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8669d6e79b
Allow to build `pyspark-notebook` image with an alternative Spark version. - Define arguments for Spark installation - Add a note in "Image Specifics" explaining how to build an image with an alternative Spark version - Remove Toree documentation from "Image Specifics" since its support has been droped in #1115
276 lines
8.8 KiB
Markdown
276 lines
8.8 KiB
Markdown
# Image Specifics
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This page provides details about features specific to one or more images.
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## Apache Spark™
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### Specific Docker Image Options
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* `-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`.
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### Build an Image with a Different Version of Spark
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You can build a `pyspark-notebook` image (and also the downstream `all-spark-notebook` image) with a different version of Spark by overriding the default value of the following arguments at build time.
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* Spark distribution is defined by the combination of the Spark and the Hadoop version and verified by the package checksum, see [Download Apache Spark](https://spark.apache.org/downloads.html) for more information.
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* `spark_version`: The Spark version to install (`3.0.0`).
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* `hadoop_version`: The Hadoop version (`3.2`).
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* `spark_checksum`: The package checksum (`BFE4540...`).
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* Spark is shipped with a version of Py4J that has to be referenced in the `PYTHONPATH`.
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* `py4j_version`: The Py4J version (`0.10.9`), see the tip below.
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* Spark can run with different OpenJDK versions.
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* `openjdk_version`: The version of (JRE headless) the OpenJDK distribution (`11`), see [Ubuntu packages](https://packages.ubuntu.com/search?keywords=openjdk).
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For example here is how to build a `pyspark-notebook` image with Spark `2.4.6`, Hadoop `2.7` and OpenJDK `8`.
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```bash
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# From the root of the project
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# Build the image with different arguments
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docker build --rm --force-rm \
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-t jupyter/pyspark-notebook:spark-2.4.6 ./pyspark-notebook \
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--build-arg spark_version=2.4.6 \
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--build-arg hadoop_version=2.7 \
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--build-arg spark_checksum=3A9F401EDA9B5749CDAFD246B1D14219229C26387017791C345A23A65782FB8B25A302BF4AC1ED7C16A1FE83108E94E55DAD9639A51C751D81C8C0534A4A9641 \
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--build-arg openjdk_version=8 \
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--build-arg py4j_version=0.10.7
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# Check the newly built image
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docker images jupyter/pyspark-notebook:spark-2.4.6
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# REPOSITORY TAG IMAGE ID CREATED SIZE
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# jupyter/pyspark-notebook spark-2.4.6 7ad7b5a9dbcd 4 minutes ago 3.44GB
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# Check the Spark version
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docker run -it --rm jupyter/pyspark-notebook:spark-2.4.6 pyspark --version
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# Welcome to
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# ____ __
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# / __/__ ___ _____/ /__
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# _\ \/ _ \/ _ `/ __/ '_/
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# /___/ .__/\_,_/_/ /_/\_\ version 2.4.6
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# /_/
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#
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# Using Scala version 2.11.12, OpenJDK 64-Bit Server VM, 1.8.0_265
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```
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**Tip**: to get the version of Py4J shipped with Spark:
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* Build a first image without changing `py4j_version` (it will not prevent the image to build it will just prevent Python to find the `pyspark` module),
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* get the version (`ls /usr/local/spark/python/lib/`),
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* set the version `--build-arg py4j_version=0.10.7`.
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*Note: At the time of writing there is an issue preventing to use Spark `2.4.6` with Python `3.8`, see [this answer on SO](https://stackoverflow.com/a/62173969/4413446) for more information.*
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```bash
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docker run -it --rm jupyter/pyspark-notebook:spark-2.4.6 ls /usr/local/spark/python/lib/
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# py4j-0.10.7-src.zip PY4J_LICENSE.txt pyspark.zip
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# You can now set the build-arg
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# --build-arg py4j_version=
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```
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### Usage Examples
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The `jupyter/pyspark-notebook` and `jupyter/all-spark-notebook` images support the use of [Apache Spark](https://spark.apache.org/) in Python, R, and Scala notebooks. The following sections provide some examples of how to get started using them.
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#### Using Spark Local Mode
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Spark **local mode** is useful for experimentation on small data when you do not have a Spark cluster available.
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##### In Python
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In a Python notebook.
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```python
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from pyspark.sql import SparkSession
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# Spark session & context
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spark = SparkSession.builder.master('local').getOrCreate()
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sc = spark.sparkContext
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# Sum of the first 100 whole numbers
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rdd = sc.parallelize(range(100 + 1))
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rdd.sum()
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# 5050
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```
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##### In R
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In a R notebook with [SparkR][sparkr].
