History

zhengruifeng a2887164bc [SPARK-32907][ML][PYTHON] adaptively blockify instances - LinearSVC ### What changes were proposed in this pull request? 1, use `maxBlockSizeInMB` instead of `blockSize`(#rows) to control the stacking of vectors; 2, infer an appropriate `maxBlockSizeInMB` if set 0; ### Why are the changes needed? the performance gain is mainly related to the nnz of block. f2jBLAS \| \| \| \| \| \| \| \| \| \| \| \| \| -- \| -- \| -- \| -- \| -- \| -- \| -- \| -- \| -- \| -- \| -- \| -- \| -- \| -- Duration(millisecond) \| branch 3.0 Impl \| blockSizeInMB=0.0625 \| blockSizeInMB=0.125 \| blockSizeInMB=0.25 \| blockSizeInMB=0.5 \| blockSizeInMB=1 \| blockSizeInMB=2 \| blockSizeInMB=4 \| blockSizeInMB=8 \| blockSizeInMB=16 \| blockSizeInMB=32 \| blockSizeInMB=64 \| blockSizeInMB=128 epsilon(100%) \| 326481 \| 26143 \| 25710 \| 24726 \| 25395 \| 25840 \| 26846 \| 25927 \| 27431 \| 26190 \| 26056 \| 26347 \| 27204 epsilon3000(67%) \| 455247 \| 35893 \| 34366 \| 34985 \| 38387 \| 38901 \| 40426 \| 40044 \| 39161 \| 38767 \| 39965 \| 39523 \| 39108 epsilon4000(50%) \| 306390 \| 42256 \| 41164 \| 43748 \| 48638 \| 50892 \| 50986 \| 51091 \| 51072 \| 51289 \| 51652 \| 53312 \| 52146 epsilon5000(40%) \| 307619 \| 43639 \| 42992 \| 44743 \| 50800 \| 51939 \| 51871 \| 52190 \| 53850 \| 52607 \| 51062 \| 52509 \| 51570 epsilon10000(20%) \| 310070 \| 58371 \| 55921 \| 56317 \| 56618 \| 53694 \| 52131 \| 51768 \| 51728 \| 52233 \| 51881 \| 51653 \| 52440 epsilon20000(10%) \| 316565 \| 109193 \| 95121 \| 82764 \| 69653 \| 60764 \| 56066 \| 53371 \| 52822 \| 52872 \| 52769 \| 52527 \| 53508 epsilon200000(1%) \| 336181 \| 1569721 \| 1069355 \| 673718 \| 375043 \| 218230 \| 145393 \| 110926 \| 94327 \| 87039 \| 83926 \| 81890 \| 81787 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| Speedup \| \| \| \| \| \| \| \| \| \| \| \| epsilon(100%) \| 1 \| 12.48827602 \| 12.69859977 \| 13.20395535 \| 12.85611341 \| 12.63471362 \| 12.16125307 \| 12.59231689 \| 11.90189931 \| 12.46586483 \| 12.5299739 \| 12.39158158 \| 12.00121306 epsilon3000(67%) \| 1 \| 12.68344803 \| 13.2470174 \| 13.01263399 \| 11.85940553 \| 11.70270687 \| 11.26124276 \| 11.36866946 \| 11.62500958 \| 11.74315784 \| 11.39114225 \| 11.51853351 \| 11.64076404 epsilon4000(50%) \| 1 \| 7.250804619 \| 7.443154212 \| 7.003520161 \| 6.299395534 \| 6.020396133 \| 6.00929667 \| 5.996946625 \| 5.999177632 \| 5.973795551 \| 5.931812902 \| 5.747111345 \| 5.875618456 epsilon5000(40%) \| 1 \| 7.049176196 \| 7.155261444 \| 6.875243055 \| 6.055492126 \| 5.92269778 \| 5.930462108 \| 5.894213451 \| 5.712516249 \| 5.847491779 \| 6.024421292 \| 5.858405226 \| 5.965076595 epsilon10000(20%) \| 1 \| 5.312055644 \| 5.544786395 \| 5.505797539 \| 5.4765269 \| 5.774760681 \| 5.947900481 \| 5.98960748 \| 5.994239097 \| 5.93628549 \| 