b8e13b0aea
## What changes were proposed in this pull request? This PR proposes to use `AtomicReference` so that parent and child threads can access to the same file block holder. Python UDF expressions are turned to a plan and then it launches a separate thread to consume the input iterator. In the separate child thread, the iterator sets `InputFileBlockHolder.set` before the parent does which the parent thread is unable to read later. 1. In this separate child thread, if it happens to call `InputFileBlockHolder.set` first without initialization of the parent's thread local (which is done when the `ThreadLocal.get()` is first called), the child thread seems calling its own `initialValue` to initialize. 2. After that, the parent calls its own `initialValue` to initializes at the first call of `ThreadLocal.get()`. 3. Both now have two different references. Updating at child isn't reflected to parent. This PR fixes it via initializing parent's thread local with `AtomicReference` for file status so that they can be used in each task, and children thread's update is reflected. I also tried to explain this a bit more at https://github.com/apache/spark/pull/24958#discussion_r297203041. ## How was this patch tested? Manually tested and unittest was added. Closes #24958 from HyukjinKwon/SPARK-28153. Authored-by: HyukjinKwon <gurwls223@apache.org> Signed-off-by: Wenchen Fan <wenchen@databricks.com> |
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| 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 (currently version 0.10.8.1), but some additional sub-packages have their own extra requirements for some features (including numpy, pandas, and pyarrow).