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hyukjinkwon 9a641e7f72 [SPARK-21945][YARN][PYTHON] Make --py-files work with PySpark shell in Yarn client mode 
## What changes were proposed in this pull request?

### Problem

When we run _PySpark shell with Yarn client mode_, specified `--py-files` are not recognised in _driver side_.

Here are the steps I took to check:

```bash
$ cat /home/spark/tmp.py
def testtest():
    return 1
```

```bash
$ ./bin/pyspark --master yarn --deploy-mode client --py-files /home/spark/tmp.py
```

```python
>>> def test():
...     import tmp
...     return tmp.testtest()
...
>>> spark.range(1).rdd.map(lambda _: test()).collect()  # executor side
[1]
>>> test()  # driver side
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "<stdin>", line 2, in test
ImportError: No module named tmp
```

### How did it happen?

Unlike Yarn cluster and client mode with Spark submit, when Yarn client mode with PySpark shell specifically,

1. It first runs Python shell via:

3cb82047f2/launcher/src/main/java/org/apache/spark/launcher/SparkSubmitCommandBuilder.java (L158) as pointed out by tgravescs in the JIRA.

2. this triggers shell.py and submit another application to launch a py4j gateway:

209b9361ac/python/pyspark/java_gateway.py (L45-L60)

3. it runs a Py4J gateway:

3cb82047f2/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala (L425)

4. it copies (or downloads) --py-files  into local temp directory:

3cb82047f2/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala (L365-L376)

and then these files are set up to `spark.submit.pyFiles`

5. Py4J JVM is launched and then the Python paths are set via:

7013eea11c/python/pyspark/context.py (L209-L216)

However, these are not actually set because those files were copied into a tmp directory in 4. whereas this code path looks for `SparkFiles.getRootDirectory` where the files are stored only when `SparkContext.addFile()` is called.

In other cluster mode, `spark.files` are set via:

3cb82047f2/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala (L554-L555)

and those files are explicitly added via:

ecb8b383af/core/src/main/scala/org/apache/spark/SparkContext.scala (L395)

So we are fine in other modes.

In case of Yarn client and cluster with _submit_, these are manually being handled. In particular https://github.com/apache/spark/pull/6360 added most of the logics. In this case, the Python path looks manually set via, for example, `deploy.PythonRunner`. We don't use `spark.files` here.

### How does the PR fix the problem?

I tried to make an isolated approach as possible as I can: simply copy py file or zip files into `SparkFiles.getRootDirectory()` in driver side if not existing. Another possible way is to set `spark.files` but it does unnecessary stuff together and sounds a bit invasive.

**Before**

```python
>>> def test():
...     import tmp
...     return tmp.testtest()
...
>>> spark.range(1).rdd.map(lambda _: test()).collect()
[1]
>>> test()
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "<stdin>", line 2, in test
ImportError: No module named tmp
```

**After**

```python
>>> def test():
...     import tmp
...     return tmp.testtest()
...
>>> spark.range(1).rdd.map(lambda _: test()).collect()
[1]
>>> test()
1
```

## How was this patch tested?

I manually tested in standalone and yarn cluster with PySpark shell. .zip and .py files were also tested with the similar steps above. It's difficult to add a test.

Author: hyukjinkwon <gurwls223@apache.org>

Closes #21267 from HyukjinKwon/SPARK-21945.
2018-05-17 12:07:58 +08:00
..
docs [PYSPARK] Update py4j to version 0.10.7. 2018-05-09 10:47:35 -07:00
lib [PYSPARK] Update py4j to version 0.10.7. 2018-05-09 10:47:35 -07:00
pyspark [SPARK-21945][YARN][PYTHON] Make --py-files work with PySpark shell in Yarn client mode 2018-05-17 12:07:58 +08:00
test_coverage [SPARK-7721][PYTHON][TESTS] Adds PySpark coverage generation script 2018-01-22 22:12:50 +09:00
test_support [SPARK-23094][SPARK-23723][SPARK-23724][SQL] Support custom encoding for json files 2018-04-29 11:25:31 +08:00
.coveragerc [SPARK-7721][PYTHON][TESTS] Adds PySpark coverage generation script 2018-01-22 22:12:50 +09:00
.gitignore [SPARK-3946] gitignore in /python includes wrong directory 2014-10-14 14:09:39 -07:00
MANIFEST.in [SPARK-18652][PYTHON] Include the example data and third-party licenses in pyspark package. 2016-12-07 06:09:27 +08:00
pylintrc [SPARK-13596][BUILD] Move misc top-level build files into appropriate subdirs 2016-03-07 14:48:02 -08:00
README.md [PYSPARK] Update py4j to version 0.10.7. 2018-05-09 10:47:35 -07:00
run-tests [SPARK-8583] [SPARK-5482] [BUILD] Refactor python/run-tests to integrate with dev/run-tests module system 2015-06-27 20:24:34 -07:00
run-tests-with-coverage [SPARK-7721][PYTHON][TESTS] Adds PySpark coverage generation script 2018-01-22 22:12:50 +09:00
run-tests.py [SPARK-24126][PYSPARK] Use build-specific temp directory for pyspark tests. 2018-05-07 13:00:18 +08:00
setup.cfg [SPARK-1267][SPARK-18129] Allow PySpark to be pip installed 2016-11-16 14:22:15 -08:00
setup.py [PYSPARK] Update py4j to version 0.10.7. 2018-05-09 10:47:35 -07:00

README.md

Apache Spark

Spark is a fast and general cluster computing system for Big Data. It provides high-level APIs in Scala, Java, Python, and R, and an optimized engine that supports general computation graphs for data analysis. It also supports a rich set of higher-level tools including Spark SQL for SQL and DataFrames, MLlib for machine learning, GraphX for graph processing, and Spark Streaming for stream processing.

http://spark.apache.org/

Online Documentation

You can find the latest Spark documentation, including a programming guide, on the project web page

Python Packaging

This README file only contains basic information related to pip installed PySpark. This packaging is currently experimental and may change in future versions (although we will do our best to keep compatibility). Using PySpark requires the Spark JARs, and if you are building this from source please see the builder instructions at "Building Spark".

The Python packaging for Spark is not intended to replace all of the other use cases. This Python packaged version of Spark is suitable for interacting with an existing cluster (be it Spark standalone, YARN, or Mesos) - but does not contain the tools required to set up your own standalone Spark cluster. You can download the full version of Spark from the Apache Spark downloads page.

NOTE: If you are using this with a Spark standalone cluster you must ensure that the version (including minor version) matches or you may experience odd errors.

Python Requirements

At its core PySpark depends on Py4J (currently version 0.10.7), but some additional sub-packages have their own extra requirements for some features (including numpy, pandas, and pyarrow).