHub that are common to all
+authenticators & spawners, we recommend using both DummyAuthenticator &
+SimpleLocalProcessSpawner. If you are working on just authenticator-related
+parts, use only SimpleLocalProcessSpawner. Similarly, if you are working on
+just spawner-related parts, use only DummyAuthenticator.
+
+## Troubleshooting
+
+This section lists common ways setting up your development environment may
+fail, and how to fix them. Please add to the list if you encounter yet
+another way it can fail!
+
+### `lessc` not found
+
+If the `python3 -m pip install --editable .` command fails and complains about
+`lessc` being unavailable, you may need to explicitly install some
+additional JavaScript dependencies:
+
+```bash
+npm install
+```
+
+This will fetch client-side JavaScript dependencies necessary to compile
+CSS.
+
+You may also need to manually update JavaScript and CSS after some
+development updates, with:
+
+```bash
+python3 setup.py js    # fetch updated client-side js
+python3 setup.py css   # recompile CSS from LESS sources
+python3 setup.py jsx   # build React admin app
+```

docs/source/contributing/tests.md

@@ -0,0 +1,157 @@
+(contributing-tests)=
+
+# Testing JupyterHub and linting code
+
+Unit testing helps to validate that JupyterHub works the way we think it does,
+and continues to do so when changes occur. They also help communicate
+precisely what we expect our code to do.
+
+JupyterHub uses [pytest](https://pytest.org) for all the tests. You
+can find them under the [jupyterhub/tests](https://github.com/jupyterhub/jupyterhub/tree/main/jupyterhub/tests) directory in the git repository.
+
+## Running the tests
+
+1. Make sure you have completed {ref}`contributing/setup`.
+   Once you are done, you would be able to run `jupyterhub` from the command line and access it from your web browser.
+   This ensures that the dev environment is properly set up for tests to run.
+
+2. You can run all tests in JupyterHub
+
+   ```bash
+   pytest -v jupyterhub/tests
+   ```
+
+   This should display progress as it runs all the tests, printing
+   information about any test failures as they occur.
+
+   If you wish to confirm test coverage the run tests with the `--cov` flag:
+
+   ```bash
+   pytest -v --cov=jupyterhub jupyterhub/tests
+   ```
+
+3. You can also run tests in just a specific file:
+
+   ```bash
+   pytest -v jupyterhub/tests/<test-file-name>
+   ```
+
+4. To run a specific test only, you can do:
+
+   ```bash
+   pytest -v jupyterhub/tests/<test-file-name>::<test-name>
+   ```
+
+   This runs the test with function name `<test-name>` defined in
+   `<test-file-name>`. This is very useful when you are iteratively
+   developing a single test.
+
+   For example, to run the test `test_shutdown` in the file `test_api.py`,
+   you would run:
+
+   ```bash
+   pytest -v jupyterhub/tests/test_api.py::test_shutdown
+   ```
+
+   For more details, refer to the [pytest usage documentation](https://pytest.readthedocs.io/en/latest/usage.html).
+
+## Test organisation
+
+The tests live in `jupyterhub/tests` and are organized roughly into:
+
+1. `test_api.py` tests the REST API
+2. `test_pages.py` tests loading the HTML pages
+
+and other collections of tests for different components.
+When writing a new test, there should usually be a test of
+similar functionality already written and related tests should
+be added nearby.
+
+The fixtures live in `jupyterhub/tests/conftest.py`. There are
+fixtures that can be used for JupyterHub components, such as:
+
+- `app`: an instance of JupyterHub with mocked parts
+- `auth_state_enabled`: enables persisting auth_state (like authentication tokens)
+- `db`: a sqlite in-memory DB session
+- `` io_loop` ``: a Tornado event loop
+- `event_loop`: a new asyncio event loop
+- `user`: creates a new temporary user
+- `admin_user`: creates a new temporary admin user
+- single user servers
+  \- `cleanup_after`: allows cleanup of single user servers between tests
+- mocked service
+  \- `MockServiceSpawner`: a spawner that mocks services for testing with a short poll interval
+  \- `` mockservice` ``: mocked service with no external service url
+  \- `mockservice_url`: mocked service with a url to test external services
+
+And fixtures to add functionality or spawning behavior:
+
+- `admin_access`: grants admin access
+- `` no_patience` ``: sets slow-spawning timeouts to zero
+- `slow_spawn`: enables the SlowSpawner (a spawner that takes a few seconds to start)
+- `never_spawn`: enables the NeverSpawner (a spawner that will never start)
+- `bad_spawn`: enables the BadSpawner (a spawner that fails immediately)
+- `slow_bad_spawn`: enables the SlowBadSpawner (a spawner that fails after a short delay)
+
+Refer to the [pytest fixtures documentation](https://pytest.readthedocs.io/en/latest/fixture.html) to learn how to use fixtures that exists already and to create new ones.
+
+### The Pytest-Asyncio Plugin
+
+When testing the various JupyterHub components and their various implementations, it sometimes becomes necessary to have a running instance of JupyterHub to test against.
+The [`app`](https://github.com/jupyterhub/jupyterhub/blob/270b61992143b29af8c2fab90c4ed32f2f6fe209/jupyterhub/tests/conftest.py#L60) fixture mocks a JupyterHub application for use in testing by:
+
+- enabling ssl if internal certificates are available
+- creating an instance of [MockHub](https://github.com/jupyterhub/jupyterhub/blob/270b61992143b29af8c2fab90c4ed32f2f6fe209/jupyterhub/tests/mocking.py#L221) using any provided configurations as arguments
+- initializing the mocked instance
+- starting the mocked instance
+- finally, a registered finalizer function performs a cleanup and stops the mocked instance
+
+The JupyterHub test suite uses the [pytest-asyncio plugin](https://pytest-asyncio.readthedocs.io/en/latest/) that handles [event-loop](https://docs.python.org/3/library/asyncio-eventloop.html) integration in [Tornado](https://www.tornadoweb.org/en/stable/) applications. This allows for the use of top-level awaits when calling async functions or [fixtures](https://docs.pytest.org/en/6.2.x/fixture.html#what-fixtures-are) during testing. All test functions and fixtures labelled as `async` will run on the same event loop.
+
+```{note}
+With the introduction of [top-level awaits](https://piccolo-orm.com/blog/top-level-await-in-python/), the use of the `io_loop` fixture of the [pytest-tornado plugin](https://www.tornadoweb.org/en/stable/ioloop.html) is no longer necessary. It was initially used to call coroutines. With the upgrades made to `pytest-asyncio`, this usage is now deprecated. It is now, only utilized within the JupyterHub test suite to ensure complete cleanup of resources used during testing such as open file descriptors. This is demonstrated in this [pull request](https://github.com/jupyterhub/jupyterhub/pull/4332).
+More information is provided below.
+```
+
+One of the general goals of the [JupyterHub Pytest Plugin project](https://github.com/jupyterhub/pytest-jupyterhub) is to ensure the MockHub cleanup fully closes and stops all utilized resources during testing so the use of the `io_loop` fixture for teardown is not necessary. This was highlighted in this [issue](https://github.com/jupyterhub/pytest-jupyterhub/issues/30)
+
+For more information on asyncio and event-loops, here are some resources:
+
+- **Read**: [Introduction to the Python event loop](https://www.pythontutorial.net/python-concurrency/python-event-loop)
+- **Read**: [Overview of Async IO in Python 3.7](https://stackabuse.com/overview-of-async-io-in-python-3-7)
+- **Watch**: [Asyncio: Understanding Async / Await in Python](https://www.youtube.com/watch?v=bs9tlDFWWdQ)
+- **Watch**: [Learn Python's AsyncIO #2 - The Event Loop](https://www.youtube.com/watch?v=E7Yn5biBZ58)
+
+## Troubleshooting Test Failures
+
+### All the tests are failing
+
+Make sure you have completed all the steps in {ref}`contributing/setup` successfully, and are able to access JupyterHub from your browser at http://localhost:8000 after starting `jupyterhub` in your command line.
+
+## Code formatting and linting
+
+JupyterHub automatically enforces code formatting. This means that pull requests
+with changes breaking this formatting will receive a commit from pre-commit.ci
+automatically.
+
+To automatically format code locally, you can install pre-commit and register a
+_git hook_ to automatically check with pre-commit before you make a commit if
+the formatting is okay.
+
+```bash
+pip install pre-commit
+pre-commit install --install-hooks
+```
+
+To run pre-commit manually you would do:
+
+```bash
+# check for changes to code not yet committed
+pre-commit run
+
+# check for changes also in already committed code
+pre-commit run --all-files
+```
+
+You may also install [black integration](https://github.com/psf/black#editor-integration)
+into your text editor to format code automatically.

docs/source/explanation/capacity-planning.md

@@ -0,0 +1,308 @@
+# Capacity planning
+
+General capacity planning advice for JupyterHub is hard to give,
+because it depends almost entirely on what your users are doing,
+and what JupyterHub users do varies _wildly_ in terms of resource consumption.
+
+**There is no single answer to "I have X users, what resources do I need?" or "How many users can I support with this machine?"**
+
+Here are three _typical_ Jupyter use patterns that require vastly different resources:
+
+- **Learning**: negligible resources because computation is mostly idle,
+  e.g. students learning programming for the first time
+- **Production code**: very intense, sustained load, e.g. training machine learning models
+- **Bursting**: _mostly_ idle, but needs a lot of resources for short periods of time
+  (interactive research often looks like this)
+
+But just because there's no single answer doesn't mean we can't help.
+So we have gathered here some useful information to help you make your decisions
+about what resources you need based on how your users work,
+including the relative invariants in terms of resources that JupyterHub itself needs.
+
+## JupyterHub infrastructure
+
+JupyterHub consists of a few components that are always running.
+These take up very little resources,
+especially relative to the resources consumed by users when you have more than a few.
+
+As an example, an instance of mybinder.org (running JupyterHub 1.5.0),
+running with typically ~100-150 users has:
+
+| Component | CPU (mean/peak) | Memory (mean/peak) |
+| --------- | --------------- | ------------------ |
+| Hub       | 4% / 13%        | (230 MB / 260 MB)  |
+| Proxy     | 6% / 13%        | (47 MB / 65 MB)    |
+
+So it would be pretty generous to allocate ~25% of one CPU core
+and ~500MB of RAM to overall JupyterHub infrastructure.
+
+The rest is going to be up to your users.
+Per-user overhead from JupyterHub is typically negligible
+up to at least a few hundred concurrent active users.
+
+```{figure} /images/mybinder-hub-components-cpu-memory.png
+JupyterHub component resource usage for mybinder.org.
+```
+
+## Factors to consider
+
+### Static vs elastic resources
+
+A big factor in planning resources is:
+**how much does it cost to change your mind?**
+If you are using a single shared machine with local storage,
+migrating to a new one because it turns out your users don't fit might be very costly.
+You will have to get a new machine, set it up, and maybe even migrate user data.
+
+On the other hand, if you are using ephemeral resources,
+such as node pools in Kubernetes,
+changing resource types costs close to nothing
+because nodes can automatically be added or removed as needed.
+
+Take that cost into account when you are picking how much memory or cpu to allocate to users.
+
+Static resources (like [the-littlest-jupyterhub][]) provide for more **stable, predictable costs**,
+but elastic resources (like [zero-to-jupyterhub][]) tend to provide **lower overall costs**
+(especially when deployed with monitoring allowing cost optimizations over time),
+but which are **less predictable**.
+
+[the-littlest-jupyterhub]: https://the-littlest-jupyterhub.readthedocs.io
+
+[zero-to-jupyterhub]: https://z2jh.jupyter.org
+
+(limits-requests)=
+
+### Limit vs Request for resources
+
+Many scheduling tools like Kubernetes have two separate ways of allocating resources to users.
+A **Request** or **Reservation** describes how much resources are _set aside_ for each user.
+Often, this doesn't have any practical effect other than deciding when a given machine is considered 'full'.
+If you are using expandable resources like an autoscaling Kubernetes cluster,
+a new node must be launched and added to the pool if you 'request' more resources than fit on currently running nodes (a cluster **scale-up event**).
+If you are running on a single VM, this describes how many users you can run at the same time, full stop.
+
+A **Limit**, on the other hand, enforces a limit to how much resources any given user can consume.
+For more information on what happens when users try to exceed their limits, see [](oversubscription).
+
+In the strictest, safest case, you can have these two numbers be the same.
+That means that each user is _limited_ to fit within the resources allocated to it.
+This avoids **[oversubscription](oversubscription)** of resources (allowing use of more than you have available),
+at the expense (in a literal, this-costs-money sense) of reserving lots of usually-idle capacity.
+
+However, you often find that a small fraction of users use more resources than others.
+In this case you may give users limits that _go beyond the amount of resources requested_.
+This is called **oversubscribing** the resources available to users.
+
+Having a gap between the request and the limit means you can fit a number of _typical_ users on a node (based on the request),
+but still limit how much a runaway user can gobble up for themselves.
+
+(oversubscription)=
+
+### Oversubscribed CPU is okay, running out of memory is bad
+
+An important consideration when assigning resources to users is: **What happens when users need more than I've given them?**
+
+A good summary to keep in mind:
+
+> When tasks don't get enough CPU, things are slow.
+> When they don't get enough memory, things are broken.
+
+This means it's **very important that users have enough memory**,
+but much less important that they always have exclusive access to all the CPU they can use.
+
+This relates to [Limits and Requests](limits-requests),
+because these are the consequences of your limits and/or requests not matching what users actually try to use.
+
+A table of mismatched resource allocation situations and their consequences:
+
+| issue                                                    | consequence                                                                           |
+| -------------------------------------------------------- | ------------------------------------------------------------------------------------- |
+| Requests too high                                        | Unnecessarily high cost and/or low capacity.                                          |
+| CPU limit too low                                        | Poor performance experienced by users                                                 |
+| CPU oversubscribed (too-low request + too-high limit)    | Poor performance across the system; may crash, if severe                              |
+| Memory limit too low                                     | Servers killed by Out-of-Memory Killer (OOM); lost work for users                     |
+| Memory oversubscribed (too-low request + too-high limit) | System memory exhaustion - all kinds of hangs and crashes and weird errors. Very bad. |
+
+Note that the 'oversubscribed' problem case is where the request is lower than _typical_ usage,
+meaning that the total reserved resources isn't enough for the total _actual_ consumption.
+This doesn't mean that _all_ your users exceed the request,
+just that the _limit_ gives enough room for the _average_ user to exceed the request.
+
+All of these considerations are important _per node_.
+Larger nodes means more users per node, and therefore more users to average over.
+It also means more chances for multiple outliers on the same node.
+
+### Example case for oversubscribing memory
+
+Take for example, this system and sampling of user behavior:
+
+- System memory = 8G
+- memory request = 1G, limit = 3G
+- typical 'heavy' user: 2G
+- typical 'light' user: 0.5G
+
+This will assign 8 users to those 8G of RAM (remember: only requests are used for deciding when a machine is 'full').
+As long as the total of 8 users _actual_ usage is under 8G, everything is fine.
+But the _limit_ allows a total of 24G to be used,
+which would be a mess if everyone used their full limit.
+But _not_ everyone uses the full limit, which is the point!
+
+This pattern is fine if 1/8 of your users are 'heavy' because _typical_ usage will be ~0.7G,
+and your total usage will be ~5G (`1 × 2 + 7 × 0.5 = 5.5`).
+
+But if _50%_ of your users are 'heavy' you have a problem because that means your users will be trying to use 10G (`4 × 2 + 4 × 0.5 = 10`),
+which you don't have.
+
+You can make guesses at these numbers, but the only _real_ way to get them is to measure (see [](measuring)).
+
+### CPU:memory ratio
+
+Most of the time, you'll find that only one resource is the limiting factor for your users.
+Most often it's memory, but for certain tasks, it could be CPU (or even GPUs).
+
+Many cloud deployments have just one or a few fixed ratios of cpu to memory
+(e.g. 'general purpose', 'high memory', and 'high cpu').
+Setting your secondary resource allocation according to this ratio
+after selecting the more important limit results in a balanced resource allocation.
+
+For instance, some of Google Cloud's ratios are:
+
+| node type   | GB RAM / CPU core |
+| ----------- | ----------------- |
+| n2-highmem  | 8                 |
+| n2-standard | 4                 |
+| n2-highcpu  | 1                 |
+
+(idleness)=
+
+### Idleness
+
+Jupyter being an interactive tool means people tend to spend a lot more time reading and thinking than actually running resource-intensive code.
+This significantly affects how much _cpu_ resources a typical active user needs,
+but often does not significantly affect the _memory_.
+
+Ways to think about this:
+
+- More idle users means unused CPU.
+  This generally means setting your CPU _limit_ higher than your CPU _request_.
+- What do your users do when they _are_ running code?
+  Is it typically single-threaded local computation in a notebook?
+  If so, there's little reason to set a limit higher than 1 CPU core.
+- Do typical computations take a long time, or just a few seconds?
+  Longer typical computations means it's more likely for users to be trying to use the CPU at the same moment,
+  suggesting a higher _request_.
+- Even with idle users, parallel computation adds up quickly - one user fully loading 4 cores and 3 using almost nothing still averages to more than a full CPU core per user.
+- Long-running intense computations suggest higher requests.
+
+Again, using mybinder.org as an example—we run around 100 users on 8-core nodes,
+and still see fairly _low_ overall CPU usage on each user node.
+The limit here is actually Kubernetes' pods per node, not memory _or_ CPU.
+This is likely a extreme case, as many Binder users come from clicking links on webpages
+without any actual intention of running code.
+
+```{figure} /images/mybinder-load5.png
+mybinder.org node CPU usage is low with 50-150 users sharing just 8 cores
+```
+
+### Concurrent users and culling idle servers
+
+Related to [][idleness], all of these resource consumptions and limits are calculated based on **concurrently active users**,
+not total users.
+You might have 10,000 users of your JupyterHub deployment, but only 100 of them running at any given time.
+That 100 is the main number you need to use for your capacity planning.
+JupyterHub costs scale very little based on the number of _total_ users,
+up to a point.
+
+There are two important definitions for **active user**:
+
+- Are they _actually_ there (i.e. a human interacting with Jupyter, or running code that might be )
+- Is their server running (this is where resource reservations and limits are actually applied)
+
+Connecting those two definitions (how long are servers running if their humans aren't using them) is an important area of deployment configuration, usually implemented via the [JupyterHub idle culler service][idle-culler].
+
+[idle-culler]: https://github.com/jupyterhub/jupyterhub-idle-culler
+
+There are a lot of considerations when it comes to culling idle users that will depend:
+
+- How much does it save me to shut down user servers? (e.g. keeping an elastic cluster small, or keeping a fixed-size deployment available to active users)
+- How much does it cost my users to have their servers shut down? (e.g. lost work if shutdown prematurely)
+- How easy do I want it to be for users to keep their servers running? (e.g. Do they want to run unattended simulations overnight? Do you want them to?)
+
+Like many other things in this guide, there are many correct answers leading to different configuration choices.
+For more detail on culling configuration and considerations, consult the [JupyterHub idle culler documentation][idle-culler].
+
+## More tips
+
+### Start strict and generous, then measure
+
+A good tip, in general, is to give your users as much resources as you can afford that you think they _might_ use.
+Then, use resource usage metrics like prometheus to analyze what your users _actually_ need,
+and tune accordingly.
+Remember: **Limits affect your user experience and stability. Requests mostly affect your costs**.
+
+For example, a sensible starting point (lacking any other information) might be:
+
+```yaml
+request:
+  cpu: 0.5
+  mem: 2G
+limit:
+  cpu: 1
+  mem: 2G
+```
+
+(more memory if significant computations are likely - machine learning models, data analysis, etc.)
+
+Some actions
+
+- If you see out-of-memory killer events, increase the limit (or talk to your users!)
+- If you see typical memory well below your limit, reduce the request (but not the limit)
+- If _nobody_ uses that much memory, reduce your limit
+- If CPU is your limiting scheduling factor and your CPUs are mostly idle,
+  reduce the cpu request (maybe even to 0!).
+- If CPU usage continues to be low, increase the limit to 2 or 4 to allow bursts of parallel execution.
+
+(measuring)=
+
+### Measuring user resource consumption
+
+It is _highly_ recommended to deploy monitoring services such as [Prometheus][]
+and [Grafana][] to get a view of your users' resource usage.
+This is the only way to truly know what your users need.
+
+JupyterHub has some experimental [grafana dashboards][] you can use as a starting point,
+to keep an eye on your resource usage.
+Here are some sample charts from (again from mybinder.org),
+showing >90% of users using less than 10% CPU and 200MB,
+but a few outliers near the limit of 1 CPU and 2GB of RAM.
+This is the kind of information you can use to tune your requests and limits.
+
+![Snapshot from JupyterHub's Grafana dashboards on mybinder.org](/images/mybinder-user-resources.png)
+
+[prometheus]: https://prometheus.io
+[grafana]: https://grafana.com
+[grafana dashboards]: https://github.com/jupyterhub/grafana-dashboards
+
+### Measuring costs
+
+Measuring costs may be as important as measuring your users activity.
+If you are using a cloud provider, you can often use cost thresholds and quotas to instruct them to notify you if your costs are too high,
+e.g. "Have AWS send me an email if I hit X spending trajectory on week 3 of the month."
+You can then use this information to tune your resources based on what you can afford.
+You can mix this information with user resource consumption to figure out if you have a problem,
+e.g. "my users really do need X resources, but I can only afford to give them 80% of X."
+This information may prove useful when asking your budget-approving folks for more funds.
+
+### Additional resources
+
+There are lots of other resources for cost and capacity planning that may be specific to JupyterHub and/or your cloud provider.
+
+Here are some useful links to other resources
+
+- [Zero to JupyterHub](https://z2jh.jupyter.org) documentation on
+  - [projecting costs](https://z2jh.jupyter.org/en/latest/administrator/cost.html)
+  - [configuring user resources](https://z2jh.jupyter.org/en/latest/jupyterhub/customizing/user-resources.html)
+- Cloud platform cost calculators:
+  - [Google Cloud](https://cloud.google.com/products/calculator/)
+  - [Amazon AWS](https://calculator.aws)
+  - [Microsoft Azure](https://azure.microsoft.com/en-us/pricing/calculator/)