5.976561747 \| 6.002942714 \| 5.912852784 epsilon20000(10%) \| 1 \| 2.899132728 \| 3.328024306 \| 3.824911797 \| 4.544886796 \| 5.209745902 \| 5.64629187 \| 5.931404695 \| 5.993052137 \| 5.987384627 \| 5.999071425 \| 6.026710073 \| 5.916218136 epsilon200000(1%) \| 1 \| 0.214166084 \| 0.314377358 \| 0.498993644 \| 0.896379882 \| 1.540489392 \| 2.312222734 \| 3.03067811 \| 3.563995463 \| 3.862417997 \| 4.005683578 \| 4.105275369 \| 4.110445425 OpenBLAS \| \| \| \| \| \| \| \| \| \| \| \| \| -- \| -- \| -- \| -- \| -- \| -- \| -- \| -- \| -- \| -- \| -- \| -- \| -- \| -- Duration(millisecond) \| branch 3.0 Impl \| blockSizeInMB=0.0625 \| blockSizeInMB=0.125 \| blockSizeInMB=0.25 \| blockSizeInMB=0.5 \| blockSizeInMB=1 \| blockSizeInMB=2 \| blockSizeInMB=4 \| blockSizeInMB=8 \| blockSizeInMB=16 \| blockSizeInMB=32 \| blockSizeInMB=64 \| blockSizeInMB=128 epsilon(100%) \| 299119 \| 26047 \| 25049 \| 25239 \| 28001 \| 35138 \| 36438 \| 36279 \| 36114 \| 35111 \| 35428 \| 36295 \| 35197 epsilon3000(67%) \| 439798 \| 33321 \| 34423 \| 34336 \| 38906 \| 51756 \| 54138 \| 54085 \| 53412 \| 54766 \| 54425 \| 54221 \| 54842 epsilon4000(50%) \| 302963 \| 42960 \| 40678 \| 43483 \| 48254 \| 50888 \| 54990 \| 52647 \| 51947 \| 51843 \| 52891 \| 53410 \| 52020 epsilon5000(40%) \| 303569 \| 44225 \| 44961 \| 45065 \| 51768 \| 52776 \| 51930 \| 53587 \| 53104 \| 51833 \| 52138 \| 52574 \| 53756 epsilon10000(20%) \| 307403 \| 58447 \| 55993 \| 56757 \| 56694 \| 54038 \| 52734 \| 52073 \| 52051 \| 52150 \| 51986 \| 52407 \| 52390 epsilon20000(10%) \| 313344 \| 107580 \| 94679 \| 83329 \| 70226 \| 60996 \| 57130 \| 55461 \| 54641 \| 52712 \| 52541 \| 53101 \| 53312 epsilon200000(1%) \| 334679 \| 1642726 \| 1073148 \| 654481 \| 364974 \| 213881 \| 140248 \| 107579 \| 91757 \| 85090 \| 81940 \| 80492 \| 80250 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| Speedup \| \| \| \| \| \| \| \| \| \| \| \| epsilon(100%) \| 1 \| 11.48381771 \| 11.94135494 \| 11.85146004 \| 10.68243991 \| 8.512692811 \| 8.208985125 \| 8.244962651 \| 8.282632774 \| 8.519238985 \| 8.443011178 \| 8.241328007 \| 8.498423161 epsilon3000(67%) \| 1 \| 13.19882356 \| 12.7762833 \| 12.80865564 \| 11.30411762 \| 8.497526857 \| 8.123646976 \| 8.131607655 \| 8.234067251 \| 8.030493372 \| 8.080808452 \| 8.111211523 \| 8.01936472 epsilon4000(50%) \| 1 \| 7.052211359 \| 7.44783421 \| 6.967389555 \| 6.278505409 \| 5.953525389 \| 5.509419895 \| 5.754610899 \| 5.832155851 \| 5.843855487 \| 5.728063376 \| 5.672402172 \| 5.823971549 epsilon5000(40%) \| 1 \| 6.86419446 \| 6.751829363 \| 6.736247642 \| 5.864027971 \| 5.752027437 \| 5.845734643 \| 5.664974714 \| 5.716499699 \| 5.856674319 \| 5.822413595 \| 5.774127896 \| 5.647164968 epsilon10000(20%) \| 1 \| 5.259517169 \| 5.490025539 \| 5.416124883 \| 5.422143437 \| 5.688645028 \| 