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Merge branch 'master' into asalikhov/improve_docs

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Peter Parente
2020-05-29 09:10:49 -04:00
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parent b6d96d1ca2 8d4bff6078
commit 40d2312b1e
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60
all-spark-notebook/test/data/local_pyspark.ipynb Normal file
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{
"cells": [
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"output_type": "error",
"ename": "Error",
"evalue": "Jupyter cannot be started. Error attempting to locate jupyter: Data Science libraries jupyter and notebook are not installed in interpreter Python 3.7.7 64-bit ('jupyter': conda).",
"traceback": [
"Error: Jupyter cannot be started. Error attempting to locate jupyter: Data Science libraries jupyter and notebook are not installed in interpreter Python 3.7.7 64-bit ('jupyter': conda).",
"at b.startServer (/Users/romain/.vscode/extensions/ms-python.python-2020.5.80290/out/client/extension.js:92:270430)",
"at async b.createServer (/Users/romain/.vscode/extensions/ms-python.python-2020.5.80290/out/client/extension.js:92:269873)",
"at async connect (/Users/romain/.vscode/extensions/ms-python.python-2020.5.80290/out/client/extension.js:92:397876)",
"at async w.ensureConnectionAndNotebookImpl (/Users/romain/.vscode/extensions/ms-python.python-2020.5.80290/out/client/extension.js:16:556625)",
"at async w.ensureConnectionAndNotebook (/Users/romain/.vscode/extensions/ms-python.python-2020.5.80290/out/client/extension.js:16:556303)",
"at async w.clearResult (/Users/romain/.vscode/extensions/ms-python.python-2020.5.80290/out/client/extension.js:16:552346)",
"at async w.reexecuteCell (/Users/romain/.vscode/extensions/ms-python.python-2020.5.80290/out/client/extension.js:16:540374)",
"at async w.reexecuteCells (/Users/romain/.vscode/extensions/ms-python.python-2020.5.80290/out/client/extension.js:16:537541)"
]
}
],
"source": [
"from pyspark.sql import SparkSession\n",
"\n",
"# Spark session & context\n",
"spark = SparkSession.builder.master('local').getOrCreate()\n",
"sc = spark.sparkContext\n",
"\n",
"# Sum of the first 100 whole numbers\n",
"rdd = sc.parallelize(range(100 + 1))\n",
"rdd.sum()\n",
"# 5050"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.6"
}
},
"nbformat": 4,
"nbformat_minor": 4
}

41
all-spark-notebook/test/data/local_sparkR.ipynb Normal file
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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"library(SparkR)\n",
"\n",
"# Spark session & context\n",
"sc <- sparkR.session(\"local\")\n",
"\n",
"# Sum of the first 100 whole numbers\n",
"sdf <- createDataFrame(list(1:100))\n",
"dapplyCollect(sdf,\n",
" function(x) \n",
" { x <- sum(x)}\n",
" )\n",
"# 5050"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "R",
"language": "R",
"name": "ir"
},
"language_info": {
"codemirror_mode": "r",
"file_extension": ".r",
"mimetype": "text/x-r-source",
"name": "R",
"pygments_lexer": "r",
"version": "3.6.3"
}
},
"nbformat": 4,
"nbformat_minor": 4
}

43
all-spark-notebook/test/data/local_sparklyr.ipynb Normal file
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@@ -0,0 +1,43 @@
{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"library(sparklyr)\n",
"\n",
"# get the default config\n",
"conf <- spark_config()\n",
"# Set the catalog implementation in-memory\n",
"conf$spark.sql.catalogImplementation <- \"in-memory\"\n",
"\n",
"# Spark session & context\n",
"sc <- spark_connect(master = \"local\", config = conf)\n",
"\n",
"# Sum of the first 100 whole numbers\n",
"sdf_len(sc, 100, repartition = 1) %>% \n",
" spark_apply(function(e) sum(e))\n",
"# 5050"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "R",
"language": "R",
"name": "ir"
},
"language_info": {
"codemirror_mode": "r",
"file_extension": ".r",
"mimetype": "text/x-r-source",