docs/source/explanation/database.md

@@ -0,0 +1,183 @@
+(hub-database)=
+
+# The Hub's Database
+
+JupyterHub uses a database to store information about users, services, and other data needed for operating the Hub.
+This is the **state** of the Hub.
+
+## Why does JupyterHub have a database?
+
+JupyterHub is a **stateful** application (more on that 'state' later).
+Updating JupyterHub's configuration or upgrading the version of JupyterHub requires restarting the JupyterHub process to apply the changes.
+We want to minimize the disruption caused by restarting the Hub process, so it can be a mundane, frequent, routine activity.
+Storing state information outside the process for later retrieval is necessary for this, and one of the main thing databases are for.
+
+A lot of the operations in JupyterHub are also **relationships**, which is exactly what SQL databases are great at.
+For example:
+
+- Given an API token, what user is making the request?
+- Which users don't have running servers?
+- Which servers belong to user X?
+- Which users have not been active in the last 24 hours?
+
+Finally, a database allows us to have more information stored without needing it all loaded in memory,
+e.g. supporting a large number (several thousands) of inactive users.
+
+## What's in the database?
+
+The short answer of what's in the JupyterHub database is "everything."
+JupyterHub's **state** lives in the database.
+That is, everything JupyterHub needs to be aware of to function that _doesn't_ come from the configuration files, such as
+
+- users, roles, role assignments
+- state, urls of running servers
+- Hashed API tokens
+- Short-lived state related to OAuth flow
+- Timestamps for when users, tokens, and servers were last used
+
+### What's _not_ in the database
+
+Not _quite_ all of JupyterHub's state is in the database.
+This mostly involves transient state, such as the 'pending' transitions of Spawners (starting, stopping, etc.).
+Anything not in the database must be reconstructed on Hub restart, and the only sources of information to do that are the database and JupyterHub configuration file(s).
+
+## How does JupyterHub use the database?
+
+JupyterHub makes some _unusual_ choices in how it connects to the database.
+These choices represent trade-offs favoring single-process simplicity and performance at the expense of horizontal scalability (multiple Hub instances).
+
+We often say that the Hub 'owns' the database.
+This ownership means that we assume the Hub is the only process that will talk to the database.
+This assumption enables us to make several caching optimizations that dramatically improve JupyterHub's performance (i.e. data written recently to the database can be read from memory instead of fetched again from the database) that would not work if multiple processes could be interacting with the database at the same time.
+
+Database operations are also synchronous, so while JupyterHub is waiting on a database operation, it cannot respond to other requests.
+This allows us to avoid complex locking mechanisms, because transaction races can only occur during an `await`, so we only need to make sure we've completed any given transaction before the next `await` in a given request.
+
+:::{note}
+We are slowly working to remove these assumptions, and moving to a more traditional db session per-request pattern.
+This will enable multiple Hub instances and enable scaling JupyterHub, but will significantly reduce the number of active users a single Hub instance can serve.
+:::
+
+### Database performance in a typical request
+
+Most authenticated requests to JupyterHub involve a few database transactions:
+
+1. look up the authenticated user (e.g. look up token by hash, then resolve owner and permissions)
+2. record activity
+3. perform any relevant changes involved in processing the request (e.g. create the records for a running server when starting one)
+
+This means that the database is involved in almost every request, but only in quite small, simple queries, e.g.:
+
+- lookup one token by hash
+- lookup one user by name
+- list tokens or servers for one user (typically 1-10)
+- etc.
+
+### The database as a limiting factor
+
+As a result of the above transactions in most requests, database performance is the _leading_ factor in JupyterHub's baseline requests-per-second performance, but that cost does not scale significantly with the number of users, active or otherwise.
+However, the database is _rarely_ a limiting factor in JupyterHub performance in a practical sense, because the main thing JupyterHub does is start, stop, and monitor whole servers, which take far more time than any small database transaction, no matter how many records you have or how slow your database is (within reason).
+Additionally, there is usually _very_ little load on the database itself.
+
+By far the most taxing activity on the database is the 'list all users' endpoint, primarily used by the [idle-culling service](https://github.com/jupyterhub/jupyterhub-idle-culler).
+Database-based optimizations have been added to make even these operations feasible for large numbers of users:
+
+1. State filtering on [GET /hub/api/users?state=active](../reference/rest-api.html#/default/get_users){.external},
+   which limits the number of results in the query to only the relevant subset (added in JupyterHub 1.3), rather than all users.
+2. [Pagination](api-pagination) of all list endpoints, allowing the request of a large number of resources to be more fairly balanced with other Hub activities across multiple requests (added in 2.0).
+
+:::{note}
+It's important to note when discussing performance and limiting factors and that all of this only applies to requests to `/hub/...`.
+The Hub and its database are not involved in most requests to single-user servers (`/user/...`), which is by design, and largely motivated by the fact that the Hub itself doesn't _need_ to be fast because its operations are infrequent and large.
+:::
+
+## Database backends
+
+JupyterHub supports a variety of database backends via [SQLAlchemy][].
+The default is sqlite, which works great for many cases, but you should be able to use many backends supported by SQLAlchemy.
+Usually, this will mean PostgreSQL or MySQL, both of which are officially supported and well tested with JupyterHub, but others may work as well.
+See [SQLAlchemy's docs][sqlalchemy-dialect] for how to connect to different database backends.
+Doing so generally involves:
+
+1. installing a Python package that provides a client implementation, and
+2. setting [](JupyterHub.db_url) to connect to your database with the specified implementation
+
+[sqlalchemy-dialect]: https://docs.sqlalchemy.org/en/20/dialects/
+[sqlalchemy]: https://www.sqlalchemy.org
+
+### Default backend: SQLite
+
+The default database backend for JupyterHub is [SQLite](https://sqlite.org).
+We have chosen SQLite as JupyterHub's default because it's simple (the 'database' is a single file) and ubiquitous (it is in the Python standard library).
+It works very well for testing, small deployments, and workshops.
+
+For production systems, SQLite has some disadvantages when used with JupyterHub:
+
+- `upgrade-db` may not always work, and you may need to start with a fresh database
+- `downgrade-db` **will not** work if you want to rollback to an earlier
+  version, so backup the `jupyterhub.sqlite` file before upgrading (JupyterHub automatically creates a date-stamped backup file when upgrading sqlite)
+
+The sqlite documentation provides a helpful page about [when to use SQLite and
+where traditional RDBMS may be a better choice](https://sqlite.org/whentouse.html).
+
+### Picking your database backend (PostgreSQL, MySQL)
+
+When running a long term deployment or a production system, we recommend using a full-fledged relational database, such as [PostgreSQL](https://www.postgresql.org) or [MySQL](https://www.mysql.com), that supports the SQL `ALTER TABLE` statement, which is used in some database upgrade steps.
+
+In general, you select your database backend with [](JupyterHub.db_url), and can further configure it (usually not necessary) with [](JupyterHub.db_kwargs).
+
+## Notes and Tips
+
+### SQLite
+
+The SQLite database should not be used on NFS. SQLite uses reader/writer locks
+to control access to the database. This locking mechanism might not work
+correctly if the database file is kept on an NFS filesystem. This is because
+`fcntl()` file locking is broken on many NFS implementations. Therefore, you
+should avoid putting SQLite database files on NFS since it will not handle well
+multiple processes which might try to access the file at the same time.
+
+### PostgreSQL
+
+We recommend using PostgreSQL for production if you are unsure whether to use
+MySQL or PostgreSQL or if you do not have a strong preference.
+There is additional configuration required for MySQL that is not needed for PostgreSQL.
+
+For example, to connect to a postgres database with psycopg2:
+
+1. install psycopg2: `pip instal psycopg2` (or `psycopg2-binary` to avoid compilation, which is [not recommended for production][psycopg2-binary])
+2. set authentication via environment variables `PGUSER` and `PGPASSWORD`
+3. configure [](JupyterHub.db_url):
+
+   ```python
+   c.JupyterHub.db_url = "postgres+psycopg2://my-postgres-server:5432/my-db-name"
+   ```
+
+[psycopg2-binary]: https://www.psycopg.org/docs/install.html#psycopg-vs-psycopg-binary
+
+### MySQL / MariaDB
+
+- You should probably use the `pymysql` or `mysqlclient` sqlalchemy provider, or another backend [recommended by sqlalchemy](https://docs.sqlalchemy.org/en/20/dialects/mysql.html#dialect-mysql)
+- You also need to set `pool_recycle` to some value (typically 60 - 300, JupyterHub will default to 60)
+  which depends on your MySQL setup. This is necessary since MySQL kills
+  connections serverside if they've been idle for a while, and the connection
+  from the hub will be idle for longer than most connections. This behavior
+  will lead to frustrating 'the connection has gone away' errors from
+  sqlalchemy if `pool_recycle` is not set.
+- If you use `utf8mb4` collation with MySQL earlier than 5.7.7 or MariaDB
+  earlier than 10.2.1 you may get an `1709, Index column size too large` error.
+  To fix this you need to set `innodb_large_prefix` to enabled and
+  `innodb_file_format` to `Barracuda` to allow for the index sizes jupyterhub
+  uses. `row_format` will be set to `DYNAMIC` as long as those options are set
+  correctly. Later versions of MariaDB and MySQL should set these values by
+  default, as well as have a default `DYNAMIC` `row_format` and pose no trouble
+  to users.
+
+For example, to connect to a mysql database with mysqlclient:
+
+1. install mysqlclient: `pip install mysqlclient`
+2. configure [](JupyterHub.db_url):
+
+   ```python
+   c.JupyterHub.db_url = "mysql+mysqldb://myuser:mypassword@my-sql-server:3306/my-db-name"
+   ```

docs/source/explanation/index.md

@@ -0,0 +1,14 @@
+# Explanation
+
+_Explanation_ documentation provide big-picture descriptions of how JupyterHub works. This section is meant to build your understanding of particular topics.
+
+```{toctree}
+:maxdepth: 1
+
+capacity-planning
+database
+websecurity
+oauth
+singleuser
+../rbac/index
+```

docs/source/explanation/oauth.md

@@ -0,0 +1,373 @@
+# JupyterHub and OAuth
+
+JupyterHub uses [OAuth 2](https://oauth.net/2/) as an internal mechanism for authenticating users.
+As such, JupyterHub itself always functions as an OAuth **provider**.
+You can find out more about what that means [below](oauth-terms).
+
+Additionally, JupyterHub is _often_ deployed with [OAuthenticator](https://oauthenticator.readthedocs.io),
+where an external identity provider, such as GitHub or KeyCloak, is used to authenticate users.
+When this is the case, there are _two_ nested OAuth flows:
+an _internal_ OAuth flow where JupyterHub is the **provider**,
+and an _external_ OAuth flow, where JupyterHub is the **client**.
+
+This means that when you are using JupyterHub, there is always _at least one_ and often two layers of OAuth involved in a user logging in and accessing their server.
+
+The following points are noteworthy:
+
+- Single-user servers _never_ need to communicate with or be aware of the upstream provider configured in your Authenticator.
+  As far as the servers are concerned, only JupyterHub is an OAuth provider,
+  and how users authenticate with the Hub itself is irrelevant.
+- When interacting with a single-user server,
+  there are ~always two tokens:
+  first, a token issued to the server itself to communicate with the Hub API,
+  and second, a per-user token in the browser to represent the completed login process and authorized permissions.
+  More on this [later](two-tokens).
+
+(oauth-terms)=
+
+## Key OAuth terms
+
+Here are some key definitions to keep in mind when we are talking about OAuth.
+You can also read more in detail [here](https://www.oauth.com/oauth2-servers/definitions/).
+
+- **provider**: The entity responsible for managing identity and authorization;
+  always a web server.
+  JupyterHub is _always_ an OAuth provider for JupyterHub's components.
+  When OAuthenticator is used, an external service, such as GitHub or KeyCloak, is also an OAuth provider.
+- **client**: An entity that requests OAuth **tokens** on a user's behalf;
+  generally a web server of some kind.
+  OAuth **clients** are services that _delegate_ authentication and/or authorization
+  to an OAuth **provider**.
+  JupyterHub _services_ or single-user _servers_ are OAuth **clients** of the JupyterHub **provider**.
+  When OAuthenticator is used, JupyterHub is itself _also_ an OAuth **client** for the external OAuth **provider**, e.g. GitHub.
+- **browser**: A user's web browser, which makes requests and stores things like cookies.
+- **token**: The secret value used to represent a user's authorization. This is the final product of the OAuth process.
+- **code**: A short-lived temporary secret that the **client** exchanges
+  for a **token** at the conclusion of OAuth,
+  in what's generally called the "OAuth callback handler."
+
+## One oauth flow
+
+OAuth **flow** is what we call the sequence of HTTP requests involved in authenticating a user and issuing a token, ultimately used for authorizing access to a service or single-user server.
+
+A single OAuth flow typically goes like this:
+
+### OAuth request and redirect
+
+1. A **browser** makes an HTTP request to an OAuth **client**.
+2. There are no credentials, so the client _redirects_ the browser to an "authorize" page on the OAuth **provider** with some extra information:
+   - the OAuth **client ID** of the client itself.
+   - the **redirect URI** to be redirected back to after completion.
+   - the **scopes** requested, which the user should be presented with to confirm.
+     This is the "X would like to be able to Y on your behalf. Allow this?" page you see on all the "Login with ..." pages around the Internet.
+3. During this authorize step,
+   the browser must be _authenticated_ with the provider.
+   This is often already stored in a cookie,
+   but if not the provider webapp must begin its _own_ authentication process before serving the authorization page.
+   This _may_ even begin another OAuth flow!
+4. After the user tells the provider that they want to proceed with the authorization,
+   the provider records this authorization in a short-lived record called an **OAuth code**.
+5. Finally, the oauth provider redirects the browser _back_ to the oauth client's "redirect URI"
+   (or "OAuth callback URI"),
+   with the OAuth code in a URL parameter.
+
+That marks the end of the requests made between the **browser** and the **provider**.
+
+### State after redirect
+
+At this point:
+
+- The browser is authenticated with the _provider_.
+- The user's authorized permissions are recorded in an _OAuth code_.
+- The _provider_ knows that the permissions requested by the OAuth client have been granted, but the client doesn't know this yet.
+- All the requests so far have been made directly by the browser.
+  No requests have originated from the client or provider.
+
+### OAuth Client Handles Callback Request
+
+At this stage, we get to finish the OAuth process.
+Let's dig into what the OAuth client does when it handles
+the OAuth callback request.
+
+- The OAuth client receives the _code_ and makes an API request to the _provider_ to exchange the code for a real _token_.
+  This is the first direct request between the OAuth _client_ and the _provider_.
+- Once the token is retrieved, the client _usually_
+  makes a second API request to the _provider_
+  to retrieve information about the owner of the token (the user).
+  This is the step where behavior diverges for different OAuth providers.
+  Up to this point, all OAuth providers are the same, following the OAuth specification.
+  However, OAuth does not define a standard for issuing tokens in exchange for information about their owner or permissions ([OpenID Connect](https://openid.net/connect/) does that),
+  so this step may be different for each OAuth provider.
+- Finally, the OAuth client stores its own record that the user is authorized in a cookie.
+  This could be the token itself, or any other appropriate representation of successful authentication.
+- Now that credentials have been established,
+  the browser can be redirected to the _original_ URL where it started,
+  to try the request again.
+  If the client wasn't able to keep track of the original URL all this time
+  (not always easy!),
+  you might end up back at a default landing page instead of where you started the login process. This is frustrating!
+
+😮‍💨 _phew_.
+
+So that's _one_ OAuth process.
+
+## Full sequence of OAuth in JupyterHub
+
+Let's go through the above OAuth process in JupyterHub,
+with specific examples of each HTTP request and what information it contains.
+For bonus points, we are using the double-OAuth example of JupyterHub configured with GitHubOAuthenticator.
+
+To disambiguate, we will call the OAuth process where JupyterHub is the **provider** "internal OAuth,"
+and the one with JupyterHub as a **client** "external OAuth."
+
+Our starting point:
+
+- a user's single-user server is running. Let's call them `danez`
+- Jupyterhub is running with GitHub as an OAuth provider (this means two full instances of OAuth),
+- Danez has a fresh browser session with no cookies yet.
+
+First request:
+
+- browser->single-user server running JupyterLab or Jupyter Classic
+- `GET /user/danez/notebooks/mynotebook.ipynb`
+- no credentials, so single-user server (as an OAuth **client**) starts internal OAuth process with JupyterHub (the **provider**)
+- response: 302 redirect -> `/hub/api/oauth2/authorize`
+  with:
+  - client-id=`jupyterhub-user-danez`
+  - redirect-uri=`/user/danez/oauth_callback` (we'll come back later!)
+
+Second request, following redirect:
+
+- browser->JupyterHub
+- `GET /hub/api/oauth2/authorize`
+- no credentials, so JupyterHub starts external OAuth process _with GitHub_
+- response: 302 redirect -> `https://github.com/login/oauth/authorize`
+  with:
+  - client-id=`jupyterhub-client-uuid`
+  - redirect-uri=`/hub/oauth_callback` (we'll come back later!)
+
+_pause_ This is where JupyterHub configuration comes into play.
+Recall, in this case JupyterHub is using:
+
+```python
+c.JupyterHub.authenticator_class = 'github'
+```
+
+That means authenticating a request to the Hub itself starts
+a _second_, external OAuth process with GitHub as a provider.
+This external OAuth process is optional, though.
+If you were using the default username+password PAMAuthenticator,
+this redirect would have been to `/hub/login` instead, to present the user
+with a login form.
+
+Third request, following redirect:
+
+- browser->GitHub
+- `GET https://github.com/login/oauth/authorize`
+
+Here, GitHub prompts for login and asks for confirmation of authorization
+(more redirects if you aren't logged in to GitHub yet, but ultimately back to this `/authorize` URL).
+
+After successful authorization
+(either by looking up a pre-existing authorization,
+or recording it via form submission)
+GitHub issues an **OAuth code** and redirects to `/hub/oauth_callback?code=github-code`
+
+Next request:
+
+- browser->JupyterHub
+- `GET /hub/oauth_callback?code=github-code`
+
+Inside the callback handler, JupyterHub makes two API requests:
+
+The first:
+
+- JupyterHub->GitHub
+- `POST https://github.com/login/oauth/access_token`
+- request made with OAuth **code** from URL parameter
+- response includes an access **token**
+
+The second:
+
+- JupyterHub->GitHub
+- `GET https://api.github.com/user`
+- request made with access **token** in the `Authorization` header
+- response is the user model, including username, email, etc.
+
+Now the external OAuth callback request completes with:
+
+- set cookie on `/hub/` path, recording jupyterhub authentication so we don't need to do external OAuth with GitHub again for a while
+- redirect -> `/hub/api/oauth2/authorize`
+
+🎉 At this point, we have completed our first OAuth flow! 🎉
+
+Now, we get our first repeated request:
+
+- browser->jupyterhub
+- `GET /hub/api/oauth2/authorize`
+- this time with credentials,
+  so jupyterhub either
+  1. serves the internal authorization confirmation page, or
+  2. automatically accepts authorization (shortcut taken when a user is visiting their own server)
+- redirect -> `/user/danez/oauth_callback?code=jupyterhub-code`
+
+Here, we start the same OAuth callback process as before, but at Danez's single-user server for the _internal_ OAuth.
+
+- browser->single-user server
+- `GET /user/danez/oauth_callback`
+
+(in handler)
+
+Inside the internal OAuth callback handler,
+Danez's server makes two API requests to JupyterHub:
+
+The first:
+
+- single-user server->JupyterHub
+- `POST /hub/api/oauth2/token`
+- request made with oauth code from url parameter
+- response includes an API token
+
+The second:
+
+- single-user server->JupyterHub
+- `GET /hub/api/user`
+- request made with token in the `Authorization` header
+- response is the user model, including username, groups, etc.
+
+Finally completing `GET /user/danez/oauth_callback`:
+
+- response sets cookie, storing encrypted access token
+- _finally_ redirects back to the original `/user/danez/notebooks/mynotebook.ipynb`
+
+Final request:
+
+- browser -> single-user server
+- `GET /user/danez/notebooks/mynotebook.ipynb`
+- encrypted jupyterhub token in cookie
+
+To authenticate this request, the single token stored in the encrypted cookie is passed to the Hub for verification:
+
+- single-user server -> Hub
+- `GET /hub/api/user`
+- browser's token in Authorization header
+- response: user model with name, groups, etc.
+
+If the user model matches who should be allowed (e.g. Danez),
+then the request is allowed.
+See [Scopes in JupyterHub](jupyterhub-scopes) for how JupyterHub uses scopes to determine authorized access to servers and services.
+
+_the end_
+
+## Token caches and expiry
+
+Because tokens represent information from an external source,
+they can become 'stale,'
+or the information they represent may no longer be accurate.
+For example: a user's GitHub account may no longer be authorized to use JupyterHub,
+that should ultimately propagate to revoking access and force logging in again.
+
+To handle this, OAuth tokens and the various places they are stored can _expire_,
+which should have the same effect as no credentials,
+and trigger the authorization process again.
+
+In JupyterHub's internal OAuth, we have these layers of information that can go stale:
+
+- The OAuth client has a **cache** of Hub responses for tokens,
+  so it doesn't need to make API requests to the Hub for every request it receives.
+  This cache has an expiry of five minutes by default,
+  and is governed by the configuration `HubAuth.cache_max_age` in the single-user server.
+- The internal OAuth token is stored in a cookie, which has its own expiry (default: 14 days),
+  governed by `JupyterHub.cookie_max_age_days`.
+- The internal OAuth token itself can also expire,
+  which is by default the same as the cookie expiry,
+  since it makes sense for the token itself and the place it is stored to expire at the same time.
+  This is governed by `JupyterHub.cookie_max_age_days` first,
+  or can overridden by `JupyterHub.oauth_token_expires_in`.
+
+That's all for _internal_ auth storage,
+but the information from the _external_ authentication provider
+(could be PAM or GitHub OAuth, etc.) can also expire.
+Authenticator configuration governs when JupyterHub needs to ask again,
+triggering the external login process anew before letting a user proceed.
+
+- `jupyterhub-hub-login` cookie stores that a browser is authenticated with the Hub.
+  This expires according to `JupyterHub.cookie_max_age_days` configuration,
+  with a default of 14 days.
+  The `jupyterhub-hub-login` cookie is encrypted with `JupyterHub.cookie_secret`
+  configuration.
+- {meth}`.Authenticator.refresh_user` is a method to refresh a user's auth info.
+  By default, it does nothing, but it can return an updated user model if a user's information has changed,
+  or force a full login process again if needed.
+- {attr}`.Authenticator.auth_refresh_age` configuration governs how often
+  `refresh_user()` will be called to check if a user must login again (default: 300 seconds).
+- {attr}`.Authenticator.refresh_pre_spawn` configuration governs whether
+  `refresh_user()` should be called prior to spawning a server,
+  to force fresh auth info when a server is launched (default: False).
+  This can be useful when Authenticators pass access tokens to spawner environments, to ensure they aren't getting a stale token that's about to expire.
+
+**So what happens when these things expire or get stale?**
+
+- If the HubAuth **token response cache** expires,
+  when a request is made with a token,
+  the Hub is asked for the latest information about the token.
+  This usually has no visible effect, since it is just refreshing a cache.
+  If it turns out that the token itself has expired or been revoked,
+  the request will be denied.
+- If the token has expired, but is still in the cookie:
+  when the token response cache expires,
+  the next time the server asks the hub about the token,
+  no user will be identified and the internal OAuth process begins again.
+- If the token _cookie_ expires, the next browser request will be made with no credentials,
+  and the internal OAuth process will begin again.
+  This will usually have the form of a transparent redirect browsers won't notice.
+  However, if this occurs on an API request in a long-lived page visit
+  such as a JupyterLab session, the API request may fail and require
+  a page refresh to get renewed credentials.
+- If the _JupyterHub_ cookie expires, the next time the browser makes a request to the Hub,
+  the Hub's authorization process must begin again (e.g. login with GitHub).
+  Hub cookie expiry on its own **does not** mean that a user can no longer access their single-user server!
+- If credentials from the upstream provider (e.g. GitHub) become stale or outdated,
+  these will not be refreshed until/unless `refresh_user` is called
+  _and_ `refresh_user()` on the given Authenticator is implemented to perform such a check.
+  At this point, few Authenticators implement `refresh_user` to support this feature.
+  If your Authenticator does not or cannot implement `refresh_user`,
+  the only way to force a check is to reset the `JupyterHub.cookie_secret` encryption key,
+  which invalidates the `jupyterhub-hub-login` cookie for all users.
+
+### Logging out
+
+Logging out of JupyterHub means clearing and revoking many of these credentials:
+
+- The `jupyterhub-hub-login` cookie is revoked, meaning the next request to the Hub itself will require a new login.
+- The token stored in the `jupyterhub-user-username` cookie for the single-user server
+  will be revoked, based on its associaton with `jupyterhub-session-id`, but the _cookie itself cannot be cleared at this point_
+- The shared `jupyterhub-session-id` is cleared, which ensures that the HubAuth **token response cache** will not be used,
+  and the next request with the expired token will ask the Hub, which will inform the single-user server that the token has expired
+
+## Extra bits
+
+(two-tokens)=
+
+### A tale of two tokens
+
+**TODO**: discuss API token issued to server at startup ($JUPYTERHUB_API_TOKEN)
+and OAuth-issued token in the cookie,
+and some details of how JupyterLab currently deals with that.
+They are different, and JupyterLab should be making requests using the token from the cookie,
+not the token from the server,
+but that is not currently the case.
+
+### Redirect loops
+
+In general, an authenticated web endpoint has this behavior,
+based on the authentication/authorization state of the browser:
+
+- If authorized, allow the request to happen
+- If authenticated (I know who you are) but not authorized (you are not allowed), fail with a 403 permission denied error
+- If not authenticated, start a redirect process to establish authorization,
+  which should end in a redirect back to the original URL to try again.
+  **This is why problems in authentication result in redirect loops!**
+  If the second request fails to detect the authentication that should have been established during the redirect,
+  it will start the authentication redirect process over again,
+  and keep redirecting in a loop until the browser balks.

