d492cc5a21
## What changes were proposed in this pull request? **Context** While reviewing https://github.com/apache/spark/pull/17227, I realised here we type-dispatch per record. The PR itself is fine in terms of performance as is but this prints a prefix, `"obj"` in exception message as below: ``` from pyspark.sql.types import * schema = StructType([StructField('s', IntegerType(), nullable=False)]) spark.createDataFrame([["1"]], schema) ... TypeError: obj.s: IntegerType can not accept object '1' in type <type 'str'> ``` I suggested to get rid of this but during investigating this, I realised my approach might bring a performance regression as it is a hot path. Only for SPARK-19507 and https://github.com/apache/spark/pull/17227, It needs more changes to cleanly get rid of the prefix and I rather decided to fix both issues together. **Propersal** This PR tried to - get rid of per-record type dispatch as we do in many code paths in Scala so that it improves the performance (roughly ~25% improvement) - SPARK-21296 This was tested with a simple code `spark.createDataFrame(range(1000000), "int")`. However, I am quite sure the actual improvement in practice is larger than this, in particular, when the schema is complicated. - improve error message in exception describing field information as prose - SPARK-19507 ## How was this patch tested? Manually tested and unit tests were added in `python/pyspark/sql/tests.py`. Benchmark - codes: https://gist.github.com/HyukjinKwon/c3397469c56cb26c2d7dd521ed0bc5a3 Error message - codes: https://gist.github.com/HyukjinKwon/b1b2c7f65865444c4a8836435100e398 **Before** Benchmark: - Results: https://gist.github.com/HyukjinKwon/4a291dab45542106301a0c1abcdca924 Error message - Results: https://gist.github.com/HyukjinKwon/57b1916395794ce924faa32b14a3fe19 **After** Benchmark - Results: https://gist.github.com/HyukjinKwon/21496feecc4a920e50c4e455f836266e Error message - Results: https://gist.github.com/HyukjinKwon/7a494e4557fe32a652ce1236e504a395 Closes #17227 Author: hyukjinkwon <gurwls223@gmail.com> Author: David Gingrich <david@textio.com> Closes #18521 from HyukjinKwon/python-type-dispatch. |
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Apache Spark
Spark is a fast and general cluster computing system for Big Data. 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 Spark 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 setup 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 0.10.4), but additional sub-packages have their own requirements (including numpy and pandas).