29c51d682b
### What changes were proposed in this pull request?
This PR manually specifies the class for the input array being used in `(SparkContext|StreamingContext).union`. It fixes a regression introduced from SPARK-25737.
```python
rdd1 = sc.parallelize([1,2,3,4,5])
rdd2 = sc.parallelize([6,7,8,9,10])
pairRDD1 = rdd1.zip(rdd2)
sc.union([pairRDD1, pairRDD1]).collect()
```
in the current master and `branch-3.0`:
```
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/.../spark/python/pyspark/context.py", line 870, in union
jrdds[i] = rdds[i]._jrdd
File "/.../spark/python/lib/py4j-0.10.9-src.zip/py4j/java_collections.py", line 238, in __setitem__
File "/.../spark/python/lib/py4j-0.10.9-src.zip/py4j/java_collections.py", line 221, in __set_item
File "/.../spark/python/lib/py4j-0.10.9-src.zip/py4j/protocol.py", line 332, in get_return_value
py4j.protocol.Py4JError: An error occurred while calling None.None. Trace:
py4j.Py4JException: Cannot convert org.apache.spark.api.java.JavaPairRDD to org.apache.spark.api.java.JavaRDD
at py4j.commands.ArrayCommand.convertArgument(ArrayCommand.java:166)
at py4j.commands.ArrayCommand.setArray(ArrayCommand.java:144)
at py4j.commands.ArrayCommand.execute(ArrayCommand.java:97)
at py4j.GatewayConnection.run(GatewayConnection.java:238)
at java.lang.Thread.run(Thread.java:748)
```
which works in Spark 2.4.5:
```
[(1, 6), (2, 7), (3, 8), (4, 9), (5, 10), (1, 6), (2, 7), (3, 8), (4, 9), (5, 10)]
```
It assumed the class of the input array is the same `JavaRDD` or `JavaDStream`; however, that can be different such as `JavaPairRDD`.
This fix is based on redsanket's initial approach, and will be co-authored.
### Why are the changes needed?
To fix a regression from Spark 2.4.5.
### Does this PR introduce _any_ user-facing change?
No, it's only in unreleased branches. This is to fix a regression.
### How was this patch tested?
Manually tested, and a unittest was added.
Closes #28648 from HyukjinKwon/SPARK-31788.
Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
|
||
|---|---|---|
| .. | ||
| docs | ||
| lib | ||
| pyspark | ||
| test_coverage | ||
| test_support | ||
| .coveragerc | ||
| .gitignore | ||
| MANIFEST.in | ||
| pylintrc | ||
| README.md | ||
| run-tests | ||
| run-tests-with-coverage | ||
| run-tests.py | ||
| setup.cfg | ||
| setup.py | ||
Apache Spark
Spark is a unified analytics engine for large-scale data processing. 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 Structured Streaming for stream processing.
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, but some additional sub-packages have their own extra requirements for some features (including numpy, pandas, and pyarrow).