docs/source/explanation/singleuser.md

@@ -0,0 +1,109 @@
+(singleuser)=
+
+# The JupyterHub single-user server
+
+When a user logs into JupyterHub, they get a 'server', which we usually call the **single-user server**, because it's a server that's meant for a single JupyterHub user.
+Each JupyterHub user gets a different one (or more than one!).
+
+A single-user server is a process running somewhere that is:
+
+1. accessible over http[s],
+2. authenticated via JupyterHub using OAuth 2.0,
+3. started by a [Spawner](spawners), and
+4. 'owned' by a single JupyterHub user
+
+## The single-user server command
+
+The Spawner's default single-user server startup command, `jupyterhub-singleuser`, launches `jupyter-server`, the same program used when you run `jupyter lab` on your laptop.
+(_It can also launch the legacy `jupyter-notebook` server_).
+That's why JupyterHub looks familiar to folks who are already using Jupyter at home or elsewhere.
+It's the same!
+`jupyterhub-singleuser` _customizes_ that program to change (approximately) one thing: **authenticate requests with JupyterHub**.
+
+(singleuser-auth)=
+
+## Single-user server authentication
+
+Implementation-wise, JupyterHub single-user servers are a special-case of {ref}`services`
+and as such use the same (OAuth) authentication mechanism (more on OAuth in JupyterHub at [](oauth)).
+This is primarily implemented in the {class}`~.HubOAuth` class.
+
+This code resides in `jupyterhub.singleuser` subpackage of JupyterHub.
+The main task of this code is to:
+
+1. resolve a JupyterHub token to a JupyterHub user (authenticate)
+2. check permissions (`access:servers`) for the token to make sure the request should be allowed (authorize)
+3. if not authorized, begin the OAuth process with a redirect to the Hub
+4. after login, store OAuth tokens in a cookie only used by this single-user server
+5. implement logout to clear the cookie
+
+Most of this is implemented in the {class}`~.HubOAuth` class. `jupyterhub.singleuser` is responsible for _adapting_ the base Jupyter Server to use HubOAuth for these tasks.
+
+### JupyterHub authentication extension
+
+By default, `jupyter-server` uses its own cookie to authenticate.
+If that cookie is not present, the server redirects you a login page and asks you to enter a password or token.
+
+Jupyter Server 2.0 introduces two new _APIs_ for customizing authentication: the [IdentityProvider](inv:jupyter-server#jupyter_server.auth.IdentityProvider) and the [Authorizer](inv:jupyter-server#jupyter_server.auth.Authorizer).
+More information can be found in the [Jupyter Server documentation](https://jupyter-server.readthedocs.io).
+
+JupyterHub implements these APIs in `jupyterhub.singleuser.extension`.
+
+The IdentityProvider is responsible for _authenticating_ requests.
+In JupyterHub, that means extracting OAuth tokens from the request and resolving them to a JupyterHub user.
+
+The Authorizer is a _separate_ API for _authorizing_ actions on particular resources.
+Because the JupyterHub IdentityProvider only allows _authenticating_ users who already have the necessary `access:servers` permission to access the server, the default Authorizer only contains a redundant check for this same permission, and ignores the resource inputs.
+However, specifying a _custom_ Authorizer allows for granular permissions, such as read-only access to subsets of a shared server.
+
+### JupyterHub authentication via subclass
+
+Prior to Jupyter Server 2 (i.e. Jupyter Server 1.x or the legacy `jupyter-notebook` server), JupyterHub authentication is applied via _subclass_.
+Originally a subclass of `NotebookApp`,
+this approach works with both `jupyter-server` and `jupyter-notebook`.
+Instead of using the extension mechanisms above,
+the server application is _subclassed_. This worked well in the `jupyter-notebook` days,
+but doesn't fit well with Jupyter Server's extension-based architecture.
+
+### Selecting jupyterhub-singleuser implementation
+
+Using the JupyterHub singleuser-server extension is the default behavior of JupyterHub 4 and Jupyter Server 2, otherwise the subclass approach is taken.
+
+You can opt-out of the extension by setting the environment variable `JUPYTERHUB_SINGLEUSER_EXTENSION=0`:
+
+```python
+c.Spawner.environment.update(
+    {
+        "JUPYTERHUB_SINGLEUSER_EXTENSION": "0",
+    }
+)
+```
+
+The subclass approach will also be taken if you've opted to use the classic notebook server with:
+
+```
+JUPYTERHUB_SINGLEUSER_APP=notebook
+```
+
+which was introduced in JupyterHub 2.
+
+## Other customizations
+
+`jupyterhub-singleuser` makes other small customizations to how the single-user server behaves:
+
+1. logs activity on the single-user server, used in [idle-culling](https://github.com/jupyterhub/jupyterhub-idle-culler).
+2. disables some features that don't make sense in JupyterHub (trash, retrying ports)
+3. loading options such as URLs and SSL configuration from the environment
+4. customize logging for consistency with JupyterHub logs
+
+## Running a single-user server that's not `jupyterhub-singleuser`
+
+By default, `jupyterhub-singleuser` is the same `jupyter-server` used by JupyterLab, Jupyter notebook (>= 7), etc.
+But technically, all JupyterHub cares about is that it is:
+
+1. an http server at the prescribed URL, accessible from the Hub and proxy, and
+2. authenticated via [OAuth](oauth) with the Hub (it doesn't even have to do this, if you want to do your own authentication, as is done in BinderHub)
+
+which means that you can customize JupyterHub to launch _any_ web application that meets these criteria, by following the specifications in {ref}`services`.
+
+Most of the time, though, it's easier to use [jupyter-server-proxy](https://jupyter-server-proxy.readthedocs.io) if you want to launch additional web applications in JupyterHub.

docs/source/explanation/websecurity.md

@@ -0,0 +1,272 @@
+(web-security)=
+
+# Security Overview
+
+The **Security Overview** section helps you learn about:
+
+- the design of JupyterHub with respect to web security
+- the semi-trusted user
+- the available mitigations to protect untrusted users from each other
+- the value of periodic security audits
+
+This overview also helps you obtain a deeper understanding of how JupyterHub
+works.
+
+## Semi-trusted and untrusted users
+
+JupyterHub is designed to be a _simple multi-user server for modestly sized
+groups_ of **semi-trusted** users. While the design reflects serving
+semi-trusted users, JupyterHub can also be suitable for serving **untrusted** users,
+but **is not suitable for untrusted users** in its default configuration.
+
+As a result, using JupyterHub with **untrusted** users means more work by the
+administrator, since much care is required to secure a Hub, with extra caution on
+protecting users from each other.
+
+One aspect of JupyterHub's _design simplicity_ for **semi-trusted** users is that
+the Hub and single-user servers are placed in a _single domain_, behind a
+[_proxy_][configurable-http-proxy]. If the Hub is serving untrusted
+users, many of the web's cross-site protections are not applied between
+single-user servers and the Hub, or between single-user servers and each
+other, since browsers see the whole thing (proxy, Hub, and single user
+servers) as a single website (i.e. single domain).
+
+## Protect users from each other
+
+To protect users from each other, a user must **never** be able to write arbitrary
+HTML and serve it to another user on the Hub's domain. This is prevented by JupyterHub's
+authentication setup because only the owner of a given single-user notebook server is
+allowed to view user-authored pages served by the given single-user notebook
+server.
+
+To protect all users from each other, JupyterHub administrators must
+ensure that:
+
+- A user **does not have permission** to modify their single-user notebook server,
+  including:
+  - the installation of new packages in the Python environment that runs
+    their single-user server;
+  - the creation of new files in any `PATH` directory that precedes the
+    directory containing `jupyterhub-singleuser` (if the `PATH` is used
+    to resolve the single-user executable instead of using an absolute path);
+  - the modification of environment variables (e.g. PATH, PYTHONPATH) for
+    their single-user server;
+  - the modification of the configuration of the notebook server
+    (the `~/.jupyter` or `JUPYTER_CONFIG_DIR` directory).
+  - unrestricted selection of the base environment (e.g. the image used in container-based Spawners)
+
+If any additional services are run on the same domain as the Hub, the services
+**must never** display user-authored HTML that is neither _sanitized_ nor _sandboxed_
+to any user that lacks authentication as the author of a file.
+
+### Sharing access to servers
+
+Because sharing access to servers (via `access:servers` scopes or the sharing feature in JupyterHub 5) by definition means users can serve each other files, enabling sharing is not suitable for untrusted users without also enabling per-user domains.
+
+JupyterHub does not enable any sharing by default.
+
+## Mitigate security issues
+
+The several approaches to mitigating security issues with configuration
+options provided by JupyterHub include:
+
+### Enable user subdomains
+
+JupyterHub provides the ability to run single-user servers on their own
+domains. This means the cross-origin protections between servers has the
+desired effect, and user servers and the Hub are protected from each other.
+
+**Subdomains are the only way to reliably isolate user servers from each other.**
+
+To enable subdomains, set:
+
+```python
+c.JupyterHub.subdomain_host = "https://jupyter.example.org"
+```
+
+When subdomains are enabled, each user's single-user server will be at e.g. `https://username.jupyter.example.org`.
+This also requires all user subdomains to point to the same address,
+which is most easily accomplished with wildcard DNS, where a single A record points to your server and a wildcard CNAME record points to your A record:
+
+```
+A        jupyter.example.org  192.168.1.123
+CNAME  *.jupyter.example.org  jupyter.example.org
+```
+
+Since this spreads the service across multiple domains, you will likely need wildcard SSL as well,
+matching `*.jupyter.example.org`.
+
+Unfortunately, for many institutional domains, wildcard DNS and SSL may not be available.
+
+We also **strongly encourage** serving JupyterHub and user content on a domain that is _not_ a subdomain of any sensitive content.
+For reasoning, see [GitHub's discussion of moving user content to github.io from \*.github.com](https://github.blog/2013-04-09-yummy-cookies-across-domains/).
+
+**If you do plan to serve untrusted users, enabling subdomains is highly encouraged**,
+as it resolves many security issues, which are difficult to unavoidable when JupyterHub is on a single-domain.
+
+:::{important}
+JupyterHub makes no guarantees about protecting users from each other unless subdomains are enabled.
+
+If you want to protect users from each other, you **_must_** enable per-user domains.
+:::
+
+### Disable user config
+
+If subdomains are unavailable or undesirable, JupyterHub provides a
+configuration option `Spawner.disable_user_config = True`, which can be set to prevent
+the user-owned configuration files from being loaded. After implementing this
+option, `PATH`s and package installation are the other things that the
+admin must enforce.
+
+### Prevent spawners from evaluating shell configuration files
+
+For most Spawners, `PATH` is not something users can influence, but it's important that
+the Spawner should _not_ evaluate shell configuration files prior to launching the server.
+
+### Isolate packages in a read-only environment
+
+The user must not have permission to install packages into the environment where the singleuser-server runs.
+On a shared system, package isolation is most easily handled by running the single-user server in
+a root-owned virtualenv with disabled system-site-packages.
+The user must not have permission to install packages into this environment.
+The same principle extends to the images used by container-based deployments.
+If users can select the images in which their servers run, they can disable all security for their own servers.
+
+It is important to note that the control over the environment is only required for the
+single-user server, and not the environment(s) in which the users' kernel(s)
+may run. Installing additional packages in the kernel environment does not
+pose additional risk to the web application's security.
+
+### Encrypt internal connections with SSL/TLS
+
+By default, all communications within JupyterHub—between the proxy, hub, and single
+-user notebooks—are performed unencrypted. Setting the `internal_ssl` flag in
+`jupyterhub_config.py` secures the aforementioned routes. Turning this
+feature on does require that the enabled `Spawner` can use the certificates
+generated by the `Hub` (the default `LocalProcessSpawner` can, for instance).
+
+It is also important to note that this encryption **does not** cover the
+`zmq tcp` sockets between the Notebook client and kernel yet. While users cannot
+submit arbitrary commands to another user's kernel, they can bind to these
+sockets and listen. When serving untrusted users, this eavesdropping can be
+mitigated by setting `KernelManager.transport` to `ipc`. This applies standard
+Unix permissions to the communication sockets thereby restricting
+communication to the socket owner. The `internal_ssl` option will eventually
+extend to securing the `tcp` sockets as well.
+
+### Mitigating same-origin deployments
+
+While per-user domains are **required** for robust protection of users from each other,
+you can mitigate many (but not all) cross-user issues.
+First, it is critical that users cannot modify their server environments, as described above.
+Second, it is important that users do not have `access:servers` permission to any server other than their own.
+
+If users can access each others' servers, additional security measures must be enabled, some of which come with distinct user-experience costs.
+
+Without the [Same-Origin Policy] (SOP) protecting user servers from each other,
+each user server is considered a trusted origin for requests to each other user server (and the Hub itself).
+Servers _cannot_ meaningfully distinguish requests originating from other user servers,
+because SOP implies a great deal of trust, losing many restrictions applied to cross-origin requests.
+
+That means pages served from each user server can:
+
+1. arbitrarily modify the path in the Referer
+2. make fully authorized requests with cookies
+3. access full page contents served from the hub or other servers via popups
+
+JupyterHub uses distinct xsrf tokens stored in cookies on each server path to attempt to limit requests across.
+This has limitations because not all requests are protected by these XSRF tokens,
+and unless additional measures are taken, the XSRF tokens from other user prefixes may be retrieved.
+
+[Same-Origin Policy]: https://developer.mozilla.org/en-US/docs/Web/Security/Same-origin_policy
+
+For example:
+
+- `Content-Security-Policy` header must prohibit popups and iframes from the same origin.
+  The following Content-Security-Policy rules are _insecure_ and readily enable users to access each others' servers:
+
+  - `frame-ancestors: 'self'`
+  - `frame-ancestors: '*'`
+  - `sandbox allow-popups`
+
+- Ideally, pages should use the strictest `Content-Security-Policy: sandbox` available,
+  but this is not feasible in general for JupyterLab pages, which need at least `sandbox allow-same-origin allow-scripts` to work.
+
+The default Content-Security-Policy for single-user servers is
+
+```
+frame-ancestors: 'none'
+```
+
+which prohibits iframe embedding, but not pop-ups.
+
+A more secure Content-Security-Policy that has some costs to user experience is:
+
+```
+frame-ancestors: 'none'; sandbox allow-same-origin allow-scripts
+```
+
+`allow-popups` is not disabled by default because disabling it breaks legitimate functionality, like "Open this in a new tab", and the "JupyterHub Control Panel" menu item.
+To reiterate, the right way to avoid these issues is to enable per-user domains, where none of these concerns come up.
+
+Note: even this level of protection requires administrators maintaining full control over the user server environment.
+If users can modify their server environment, these methods are ineffective, as users can readily disable them.
+
+### Cookie tossing
+
+Cookie tossing is a technique where another server on a subdomain or peer subdomain can set a cookie
+which will be read on another domain.
+This is not relevant unless there are other user-controlled servers on a peer domain.
+
+"Domain-locked" cookies avoid this issue, but have their own restrictions:
+
+- JupyterHub must be served over HTTPS
+- All secure cookies must be set on `/`, not on sub-paths, which means they are shared by all JupyterHub components in a single-domain deployment.
+
+As a result, this option is only recommended when per-user subdomains are enabled,
+to prevent sending all jupyterhub cookies to all user servers.
+
+To enable domain-locked cookies, set:
+
+```python
+c.JupyterHub.cookie_host_prefix_enabled = True
+```
+
+```{versionadded} 4.1
+
+```
+
+### Forced-login
+
+Jupyter servers can share links with `?token=...`.
+JupyterHub prior to 5.0 will accept this request and persist the token for future requests.
+This is useful for enabling admins to create 'fully authenticated' links bypassing login.
+However, it also means users can share their own links that will log other users into their own servers,
+enabling them to serve each other notebooks and other arbitrary HTML, depending on server configuration.
+
+```{versionadded} 4.1
+Setting environment variable `JUPYTERHUB_ALLOW_TOKEN_IN_URL=0` in the single-user environment can opt out of accepting token auth in URL parameters.
+```
+
+```{versionadded} 5.0
+Accepting tokens in URLs is disabled by default, and `JUPYTERHUB_ALLOW_TOKEN_IN_URL=1` environment variable must be set to _allow_ token auth in URL parameters.
+```
+
+## Security audits
+
+We recommend that you do periodic reviews of your deployment's security. It's
+good practice to keep [JupyterHub](https://readthedocs.org/projects/jupyterhub/), [configurable-http-proxy][], and [nodejs
+versions](https://github.com/nodejs/Release) up to date.
+
+A handy website for testing your deployment is
+[Qualsys' SSL analyzer tool](https://www.ssllabs.com/ssltest/analyze.html).
+
+[configurable-http-proxy]: https://github.com/jupyterhub/configurable-http-proxy
+
+## Vulnerability reporting
+
+If you believe you have found a security vulnerability in JupyterHub, or any
+Jupyter project, please report it to
+[security@ipython.org](mailto:security@ipython.org). If you prefer to encrypt
+your security reports, you can use [this PGP public
+key](https://jupyter.org/assets/ipython_security.asc).