5.829313157 \| 5.903308816 \| 5.905803923 \| 5.894592522 \| 5.913188166 \| 5.865685882 \| 5.867589235 epsilon20000(10%) \| 1 \| 2.912660346 \| 3.309540658 \| 3.760323537 \| 4.461937174 \| 5.137123746 \| 5.48475407 \| 5.649807973 \| 5.734594901 \| 5.944452876 \| 5.963799699 \| 5.900905821 \| 5.87755102 epsilon200000(1%) \| 1 \| 0.203733915 \| 0.311866583 \| 0.511365494 \| 0.916994087 \| 1.564790701 \| 2.38633706 \| 3.111006795 \| 3.647449241 \| 3.933235398 \| 4.084439834 \| 4.157916315 \| 4.170454829 ### Does this PR introduce _any_ user-facing change? yes, param `blockSize` -> `blockSizeInMB` in master ### How was this patch tested? added testsuites and performance test (result attached in [ticket](https://issues.apache.org/jira/browse/SPARK-32907)) Closes #30009 from zhengruifeng/adaptively_blockify_linear_svc_II. Lead-authored-by: zhengruifeng <ruifengz@foxmail.com> Co-authored-by: Weichen Xu <weichen.xu@databricks.com> Signed-off-by: Weichen Xu <weichen.xu@databricks.com>		2020-11-12 19:14:07 +08:00
..
docs	[SPARK-33243][PYTHON][BUILD] Add numpydoc into documentation dependency	2020-10-27 14:03:57 +09:00
lib	[SPARK-30884][PYSPARK] Upgrade to Py4J 0.10.9	2020-02-20 09:09:30 -08:00
pyspark	[SPARK-32907][ML][PYTHON] adaptively blockify instances - LinearSVC	2020-11-12 19:14:07 +08:00
test_coverage	[SPARK-7721][PYTHON][TESTS] Adds PySpark coverage generation script	2018-01-22 22:12:50 +09:00
test_support	[SPARK-23094][SPARK-23723][SPARK-23724][SQL] Support custom encoding for json files	2018-04-29 11:25:31 +08:00
.coveragerc	[SPARK-7721][PYTHON][TESTS] Adds PySpark coverage generation script	2018-01-22 22:12:50 +09:00
.gitignore	[SPARK-3946] gitignore in /python includes wrong directory	2014-10-14 14:09:39 -07:00
MANIFEST.in	[SPARK-32714][PYTHON] Initial pyspark-stubs port	2020-09-24 14:15:36 +09:00
mypy.ini	[SPARK-33002][PYTHON] Remove non-API annotations	2020-10-07 19:53:59 +09:00
pylintrc	[SPARK-32435][PYTHON] Remove heapq3 port from Python 3	2020-07-27 20:10:13 +09:00
README.md	[SPARK-30884][PYSPARK] Upgrade to Py4J 0.10.9	2020-02-20 09:09:30 -08:00
run-tests	[SPARK-29672][PYSPARK] update spark testing framework to use python3	2019-11-14 10:18:55 -08:00
run-tests-with-coverage	[SPARK-26252][PYTHON] Add support to run specific unittests and/or doctests in python/run-tests script	2018-12-05 15:22:08 +08:00
run-tests.py	[SPARK-33189][PYTHON][TESTS] Add env var to tests for legacy nested timestamps in pyarrow	2020-10-21 09:13:33 +09:00
setup.cfg	[SPARK-1267][SPARK-18129] Allow PySpark to be pip installed	2016-11-16 14:22:15 -08:00
setup.py	[SPARK-33371][PYTHON] Update setup.py and tests for Python 3.9	2020-11-06 15:05:37 -08:00

README.md

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.

https://spark.apache.org/

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).