"name": "R",
"pygments_lexer": "r",
"version": "3.6.3"
}
},
"nbformat": 4,
"nbformat_minor": 4
}

63
all-spark-notebook/test/data/local_spylon.ipynb Normal file
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{
"cells": [
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"%%init_spark\n",
"# Spark session & context\n",
"launcher.master = \"local\"\n",
"launcher.conf.spark.executor.cores = 1"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"rdd: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD[8] at parallelize at <console>:28\n",
"res4: Double = 5050.0\n"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"// Sum of the first 100 whole numbers\n",
"val rdd = sc.parallelize(0 to 100)\n",
"rdd.sum()\n",
"// 5050"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "spylon-kernel",
"language": "scala",
"name": "spylon-kernel"
},
"language_info": {
"codemirror_mode": "text/x-scala",
"file_extension": ".scala",
"help_links": [
{
"text": "MetaKernel Magics",
"url": "https://metakernel.readthedocs.io/en/latest/source/README.html"
}
],
"mimetype": "text/x-scala",
"name": "scala",
"pygments_lexer": "scala",
"version": "0.4.1"
}
},
"nbformat": 4,
"nbformat_minor": 4
}

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all-spark-notebook/test/data/local_toree.ipynb Normal file
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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Waiting for a Spark session to start..."
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"spark://master:7077\n"
]
}
],
"source": [
"// should print the value of --master in the kernel spec\n",
"println(sc.master)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Waiting for a Spark session to start..."
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"rdd = ParallelCollectionRDD[0] at parallelize at <console>:28\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"5050.0"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"// Sum of the first 100 whole numbers\n",
"val rdd = sc.parallelize(0 to 100)\n",
"rdd.sum()\n",
"// 5050"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Apache Toree - Scala",
"language": "scala",
"name": "apache_toree_scala"
},
"language_info": {
"codemirror_mode": "text/x-scala",
"file_extension": ".scala",
"mimetype": "text/x-scala",
"name": "scala",
"pygments_lexer": "scala",
"version": "2.11.12"
}
},
"nbformat": 4,
"nbformat_minor": 4
}

35
all-spark-notebook/test/test_spark_notebooks.py Normal file
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# Copyright (c) Jupyter Development Team.
# Distributed under the terms of the Modified BSD License.
import logging
import pytest
import os
LOGGER = logging.getLogger(__name__)
@pytest.mark.parametrize(
"test_file",
# TODO: add local_sparklyr
["local_pyspark", "local_spylon", "local_toree", "local_sparkR"],
)
def test_nbconvert(container, test_file):
"""Check if Spark notebooks can be executed"""
host_data_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)), "data")
cont_data_dir = "/home/jovyan/data"
output_dir = "/tmp"
timeout_ms = 600
LOGGER.info(f"Test that {test_file} notebook can be executed ...")
command = f"jupyter nbconvert --to markdown --ExecutePreprocessor.timeout={timeout_ms} --output-dir {output_dir} --execute {cont_data_dir}/{test_file}.ipynb"
c = container.run(
volumes={host_data_dir: {"bind": cont_data_dir, "mode": "ro"}},
tty=True,
command=["start.sh", "bash", "-c", command],
)
rv = c.wait(timeout=timeout_ms / 10 + 10)
assert rv == 0 or rv["StatusCode"] == 0, f"Command {command} failed"
logs = c.logs(stdout=True).decode("utf-8")
LOGGER.debug(logs)
expected_file = f"{output_dir}/{test_file}.md"
assert expected_file in logs, f"Expected file {expected_file} not generated"

211
docs/using/specifics.md
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@@ -5,7 +5,8 @@ This page provides details about features specific to one or more images.