docs/source/faq/faq.md

@@ -0,0 +1,76 @@
+# Frequently asked questions
+
+## How do I share links to notebooks?
+
+Sharing links to notebooks is a common activity,
+and can look different depending on what you mean by 'share.'
+Your first instinct might be to copy the URL you see in the browser,
+e.g. `jupyterhub.example/user/yourname/notebooks/coolthing.ipynb`,
+but this usually won't work, depending on the permissions of the person you share the link with.
+
+Unfortunately, 'share' means at least a few things to people in a JupyterHub context.
+We'll cover 3 common cases here, when they are applicable, and what assumptions they make:
+
+1. sharing links that will open the same file on the visitor's own server
+2. sharing links that will bring the visitor to _your_ server (e.g. for real-time collaboration, or RTC)
+3. publishing notebooks and sharing links that will download the notebook into the user's server
+
+### link to the same file on the visitor's server
+
+This is for the case where you have JupyterHub on a shared (or sufficiently similar) filesystem, where you want to share a link that will cause users to login and start their _own_ server, to view or edit the file.
+
+**Assumption:** the same path on someone else's server is valid and points to the same file
+
+This is useful in e.g. classes where you know students have certain files in certain locations, or collaborations where you know you have a shared filesystem where everyone has access to the same files.
+
+A link should look like `https://jupyterhub.example/hub/user-redirect/lab/tree/foo.ipynb`.
+You can hand-craft these URLs from the URL you are looking at, where you see `/user/name/lab/tree/foo.ipynb` use `/hub/user-redirect/lab/tree/foo.ipynb` (replace `/user/name/` with `/hub/user-redirect/`).
+Or you can use JupyterLab's "copy shareable link" in the context menu in the file browser:
+
+![copy shareable link in JupyterLab](../images/shareable_link.webp)
+
+which will produce a correct URL with `/hub/user-redirect/` in it.
+
+### link to the file on your server
+
+This is for the case where you want to both be using _your_ server, e.g. for real-time collaboration (RTC).
+
+**Assumption:** the user has (or should have) access to your server.
+
+**Assumption:** your server is running _or_ the user has permission to start it.
+
+By default, JupyterHub users don't have access to each other's servers, but JupyterHub 2.0 administrators can grant users limited access permissions to each other's servers.
+If the visitor doesn't have access to the server, these links will result in a 403 Permission Denied error.
+
+In many cases, for this situation you can copy the link in your URL bar (`/user/yourname/lab`), or you can add `/tree/path/to/specific/notebook.ipynb` to open a specific file.
+
+The [jupyterlab-link-share] JupyterLab extension generates these links, and even can _grant_ other users access to your server.
+
+[jupyterlab-link-share]: https://github.com/jupyterlab-contrib/jupyterlab-link-share
+
+:::{warning}
+Note that the way the extension _grants_ access is handing over credentials to allow the other user to **_BECOME YOU_**.
+This is usually not appropriate in JupyterHub.
+:::
+
+### link to a published copy
+
+Another way to 'share' notebooks is to publish copies, e.g. pushing the notebook to a git repository and sharing a download link.
+This way is especially useful for course materials,
+where no assumptions are necessary about the user's environment,
+except for having one package installed.
+
+**Assumption:** The [nbgitpuller](inv:nbgitpuller#index) server extension is installed
+
+Unlike the other two methods, nbgitpuller doesn't provide an extension to copy a shareable link for the document you're currently looking at,
+but it does provide a [link generator](inv:nbgitpuller#link),
+which uses the `user-redirect` approach above.
+
+When visiting an nbgitpuller link:
+
+- The visitor will be directed to their own server
+- Your repo will be cloned (or updated if it's already been cloned)
+- and then the file opened when it's ready
+
+[nbgitpuller]: https://nbgitpuller.readthedocs.io
+[nbgitpuller-link]: https://nbgitpuller.readthedocs.io/en/latest/link.html

docs/source/faq/index.md

@@ -0,0 +1,11 @@
+# FAQs
+
+Find answers to some of the most frequently-asked questions around JupyterHub and how it works.
+
+```{toctree}
+:maxdepth: 2
+
+faq
+institutional-faq
+troubleshooting
+```

docs/source/faq/institutional-faq.md

@@ -0,0 +1,267 @@
+# Institutional FAQ
+
+This page contains common questions from users of JupyterHub,
+broken down by their roles within organizations.
+
+## For all
+
+### Is it appropriate for adoption within a larger institutional context?
+
+Yes! JupyterHub has been used at-scale for large pools of users, as well
+as complex and high-performance computing.
+For example,
+
+- UC Berkeley uses
+  JupyterHub for its Data Science Education Program courses (serving over
+  3,000 students).
+- The Pangeo project uses JupyterHub to provide access
+  to scalable cloud computing with Dask.
+
+JupyterHub is stable and customizable
+to the use-cases of large organizations.
+
+### I keep hearing about Jupyter Notebook, JupyterLab, and now JupyterHub. What’s the difference?
+
+Here is a quick breakdown of these three tools:
+
+- **The Jupyter Notebook** is a document specification (the `.ipynb`) file that interweaves
+  narrative text with code cells and their outputs. It is also a graphical interface
+  that allows users to edit these documents. There are also several other graphical interfaces
+  that allow users to edit the `.ipynb` format (nteract, Jupyter Lab, Google Colab, Kaggle, etc).
+- **JupyterLab** is a flexible and extendible user interface for interactive computing. It
+  has several extensions that are tailored for using Jupyter Notebooks, as well as extensions
+  for other parts of the data science stack.
+- **JupyterHub** is an application that manages interactive computing sessions for **multiple users**.
+  It also connects users with infrastructure they wish to access. It can provide
+  remote access to Jupyter Notebooks and JupyterLab for many people.
+
+## For management
+
+### Briefly, what problem does JupyterHub solve for us?
+
+JupyterHub provides a shared platform for data science and collaboration.
+It allows users to utilize familiar data science workflows (such as the scientific Python stack,
+the R tidyverse, and Jupyter Notebooks) on institutional infrastructure. It also gives administrators
+some control over access to resources, security, environments, and authentication.
+
+### Is JupyterHub mature? Why should we trust it?
+
+Yes - the core JupyterHub application recently
+reached 1.0 status, and is considered stable and performant for most institutions.
+JupyterHub has also been deployed (along with other tools) to work on
+scalable infrastructure, large datasets, and high-performance computing.
+
+### Who else uses JupyterHub?
+
+JupyterHub is used at a variety of institutions in academia,
+industry, and government research labs. It is most-commonly used by two kinds of groups:
+
+- Small teams (e.g., data science teams, research labs, or collaborative projects) to provide a
+  shared resource for interactive computing, collaboration, and analytics.
+- Large teams (e.g., a department, a large class, or a large group of remote users) to provide
+  access to organizational hardware, data, and analytics environments at scale.
+
+Here is a sample of organizations that use JupyterHub:
+
+- **Universities and colleges**: UC Berkeley, UC San Diego, Cal Poly SLO, Harvard University, University of Chicago,
+  University of Oslo, University of Sheffield, Université Paris Sud, University of Versailles
+- **Research laboratories**: NASA, NCAR, NOAA, the Large Synoptic Survey Telescope, Brookhaven National Lab,
+  Minnesota Supercomputing Institute, ALCF, CERN, Lawrence Livermore National Laboratory, HUNT
+- **Online communities**: Pangeo, Quantopian, mybinder.org, MathHub, Open Humans
+- **Computing infrastructure providers**: NERSC, San Diego Supercomputing Center, Compute Canada
+- **Companies**: Capital One, SANDVIK code, Globus
+
+See the [Gallery of JupyterHub deployments](gallery-of-deployments) for
+a more complete list of JupyterHub deployments at institutions.
+
+### How does JupyterHub compare with hosted products, like Google Colaboratory, RStudio.cloud, or Anaconda Enterprise?
+
+JupyterHub puts you in control of your data, infrastructure, and coding environment.
+In addition, it is vendor neutral, which reduces lock-in to a particular vendor or service.
+JupyterHub provides access to interactive computing environments in the cloud (similar to each of these services).
+Compared with the tools above, it is more flexible, more customizable, free, and
+gives administrators more control over their setup and hardware.
+
+Because JupyterHub is an open-source, community-driven tool, it can be extended and
+modified to fit an institution's needs. It plays nicely with the open source data science
+stack, and can serve a variety of computing environments, user interfaces, and
+computational hardware. It can also be deployed anywhere - on enterprise cloud infrastructure, on
+High-Performance-Computing machines, on local hardware, or even on a single laptop, which
+is not possible with most other tools for shared interactive computing.
+
+## For IT
+
+### How would I set up JupyterHub on institutional hardware?
+
+That depends on what kind of hardware you've got. JupyterHub is flexible enough to be deployed
+on a variety of hardware, including in-room hardware, on-prem clusters, cloud infrastructure,
+etc.
+
+The most common way to set up a JupyterHub is to use a JupyterHub distribution, these are pre-configured
+and opinionated ways to set up a JupyterHub on particular kinds of infrastructure. The two distributions
+that we currently suggest are:
+
+- [Zero to JupyterHub for Kubernetes](https://z2jh.jupyter.org) is a scalable JupyterHub deployment and
+  guide that runs on Kubernetes. Better for larger or dynamic user groups (50-10,000) or more complex
+  compute/data needs.
+- [The Littlest JupyterHub](https://tljh.jupyter.org) is a lightweight JupyterHub that runs on a single
+  machine (in the cloud or under your desk). Better for smaller user groups (4-80) or more
+  lightweight computational resources.
+
+### Does JupyterHub run well in the cloud?
+
+**Yes** - most deployments of JupyterHub are run via cloud infrastructure and on a variety of cloud providers.
+Depending on the distribution of JupyterHub that you'd like to use, you can also connect your JupyterHub
+deployment with a number of other cloud-native services so that users have access to other resources from
+their interactive computing sessions.
+
+For example, if you use the [Zero to JupyterHub for Kubernetes](https://z2jh.jupyter.org) distribution,
+you'll be able to utilize container-based workflows of other technologies such as the [dask-kubernetes](https://kubernetes.dask.org/en/latest/)
+project for distributed computing.
+
+The Z2JH Helm Chart also has some functionality built in for auto-scaling your cluster up and down
+as more resources are needed - allowing you to utilize the benefits of a flexible cloud-based deployment.
+
+### Is JupyterHub secure?
+
+The short answer: yes.
+JupyterHub as a standalone application has been battle-tested at an institutional
+level for several years, and makes a number of "default" security decisions that are reasonable for most
+users.
+
+- For security considerations in the base JupyterHub application,
+  [see the JupyterHub security page](web-security).
+- For security considerations when deploying JupyterHub on Kubernetes, see the
+  [JupyterHub on Kubernetes security page](https://z2jh.jupyter.org/en/latest/security.html).
+
+The longer answer: it depends on your deployment. Because JupyterHub is very flexible, it can be used
+in a variety of deployment setups. This often entails connecting your JupyterHub to **other** infrastructure
+(such as a [Dask Gateway service](https://gateway.dask.org/)). There are many security decisions to be made
+in these cases, and the security of your JupyterHub deployment will often depend on these decisions.
+
+If you are worried about security, don't hesitate to reach out to the JupyterHub community in the
+[Jupyter Community Forum](https://discourse.jupyter.org/c/jupyterhub). This community of practice has many
+individuals with experience running secure JupyterHub deployments and will be very glad to help you out.
+
+### Does JupyterHub provide computing or data infrastructure?
+
+**No** - JupyterHub manages user sessions and can _control_ computing infrastructure, but it does not provide these
+things itself. You are expected to run JupyterHub on your own infrastructure (local or in the cloud). Moreover,
+JupyterHub has no internal concept of "data", but is designed to be able to communicate with data repositories
+(again, either locally or remotely) for use within interactive computing sessions.
+
+### How do I manage users?
+
+JupyterHub offers a few options for managing your users. Upon setting up a JupyterHub, you can choose what
+kind of **authentication** you'd like to use. For example, you can have users sign up with an institutional
+email address, or choose a username / password when they first log-in, or offload authentication onto
+another service such as an organization's OAuth.
+
+The users of a JupyterHub are stored locally, and can be modified manually by an administrator of the JupyterHub.
+Moreover, the _active_ users on a JupyterHub can be found on the administrator's page. This page
+gives you the abiltiy to stop or restart kernels, inspect user filesystems, and even take over user
+sessions to assist them with debugging.
+
+### How do I manage software environments?
+
+A key benefit of JupyterHub is the ability for an administrator to define the environment(s) that users
+have access to. There are many ways to do this, depending on what kind of infrastructure you're using for
+your JupyterHub.
+
+For example, **The Littlest JupyterHub** runs on a single VM. In this case, the administrator defines
+an environment by installing packages to a shared folder that exists on the path of all users. The
+**JupyterHub for Kubernetes** deployment uses Docker images to define environments. You can create your
+own list of Docker images that users can select from, and can also control things like the amount of
+RAM available to users, or the types of machines that their sessions will use in the cloud.
+
+### How does JupyterHub manage computational resources?
+
+For interactive computing sessions, JupyterHub controls computational resources via a **spawner**.
+Spawners define how a new user session is created, and are customized for particular kinds of
+infrastructure. For example, the KubeSpawner knows how to control a Kubernetes deployment
+to create new pods when users log in.
+
+For more sophisticated computational resources (like distributed computing), JupyterHub can
+connect with other infrastructure tools (like Dask or Spark). This allows users to control
+scalable or high-performance resources from within their JupyterHub sessions. The logic of
+how those resources are controlled is taken care of by the non-JupyterHub application.
+
+### Can JupyterHub be used with my high-performance computing resources?
+
+Yes - JupyterHub can provide access to many kinds of computing infrastructure.
+Especially when combined with other open-source schedulers such as Dask, you can manage fairly
+complex computing infrastructures from the interactive sessions of a JupyterHub. For example
+[see the Dask HPC page](https://docs.dask.org/en/latest/setup/hpc.html).
+
+### How much resources do user sessions take?
+
+This is highly configurable by the administrator. If you wish for your users to have simple
+data analytics environments for prototyping and light data exploring, you can restrict their
+memory and CPU based on the resources that you have available. If you'd like your JupyterHub
+to serve as a gateway to high-performance computing or data resources, you may increase the
+resources available on user machines, or connect them with computing infrastructures elsewhere.
+
+### Can I customize the look and feel of a JupyterHub?
+
+JupyterHub provides some customization of the graphics displayed to users. The most common
+modification is to add custom branding to the JupyterHub login page, loading pages, and
+various elements that persist across all pages (such as headers).
+
+## For Technical Leads
+
+### Will JupyterHub “just work” with our team's interactive computing setup?
+
+Depending on the complexity of your setup, you'll have different experiences with "out of the box"
+distributions of JupyterHub. If all of the resources you need will fit on a single VM, then
+[The Littlest JupyterHub](https://tljh.jupyter.org) should get you up-and-running within
+a half day or so. For more complex setups, such as scalable Kubernetes clusters or access
+to high-performance computing and data, it will require more time and expertise with
+the technologies your JupyterHub will use (e.g., dev-ops knowledge with cloud computing).
+
+In general, the base JupyterHub deployment is not the bottleneck for setup, it is connecting
+your JupyterHub with the various services and tools that you wish to provide to your users.
+
+### How well does JupyterHub scale? What are JupyterHub's limitations?
+
+JupyterHub works well at both a small scale (e.g., a single VM or machine) as well as a
+high scale (e.g., a scalable Kubernetes cluster). It can be used for teams as small as 2, and
+for user bases as large as 10,000. The scalability of JupyterHub largely depends on the
+infrastructure on which it is deployed. JupyterHub has been designed to be lightweight and
+flexible, so you can tailor your JupyterHub deployment to your needs.
+
+### Is JupyterHub resilient? What happens when a machine goes down?
+
+For JupyterHubs that are deployed in a containerized environment (e.g., Kubernetes), it is
+possible to configure the JupyterHub to be fairly resistant to failures in the system.
+For example, if JupyterHub fails, then user sessions will not be affected (though new
+users will not be able to log in). When a JupyterHub process is restarted, it should
+seamlessly connect with the user database and the system will return to normal.
+Again, the details of your JupyterHub deployment (e.g., whether it's deployed on a scalable cluster)
+will affect the resiliency of the deployment.
+
+### What interfaces does JupyterHub support?
+
+Out of the box, JupyterHub supports a variety of popular data science interfaces for user sessions,
+such as JupyterLab, Jupyter Notebooks, and RStudio. Any interface that can be served
+via a web address can be served with a JupyterHub (with the right setup).
+
+### Does JupyterHub make it easier for our team to collaborate?
+
+JupyterHub provides a standardized environment and access to shared resources for your teams.
+This greatly reduces the cost associated with sharing analyses and content with other team
+members, and makes it easier to collaborate and build off of one another's ideas. Combined with
+access to high-performance computing and data, JupyterHub provides a common resource to
+amplify your team's ability to prototype their analyses, scale them to larger data, and then
+share their results with one another.
+
+JupyterHub also provides a computational framework to share computational narratives between
+different levels of an organization. For example, data scientists can share Jupyter Notebooks
+rendered as [Voilà dashboards](https://voila.readthedocs.io/en/stable/) with those who are not
+familiar with programming, or create publicly-available interactive analyses to allow others to
+interact with your work.
+
+### Can I use JupyterHub with R/RStudio or other languages and environments?
+
+Yes, Jupyter is a polyglot project, and there are over 40 community-provided kernels for a variety
+of languages (the most common being Python, Julia, and R). You can also use a JupyterHub to provide
+access to other interfaces, such as RStudio, that provide their own access to a language kernel.

