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### What changes were proposed in this pull request? The style of `EXPLAIN FORMATTED` output needs to be improved. We’ve already got some observations/ideas in https://github.com/apache/spark/pull/27368#discussion_r376694496 https://github.com/apache/spark/pull/27368#discussion_r376927143 Observations/Ideas: 1. Using comma as the separator is not clear, especially commas are used inside the expressions too. 2. Show the column counts first? For example, `Results [4]: …` 3. Currently the attribute names are automatically generated, this need to refined. 4. Add arguments field in common implementations as `EXPLAIN EXTENDED` did by calling `argString` in `TreeNode.simpleString`. This will eliminate most existing minor differences between `EXPLAIN EXTENDED` and `EXPLAIN FORMATTED`. 5. Another improvement we can do is: the generated alias shouldn't include attribute id. collect_set(val, 0, 0)#123 looks clearer than collect_set(val#456, 0, 0)#123 This PR is currently addressing comments 2 & 4, and open for more discussions on improving readability. ### Why are the changes needed? The readability of `EXPLAIN FORMATTED` need to be improved, which will help user better understand the query plan. ### Does this PR introduce any user-facing change? Yes, `EXPLAIN FORMATTED` output style changed. ### How was this patch tested? Update expect results of test cases in explain.sql Closes #27509 from Eric5553/ExplainFormattedRefine. Authored-by: Eric Wu <492960551@qq.com> Signed-off-by: Wenchen Fan <wenchen@databricks.com> |
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| pyspark | ||
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| .gitignore | ||
| MANIFEST.in | ||
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| README.md | ||
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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).