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Bunch of small refactorings and updates

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This commit is contained in:
Ayaz Salikhov
2021-04-17 13:25:23 +03:00
parent e28c630dfc
commit bf9660a054
13 changed files with 32 additions and 36 deletions
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2
docs/using/common.md
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@@ -116,7 +116,7 @@ mamba install some-package
### Using alternative channels
Conda is configured by default to use only the [`conda-forge`](https://anaconda.org/conda-forge) channel.
Conda is configured by default to use only the [`conda-forge`](https://anaconda.org/conda-forge) channel.
However, alternative channels can be used either one shot by overwriting the default channel in the installation command or by configuring `conda` to use different channels.
The examples below show how to use the [anaconda default channels](https://repo.anaconda.com/pkgs/main) instead of `conda-forge` to install packages.

2
docs/using/recipes.md
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@@ -130,7 +130,7 @@ RUN $CONDA_DIR/envs/${conda_env}/bin/python -m ipykernel install --user --name=$
fix-permissions /home/$NB_USER
# any additional pip installs can be added by uncommenting the following line
# RUN $CONDA_DIR/envs/${conda_env}/bin/pip install
# RUN $CONDA_DIR/envs/${conda_env}/bin/pip install
# prepend conda environment to path
ENV PATH $CONDA_DIR/envs/${conda_env}/bin:$PATH

2
docs/using/running.md
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@@ -76,7 +76,7 @@ Executing the command: jupyter notebook
Pressing `Ctrl-C` shuts down the notebook server and immediately destroys the Docker container. Files written to `~/work` in the container remain touched. Any other changes made in the container are lost.
**Example 3** This command pulls the `jupyter/all-spark-notebook` image currently tagged `latest` from Docker Hub if an image tagged `latest` is not already present on the local host. It then starts a container named `notebook` running a JupyterLab server and exposes the server on a randomly selected port.
**Example 3** This command pulls the `jupyter/all-spark-notebook` image currently tagged `latest` from Docker Hub if an image tagged `latest` is not already present on the local host. It then starts a container named `notebook` running a JupyterLab server and exposes the server on a randomly selected port.
```
docker run -d -P --name notebook jupyter/all-spark-notebook

8
docs/using/specifics.md
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@@ -82,7 +82,7 @@ sc <- sparkR.session("local")
# Sum of the first 100 whole numbers
sdf <- createDataFrame(list(1:100))
dapplyCollect(sdf,
function(x)
function(x)
{ x <- sum(x)}
)
# 5050
@@ -102,7 +102,7 @@ conf$spark.sql.catalogImplementation <- "in-memory"
sc <- spark_connect(master = "local", config = conf)
# Sum of the first 100 whole numbers
sdf_len(sc, 100, repartition = 1) %>%
sdf_len(sc, 100, repartition = 1) %>%
spark_apply(function(e) sum(e))
# 5050
```
@@ -171,7 +171,7 @@ sc <- sparkR.session("spark://master:7077")
# Sum of the first 100 whole numbers
sdf <- createDataFrame(list(1:100))
dapplyCollect(sdf,
function(x)
function(x)
{ x <- sum(x)}
)
# 5050
@@ -190,7 +190,7 @@ conf$spark.sql.catalogImplementation <- "in-memory"
sc <- spark_connect(master = "spark://master:7077", config = conf)
# Sum of the first 100 whole numbers
sdf_len(sc, 100, repartition = 1) %>%
sdf_len(sc, 100, repartition = 1) %>%
spark_apply(function(e) sum(e))
# 5050
```