docs/source/faq/troubleshooting.md

@@ -0,0 +1,395 @@
+(troubleshooting)=
+
+# Troubleshooting
+
+When troubleshooting, you may see unexpected behaviors or receive an error
+message. This section provides links for identifying the cause of the
+problem and how to resolve it.
+
+## Behavior
+
+### JupyterHub proxy fails to start
+
+If you have tried to start the JupyterHub proxy and it fails to start:
+
+- check if the JupyterHub IP configuration setting is
+  `c.JupyterHub.ip = '*'`; if it is, try `c.JupyterHub.ip = ''`
+- Try starting with `jupyterhub --ip=0.0.0.0`
+
+**Note**: If this occurs on Ubuntu/Debian, check that you are using a
+recent version of [Node](https://nodejs.org). Some versions of Ubuntu/Debian come with a very old version
+of Node and it is necessary to update Node.
+
+### sudospawner fails to run
+
+If the sudospawner script is not found in the path, sudospawner will not run.
+To avoid this, specify sudospawner's absolute path. For example, start
+jupyterhub with:
+
+    jupyterhub --SudoSpawner.sudospawner_path='/absolute/path/to/sudospawner'
+
+or add:
+
+    c.SudoSpawner.sudospawner_path = '/absolute/path/to/sudospawner'
+
+to the config file, `jupyterhub_config.py`.
+
+### What is the default behavior when none of the lists (admin, allowed, allowed groups) are set?
+
+When nothing is given for these lists, there will be no admins, and all users
+who can authenticate on the system (i.e. all the Unix users on the server with
+a password) will be allowed to start a server. The allowed username set lets you limit
+this to a particular set of users, and admin_users lets you specify who
+among them may use the admin interface (not necessary, unless you need to do
+things like inspect other users' servers or modify the user list at runtime).
+
+### JupyterHub Docker container is not accessible at localhost
+
+Even though the command to start your Docker container exposes port 8000
+(`docker run -p 8000:8000 -d --name jupyterhub quay.io/jupyterhub/jupyterhub jupyterhub`),
+it is possible that the IP address itself is not accessible/visible. As a result,
+when you try http://localhost:8000 in your browser, you are unable to connect
+even though the container is running properly. One workaround is to explicitly
+tell Jupyterhub to start at `0.0.0.0` which is visible to everyone. Try this
+command:
+`docker run -p 8000:8000 -d --name jupyterhub quay.io/jupyterhub/jupyterhub jupyterhub --ip 0.0.0.0 --port 8000`
+
+### How can I kill ports from JupyterHub-managed services that have been orphaned?
+
+I started JupyterHub + nbgrader on the same host without containers. When I try to restart JupyterHub + nbgrader with this configuration, errors appear that the service accounts cannot start because the ports are being used.
+
+How can I kill the processes that are using these ports?
+
+Run the following command:
+
+    sudo kill -9 $(sudo lsof -t -i:<service_port>)
+
+Where `<service_port>` is the port used by the nbgrader course service. This configuration is specified in `jupyterhub_config.py`.
+
+### Why am I getting a Spawn failed error message?
+
+After successfully logging in to JupyterHub with a compatible authenticator, I get a 'Spawn failed' error message in the browser. The JupyterHub logs have `jupyterhub KeyError: "getpwnam(): name not found: <my_user_name>`.
+
+This issue occurs when the authenticator requires a local system user to exist. In these cases, you need to use a spawner
+that does not require an existing system user account, such as `DockerSpawner` or `KubeSpawner`.
+
+### How can I run JupyterHub with sudo but use my current environment variables and virtualenv location?
+
+When launching JupyterHub with `sudo jupyterhub` I get import errors and my environment variables don't work.
+
+When launching services with `sudo ...` the shell won't have the same environment variables or `PATH`s in place. The most direct way to solve this issue is to use the full path to your python environment and add environment variables. For example:
+
+```bash
+sudo MY_ENV=abc123 \
+  /home/foo/venv/bin/python3 \
+  /srv/jupyterhub/jupyterhub
+```
+
+## Errors
+
+### Error 500 after spawning my single-user server
+
+You receive a 500 error while accessing the URL `/user/<your_name>/...`.
+This is often seen when your single-user server cannot verify your user cookie
+with the Hub.
+
+There are two likely reasons for this:
+
+1. The single-user server cannot connect to the Hub's API (networking
+   configuration problems)
+2. The single-user server cannot _authenticate_ its requests (invalid token)
+
+#### Symptoms
+
+The main symptom is a failure to load _any_ page served by the single-user
+server, met with a 500 error. This is typically the first page at `/user/<your_name>`
+after logging in or clicking "Start my server". When a single-user notebook server
+receives a request, the notebook server makes an API request to the Hub to
+check if the cookie corresponds to the right user. This request is logged.
+
+If everything is working, the response logged will be similar to this:
+
+```
+200 GET /hub/api/authorizations/cookie/jupyterhub-token-name/[secret] (@10.0.1.4) 6.10ms
+```
+
+You should see a similar 200 message, as above, in the Hub log when you first
+visit your single-user notebook server. If you don't see this message in the log, it
+may mean that your single-user notebook server is not connecting to your Hub.
+
+If you see 403 (forbidden) like this, it is likely a token problem:
+
+```
+403 GET /hub/api/authorizations/cookie/jupyterhub-token-name/[secret] (@10.0.1.4) 4.14ms
+```
+
+Check the logs of the single-user notebook server, which may have more detailed
+information on the cause.
+
+#### Causes and resolutions
+
+##### No authorization request
+
+If you make an API request and it is not received by the server, you likely
+have a network configuration issue. Often, this happens when the Hub is only
+listening on 127.0.0.1 (default) and the single-user servers are not on the
+same 'machine' (can be physically remote, or in a docker container or VM). The
+fix for this case is to make sure that `c.JupyterHub.hub_ip` is an address
+that all single-user servers can connect to, e.g.:
+
+```python
+c.JupyterHub.hub_ip = '10.0.0.1'
+```
+
+##### 403 GET /hub/api/authorizations/cookie
+
+If you receive a 403 error, the API token for the single-user server is likely
+invalid. Commonly, the 403 error is caused by resetting the JupyterHub
+database (either removing jupyterhub.sqlite or some other action) while
+leaving single-user servers running. This happens most frequently when using
+DockerSpawner because Docker's default behavior is to stop/start containers
+that reset the JupyterHub database, rather than destroying and recreating
+the container every time. This means that the same API token is used by the
+server for its whole life until the container is rebuilt.
+
+The fix for this Docker case is to remove any Docker containers seeing this
+issue (typically all containers created before a certain point in time):
+
+    docker rm -f jupyter-name
+
+After this, when you start your server via JupyterHub, it will build a
+new container. If this was the underlying cause of the issue, you should see
+your server again.
+
+##### Proxy settings (403 GET)
+
+When your whole JupyterHub sits behind an organization proxy (_not_ a reverse proxy like NGINX as part of your setup and _not_ the configurable-http-proxy) the environment variables `HTTP_PROXY`, `HTTPS_PROXY`, `http_proxy`, and `https_proxy` might be set. This confuses the JupyterHub single-user servers: When connecting to the Hub for authorization they connect via the proxy instead of directly connecting to the Hub on localhost. The proxy might deny the request (403 GET). This results in the single-user server thinking it has the wrong auth token. To circumvent this you should add `<hub_url>,<hub_ip>,localhost,127.0.0.1` to the environment variables `NO_PROXY` and `no_proxy`.
+
+### Launching Jupyter Notebooks to run as an externally managed JupyterHub service with the `jupyterhub-singleuser` command returns a `JUPYTERHUB_API_TOKEN` error
+
+{ref}`services` allow processes to interact with JupyterHub's REST API. Example use-cases include:
+
+- **Secure Testing**: provide a canonical Jupyter Notebook for testing production data to reduce the number of entry points into production systems.
+- **Grading Assignments**: provide access to shared Jupyter Notebooks that may be used for management tasks such as grading assignments.
+- **Private Dashboards**: share dashboards with certain group members.
+
+If possible, try to run the Jupyter Notebook as an externally managed service with one of the provided [jupyter/docker-stacks](https://github.com/jupyter/docker-stacks).
+
+Standard JupyterHub installations include a [jupyterhub-singleuser](https://github.com/jupyterhub/jupyterhub/blob/9fdab027daa32c9017845572ad9d5ba1722dbc53/setup.py#L116) command which is built from the `jupyterhub.singleuser:main` method. The `jupyterhub-singleuser` command is the default command when JupyterHub launches single-user Jupyter Notebooks. One of the goals of this command is to make sure the version of JupyterHub installed within the Jupyter Notebook coincides with the version of the JupyterHub server itself.
+
+If you launch a Jupyter Notebook with the `jupyterhub-singleuser` command directly from the command line, the Jupyter Notebook won't have access to the `JUPYTERHUB_API_TOKEN` and will return:
+
+```
+    JUPYTERHUB_API_TOKEN env is required to run jupyterhub-singleuser.
+    Did you launch it manually?
+```
+
+If you plan on testing `jupyterhub-singleuser` independently from JupyterHub, then you can set the API token environment variable. For example, if you were to run the single-user Jupyter Notebook on the host, then:
+
+    export JUPYTERHUB_API_TOKEN=my_secret_token
+    jupyterhub-singleuser
+
+With a docker container, pass in the environment variable with the run command:
+
+    docker run -d \
+      -p 8888:8888 \
+      -e JUPYTERHUB_API_TOKEN=my_secret_token \
+      jupyter/datascience-notebook:latest
+
+[This example](https://github.com/jupyterhub/jupyterhub/tree/HEAD/examples/service-notebook/external) demonstrates how to combine the use of the `jupyterhub-singleuser` environment variables when launching a Notebook as an externally managed service.
+
+## How do I...?
+
+### Use a chained SSL certificate
+
+Some certificate providers, i.e. Entrust, may provide you with a chained
+certificate that contains multiple files. If you are using a chained
+certificate you will need to concatenate the individual files by appending the
+chained cert and root cert to your host cert:
+
+    cat your_host.crt chain.crt root.crt > your_host-chained.crt
+
+You would then set in your `jupyterhub_config.py` file the `ssl_key` and
+`ssl_cert` as follows:
+
+    c.JupyterHub.ssl_cert = your_host-chained.crt
+    c.JupyterHub.ssl_key = your_host.key
+
+#### Example
+
+Your certificate provider gives you the following files: `example_host.crt`,
+`Entrust_L1Kroot.txt`, and `Entrust_Root.txt`.
+
+Concatenate the files appending the chain cert and root cert to your host cert:
+
+    cat example_host.crt Entrust_L1Kroot.txt Entrust_Root.txt > example_host-chained.crt
+
+You would then use the `example_host-chained.crt` as the value for
+JupyterHub's `ssl_cert`. You may pass this value as a command line option
+when starting JupyterHub or more conveniently set the `ssl_cert` variable in
+JupyterHub's configuration file, `jupyterhub_config.py`. In `jupyterhub_config.py`,
+set:
+
+    c.JupyterHub.ssl_cert = /path/to/example_host-chained.crt
+    c.JupyterHub.ssl_key = /path/to/example_host.key
+
+where `ssl_cert` is example-chained.crt and ssl_key to your private key.
+
+Then restart JupyterHub.
+
+See also {ref}`ssl-encryption`.
+
+### Install JupyterHub without a network connection
+
+Both conda and pip can be used without a network connection. You can make your
+own repository (directory) of conda packages and/or wheels, and then install
+from there instead of the internet.
+
+For instance, you can install JupyterHub with pip and configurable-http-proxy
+with npmbox:
+
+    python3 -m pip wheel jupyterhub
+    npmbox configurable-http-proxy
+
+### I want access to the whole filesystem and still default users to their home directory
+
+Setting the following in `jupyterhub_config.py` will configure access to
+the entire filesystem and set the default to the user's home directory.
+
+    c.Spawner.notebook_dir = '/'
+    c.Spawner.default_url = '/home/%U' # %U will be replaced with the username
+
+### How do I increase the number of pySpark executors on YARN?
+
+From the command line, pySpark executors can be configured using a command
+similar to this one:
+
+    pyspark --total-executor-cores 2 --executor-memory 1G
+
+[Cloudera documentation for configuring spark on YARN applications](https://www.cloudera.com/documentation/enterprise/latest/topics/cdh_ig_running_spark_on_yarn.html#spark_on_yarn_config_apps)
+provides additional information. The [pySpark configuration documentation](https://spark.apache.org/docs/0.9.0/configuration.html)
+is also helpful for programmatic configuration examples.
+
+### How do I use JupyterLab's pre-release version with JupyterHub?
+
+While JupyterLab is still under active development, we have had users
+ask about how to try out JupyterLab with JupyterHub.
+
+You need to install and enable the JupyterLab extension system-wide,
+then you can change the default URL to `/lab`.
+
+For instance:
+
+    python3 -m pip install jupyterlab
+    jupyter serverextension enable --py jupyterlab --sys-prefix
+
+The important thing is that JupyterLab is installed and enabled in the
+single-user notebook server environment. For system users, this means
+system-wide, as indicated above. For Docker containers, it means inside
+the single-user docker image, etc.
+
+In `jupyterhub_config.py`, configure the Spawner to tell the single-user
+notebook servers to default to JupyterLab:
+
+    c.Spawner.default_url = '/lab'
+
+### How do I set up JupyterHub for a workshop (when users are not known ahead of time)?
+
+1. Set up JupyterHub using OAuthenticator for GitHub authentication
+2. Configure the admin list to have workshop leaders listed with administrator privileges.
+
+Users will need a GitHub account to log in and be authenticated by the Hub.
+
+### How do I set up rotating daily logs?
+
+You can do this with [logrotate](https://linux.die.net/man/8/logrotate),
+or pipe to `logger` to use Syslog instead of directly to a file.
+
+For example, with this logrotate config file:
+
+```
+/var/log/jupyterhub.log {
+  copytruncate
+  daily
+}
+```
+
+and run this daily by putting a script in `/etc/cron.daily/`:
+
+```bash
+logrotate /path/to/above-config
+```
+
+Or use syslog:
+
+    jupyterhub | logger -t jupyterhub
+
+### Toree integration with HDFS rack awareness script
+
+The Apache Toree kernel will have an issue when running with JupyterHub if the standard HDFS rack awareness script is used. This will materialize in the logs as a repeated WARN:
+
+```bash
+16/11/29 16:24:20 WARN ScriptBasedMapping: Exception running /etc/hadoop/conf/topology_script.py some.ip.address
+ExitCodeException exitCode=1:   File "/etc/hadoop/conf/topology_script.py", line 63
+    print rack
+             ^
+SyntaxError: Missing parentheses in call to 'print'
+
+    at `org.apache.hadoop.util.Shell.runCommand(Shell.java:576)`
+```
+
+In order to resolve this issue, there are two potential options.
+
+1. Update HDFS core-site.xml, so the parameter "net.topology.script.file.name" points to a custom
+   script (e.g. /etc/hadoop/conf/custom_topology_script.py). Copy the original script and change the first line point
+   to a python two installation (e.g. /usr/bin/python).
+2. In spark-env.sh add a Python 2 installation to your path (e.g. export PATH=/opt/anaconda2/bin:$PATH).
+
+### Where do I find Docker images and Dockerfiles related to JupyterHub?
+
+Docker images can be found at the [JupyterHub organization on Quay.io](https://quay.io/organization/jupyterhub).
+The Docker image [jupyterhub/singleuser](https://quay.io/repository/jupyterhub/singleuser)
+provides an example single-user notebook server for use with DockerSpawner.
+
+Additional single-user notebook server images can be found at the [Jupyter
+organization on Quay.io](https://quay.io/organization/jupyter) and information
+about each image at the [jupyter/docker-stacks repo](https://github.com/jupyter/docker-stacks).
+
+### How can I view the logs for JupyterHub or the user's Notebook servers when using the DockerSpawner?
+
+Use `docker logs <container>` where `<container>` is the container name defined within `docker-compose.yml`. For example, to view the logs of the JupyterHub container use:
+
+    docker logs hub
+
+By default, the user's notebook server is named `jupyter-<username>` where `username` is the user's username within JupyterHub's database.
+So if you wanted to see the logs for user `foo` you would use:
+
+    docker logs jupyter-foo
+
+You can also tail logs to view them in real-time using the `-f` option:
+
+    docker logs -f hub
+
+## Troubleshooting commands
+
+The following commands provide additional detail about installed packages,
+versions, and system information that may be helpful when troubleshooting
+a JupyterHub deployment. The commands are:
+
+- System and deployment information
+
+```bash
+jupyter troubleshoot
+```
+
+- Kernel information
+
+```bash
+jupyter kernelspec list
+```
+
+- Debug logs when running JupyterHub
+
+```bash
+jupyterhub --debug
+```

docs/source/howto/api-only.md

@@ -0,0 +1,128 @@
+(api-only)=
+
+# Deploying JupyterHub in "API only mode"
+
+As a service for deploying and managing Jupyter servers for users, JupyterHub
+exposes this functionality _primarily_ via a [REST API](rest).
+For convenience, JupyterHub also ships with a _basic_ web UI built using that REST API.
+The basic web UI enables users to click a button to quickly start and stop their servers,
+and it lets admins perform some basic user and server management tasks.
+
+The REST API has always provided additional functionality beyond what is available in the basic web UI.
+Similarly, we avoid implementing UI functionality that is also not available via the API.
+With JupyterHub 2.0, the basic web UI will **always** be composed using the REST API.
+In other words, no UI pages should rely on information not available via the REST API.
+Previously, some admin UI functionality could only be achieved via admin pages,
+such as paginated requests.
+
+## Limited UI customization via templates
+
+The JupyterHub UI is customizable via extensible HTML [templates](templates),
+but this has some limited scope to what can be customized.
+Adding some content and messages to existing pages is well supported,
+but changing the page flow and what pages are available are beyond the scope of what is customizable.
+
+## Rich UI customization with REST API based apps
+
+Increasingly, JupyterHub is used purely as an API for managing Jupyter servers
+for other Jupyter-based applications that might want to present a different user experience.
+If you want a fully customized user experience,
+you can now disable the Hub UI and use your own pages together with the JupyterHub REST API
+to build your own web application to serve your users,
+relying on the Hub only as an API for managing users and servers.
+
+One example of such an application is [BinderHub][], which powers https://mybinder.org,
+and motivates many of these changes.
+
+BinderHub is distinct from a traditional JupyterHub deployment
+because it uses temporary users created for each launch.
+Instead of presenting a login page,
+users are presented with a form to specify what environment they would like to launch:
+
+![Binder launch form](../images/binderhub-form.png)
+
+When a launch is requested:
+
+1. an image is built, if necessary
+2. a temporary user is created,
+3. a server is launched for that user, and
+4. when running, users are redirected to an already running server with an auth token in the URL
+5. after the session is over, the user is deleted
+
+This means that a lot of JupyterHub's UI flow doesn't make sense:
+
+- there is no way for users to login
+- the human user doesn't map onto a JupyterHub `User` in a meaningful way
+- when a server isn't running, there isn't a 'restart your server' action available because the user has been deleted
+- users do not have any access to any Hub functionality, so presenting pages for those features would be confusing
+
+BinderHub is one of the motivating use cases for JupyterHub supporting being used _only_ via its API.
+We'll use BinderHub here as an example of various configuration options.
+
+[binderhub]: https://binderhub.readthedocs.io
+
+## Disabling Hub UI
+
+`c.JupyterHub.hub_routespec` is a configuration option to specify which URL prefix should be routed to the Hub.
+The default is `/` which means that the Hub will receive all requests not already specified to be routed somewhere else.
+
+There are three values that are most logical for `hub_routespec`:
+
+- `/` - this is the default, and used in most deployments.
+  It is also the only option prior to JupyterHub 1.4.
+- `/hub/` - this serves only Hub pages, both UI and API
+- `/hub/api` - this serves _only the Hub API_, so all Hub UI is disabled,
+  aside from the OAuth confirmation page, if used.
+
+If you choose a hub routespec other than `/`,
+the main JupyterHub feature you will lose is the automatic handling of requests for `/user/:username`
+when the requested server is not running.
+
+JupyterHub's handling of this request shows this page,
+telling you that the server is not running,
+with a button to launch it again:
+
+![screenshot of hub page for server not running](../images/server-not-running.png)
+
+If you set `hub_routespec` to something other than `/`,
+it is likely that you also want to register another destination for `/` to handle requests to not-running servers.
+If you don't, you will see a default 404 page from the proxy:
+
+![screenshot of CHP default 404](../images/chp-404.png)
+
+For mybinder.org, the default "start my server" page doesn't make sense,
+because when a server is gone, there is no restart action.
+Instead, we provide hints about how to get back to a link to start a _new_ server:
+
+![screenshot of mybinder.org 404](../images/binder-404.png)
+
+To achieve this, mybinder.org registers a route for `/` that goes to a custom endpoint
+that runs nginx and only serves this static HTML error page.
+This is set with
+
+```python
+c.Proxy.extra_routes = {
+    "/": "http://custom-404-entpoint/",
+}
+```
+
+You may want to use an alternate behavior, such as redirecting to a landing page,
+or taking some other action based on the requested page.
+
+If you use `c.JupyterHub.hub_routespec = "/hub/"`,
+then all the Hub pages will be available,
+and only this default-page-404 issue will come up.
+
+If you use `c.JupyterHub.hub_routespec = "/hub/api/"`,
+then only the Hub _API_ will be available,
+and all UI will be up to you.
+mybinder.org takes this last option,
+because none of the Hub UI pages really make sense.
+Binder users don't have any reason to know or care that JupyterHub happens
+to be an implementation detail of how their environment is managed.
+Seeing Hub error pages and messages in that situation is more likely to be confusing than helpful.
+
+:::{versionadded} 1.4
+
+`c.JupyterHub.hub_routespec` and `c.Proxy.extra_routes` are new in JupyterHub 1.4.
+:::

docs/source/howto/configuration/config-ghoauth.md

@@ -0,0 +1,83 @@
+# Configure GitHub OAuth
+
+In this example, we show a configuration file for a fairly standard JupyterHub
+deployment with the following assumptions:
+
+- Running JupyterHub on a single cloud server
+- Using SSL on the standard HTTPS port 443
+- Using GitHub OAuth (using [OAuthenticator](https://oauthenticator.readthedocs.io/en/latest)) for login
+- Using the default spawner (to configure other spawners, uncomment and edit
+  `spawner_class` as well as follow the instructions for your desired spawner)
+- Users exist locally on the server
+- Users' notebooks to be served from `~/assignments` to allow users to browse
+  for notebooks within other users' home directories
+- You want the landing page for each user to be a `Welcome.ipynb` notebook in
+  their assignments directory
+- All runtime files are put into `/srv/jupyterhub` and log files in `/var/log`
+
+The `jupyterhub_config.py` file would have these settings:
+
+```python
+# jupyterhub_config.py file
+c = get_config()
+
+import os
+pjoin = os.path.join
+
+runtime_dir = os.path.join('/srv/jupyterhub')
+ssl_dir = pjoin(runtime_dir, 'ssl')
+if not os.path.exists(ssl_dir):
+    os.makedirs(ssl_dir)
+
+# Allows multiple single-server per user
+c.JupyterHub.allow_named_servers = True
+
+# https on :443
+c.JupyterHub.port = 443
+c.JupyterHub.ssl_key = pjoin(ssl_dir, 'ssl.key')
+c.JupyterHub.ssl_cert = pjoin(ssl_dir, 'ssl.cert')
+
+# put the JupyterHub cookie secret and state db
+# in /var/run/jupyterhub
+c.JupyterHub.cookie_secret_file = pjoin(runtime_dir, 'cookie_secret')
+c.JupyterHub.db_url = pjoin(runtime_dir, 'jupyterhub.sqlite')
+# or `--db=/path/to/jupyterhub.sqlite` on the command-line
+
+# use GitHub OAuthenticator for local users
+c.JupyterHub.authenticator_class = 'oauthenticator.LocalGitHubOAuthenticator'
+c.GitHubOAuthenticator.oauth_callback_url = os.environ['OAUTH_CALLBACK_URL']
+
+# create system users that don't exist yet
+c.LocalAuthenticator.create_system_users = True
+
+# specify users and admin
+c.Authenticator.allowed_users = {'rgbkrk', 'minrk', 'jhamrick'}
+c.Authenticator.admin_users = {'jhamrick', 'rgbkrk'}
+
+# uses the default spawner
+# To use a different spawner, uncomment `spawner_class` and set to desired
+# spawner (e.g. SudoSpawner). Follow instructions for desired spawner
+# configuration.
+# c.JupyterHub.spawner_class = 'sudospawner.SudoSpawner'
+
+# start single-user notebook servers in ~/assignments,
+# with ~/assignments/Welcome.ipynb as the default landing page
+# this config could also be put in
+# /etc/jupyter/jupyter_notebook_config.py
+c.Spawner.notebook_dir = '~/assignments'
+c.Spawner.args = ['--NotebookApp.default_url=/notebooks/Welcome.ipynb']
+```
+
+Using the GitHub Authenticator requires a few additional
+environment variables to be set prior to launching JupyterHub:
+
+```bash
+export GITHUB_CLIENT_ID=github_id
+export GITHUB_CLIENT_SECRET=github_secret
+export OAUTH_CALLBACK_URL=https://example.com/hub/oauth_callback
+export CONFIGPROXY_AUTH_TOKEN=super-secret
+# append log output to log file /var/log/jupyterhub.log
+jupyterhub -f /etc/jupyterhub/jupyterhub_config.py &>> /var/log/jupyterhub.log
+```
+
+Visit the [Github OAuthenticator reference](https://oauthenticator.readthedocs.io/en/latest/api/gen/oauthenticator.github.html) to see the full list of options for configuring Github OAuth with JupyterHub.

