07593d362f
### What changes were proposed in this pull request? This is a follow-up of https://github.com/apache/spark/pull/26780 In https://github.com/apache/spark/pull/26780, a new Avro data source option `actualSchema` is introduced for setting the original Avro schema in function `from_avro`, while the expected schema is supposed to be set in the parameter `jsonFormatSchema` of `from_avro`. However, there is another Avro data source option `avroSchema`. It is used for setting the expected schema in readiong and writing. This PR is to use the option `avroSchema` option for reading Avro data with an evolved schema and remove the new one `actualSchema` ### Why are the changes needed? Unify and simplify the Avro data source options. ### Does this PR introduce any user-facing change? Yes. To deserialize Avro data with an evolved schema, before changes: ``` from_avro('col, expectedSchema, ("actualSchema" -> actualSchema)) ``` After changes: ``` from_avro('col, actualSchema, ("avroSchema" -> expectedSchema)) ``` The second parameter is always the actual Avro schema after changes. ### How was this patch tested? Update the existing tests in https://github.com/apache/spark/pull/26780 Closes #27045 from gengliangwang/renameAvroOption. Authored-by: Gengliang Wang <gengliang.wang@databricks.com> Signed-off-by: HyukjinKwon <gurwls223@apache.org> |
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| pyspark | ||
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| .coveragerc | ||
| .gitignore | ||
| MANIFEST.in | ||
| pylintrc | ||
| README.md | ||
| run-tests | ||
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| run-tests.py | ||
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| 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).