## Apache Spark ## Apache Spark
**Specific Docker Image Options** **Specific Docker Image Options**
* `-p 4040:4040` - The `jupyter/pyspark-notebook` and `jupyter/all-spark-notebook` images open [SparkUI (Spark Monitoring and Instrumentation UI)](http://spark.apache.org/docs/latest/monitoring.html) at default port `4040`, this option map `4040` port inside docker container to `4040` port on host machine . Note every new spark context that is created is put onto an incrementing port (ie. 4040, 4041, 4042, etc.), and it might be necessary to open multiple ports. For example: `docker run -d -p 8888:8888 -p 4040:4040 -p 4041:4041 jupyter/pyspark-notebook`
* `-p 4040:4040` - The `jupyter/pyspark-notebook` and `jupyter/all-spark-notebook` images open [SparkUI (Spark Monitoring and Instrumentation UI)](http://spark.apache.org/docs/latest/monitoring.html) at default port `4040`, this option map `4040` port inside docker container to `4040` port on host machine . Note every new spark context that is created is put onto an incrementing port (ie. 4040, 4041, 4042, etc.), and it might be necessary to open multiple ports. For example: `docker run -d -p 8888:8888 -p 4040:4040 -p 4041:4041 jupyter/pyspark-notebook`.
**Usage Examples** **Usage Examples**
@@ -13,30 +14,66 @@ The `jupyter/pyspark-notebook` and `jupyter/all-spark-notebook` images support t
### Using Spark Local Mode ### Using Spark Local Mode
Spark local mode is useful for experimentation on small data when you do not have a Spark cluster available. Spark **local mode** is useful for experimentation on small data when you do not have a Spark cluster available.
#### In a Python Notebook #### In Python
In a Python notebook.
```python ```python
from pyspark.sql import SparkSession from pyspark.sql import SparkSession
spark = SparkSession.builder.appName("SimpleApp").getOrCreate()
# do something to prove it works # Spark session & context
spark.sql('SELECT "Test" as c1').show() spark = SparkSession.builder.master('local').getOrCreate()
sc = spark.sparkContext
# Sum of the first 100 whole numbers
rdd = sc.parallelize(range(100 + 1))
rdd.sum()
# 5050
``` ```
#### In a R Notebook #### In R
```r In a R notebook with [SparkR][sparkr].
```R
library(SparkR) library(SparkR)
as <- sparkR.session("local[*]") # Spark session & context
sc <- sparkR.session("local")
# do something to prove it works # Sum of the first 100 whole numbers
df <- as.DataFrame(iris) sdf <- createDataFrame(list(1:100))
head(filter(df, df$Petal_Width > 0.2)) dapplyCollect(sdf,
function(x)
{ x <- sum(x)}
)
# 5050
``` ```
#### In a Spylon Kernel Scala Notebook In a R notebook with [sparklyr][sparklyr].
```R
library(sparklyr)
# Spark configuration
conf <- spark_config()
# Set the catalog implementation in-memory
conf$spark.sql.catalogImplementation <- "in-memory"
# Spark session & context
sc <- spark_connect(master = "local", config = conf)
# Sum of the first 100 whole numbers
sdf_len(sc, 100, repartition = 1) %>%
spark_apply(function(e) sum(e))
# 5050
```
#### In Scala
##### In a Spylon Kernel
Spylon kernel instantiates a `SparkContext` for you in variable `sc` after you configure Spark Spylon kernel instantiates a `SparkContext` for you in variable `sc` after you configure Spark
options in a `%%init_spark` magic cell. options in a `%%init_spark` magic cell.
@@ -44,27 +81,30 @@ options in a `%%init_spark` magic cell.