docs/source/howto/configuration/config-proxy.md

@@ -0,0 +1,247 @@
+# Using a reverse proxy
+
+In the following example, we show configuration files for a JupyterHub server
+running locally on port `8000` but accessible from the outside on the standard
+SSL port `443`. This could be useful if the JupyterHub server machine is also
+hosting other domains or content on `443`. The goal in this example is to
+satisfy the following:
+
+- JupyterHub is running on a server, accessed _only_ via `HUB.DOMAIN.TLD:443`
+- On the same machine, `NO_HUB.DOMAIN.TLD` strictly serves different content,
+  also on port `443`
+- `nginx` or `apache` is used as the public access point (which means that
+  only nginx/apache will bind to `443`)
+- After testing, the server in question should be able to score at least an A on the
+  Qualys SSL Labs [SSL Server Test](https://www.ssllabs.com/ssltest/)
+
+Let's start out with the needed JupyterHub configuration in `jupyterhub_config.py`:
+
+```python
+# Force the proxy to only listen to connections to 127.0.0.1 (on port 8000)
+c.JupyterHub.bind_url = 'http://127.0.0.1:8000'
+```
+
+(For Jupyterhub < 0.9 use `c.JupyterHub.ip = '127.0.0.1'`.)
+
+For high-quality SSL configuration, we also generate Diffie-Helman parameters.
+This can take a few minutes:
+
+```bash
+openssl dhparam -out /etc/ssl/certs/dhparam.pem 4096
+```
+
+## Nginx
+
+This **`nginx` config file** is fairly standard fare except for the two
+`location` blocks within the main section for HUB.DOMAIN.tld.
+To create a new site for jupyterhub in your Nginx config, make a new file
+in `sites.enabled`, e.g. `/etc/nginx/sites.enabled/jupyterhub.conf`:
+
+```bash
+# Top-level HTTP config for WebSocket headers
+# If Upgrade is defined, Connection = upgrade
+# If Upgrade is empty, Connection = close
+map $http_upgrade $connection_upgrade {
+    default upgrade;
+    ''      close;
+}
+
+# HTTP server to redirect all 80 traffic to SSL/HTTPS
+server {
+    listen 80;
+    server_name HUB.DOMAIN.TLD;
+
+    # Redirect the request to HTTPS
+    return 302 https://$host$request_uri;
+}
+
+# HTTPS server to handle JupyterHub
+server {
+    listen 443;
+    ssl on;
+
+    server_name HUB.DOMAIN.TLD;
+
+    ssl_certificate /etc/letsencrypt/live/HUB.DOMAIN.TLD/fullchain.pem;
+    ssl_certificate_key /etc/letsencrypt/live/HUB.DOMAIN.TLD/privkey.pem;
+
+    ssl_protocols TLSv1 TLSv1.1 TLSv1.2;
+    ssl_prefer_server_ciphers on;
+    ssl_dhparam /etc/ssl/certs/dhparam.pem;
+    ssl_ciphers 'ECDHE-RSA-AES128-GCM-SHA256:ECDHE-ECDSA-AES128-GCM-SHA256:ECDHE-RSA-AES256-GCM-SHA384:ECDHE-ECDSA-AES256-GCM-SHA384:DHE-RSA-AES128-GCM-SHA256:DHE-DSS-AES128-GCM-SHA256:kEDH+AESGCM:ECDHE-RSA-AES128-SHA256:ECDHE-ECDSA-AES128-SHA256:ECDHE-RSA-AES128-SHA:ECDHE-ECDSA-AES128-SHA:ECDHE-RSA-AES256-SHA384:ECDHE-ECDSA-AES256-SHA384:ECDHE-RSA-AES256-SHA:ECDHE-ECDSA-AES256-SHA:DHE-RSA-AES128-SHA256:DHE-RSA-AES128-SHA:DHE-DSS-AES128-SHA256:DHE-RSA-AES256-SHA256:DHE-DSS-AES256-SHA:DHE-RSA-AES256-SHA:AES128-GCM-SHA256:AES256-GCM-SHA384:AES128-SHA256:AES256-SHA256:AES128-SHA:AES256-SHA:AES:CAMELLIA:DES-CBC3-SHA:!aNULL:!eNULL:!EXPORT:!DES:!RC4:!MD5:!PSK:!aECDH:!EDH-DSS-DES-CBC3-SHA:!EDH-RSA-DES-CBC3-SHA:!KRB5-DES-CBC3-SHA';
+    ssl_session_timeout 1d;
+    ssl_session_cache shared:SSL:50m;
+    ssl_stapling on;
+    ssl_stapling_verify on;
+    add_header Strict-Transport-Security max-age=15768000;
+
+    # Managing literal requests to the JupyterHub frontend
+    location / {
+        proxy_pass http://127.0.0.1:8000;
+        proxy_set_header X-Real-IP $remote_addr;
+        proxy_set_header Host $host;
+        proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for;
+
+        # websocket headers
+        proxy_http_version 1.1;
+        proxy_set_header Upgrade $http_upgrade;
+        proxy_set_header Connection $connection_upgrade;
+        proxy_set_header X-Scheme $scheme;
+
+        proxy_buffering off;
+    }
+
+    # Managing requests to verify letsencrypt host
+    location ~ /.well-known {
+        allow all;
+    }
+}
+```
+
+If `nginx` is not running on port 443, substitute `$http_host` for `$host` on
+the lines setting the `Host` header.
+
+`nginx` will now be the front-facing element of JupyterHub on `443` which means
+it is also free to bind other servers, like `NO_HUB.DOMAIN.TLD` to the same port
+on the same machine and network interface. In fact, one can simply use the same
+server blocks as above for `NO_HUB` and simply add a line for the root directory
+of the site as well as the applicable location call:
+
+```bash
+server {
+    listen 80;
+    server_name NO_HUB.DOMAIN.TLD;
+
+    # Redirect the request to HTTPS
+    return 302 https://$host$request_uri;
+}
+
+server {
+    listen 443;
+    ssl on;
+
+    # INSERT OTHER SSL PARAMETERS HERE AS ABOVE
+    # SSL cert may differ
+
+    # Set the appropriate root directory
+    root /var/www/html
+
+    # Set URI handling
+    location / {
+        try_files $uri $uri/ =404;
+    }
+
+    # Managing requests to verify letsencrypt host
+    location ~ /.well-known {
+        allow all;
+    }
+
+}
+```
+
+Now restart `nginx`, restart the JupyterHub, and enjoy accessing
+`https://HUB.DOMAIN.TLD` while serving other content securely on
+`https://NO_HUB.DOMAIN.TLD`.
+
+### SELinux permissions for Nginx
+
+On distributions with SELinux enabled (e.g. Fedora), one may encounter permission errors
+when the Nginx service is started.
+
+We need to allow Nginx to perform network relay and connect to the JupyterHub port. The
+following commands do that:
+
+```bash
+semanage port -a -t http_port_t -p tcp 8000
+setsebool -P httpd_can_network_relay 1
+setsebool -P httpd_can_network_connect 1
+```
+
+Replace 8000 with the port the JupyterHub server is running from.
+
+## Apache
+
+As with Nginx above, you can use [Apache](https://httpd.apache.org) as the reverse proxy.
+First, we will need to enable the Apache modules that we are going to need:
+
+```bash
+a2enmod ssl rewrite proxy headers proxy_http proxy_wstunnel
+```
+
+Our Apache configuration is equivalent to the Nginx configuration above:
+
+- Redirect HTTP to HTTPS
+- Good SSL Configuration
+- Support for WebSocket on any proxied URL
+- JupyterHub is running locally at http://127.0.0.1:8000
+
+```bash
+# Redirect HTTP to HTTPS
+Listen 80
+<VirtualHost HUB.DOMAIN.TLD:80>
+  ServerName HUB.DOMAIN.TLD
+  Redirect / https://HUB.DOMAIN.TLD/
+</VirtualHost>
+
+Listen 443
+<VirtualHost HUB.DOMAIN.TLD:443>
+
+  ServerName HUB.DOMAIN.TLD
+
+  # Enable HTTP/2, if available
+  Protocols h2 http/1.1
+
+  # HTTP Strict Transport Security (mod_headers is required) (63072000 seconds)
+  Header always set Strict-Transport-Security "max-age=63072000"
+
+  # Configure SSL
+  SSLEngine on
+  SSLCertificateFile /etc/letsencrypt/live/HUB.DOMAIN.TLD/fullchain.pem
+  SSLCertificateKeyFile /etc/letsencrypt/live/HUB.DOMAIN.TLD/privkey.pem
+  SSLOpenSSLConfCmd DHParameters /etc/ssl/certs/dhparam.pem
+
+  # Intermediate configuration from SSL-config.mozilla.org (2022-03-03)
+  # Please note, that this configuration might be outdated - please update it accordingly using https://ssl-config.mozilla.org/
+  SSLProtocol             all -SSLv3 -TLSv1 -TLSv1.1
+  SSLCipherSuite          ECDHE-ECDSA-AES128-GCM-SHA256:ECDHE-RSA-AES128-GCM-SHA256:ECDHE-ECDSA-AES256-GCM-SHA384:ECDHE-RSA-AES256-GCM-SHA384:ECDHE-ECDSA-CHACHA20-POLY1305:ECDHE-RSA-CHACHA20-POLY1305:DHE-RSA-AES128-GCM-SHA256:DHE-RSA-AES256-GCM-SHA384
+  SSLHonorCipherOrder     off
+  SSLSessionTickets       off
+
+  # Use RewriteEngine to handle WebSocket connection upgrades
+  RewriteEngine On
+  RewriteCond %{HTTP:Connection} Upgrade [NC]
+  RewriteCond %{HTTP:Upgrade} websocket [NC]
+  RewriteRule /(.*) ws://127.0.0.1:8000/$1 [P,L]
+
+  <Location "/">
+    # preserve Host header to avoid cross-origin problems
+    ProxyPreserveHost on
+    # proxy to JupyterHub
+    ProxyPass         http://127.0.0.1:8000/
+    ProxyPassReverse  http://127.0.0.1:8000/
+    RequestHeader     set "X-Forwarded-Proto" expr=%{REQUEST_SCHEME}
+  </Location>
+</VirtualHost>
+```
+
+In case of the need to run JupyterHub under /jhub/ or another location please use the below configurations:
+
+- JupyterHub running locally at http://127.0.0.1:8000/jhub/ or other location
+
+httpd.conf amendments:
+
+```bash
+ RewriteRule /jhub/(.*) ws://127.0.0.1:8000/jhub/$1 [P,L]
+ RewriteRule /jhub/(.*) http://127.0.0.1:8000/jhub/$1 [P,L]
+
+ ProxyPass /jhub/ http://127.0.0.1:8000/jhub/
+ ProxyPassReverse /jhub/  http://127.0.0.1:8000/jhub/
+```
+
+jupyterhub_config.py amendments:
+
+```python
+# The public facing URL of the whole JupyterHub application.
+# This is the address on which the proxy will bind. Sets protocol, IP, base_url
+c.JupyterHub.bind_url = 'http://127.0.0.1:8000/jhub/'
+```

docs/source/howto/configuration/config-sudo.md

@@ -0,0 +1,261 @@
+# Run JupyterHub without root privileges using `sudo`
+
+**Note:** Setting up `sudo` permissions involves many pieces of system
+configuration. It is quite easy to get wrong and very difficult to debug.
+Only do this if you are very sure you must.
+
+## Overview
+
+There are many [Authenticators](authenticators) and [Spawners](spawners) available for JupyterHub. Some, such
+as [DockerSpawner](https://github.com/jupyterhub/dockerspawner) or [OAuthenticator](https://github.com/jupyterhub/oauthenticator), do not need any elevated permissions. This
+document describes how to get the full default behavior of JupyterHub while
+running notebook servers as real system users on a shared system, without
+running the Hub itself as root.
+
+Since JupyterHub needs to spawn processes as other users, the simplest way
+is to run it as root, spawning user servers with [setuid](https://linux.die.net/man/2/setuid).
+But this isn't especially safe, because you have a process running on the
+public web as root.
+
+A **more prudent way** to run the server while preserving functionality is to
+create a dedicated user with `sudo` access restricted to launching and
+monitoring single-user servers.
+
+## Create a user
+
+To do this, first create a user that will run the Hub:
+
+```bash
+sudo useradd rhea
+```
+
+This user shouldn't have a login shell or password (possible with -r).
+
+## Set up sudospawner
+
+Next, you will need [sudospawner](https://github.com/jupyter/sudospawner)
+to enable monitoring the single-user servers with sudo:
+
+```bash
+sudo python3 -m pip install sudospawner
+```
+
+Now we have to configure sudo to allow the Hub user (`rhea`) to launch
+the sudospawner script on behalf of our hub users (here `zoe` and `wash`).
+We want to confine these permissions to only what we really need.
+
+## Edit `/etc/sudoers`
+
+To do this we add to `/etc/sudoers` (use `visudo` for safe editing of sudoers):
+
+- specify the list of users `JUPYTER_USERS` for whom `rhea` can spawn servers
+- set the command `JUPYTER_CMD` that `rhea` can execute on behalf of users
+- give `rhea` permission to run `JUPYTER_CMD` on behalf of `JUPYTER_USERS`
+  without entering a password
+
+For example:
+
+```bash
+# comma-separated list of users that can spawn single-user servers
+# this should include all of your Hub users
+Runas_Alias JUPYTER_USERS = rhea, zoe, wash
+
+# the command(s) the Hub can run on behalf of the above users without needing a password
+# the exact path may differ, depending on how sudospawner was installed
+Cmnd_Alias JUPYTER_CMD = /usr/local/bin/sudospawner
+
+# actually give the Hub user permission to run the above command on behalf
+# of the above users without prompting for a password
+rhea ALL=(JUPYTER_USERS) NOPASSWD:JUPYTER_CMD
+```
+
+It might be useful to modify `secure_path` to add commands in path. (Search for
+`secure_path` in the [sudo docs](https://www.sudo.ws/man/1.8.14/sudoers.man.html)
+
+As an alternative to adding every user to the `/etc/sudoers` file, you can
+use a group in the last line above, instead of `JUPYTER_USERS`:
+
+```bash
+rhea ALL=(%jupyterhub) NOPASSWD:JUPYTER_CMD
+```
+
+If the `jupyterhub` group exists, there will be no need to edit `/etc/sudoers`
+again. A new user will gain access to the application when added to the group:
+
+```bash
+$ adduser -G jupyterhub newuser
+```
+
+## Test `sudo` setup
+
+Test that the new user doesn't need to enter a password to run the sudospawner
+command.
+
+This should prompt for your password to switch to `rhea`, but _not_ prompt for
+any password for the second switch. It should show some help output about
+logging options:
+
+```bash
+$ sudo -u rhea sudo -n -u $USER /usr/local/bin/sudospawner --help
+Usage: /usr/local/bin/sudospawner [OPTIONS]
+
+Options:
+
+--help          show this help information
+...
+```
+
+And this should fail:
+
+```bash
+$ sudo -u rhea sudo -n -u $USER echo 'fail'
+sudo: a password is required
+```
+
+## Enable PAM for non-root
+
+By default, [PAM authentication](https://en.wikipedia.org/wiki/Pluggable_authentication_module)
+is used by JupyterHub. To use PAM, the process may need to be able to read
+the shadow password database.
+
+### Shadow group (Linux)
+
+**Note:** On [Fedora based distributions](https://fedoraproject.org/wiki/List_of_Fedora_remixes) there is no clear way to configure
+the PAM database to allow sufficient access for authenticating with the target user's password
+from JupyterHub. As a workaround we recommend use an
+[alternative authentication method](https://github.com/jupyterhub/jupyterhub/wiki/Authenticators).
+
+```bash
+$ ls -l /etc/shadow
+-rw-r-----  1 root shadow   2197 Jul 21 13:41 shadow
+```
+
+If there's already a shadow group, you are set. If its permissions are more like:
+
+```bash
+    $ ls -l /etc/shadow
+    -rw-------  1 root wheel   2197 Jul 21 13:41 shadow
+```
+
+Then you may want to add a shadow group, and make the shadow file group-readable:
+
+```bash
+$ sudo groupadd shadow
+$ sudo chgrp shadow /etc/shadow
+$ sudo chmod g+r /etc/shadow
+```
+
+We want our new user to be able to read the shadow passwords, so add it to the shadow group:
+
+```bash
+    $ sudo usermod -a -G shadow rhea
+```
+
+If you want jupyterhub to serve pages on a restricted port (such as port 80 for HTTP),
+then you will need to give `node` permission to do so:
+
+```bash
+sudo setcap 'cap_net_bind_service=+ep' /usr/bin/node
+```
+
+However, you may want to further understand the consequences of this.
+([Further reading](https://man7.org/linux/man-pages/man7/capabilities.7.html))
+
+You may also be interested in limiting the amount of CPU any process can use
+on your server. `cpulimit` is a useful tool that is available for many Linux
+distributions' packaging system. This can be used to keep any user's process
+from using too much CPU cycles. You can configure it accoring to [these
+instructions](https://ubuntuforums.org/showthread.php?t=992706).
+
+### Shadow group (FreeBSD)
+
+**NOTE:** This has not been tested on FreeBSD and may not work as expected on
+the FreeBSD platform. _Do not use in production without verifying that it works properly!_
+
+```bash
+$ ls -l /etc/spwd.db /etc/master.passwd
+-rw-------  1 root  wheel   2516 Aug 22 13:35 /etc/master.passwd
+-rw-------  1 root  wheel  40960 Aug 22 13:35 /etc/spwd.db
+```
+
+Add a shadow group if there isn't one, and make the shadow file group-readable:
+
+```bash
+$ sudo pw group add shadow
+$ sudo chgrp shadow /etc/spwd.db
+$ sudo chmod g+r /etc/spwd.db
+$ sudo chgrp shadow /etc/master.passwd
+$ sudo chmod g+r /etc/master.passwd
+```
+
+We want our new user to be able to read the shadow passwords, so add it to the
+shadow group:
+
+```bash
+$ sudo pw user mod rhea -G shadow
+```
+
+## Test that PAM works
+
+We can verify that PAM is working, with:
+
+```bash
+$ sudo -u rhea python3 -c "import pamela, getpass; print(pamela.authenticate('$USER', getpass.getpass()))"
+Password: [enter your unix password]
+```
+
+## Make a directory for JupyterHub
+
+JupyterHub stores its state in a database, so it needs write access to a directory.
+The simplest way to deal with this is to make a directory owned by your Hub user,
+and use that as the CWD when launching the server.
+
+```bash
+$ sudo mkdir /etc/jupyterhub
+$ sudo chown rhea /etc/jupyterhub
+```
+
+## Start jupyterhub
+
+Finally, start the server as our newly configured user, `rhea`:
+
+```bash
+$ cd /etc/jupyterhub
+$ sudo -u rhea jupyterhub --JupyterHub.spawner_class=sudospawner.SudoSpawner
+```
+
+And try logging in.
+
+## Troubleshooting: SELinux
+
+If you still get a generic `Permission denied` `PermissionError`, it's possible SELinux is blocking you.
+Here's how you can make a module to resolve this.
+First, put this in a file named `sudo_exec_selinux.te`:
+
+```bash
+module sudo_exec_selinux 1.1;
+
+require {
+        type unconfined_t;
+        type sudo_exec_t;
+        class file { read entrypoint };
+}
+
+#============= unconfined_t ==============
+allow unconfined_t sudo_exec_t:file entrypoint;
+```
+
+Then run all of these commands as root:
+
+```bash
+$ checkmodule -M -m -o sudo_exec_selinux.mod sudo_exec_selinux.te
+$ semodule_package -o sudo_exec_selinux.pp -m sudo_exec_selinux.mod
+$ semodule -i sudo_exec_selinux.pp
+```
+
+## Troubleshooting: PAM session errors
+
+If the PAM authentication doesn't work and you see errors for
+`login:session-auth`, or similar, consider updating to a more recent version
+of jupyterhub and disabling the opening of PAM sessions with
+`c.PAMAuthenticator.open_sessions=False`.

