ab8a9a0ceb
### What changes were proposed in this pull request? pyrolite 4.21 introduced and enabled value comparison by default (`valueCompare=true`) during object memoization and serialization: https://github.com/irmen/Pyrolite/blob/pyrolite-4.21/java/src/main/java/net/razorvine/pickle/Pickler.java#L112-L122 This change has undesired effect when we serialize a row (actually `GenericRowWithSchema`) to be passed to python: https://github.com/apache/spark/blob/branch-3.0/sql/core/src/main/scala/org/apache/spark/sql/execution/python/EvaluatePython.scala#L60. A simple example is that ``` new GenericRowWithSchema(Array(1.0, 1.0), StructType(Seq(StructField("_1", DoubleType), StructField("_2", DoubleType)))) ``` and ``` new GenericRowWithSchema(Array(1, 1), StructType(Seq(StructField("_1", IntegerType), StructField("_2", IntegerType)))) ``` are currently equal and the second instance is replaced to the short code of the first one during serialization. ### Why are the changes needed? The above can cause nasty issues like the one in https://issues.apache.org/jira/browse/SPARK-34545 description: ``` >>> from pyspark.sql.functions import udf >>> from pyspark.sql.types import * >>> >>> def udf1(data_type): def u1(e): return e[0] return udf(u1, data_type) >>> >>> df = spark.createDataFrame([((1.0, 1.0), (1, 1))], ['c1', 'c2']) >>> >>> df = df.withColumn("c3", udf1(DoubleType())("c1")) >>> df = df.withColumn("c4", udf1(IntegerType())("c2")) >>> >>> df.select("c3").show() +---+ | c3| +---+ |1.0| +---+ >>> df.select("c4").show() +---+ | c4| +---+ | 1| +---+ >>> df.select("c3", "c4").show() +---+----+ | c3| c4| +---+----+ |1.0|null| +---+----+ ``` This is because during serialization from JVM to Python `GenericRowWithSchema(1.0, 1.0)` (`c1`) is memoized first and when `GenericRowWithSchema(1, 1)` (`c2`) comes next, it is replaced to some short code of the `c1` (instead of serializing `c2` out) as they are `equal()`. The python functions then runs but the return type of `c4` is expected to be `IntegerType` and if a different type (`DoubleType`) comes back from python then it is discarded: https://github.com/apache/spark/blob/branch-3.0/sql/core/src/main/scala/org/apache/spark/sql/execution/python/EvaluatePython.scala#L108-L113 After this PR: ``` >>> df.select("c3", "c4").show() +---+---+ | c3| c4| +---+---+ |1.0| 1| +---+---+ ``` ### Does this PR introduce _any_ user-facing change? Yes, fixes a correctness issue. ### How was this patch tested? Added new UT + manual tests. Closes #31682 from peter-toth/SPARK-34545-fix-row-comparison. Authored-by: Peter Toth <peter.toth@gmail.com> Signed-off-by: Sean Owen <srowen@gmail.com> |
||
|---|---|---|
| .. | ||
| docs | ||
| lib | ||
| pyspark | ||
| test_coverage | ||
| test_support | ||
| .coveragerc | ||
| .gitignore | ||
| MANIFEST.in | ||
| mypy.ini | ||
| 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).