```python ```python
%%init_spark %%init_spark
# Configure Spark to use a local master # Configure Spark to use a local master
launcher.master = "local[*]" launcher.master = "local"
``` ```
```scala ```scala
// Now run Scala code that uses the initialized SparkContext in sc // Sum of the first 100 whole numbers
val rdd = sc.parallelize(0 to 999) val rdd = sc.parallelize(0 to 100)
rdd.takeSample(false, 5) rdd.sum()
// 5050
``` ```
#### In an Apache Toree Scala Notebook ##### In an Apache Toree Kernel
Apache Toree instantiates a local `SparkContext` for you in variable `sc` when the kernel starts. Apache Toree instantiates a local `SparkContext` for you in variable `sc` when the kernel starts.
```scala ```scala
val rdd = sc.parallelize(0 to 999) // Sum of the first 100 whole numbers
rdd.takeSample(false, 5) val rdd = sc.parallelize(0 to 100)
rdd.sum()
// 5050
``` ```
### Connecting to a Spark Cluster in Standalone Mode ### Connecting to a Spark Cluster in Standalone Mode
Connection to Spark Cluster on Standalone Mode requires the following set of steps: Connection to Spark Cluster on **[Standalone Mode](https://spark.apache.org/docs/latest/spark-standalone.html)** requires the following set of steps:
0. Verify that the docker image (check the Dockerfile) and the Spark Cluster which is being 0. Verify that the docker image (check the Dockerfile) and the Spark Cluster which is being
deployed, run the same version of Spark. deployed, run the same version of Spark.
@@ -75,97 +115,104 @@ Connection to Spark Cluster on Standalone Mode requires the following set of ste
* NOTE: When using `--net=host`, you must also use the flags `--pid=host -e * NOTE: When using `--net=host`, you must also use the flags `--pid=host -e
TINI_SUBREAPER=true`. See https://github.com/jupyter/docker-stacks/issues/64 for details. TINI_SUBREAPER=true`. See https://github.com/jupyter/docker-stacks/issues/64 for details.
#### In a Python Notebook **Note**: In the following examples we are using the Spark master URL `spark://master:7077` that shall be replaced by the URL of the Spark master.
#### In Python
The **same Python version** need to be used on the notebook (where the driver is located) and on the Spark workers.
The python version used at driver and worker side can be adjusted by setting the environment variables `PYSPARK_PYTHON` and / or `PYSPARK_DRIVER_PYTHON`, see [Spark Configuration][spark-conf] for more information.
```python ```python
import os from pyspark.sql import SparkSession
# make sure pyspark tells workers to use python3 not 2 if both are installed
os.environ['PYSPARK_PYTHON'] = '/usr/bin/python3'
import pyspark # Spark session & context
conf = pyspark.SparkConf() spark = SparkSession.builder.master('spark://master:7077').getOrCreate()
sc = spark.sparkContext
# Point to spark master # Sum of the first 100 whole numbers
conf.setMaster("spark://10.10.10.10:7070") rdd = sc.parallelize(range(100 + 1))
# point to spark binary package in HDFS or on local filesystem on all slave rdd.sum()
# nodes (e.g., file:///opt/spark/spark-2.2.0-bin-hadoop2.7.tgz) # 5050
conf.set("spark.executor.uri", "hdfs://10.10.10.10/spark/spark-2.2.0-bin-hadoop2.7.tgz")
# set other options as desired
conf.set("spark.executor.memory", "8g")
conf.set("spark.core.connection.ack.wait.timeout", "1200")
# create the context
sc = pyspark.SparkContext(conf=conf)
# do something to prove it works
rdd = sc.parallelize(range(100000000))
rdd.sumApprox(3)
``` ```
#### In a R Notebook #### In R
```r In a R notebook with [SparkR][sparkr].