docs/source/howto/configuration/config-user-env.md

@@ -0,0 +1,263 @@
+# Configuring user environments
+
+To deploy JupyterHub means you are providing Jupyter notebook environments for
+multiple users. Often, this includes a desire to configure the user
+environment in a custom way.
+
+Since the `jupyterhub-singleuser` server extends the standard Jupyter notebook
+server, most configuration and documentation that applies to Jupyter Notebook
+applies to the single-user environments. Configuration of user environments
+typically does not occur through JupyterHub itself, but rather through system-wide
+configuration of Jupyter, which is inherited by `jupyterhub-singleuser`.
+
+**Tip:** When searching for configuration tips for JupyterHub user environments, you might want to remove JupyterHub from your search because there are a lot more people out there configuring Jupyter than JupyterHub and the configuration is the same.
+
+This section will focus on user environments, which includes the following:
+
+- [Installing packages](#installing-packages)
+- [Configuring Jupyter and IPython](#configuring-jupyter-and-ipython)
+- [Installing kernelspecs](#installing-kernelspecs)
+- [Using containers vs. multi-user hosts](#multi-user-hosts-vs-containers)
+
+## Installing packages
+
+To make packages available to users, you will typically install packages system-wide or in a shared environment.
+
+This installation location should always be in the same environment where
+`jupyterhub-singleuser` itself is installed in, and must be _readable and
+executable_ by your users. If you want your users to be able to install additional
+packages, the installation location must also be _writable_ by your users.
+
+If you are using a standard Python installation on your system, use the following command:
+
+```bash
+sudo python3 -m pip install numpy
+```
+
+to install the numpy package in the default Python 3 environment on your system
+(typically `/usr/local`).
+
+You may also use conda to install packages. If you do, you should make sure
+that the conda environment has appropriate permissions for users to be able to
+run Python code in the env. The env must be _readable and executable_ by all
+users. Additionally it must be _writeable_ if you want users to install
+additional packages.
+
+## Configuring Jupyter and IPython
+
+[Jupyter](https://jupyter-notebook.readthedocs.io/en/stable/configuring/config_overview.html)
+and [IPython](https://ipython.readthedocs.io/en/stable/development/config.html)
+have their own configuration systems.
+
+As a JupyterHub administrator, you will typically want to install and configure environments for all JupyterHub users. For example, let's say you wish for each student in a class to have the same user environment configuration.
+
+Jupyter and IPython support **"system-wide"** locations for configuration, which is the logical place to put global configuration that you want to affect all users. It's generally more efficient to configure user environments "system-wide", and it's a good practice to avoid creating files in the users' home directories.
+The typical locations for these config files are:
+
+- **system-wide** in `/etc/{jupyter|ipython}`
+- **env-wide** (environment wide) in `{sys.prefix}/etc/{jupyter|ipython}`.
+
+### Jupyter environment configuration priority
+
+When Jupyter runs in an environment (conda or virtualenv), it prefers to load configuration from the environment over each user's own configuration (e.g. in `~/.jupyter`).
+This may cause issues if you use a _shared_ conda environment or virtualenv for users, because e.g. jupyterlab may try to write information like workspaces or settings to the environment instead of the user's own directory.
+This could fail with something like `Permission denied: $PREFIX/etc/jupyter/lab`.
+
+To avoid this issue, set `JUPYTER_PREFER_ENV_PATH=0` in the user environment:
+
+```python
+c.Spawner.environment.update(
+    {
+        "JUPYTER_PREFER_ENV_PATH": "0",
+    }
+)
+```
+
+which tells Jupyter to prefer _user_ configuration paths (e.g. in `~/.jupyter`) to configuration set in the environment.
+
+### Example: Enable an extension system-wide
+
+For example, to enable the `cython` IPython extension for all of your users, create the file `/etc/ipython/ipython_config.py`:
+
+```python
+c.InteractiveShellApp.extensions.append("cython")
+```
+
+### Example: Enable a Jupyter notebook configuration setting for all users
+
+:::{note}
+These examples configure the Jupyter ServerApp, which is used by JupyterLab, the default in JupyterHub 2.0.
+
+If you are using the classing Jupyter Notebook server,
+the same things should work,
+with the following substitutions:
+
+- Search for `jupyter_server_config`, and replace with `jupyter_notebook_config`
+- Search for `NotebookApp`, and replace with `ServerApp`
+
+:::
+
+To enable Jupyter notebook's internal idle-shutdown behavior (requires notebook ≥ 5.4), set the following in the `/etc/jupyter/jupyter_server_config.py` file:
+
+```python
+# shutdown the server after no activity for an hour
+c.ServerApp.shutdown_no_activity_timeout = 60 * 60
+# shutdown kernels after no activity for 20 minutes
+c.MappingKernelManager.cull_idle_timeout = 20 * 60
+# check for idle kernels every two minutes
+c.MappingKernelManager.cull_interval = 2 * 60
+```
+
+## Installing kernelspecs
+
+You may have multiple Jupyter kernels installed and want to make sure that they are available to all of your users. This means installing kernelspecs either system-wide (e.g. in /usr/local/) or in the `sys.prefix` of JupyterHub
+itself.
+
+Jupyter kernelspec installation is system-wide by default, but some kernels
+may default to installing kernelspecs in your home directory. These will need
+to be moved system-wide to ensure that they are accessible.
+
+To see where your kernelspecs are, you can use the following command:
+
+```bash
+jupyter kernelspec list
+```
+
+### Example: Installing kernels system-wide
+
+Let's assume that I have a Python 2 and Python 3 environment that I want to make sure are available, I can install their specs **system-wide** (in /usr/local) using the following command:
+
+```bash
+/path/to/python3 -m ipykernel install --prefix=/usr/local
+/path/to/python2 -m ipykernel install --prefix=/usr/local
+```
+
+## Multi-user hosts vs. Containers
+
+There are two broad categories of user environments that depend on what
+Spawner you choose:
+
+- Multi-user hosts (shared system)
+- Container-based
+
+How you configure user environments for each category can differ a bit
+depending on what Spawner you are using.
+
+The first category is a **shared system (multi-user host)** where
+each user has a JupyterHub account, a home directory as well as being
+a real system user. In this example, shared configuration and installation
+must be in a 'system-wide' location, such as `/etc/`, or `/usr/local`
+or a custom prefix such as `/opt/conda`.
+
+When JupyterHub uses **container-based** Spawners (e.g. KubeSpawner or
+DockerSpawner), the 'system-wide' environment is really the container image used for users.
+
+In both cases, you want to _avoid putting configuration in user home
+directories_ because users can change those configuration settings. Also, home directories typically persist once they are created, thereby making it difficult for admins to update later.
+
+## Named servers
+
+By default, in a JupyterHub deployment, each user has one server only.
+
+JupyterHub can, however, have multiple servers per user.
+This is mostly useful in deployments where users can configure the environment in which their server will start (e.g. resource requests on an HPC cluster), so that a given user can have multiple configurations running at the same time, without having to stop and restart their own server.
+
+To allow named servers, include this code snippet in your config file:
+
+```python
+c.JupyterHub.allow_named_servers = True
+```
+
+Named servers were implemented in the REST API in JupyterHub 0.8,
+and JupyterHub 1.0 introduces UI for managing named servers via the user home page:
+
+![named servers on the home page](/images/named-servers-home.png)
+
+as well as the admin page:
+
+![named servers on the admin page](/images/named-servers-admin.png)
+
+Named servers can be accessed, created, started, stopped, and deleted
+from these pages. Activity tracking is now per server as well.
+
+To limit the number of **named server** per user by setting a constant value, include this code snippet in your config file:
+
+```python
+c.JupyterHub.named_server_limit_per_user = 5
+```
+
+Alternatively, to use a callable/awaitable based on the handler object, include this code snippet in your config file:
+
+```python
+def named_server_limit_per_user_fn(handler):
+    user = handler.current_user
+    if user and user.admin:
+        return 0
+    return 5
+
+c.JupyterHub.named_server_limit_per_user = named_server_limit_per_user_fn
+```
+
+This can be useful for quota service implementations. The example above limits the number of named servers for non-admin users only.
+
+If `named_server_limit_per_user` is set to `0`, no limit is enforced.
+
+When using named servers, Spawners may need additional configuration to take the `servername` into account. Whilst `KubeSpawner` takes the `servername` into account by default in [`pod_name_template`](https://jupyterhub-kubespawner.readthedocs.io/en/latest/spawner.html#kubespawner.KubeSpawner.pod_name_template), other Spawners may not. Check the documentation for the specific Spawner to see how singleuser servers are named, for example in `DockerSpawner` this involves modifying the [`name_template`](https://jupyterhub-dockerspawner.readthedocs.io/en/latest/api/index.html) setting to include `servername`, eg. `"{prefix}-{username}-{servername}"`.
+
+(classic-notebook-ui)=
+
+## Switching back to the classic notebook
+
+By default, the single-user server launches JupyterLab,
+which is based on [Jupyter Server][].
+
+This is the default server when running JupyterHub ≥ 2.0.
+To switch to using the legacy Jupyter Notebook server (notebook < 7.0), you can set the `JUPYTERHUB_SINGLEUSER_APP` environment variable
+(in the single-user environment) to:
+
+```bash
+export JUPYTERHUB_SINGLEUSER_APP='notebook.notebookapp.NotebookApp'
+```
+
+:::{note}
+
+```
+JUPYTERHUB_SINGLEUSER_APP='notebook.notebookapp.NotebookApp'
+```
+
+is only valid for notebook < 7. notebook v7 is based on jupyter-server,
+and the default jupyter-server application must be used.
+Selecting the new notebook UI is no longer a matter of selecting the server app to launch,
+but only the default URL for users to visit.
+To use notebook v7 with JupyterHub, leave the default singleuser app config alone (or specify `JUPYTERHUB_SINGLEUSER_APP=jupyter-server`) and set the default _URL_ for user servers:
+
+```python
+c.Spawner.default_url = '/tree/'
+```
+
+:::
+
+[jupyter server]: https://jupyter-server.readthedocs.io
+[jupyter notebook]: https://jupyter-notebook.readthedocs.io
+
+:::{versionchanged} 2.0
+
+JupyterLab is now the default single-user UI, if available,
+which is based on the [Jupyter Server][],
+no longer the legacy [Jupyter Notebook][] server.
+JupyterHub prior to 2.0 launched the legacy notebook server (`jupyter notebook`),
+and the Jupyter server could be selected by specifying the following:
+
+```python
+# jupyterhub_config.py
+c.Spawner.cmd = ["jupyter-labhub"]
+```
+
+Alternatively, for an otherwise customized Jupyter Server app,
+set the environment variable using the following command:
+
+```bash
+export JUPYTERHUB_SINGLEUSER_APP='jupyter_server.serverapp.ServerApp'
+```
+
+:::

docs/source/howto/index.md

@@ -0,0 +1,34 @@
+# How-to
+
+The _How-to_ guides provide practical step-by-step details to help you achieve a particular goal. They are useful when you are trying to get something done but require you to understand and adapt the steps to your specific usecase.
+
+Use the following guides when:
+
+```{toctree}
+:maxdepth: 1
+
+api-only
+proxy
+rest
+separate-proxy
+templates
+upgrading
+log-messages
+
+```
+
+(config-examples)=
+
+## Configuration
+
+The following guides provide examples, including configuration files and tips, for the
+following:
+
+```{toctree}
+:maxdepth: 1
+
+configuration/config-user-env
+configuration/config-ghoauth
+configuration/config-proxy
+configuration/config-sudo
+```

docs/source/howto/log-messages.md

@@ -0,0 +1,72 @@
+# Interpreting common log messages
+
+When debugging errors and outages, looking at the logs emitted by
+JupyterHub is very helpful. This document intends to describe some common
+log messages, what they mean and what are the most common causes that generated them, as well as some possible ways to fix them.
+
+## Failing suspected API request to not-running server
+
+### Example
+
+Your logs might be littered with lines that look scary
+
+```
+[W 2022-03-10 17:25:19.774 JupyterHub base:1349] Failing suspected API request to not-running server: /hub/user/<user-name>/api/metrics/v1
+```
+
+### Cause
+
+This likely means that the user's server has stopped running but they
+still have a browser tab open. For example, you might have 3 tabs open and you shut
+the server down via one.
+Another possible reason could be that you closed your laptop and the server was culled for inactivity, then reopened the laptop!
+However, the client-side code (JupyterLab, Classic Notebook, etc) doesn't interpret the shut-down server and continues to make some API requests.
+
+JupyterHub's architecture means that the proxy routes all requests that
+don't go to a running user server to the hub process itself. The hub
+process then explicitly returns a failure response, so the client knows
+that the server is not running anymore. This is used by JupyterLab to
+inform the user that the server is not running anymore, and provide an option
+to restart it.
+
+Most commonly, you'll see this in reference to the `/api/metrics/v1`
+URL, used by [jupyter-resource-usage](https://github.com/jupyter-server/jupyter-resource-usage).
+
+### Actions you can take
+
+This log message is benign, and there is usually no action for you to take.
+
+## JupyterHub Singleuser Version mismatch
+
+### Example
+
+```
+ jupyterhub version 1.5.0 != jupyterhub-singleuser version 1.3.0. This could cause failure to authenticate and result in redirect loops!
+```
+
+### Cause
+
+JupyterHub requires the `jupyterhub` python package installed inside the image or
+environment, the user server starts in. This message indicates that the version of
+the `jupyterhub` package installed inside the user image or environment is not
+the same as the JupyterHub server's version itself. This is not necessarily always a
+problem - some version drift is mostly acceptable, and the only two known cases of
+breakage are across the 0.7 and 2.0 version releases. In those cases, issues pop
+up immediately after upgrading your version of JupyterHub, so **always check the JupyterHub
+changelog before upgrading!**. The primary problems this _could_ cause are:
+
+1. Infinite redirect loops after the user server starts
+2. Missing expected environment variables in the user server once it starts
+3. Failure for the started user server to authenticate with the JupyterHub server -
+   note that this is _not_ the same as _user authentication_ failing!
+
+However, for the most part, unless you are seeing these specific issues, the log
+message should be counted as a warning to get the `jupyterhub` package versions
+aligned, rather than as an indicator of an existing problem.
+
+### Actions you can take
+
+Upgrade the version of the `jupyterhub` package in your user environment or image
+so that it matches the version of JupyterHub running your JupyterHub server! If you
+are using the [zero-to-jupyterhub](https://z2jh.jupyter.org) helm chart, you can find the appropriate
+version of the `jupyterhub` package to install in your user image [here](https://jupyterhub.github.io/helm-chart/)

docs/source/howto/proxy.md

@@ -0,0 +1,233 @@
+# Writing a custom Proxy implementation
+
+JupyterHub 0.8 introduced the ability to write a custom implementation of the
+proxy. This enables deployments with different needs than the default proxy,
+configurable-http-proxy (CHP). CHP is a single-process nodejs proxy that the
+Hub manages by default as a subprocess (it can be run externally, as well, and
+typically is in production deployments).
+
+The upside to CHP, and why we use it by default, is that it's easy to install
+and run (if you have nodejs, you are set!). The downsides are that
+
+- it's a single process and
+- does not support any persistence of the routing table.
+
+So if the proxy process dies, your whole JupyterHub instance is inaccessible
+until the Hub notices, restarts the proxy, and restores the routing table. For
+deployments that want to avoid such a single point of failure, or leverage
+existing proxy infrastructure in their chosen deployment (such as Kubernetes
+ingress objects), the Proxy API provides a way to do that.
+
+In general, for a proxy to be usable by JupyterHub, it must:
+
+1. support websockets without prior knowledge of the URL where websockets may
+   occur
+2. support trie-based routing (i.e. allow different routes on `/foo` and
+   `/foo/bar` and route based on specificity)
+3. adding or removing a route should not cause existing connections to drop
+
+Optionally, if the JupyterHub deployment is to use host-based routing,
+the Proxy must additionally support routing based on the Host of the request.
+
+## Subclassing Proxy
+
+To start, any Proxy implementation should subclass the base Proxy class,
+as is done with custom Spawners and Authenticators.
+
+```python
+from jupyterhub.proxy import Proxy
+
+class MyProxy(Proxy):
+    """My Proxy implementation"""
+    ...
+```
+
+## Starting and stopping the proxy
+
+If your proxy should be launched when the Hub starts, you must define how
+to start and stop your proxy:
+
+```python
+class MyProxy(Proxy):
+    ...
+    async def start(self):
+        """Start the proxy"""
+
+    async def stop(self):
+        """Stop the proxy"""
+```
+
+These methods **may** be coroutines.
+
+`c.Proxy.should_start` is a configurable flag that determines whether the
+Hub should call these methods when the Hub itself starts and stops.
+
+## Encryption
+
+When using `internal_ssl` to encrypt traffic behind the proxy, at minimum,
+your `Proxy` will need client ssl certificates which the `Hub` must be made
+aware of. These can be generated with the command `jupyterhub --generate-certs`
+which will write them to the `internal_certs_location` in folders named
+`proxy_api` and `proxy_client`. Alternatively, these can be provided to the
+hub via the `jupyterhub_config.py` file by providing a `dict` of named paths
+to the `external_authorities` option. The hub will include all certificates
+provided in that `dict` in the trust bundle utilized by all internal
+components.
+
+### Purely external proxies
+
+Probably most custom proxies will be externally managed,
+such as Kubernetes ingress-based implementations.
+In this case, you do not need to define `start` and `stop`.
+To disable the methods, you can define `should_start = False` at the class level:
+
+```python
+class MyProxy(Proxy):
+    should_start = False
+```
+
+## Routes
+
+At its most basic, a Proxy implementation defines a mechanism to add, remove,
+and retrieve routes. A proxy that implements these three methods is complete.
+Each of these methods **may** be a coroutine.
+
+**Definition:** routespec
+
+A routespec, which will appear in these methods, is a string describing a
+route to be proxied, such as `/user/name/`. A routespec will:
+
+1. always end with `/`
+2. always start with `/` if it is a path-based route `/proxy/path/`
+3. precede the leading `/` with a host for host-based routing, e.g.
+   `host.tld/proxy/path/`
+
+### Adding a route
+
+When adding a route, JupyterHub may pass a JSON-serializable dict as a `data`
+argument that should be attached to the proxy route. When that route is
+retrieved, the `data` argument should be returned as well. If your proxy
+implementation doesn't support storing data attached to routes, then your
+Python wrapper may have to handle storing the `data` piece itself, e.g in a
+simple file or database.
+
+```python
+async def add_route(self, routespec, target, data):
+    """Proxy `routespec` to `target`.
+
+    Store `data` associated with the routespec
+    for retrieval later.
+    """
+```
+
+Adding a route for a user looks like this:
+
+```python
+await proxy.add_route('/user/pgeorgiou/', 'http://127.0.0.1:1227',
+                {'user': 'pgeorgiou'})
+```
+
+### Removing routes
+
+`delete_route()` is given a routespec to delete. If there is no such route,
+`delete_route` should still succeed, but a warning may be issued.
+
+```python
+async def delete_route(self, routespec):
+    """Delete the route"""
+```
+
+### Retrieving routes
+
+For retrieval, you only _need_ to implement a single method that retrieves all
+routes. The return value for this function should be a dictionary, keyed by
+`routespec`, of dicts whose keys are the same three arguments passed to
+`add_route` (`routespec`, `target`, `data`)
+
+```python
+async def get_all_routes(self):
+    """Return all routes, keyed by routespec"""
+```
+
+```python
+{
+  '/proxy/path/': {
+    'routespec': '/proxy/path/',
+    'target': 'http://...',
+    'data': {},
+  },
+}
+```
+
+## Note on activity tracking
+
+JupyterHub can track activity of users, for use in services such as culling
+idle servers. As of JupyterHub 0.8, this activity tracking is the
+responsibility of the proxy. If your proxy implementation can track activity
+to endpoints, it may add a `last_activity` key to the `data` of routes
+retrieved in `.get_all_routes()`. If present, the value of `last_activity`
+should be an [ISO8601](https://en.wikipedia.org/wiki/ISO_8601) UTC date
+string:
+
+```python
+{
+  '/user/pgeorgiou/': {
+    'routespec': '/user/pgeorgiou/',
+    'target': 'http://127.0.0.1:1227',
+    'data': {
+      'user': 'pgeourgiou',
+      'last_activity': '2017-10-03T10:33:49.570Z',
+    },
+  },
+}
+```
+
+If the proxy does not track activity, then only activity to the Hub itself is
+tracked, and services such as cull-idle will not work.
+
+Now that `notebook-5.0` tracks activity internally, we can retrieve activity
+information from the single-user servers instead, removing the need to track
+activity in the proxy. But this is not yet implemented in JupyterHub 0.8.0.
+
+### Registering custom Proxies via entry points
+
+As of JupyterHub 1.0, custom proxy implementations can register themselves via
+the `jupyterhub.proxies` entry point metadata.
+To do this, in your `setup.py` add:
+
+```python
+setup(
+  ...
+  entry_points={
+    'jupyterhub.proxies': [
+        'mything = mypackage:MyProxy',
+    ],
+  },
+)
+```
+
+If you have added this metadata to your package,
+admins can select your authenticator with the configuration:
+
+```python
+c.JupyterHub.proxy_class = 'mything'
+```
+
+instead of the full
+
+```python
+c.JupyterHub.proxy_class = 'mypackage:MyProxy'
+```
+
+as previously required.
+Additionally, configurable attributes for your proxy will
+appear in jupyterhub help output and auto-generated configuration files
+via `jupyterhub --generate-config`.
+
+### Index of proxies
+
+A list of the proxies that are currently available for JupyterHub (that we know about).
+
+1. [`jupyterhub/configurable-http-proxy`](https://github.com/jupyterhub/configurable-http-proxy) The default proxy which uses node-http-proxy
+2. [`jupyterhub/traefik-proxy`](https://github.com/jupyterhub/traefik-proxy) The proxy which configures traefik proxy server for jupyterhub
+3. [`AbdealiJK/configurable-http-proxy`](https://github.com/AbdealiJK/configurable-http-proxy) A pure python implementation of the configurable-http-proxy