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```R
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library(SparkR)
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# Spark session & context
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sc <- sparkR.session("local")
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# Sum of the first 100 whole numbers
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sdf <- createDataFrame(list(1:100))
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dapplyCollect(sdf,
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function(x)
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{ x <- sum(x)}
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)
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# 5050
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```
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In a R notebook with [sparklyr][sparklyr].
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```R
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library(sparklyr)
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# Spark configuration
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conf <- spark_config()
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# Set the catalog implementation in-memory
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conf$spark.sql.catalogImplementation <- "in-memory"
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# Spark session & context
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sc <- spark_connect(master = "local", config = conf)
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# Sum of the first 100 whole numbers
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sdf_len(sc, 100, repartition = 1) %>%
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spark_apply(function(e) sum(e))
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# 5050
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```
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##### In Scala
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Spylon kernel instantiates a `SparkContext` for you in variable `sc` after you configure Spark
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options in a `%%init_spark` magic cell.
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```python
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%%init_spark
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# Configure Spark to use a local master
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launcher.master = "local"
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```
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```scala
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// Sum of the first 100 whole numbers
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val rdd = sc.parallelize(0 to 100)
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rdd.sum()
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// 5050
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```
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#### Connecting to a Spark Cluster in Standalone Mode
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Connection to Spark Cluster on **[Standalone Mode](https://spark.apache.org/docs/latest/spark-standalone.html)** requires the following set of steps:
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0. Verify that the docker image (check the Dockerfile) and the Spark Cluster which is being
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deployed, run the same version of Spark.
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1. [Deploy Spark in Standalone Mode](http://spark.apache.org/docs/latest/spark-standalone.html).
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2. Run the Docker container with `--net=host` in a location that is network addressable by all of
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your Spark workers. (This is a [Spark networking
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requirement](http://spark.apache.org/docs/latest/cluster-overview.html#components).)
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* NOTE: When using `--net=host`, you must also use the flags `--pid=host -e
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TINI_SUBREAPER=true`. See https://github.com/jupyter/docker-stacks/issues/64 for details.
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**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.
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##### In Python
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The **same Python version** need to be used on the notebook (where the driver is located) and on the Spark workers.
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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.
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```python
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from pyspark.sql import SparkSession
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# Spark session & context
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spark = SparkSession.builder.master('spark://master:7077').getOrCreate()
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sc = spark.sparkContext
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# Sum of the first 100 whole numbers
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rdd = sc.parallelize(range(100 + 1))
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rdd.sum()
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# 5050
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```
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##### In R
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In a R notebook with [SparkR][sparkr].
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```R
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library(SparkR)
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# Spark session & context
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sc <- sparkR.session("spark://master:7077")
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# Sum of the first 100 whole numbers
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sdf <- createDataFrame(list(1:100))
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dapplyCollect(sdf,
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function(x)
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{ x <- sum(x)}
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)
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# 5050
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```
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In a R notebook with [sparklyr][sparklyr].
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```R
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library(sparklyr)
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# Spark session & context
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# Spark configuration
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conf <- spark_config()
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# Set the catalog implementation in-memory
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conf$spark.sql.catalogImplementation <- "in-memory"
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sc <- spark_connect(master = "spark://master:7077", config = conf)
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# Sum of the first 100 whole numbers
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sdf_len(sc, 100, repartition = 1) %>%
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spark_apply(function(e) sum(e))
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# 5050
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```
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##### In Scala
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Spylon kernel instantiates a `SparkContext` for you in variable `sc` after you configure Spark
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options in a `%%init_spark` magic cell.
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```python
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%%init_spark
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# Configure Spark to use a local master
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launcher.master = "spark://master:7077"
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```
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```scala
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// Sum of the first 100 whole numbers
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val rdd = sc.parallelize(0 to 100)
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rdd.sum()
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// 5050
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```
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## Tensorflow
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The `jupyter/tensorflow-notebook` image supports the use of
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[Tensorflow](https://www.tensorflow.org/) in single machine or distributed mode.
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### Single Machine Mode
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```python
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import tensorflow as tf
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hello = tf.Variable('Hello World!')
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sess = tf.Session()
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init = tf.global_variables_initializer()
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sess.run(init)
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sess.run(hello)
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```
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### Distributed Mode
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```python
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import tensorflow as tf
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hello = tf.Variable('Hello Distributed World!')
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server = tf.train.Server.create_local_server()
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sess = tf.Session(server.target)
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init = tf.global_variables_initializer()
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sess.run(init)
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sess.run(hello)
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```
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[sparkr]: https://spark.apache.org/docs/latest/sparkr.html
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[sparklyr]: https://spark.rstudio.com/
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[spark-conf]: https://spark.apache.org/docs/latest/configuration.html |