```R
library(SparkR) library(SparkR)
# Point to spark master # Spark session & context
# Point to spark binary package in HDFS or on local filesystem on all worker sc <- sparkR.session("spark://master:7077")
# nodes (e.g., file:///opt/spark/spark-2.2.0-bin-hadoop2.7.tgz) in sparkEnvir
# Set other options in sparkEnvir
sc <- sparkR.session(
"spark://10.10.10.10:7070",
sparkEnvir=list(
spark.executor.uri="hdfs://10.10.10.10/spark/spark-2.4.3-bin-hadoop2.7.tgz",
spark.executor.memory="8g"
)
)
# do something to prove it works # Sum of the first 100 whole numbers
data(iris) sdf <- createDataFrame(list(1:100))
df <- as.DataFrame(iris) dapplyCollect(sdf,
head(filter(df, df$Petal_Width > 0.2)) function(x)
{ x <- sum(x)}
)
# 5050
``` ```
#### In a Spylon Kernel Scala Notebook In a R notebook with [sparklyr][sparklyr].
```R
library(sparklyr)
# Spark session & context
# Spark configuration
conf <- spark_config()
# Set the catalog implementation in-memory
conf$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) %>%
spark_apply(function(e) sum(e))
# 5050
```
#### In Scala
##### In a Spylon Kernel
Spylon kernel instantiates a `SparkContext` for you in variable `sc` after you configure Spark
options in a `%%init_spark` magic cell.
```python ```python
%%init_spark %%init_spark
# Point to spark master # Configure Spark to use a local master
launcher.master = "spark://10.10.10.10:7070" launcher.master = "spark://master:7077"
launcher.conf.spark.executor.uri=hdfs://10.10.10.10/spark/spark-2.4.3-bin-hadoop2.7.tgz
``` ```
```scala ```scala
// Now run Scala code that uses the initialized SparkContext in sc // Sum of the first 100 whole numbers
val rdd = sc.parallelize(0 to 999) val rdd = sc.parallelize(0 to 100)
rdd.takeSample(false, 5) rdd.sum()
// 5050
``` ```
#### In an Apache Toree Scala Notebook ##### In an Apache Toree Scala Notebook
The Apache Toree kernel automatically creates a `SparkContext` when it starts based on configuration The Apache Toree kernel automatically creates a `SparkContext` when it starts based on configuration information from its command line arguments and environment variables. You can pass information about your cluster via the `SPARK_OPTS` environment variable when you spawn a container.
information from its command line arguments and environment variables. You can pass information
about your cluster via the `SPARK_OPTS` environment variable when you spawn a container.
For instance, to pass information about a standalone Spark master, Spark binary location in HDFS, For instance, to pass information about a standalone Spark master, you could start the container like so:
and an executor options, you could start the container like so:
```bash ```bash
docker run -d -p 8888:8888 -e SPARK_OPTS='--master=spark://10.10.10.10:7070 \ docker run -d -p 8888:8888 -e SPARK_OPTS='--master=spark://master:7077' \
--spark.executor.uri=hdfs://10.10.10.10/spark/spark-2.4.3-bin-hadoop2.7.tgz \ jupyter/all-spark-notebook
--spark.executor.memory=8g' jupyter/all-spark-notebook
``` ```
Note that this is the same information expressed in a notebook in the Python case above. Once the Note that this is the same information expressed in a notebook in the Python case above. Once the kernel spec has your cluster information, you can test your cluster in an Apache Toree notebook like so:
kernel spec has your cluster information, you can test your cluster in an Apache Toree notebook like
so:
```scala ```scala
// should print the value of --master in the kernel spec // should print the value of --master in the kernel spec
println(sc.master) println(sc.master)
// do something to prove it works // Sum of the first 100 whole numbers
val rdd = sc.parallelize(0 to 99999999) val rdd = sc.parallelize(0 to 100)
rdd.sum() rdd.sum()
// 5050
``` ```
## Tensorflow ## Tensorflow
@@ -201,3 +248,7 @@ init = tf.global_variables_initializer()
sess.run(init) sess.run(init)
sess.run(hello) sess.run(hello)
``` ```
[sparkr]: https://spark.apache.org/docs/latest/sparkr.html
[sparklyr]: https://spark.rstudio.com/
[spark-conf]: https://spark.apache.org/docs/latest/configuration.html