docs/source/howto/rest.md

@@ -0,0 +1,339 @@
+(using-jupyterhub-rest-api)=
+
+# Using JupyterHub's REST API
+
+This section will give you information on:
+
+- What you can do with the API
+- How to create an API token
+- Assigning permissions to a token
+- Updating to admin services
+- Making an API request programmatically using the requests library
+- Paginating API requests
+- Enabling users to spawn multiple named-servers via the API
+- Learn more about JupyterHub's API
+
+Before we discuss about JupyterHub's REST API, you can learn about [REST APIs here](https://en.wikipedia.org/wiki/Representational_state_transfer). A REST
+API provides a standard way for users to get and send information to the
+Hub.
+
+## What you can do with the API
+
+Using the [JupyterHub REST API](jupyterhub-rest-API), you can perform actions on the Hub,
+such as:
+
+- Checking which users are active
+- Adding or removing users
+- Stopping or starting single user notebook servers
+- Authenticating services
+- Communicating with an individual Jupyter server's REST API
+
+## Create an API token
+
+To send requests using the JupyterHub API, you must pass an API token with
+the request.
+
+While JupyterHub is running, any JupyterHub user can request a token via the `token` page.
+This is accessible via a `token` link in the top nav bar from the JupyterHub home page,
+or at the URL `/hub/token`.
+
+:::{figure-md}
+
+![token request page](../images/token-page.png)
+
+JupyterHub's API token page
+:::
+
+:::{figure-md}
+![token-request-success](../images/token-request-success.png)
+
+JupyterHub's token page after successfully requesting a token.
+
+:::
+
+### Register API tokens via configuration
+
+Sometimes, you'll want to pre-generate a token for access to JupyterHub,
+typically for use by external services,
+so that both JupyterHub and the service have access to the same value.
+
+First, you need to generate a good random secret.
+A good way of generating an API token is by running:
+
+```bash
+openssl rand -hex 32
+```
+
+This `openssl` command generates a random token that can be added to the JupyterHub configuration in `jupyterhub_config.py`.
+
+For external services, this would be registered with JupyterHub via configuration:
+
+```python
+c.JupyterHub.services = [
+    {
+        "name": "my-service",
+        "api_token": the_secret_value,
+    },
+]
+```
+
+At this point, requests authenticated with the token will be associated with The service `my-service`.
+
+```{note}
+You can also load additional tokens for users via the `JupyterHub.api_tokens` configuration.
+
+However, this option has been deprecated since the introduction of services.
+```
+
+## Assigning permissions to a token
+
+Prior to JupyterHub 2.0, there were two levels of permissions:
+
+1. user, and
+2. admin
+
+where a token would always have full permissions to do whatever its owner could do.
+
+In JupyterHub 2.0,
+specific permissions are now defined as '**scopes**',
+and can be assigned both at the user/service level,
+and at the individual token level.
+
+This allows e.g. a user with full admin permissions to request a token with limited permissions.
+
+## Updating to admin services
+
+```{note}
+The `api_tokens` configuration has been softly deprecated since the introduction of services.
+We have no plans to remove it,
+but deployments are encouraged to use service configuration instead.
+```
+
+If you have been using `api_tokens` to create an admin user
+and the token for that user to perform some automations, then
+the services' mechanism may be a better fit if you have the following configuration:
+
+```python
+c.JupyterHub.admin_users = {"service-admin"}
+c.JupyterHub.api_tokens = {
+    "secret-token": "service-admin",
+}
+```
+
+This can be updated to create a service, with the following configuration:
+
+```python
+c.JupyterHub.services = [
+    {
+        # give the token a name
+        "name": "service-admin",
+        "api_token": "secret-token",
+        # "admin": True, # if using JupyterHub 1.x
+    },
+]
+
+# roles were introduced in JupyterHub 2.0
+# prior to 2.0, only "admin": True or False was available
+
+c.JupyterHub.load_roles = [
+    {
+        "name": "service-role",
+        "scopes": [
+            # specify the permissions the token should have
+            "admin:users",
+        ],
+        "services": [
+            # assign the service the above permissions
+            "service-admin",
+        ],
+    }
+]
+```
+
+The token will have the permissions listed in the role
+(see [scopes][] for a list of available permissions),
+but there will no longer be a user account created to house it.
+The main noticeable difference between a user and a service is that there will be no notebook server associated with the account
+and the service will not show up in the various user list pages and APIs.
+
+## Make an API request
+
+To authenticate your requests, pass the API token in the request's
+Authorization header.
+
+### Use requests
+
+Using the popular Python [requests](https://docs.python-requests.org)
+library, an API GET request is made, and the request sends an API token for
+authorization. The response contains information about the users, here's example code to make an API request for the users of a JupyterHub deployment
+
+```python
+import requests
+
+api_url = 'http://127.0.0.1:8081/hub/api'
+
+r = requests.get(api_url + '/users',
+    headers={
+        'Authorization': f'token {token}',
+    }
+)
+
+r.raise_for_status()
+users = r.json()
+```
+
+This example provides a slightly more complicated request, yet the
+process is very similar:
+
+```python
+import requests
+
+api_url = 'http://127.0.0.1:8081/hub/api'
+
+data = {'name': 'mygroup', 'users': ['user1', 'user2']}
+
+r = requests.post(api_url + '/groups/formgrade-data301/users',
+    headers={
+        'Authorization': f'token {token}',
+    },
+    json=data,
+)
+r.raise_for_status()
+r.json()
+```
+
+The same API token can also authorize access to the [Jupyter Notebook REST API][]
+
+provided by notebook servers managed by JupyterHub if it has the necessary `access:servers` scope.
+
+(api-pagination)=
+
+## Paginating API requests
+
+```{versionadded} 2.0
+
+```
+
+Pagination is available through the `offset` and `limit` query parameters on
+list endpoints, which can be used to return ideally sized windows of results.
+Here's example code demonstrating pagination on the `GET /users`
+endpoint to fetch the first 20 records.
+
+```python
+import os
+import requests
+
+api_url = 'http://127.0.0.1:8081/hub/api'
+
+r = requests.get(
+    api_url + '/users?offset=0&limit=20',
+    headers={
+        "Accept": "application/jupyterhub-pagination+json",
+        "Authorization": f"token {token}",
+    },
+)
+r.raise_for_status()
+r.json()
+```
+
+For backward-compatibility, the default structure of list responses is unchanged.
+However, this lacks pagination information (e.g. is there a next page),
+so if you have enough users that they won't fit in the first response,
+it is a good idea to opt-in to the new paginated list format.
+There is a new schema for list responses which include pagination information.
+You can request this by including the header:
+
+```
+Accept: application/jupyterhub-pagination+json
+```
+
+with your request, in which case a response will look like:
+
+```python
+{
+  "items": [
+    {
+      "name": "username",
+      "kind": "user",
+      ...
+    },
+  ],
+  "_pagination": {
+    "offset": 0,
+    "limit": 20,
+    "total": 50,
+    "next": {
+      "offset": 20,
+      "limit": 20,
+      "url": "http://127.0.0.1:8081/hub/api/users?limit=20&offset=20"
+    }
+  }
+}
+```
+
+where the list results (same as pre-2.0) will be in `items`,
+and pagination info will be in `_pagination`.
+The `next` field will include the `offset`, `limit`, and `url` for requesting the next page.
+`next` will be `null` if there is no next page.
+
+Pagination is governed by two configuration options:
+
+- `JupyterHub.api_page_default_limit` - the page size, if `limit` is unspecified in the request
+  and the new pagination API is requested
+  (default: 50)
+- `JupyterHub.api_page_max_limit` - the maximum page size a request can ask for (default: 200)
+
+Pagination is enabled on the `GET /users`, `GET /groups`, and `GET /proxy` REST endpoints.
+
+## Enabling users to spawn multiple named-servers via the API
+
+Support for multiple servers per user was introduced in JupyterHub [version 0.8.](changelog)
+Prior to that, each user could only launch a single default server via the API
+like this:
+
+```bash
+curl -X POST -H "Authorization: token <token>" "http://127.0.0.1:8081/hub/api/users/<user>/server"
+```
+
+With the named-server functionality, it's now possible to launch more than one
+specifically named servers against a given user. This could be used, for instance,
+to launch each server based on a different image.
+
+First you must enable named-servers by including the following setting in the `jupyterhub_config.py` file.
+
+`c.JupyterHub.allow_named_servers = True`
+
+If you are using the [zero-to-jupyterhub-k8s](https://github.com/jupyterhub/zero-to-jupyterhub-k8s) set-up to run JupyterHub,
+then instead of editing the `jupyterhub_config.py` file directly, you could pass
+the following as part of the `config.yaml` file, as per the [tutorial](https://z2jh.jupyter.org/en/latest/):
+
+```bash
+hub:
+  extraConfig: |
+    c.JupyterHub.allow_named_servers = True
+```
+
+With that setting in place, a new named-server is activated like this:
+
+```bash
+curl -X POST -H "Authorization: token <token>" "http://127.0.0.1:8081/hub/api/users/<user>/servers/<serverA>"
+curl -X POST -H "Authorization: token <token>" "http://127.0.0.1:8081/hub/api/users/<user>/servers/<serverB>"
+```
+
+The same servers can be stopped by substituting `DELETE` for `POST` above.
+
+### Some caveats for using named-servers
+
+For named-servers via the API to work, the spawner used to spawn these servers
+will need to be able to handle the case of multiple servers per user and ensure
+uniqueness of names, particularly if servers are spawned via docker containers
+or kubernetes pods.
+
+## Learn more about the API
+
+You can see the full [JupyterHub REST API](jupyterhub-rest-api) for more details.
+
+[openapi initiative]: https://www.openapis.org/
+[jupyterhub rest api]: ./rest-api
+[scopes]: ../rbac/scopes.md
+[jupyter notebook rest api]: https://petstore3.swagger.io/?url=https://raw.githubusercontent.com/jupyter/notebook/HEAD/notebook/services/api/api.yaml

docs/source/howto/separate-proxy.md

@@ -0,0 +1,78 @@
+(separate-proxy)=
+
+# Running proxy separately from the hub
+
+## Background
+
+The thing which users directly connect to is the proxy, which by default is
+`configurable-http-proxy`. The proxy either redirects users to the
+hub (for login and managing servers), or to their own single-user
+servers. Thus, as long as the proxy stays running, access to existing
+servers continues, even if the hub itself restarts or goes down.
+
+When you first configure the hub, you may not even realize this
+because the proxy is automatically managed by the hub. This is great
+for getting started and even most use-cases, although, everytime you restart the
+hub, all user connections are also restarted. However, it is also simple to
+run the proxy as a service separate from the hub, so that you are free
+to reconfigure the hub while only interrupting users who are waiting for their notebook server to start.
+starting their notebook server.
+
+The default JupyterHub proxy is
+[configurable-http-proxy](https://github.com/jupyterhub/configurable-http-proxy). If you are using a different proxy, such
+as [Traefik](https://github.com/traefik/traefik), these instructions are probably not relevant to you.
+
+## Configuration options
+
+`c.JupyterHub.cleanup_servers = False` should be set, which tells the
+hub to not stop servers when the hub restarts (this is useful even if
+you don't run the proxy separately).
+
+`c.ConfigurableHTTPProxy.should_start = False` should be set, which
+tells the hub that the proxy should not be started (because you start
+it yourself).
+
+`c.ConfigurableHTTPProxy.auth_token = "CONFIGPROXY_AUTH_TOKEN"` should be set to a
+token for authenticating communication with the proxy.
+
+`c.ConfigurableHTTPProxy.api_url = 'http://localhost:8001'` should be
+set to the URL which the hub uses to connect _to the proxy's API_.
+
+## Proxy configuration
+
+You need to configure a service to start the proxy. An example
+command line argument for this is:
+
+```bash
+$ configurable-http-proxy --ip=127.0.0.1 --port=8000 --api-ip=127.0.0.1 --api-port=8001 --default-target=http://localhost:8081 --error-target=http://localhost:8081/hub/error
+```
+
+(Details on how to do this is out of the scope of this tutorial. For example, it might be a
+systemd service configured within another docker container). The proxy has no
+configuration files, all configuration is via the command line and
+environment variables.
+
+`--api-ip` and `--api-port` (which tells the proxy where to listen) should match the hub's `ConfigurableHTTPProxy.api_url`.
+
+`--ip`, `-port`, and other options configure the _user_ connections to the proxy.
+
+`--default-target` and `--error-target` should point to the hub, and used when users navigate to the proxy originally.
+
+You must define the environment variable `CONFIGPROXY_AUTH_TOKEN` to
+match the token given to `c.ConfigurableHTTPProxy.auth_token`.
+
+You should check the [configurable-http-proxy
+options](https://github.com/jupyterhub/configurable-http-proxy) to see
+what other options are needed, for example, SSL options. Note that
+these options are configured in the hub if the hub is starting the proxy, so you
+need to configure the options there.
+
+## Docker image
+
+You can use [jupyterhub configurable-http-proxy docker
+image](https://quay.io/repository/jupyterhub/configurable-http-proxy)
+to run the proxy.
+
+## See also
+
+- [jupyterhub configurable-http-proxy](https://github.com/jupyterhub/configurable-http-proxy)

docs/source/howto/templates.md

@@ -0,0 +1,89 @@
+# Working with templates and UI
+
+The pages of the JupyterHub application are generated from
+[Jinja](https://jinja.palletsprojects.com) templates. These allow the header, for
+example, to be defined once and incorporated into all pages. By providing
+your own template(s), you can have complete control over JupyterHub's
+appearance.
+
+## Custom Templates
+
+JupyterHub will look for custom templates in all paths included in the
+`JupyterHub.template_paths` configuration option, falling back on these
+[default templates](https://github.com/jupyterhub/jupyterhub/tree/HEAD/share/jupyterhub/templates)
+if no custom template(s) with specified name(s) are found. This fallback
+behavior is new in version 0.9; previous versions searched only the paths
+explicitly included in `template_paths`. You may override as many
+or as few templates as you desire.
+
+## Extending Templates
+
+Jinja provides a mechanism to [extend templates](https://jinja.palletsprojects.com/en/3.0.x/templates/#template-inheritance).
+
+A base template can define `block`(s) within itself that child templates can fill up or
+supply content to. The
+[JupyterHub default templates](https://github.com/jupyterhub/jupyterhub/tree/HEAD/share/jupyterhub/templates)
+make extensive use of blocks, thus allowing you to customize parts of the
+interface easily.
+
+In general, a child template can extend a base template, `page.html`, by beginning with:
+
+```html
+{% extends "page.html" %}
+```
+
+This works, unless you are trying to extend the default template for the same
+file name. Starting in version 0.9, you may refer to the base file with a
+`templates/` prefix. Thus, if you are writing a custom `page.html`, start the
+file with this block:
+
+```html
+{% extends "templates/page.html" %}
+```
+
+By defining `block`s with the same name as in the base template, child templates
+can replace those sections with custom content. The content from the base
+template can be included in the child template with the `{{ super() }}` directive.
+
+### Example
+
+To add an additional message to the spawn-pending page, below the existing
+text about the server starting up, place the content below in a file named
+`spawn_pending.html`. This directory must also be included in the
+`JupyterHub.template_paths` configuration option.
+
+```html
+{% extends "templates/spawn_pending.html" %} {% block message %} {{ super() }}
+<p>Patience is a virtue.</p>
+{% endblock %}
+```
+
+## Page Announcements
+
+To add announcements to be displayed on a page, you have two options:
+
+- [Extend the page templates as described above](#extending-templates)
+- Use configuration variables
+
+### Announcement Configuration Variables
+
+If you set the configuration variable `JupyterHub.template_vars = {'announcement': 'some_text'}`, the given `some_text` will be placed on
+the top of all pages. The more specific variables
+`announcement_login`, `announcement_spawn`, `announcement_home`, and
+`announcement_logout` are more specific and only show on their
+respective pages (overriding the global `announcement` variable).
+Note that changing these variables requires a restart, unlike direct
+template extension.
+
+Alternatively, you can get the same effect by extending templates, which allows you
+to update the messages without restarting. Set
+`c.JupyterHub.template_paths` as mentioned above, and then create a
+template (for example, `login.html`) with:
+
+```html
+{% extends "templates/login.html" %} {% set announcement = 'some message' %}
+```
+
+Extending `page.html` puts the message on all pages, but note that
+extending `page.html` takes precedence over an extension of a specific
+page (unlike the variable-based approach above).

docs/source/howto/upgrading.md

@@ -0,0 +1,141 @@
+(upgrading-jupyterhub)=
+
+# Upgrading JupyterHub
+
+JupyterHub offers easy upgrade pathways between minor versions. This
+document describes how to do these upgrades.
+
+If you are using {ref}`a JupyterHub distribution <index/distributions>`, you
+should consult the distribution's documentation on how to upgrade. This documentation is
+for those who have set up their JupyterHub without using a distribution.
+
+This documentation is lengthy because it is quite detailed. Most likely, upgrading
+JupyterHub is painless, quick and with minimal user interruption.
+
+The steps are discussed in detail, so if you get stuck at any step you can always refer to this guide.
+
+## Read the Changelog
+
+The [changelog](changelog) contains information on what has
+changed with the new JupyterHub release and any deprecation warnings.
+Read these notes to familiarize yourself with the coming changes. There
+might be new releases of the authenticators & spawners you use, so
+read the changelogs for those too!
+
+## Notify your users
+
+If you use the default configuration where `configurable-http-proxy`
+is managed by JupyterHub, your users will see service disruption during
+the upgrade process. You should notify them, and pick a time to do the
+upgrade where they will be least disrupted.
+
+If you use a different proxy or run `configurable-http-proxy`
+independent of JupyterHub, your users will be able to continue using notebook
+servers they had already launched, but will not be able to launch new servers or sign in.
+
+## Backup database & config
+
+Before doing an upgrade, it is critical to back up:
+
+1. Your JupyterHub database (SQLite by default, or MySQL / Postgres if you used those).
+   If you use SQLite (the default), you should backup the `jupyterhub.sqlite` file.
+2. Your `jupyterhub_config.py` file.
+3. Your users' home directories. This is unlikely to be affected directly by
+   a JupyterHub upgrade, but we recommend a backup since user data is critical.
+
+## Shut down JupyterHub
+
+Shut down the JupyterHub process. This would vary depending on how you
+have set up JupyterHub to run. It is most likely using a process
+supervisor of some sort (`systemd` or `supervisord` or even `docker`).
+Use the supervisor-specific command to stop the JupyterHub process.
+
+## Upgrade JupyterHub packages
+
+There are two environments where the `jupyterhub` package is installed:
+
+1. The _hub environment_: where the JupyterHub server process
+   runs. This is started with the `jupyterhub` command, and is what
+   people generally think of as JupyterHub.
+2. The _notebook user environments_: where the user notebook
+   servers are launched from, and is probably custom to your own
+   installation. This could be just one environment (different from the
+   hub environment) that is shared by all users, one environment
+   per user, or the same environment as the hub environment. The hub
+   launched the `jupyterhub-singleuser` command in this environment,
+   which in turn starts the notebook server.
+
+You need to make sure the version of the `jupyterhub` package matches
+in both these environments. If you installed `jupyterhub` with pip,
+you can upgrade it with:
+
+```bash
+python3 -m pip install --upgrade jupyterhub==<version>
+```
+
+Where `<version>` is the version of JupyterHub you are upgrading to.
+
+If you used `conda` to install `jupyterhub`, you should upgrade it
+with:
+
+```bash
+conda install -c conda-forge jupyterhub==<version>
+```
+
+You should also check for new releases of the authenticator & spawner you
+are using. You might wish to upgrade those packages, too, along with JupyterHub
+or upgrade them separately.
+
+## Upgrade JupyterHub database
+
+Once new packages are installed, you need to upgrade the JupyterHub
+database. From the hub environment, in the same directory as your
+`jupyterhub_config.py` file, you should run:
+
+```bash
+jupyterhub upgrade-db
+```
+
+This should find the location of your database, and run the necessary upgrades
+for it.
+
+### SQLite database disadvantages
+
+SQLite has some disadvantages when it comes to upgrading JupyterHub. These
+are:
+
+- `upgrade-db` may not work, and you may need to delete your database
+  and start with a fresh one.
+- `downgrade-db` **will not** work if you want to rollback to an
+  earlier version, so backup the `jupyterhub.sqlite` file before
+  upgrading.
+
+### What happens if I delete my database?
+
+Losing the Hub database is often not a big deal. Information that
+resides only in the Hub database includes:
+
+- active login tokens (user cookies, service tokens)
+- users added via JupyterHub UI, instead of config files
+- info about running servers
+
+If the following conditions are true, you should be fine clearing the
+Hub database and starting over:
+
+- users specified in the config file, or login using an external
+  authentication provider (Google, GitHub, LDAP, etc)
+- user servers are stopped during the upgrade
+- don't mind causing users to log in again after the upgrade
+
+## Start JupyterHub
+
+Once the database upgrade is completed, start the `jupyterhub`
+process again.
+
+1. Log in and start the server to make sure things work as
+   expected.
+2. Check the logs for any errors or deprecation warnings. You
+   might have to update your `jupyterhub_config.py` file to
+   deal with any deprecated options.
+
+Congratulations, your JupyterHub has been upgraded!