[
https://issues.apache.org/jira/browse/SPARK-20392?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Barry Becker updated SPARK-20392:
---------------------------------
Attachment: blockbuster.csv
Attaching blockbuster.csv data file with many columns, but few rows.
> Slow performance when calling fit on ML pipeline for dataset with many
> columns but few rows
> -------------------------------------------------------------------------------------------
>
> Key: SPARK-20392
> URL: https://issues.apache.org/jira/browse/SPARK-20392
> Project: Spark
> Issue Type: Bug
> Components: ML
> Affects Versions: 2.1.0
> Reporter: Barry Becker
> Attachments: blockbuster.csv
>
>
> This started as a [question on stack
> overflow|http://stackoverflow.com/questions/43484006/why-is-it-slow-to-apply-a-spark-pipeline-to-dataset-with-many-columns-but-few-ro],
> but it seems like a bug.
> I am testing spark pipelines using a simple dataset (attached) with 312
> (mostly numeric) columns, but only 421 rows. It is small, but it takes 3
> minutes to apply my ML pipeline to it on a 24 core server with 60G of memory.
> This seems much to long for such a tiny dataset. Similar pipelines run
> quickly on datasets that have fewer columns and more rows. It's something
> about the number of columns that is causing the slow performance.
> Here are a list of the stages in my pipeline:
> {code}
> 000_strIdx_5708525b2b6c
> 001_strIdx_ec2296082913
> 002_bucketizer_3cbc8811877b
> 003_bucketizer_5a01d5d78436
> 004_bucketizer_bf290d11364d
> 005_bucketizer_c3296dfe94b2
> 006_bucketizer_7071ca50eb85
> 007_bucketizer_27738213c2a1
> 008_bucketizer_bd728fd89ba1
> 009_bucketizer_e1e716f51796
> 010_bucketizer_38be665993ba
> 011_bucketizer_5a0e41e5e94f
> 012_bucketizer_b5a3d5743aaa
> 013_bucketizer_4420f98ff7ff
> 014_bucketizer_777cc4fe6d12
> 015_bucketizer_f0f3a3e5530e
> 016_bucketizer_218ecca3b5c1
> 017_bucketizer_0b083439a192
> 018_bucketizer_4520203aec27
> 019_bucketizer_462c2c346079
> 020_bucketizer_47435822e04c
> 021_bucketizer_eb9dccb5e6e8
> 022_bucketizer_b5f63dd7451d
> 023_bucketizer_e0fd5041c841
> 024_bucketizer_ffb3b9737100
> 025_bucketizer_e06c0d29273c
> 026_bucketizer_36ee535a425f
> 027_bucketizer_ee3a330269f1
> 028_bucketizer_094b58ea01c0
> 029_bucketizer_e93ea86c08e2
> 030_bucketizer_4728a718bc4b
> 031_bucketizer_08f6189c7fcc
> 032_bucketizer_11feb74901e6
> 033_bucketizer_ab4add4966c7
> 034_bucketizer_4474f7f1b8ce
> 035_bucketizer_90cfa5918d71
> 036_bucketizer_1a9ff5e4eccb
> 037_bucketizer_38085415a4f4
> 038_bucketizer_9b5e5a8d12eb
> 039_bucketizer_082bb650ecc3
> 040_bucketizer_57e1e363c483
> 041_bucketizer_337583fbfd65
> 042_bucketizer_73e8f6673262
> 043_bucketizer_0f9394ed30b8
> 044_bucketizer_8530f3570019
> 045_bucketizer_c53614f1e507
> 046_bucketizer_8fd99e6ec27b
> 047_bucketizer_6a8610496d8a
> 048_bucketizer_888b0055c1ad
> 049_bucketizer_974e0a1433a6
> 050_bucketizer_e848c0937cb9
> 051_bucketizer_95611095a4ac
> 052_bucketizer_660a6031acd9
> 053_bucketizer_aaffe5a3140d
> 054_bucketizer_8dc569be285f
> 055_bucketizer_83d1bffa07bc
> 056_bucketizer_0c6180ba75e6
> 057_bucketizer_452f265a000d
> 058_bucketizer_38e02ddfb447
> 059_bucketizer_6fa4ad5d3ebd
> 060_bucketizer_91044ee766ce
> 061_bucketizer_9a9ef04a173d
> 062_bucketizer_3d98eb15f206
> 063_bucketizer_c4915bb4d4ed
> 064_bucketizer_8ca2b6550c38
> 065_bucketizer_417ee9b760bc
> 066_bucketizer_67f3556bebe8
> 067_bucketizer_0556deb652c6
> 068_bucketizer_067b4b3d234c
> 069_bucketizer_30ba55321538
> 070_bucketizer_ad826cc5d746
> 071_bucketizer_77676a898055
> 072_bucketizer_05c37a38ce30
> 073_bucketizer_6d9ae54163ed
> 074_bucketizer_8cd668b2855d
> 075_bucketizer_d50ea1732021
> 076_bucketizer_c68f467c9559
> 077_bucketizer_ee1dfc840db1
> 078_bucketizer_83ec06a32519
> 079_bucketizer_741d08c1b69e
> 080_bucketizer_b7402e4829c7
> 081_bucketizer_8adc590dc447
> 082_bucketizer_673be99bdace
> 083_bucketizer_77693b45f94c
> 084_bucketizer_53529c6b1ac4
> 085_bucketizer_6a3ca776a81e
> 086_bucketizer_6679d9588ac1
> 087_bucketizer_6c73af456f65
> 088_bucketizer_2291b2c5ab51
> 089_bucketizer_cb3d0fe669d8
> 090_bucketizer_e71f913c1512
> 091_bucketizer_156528f65ce7
> 092_bucketizer_f3ec5dae079b
> 093_bucketizer_809fab77eee1
> 094_bucketizer_6925831511e6
> 095_bucketizer_c5d853b95707
> 096_bucketizer_e677659ca253
> 097_bucketizer_396e35548c72
> 098_bucketizer_78a6410d7a84
> 099_bucketizer_e3ae6e54bca1
> 100_bucketizer_9fed5923fe8a
> 101_bucketizer_8925ba4c3ee2
> 102_bucketizer_95750b6942b8
> 103_bucketizer_6e8b50a1918b
> 104_bucketizer_36cfcc13d4ba
> 105_bucketizer_2716d0455512
> 106_bucketizer_9bcf2891652f
> 107_bucketizer_8c3d352915f7
> 108_bucketizer_0786c17d5ef9
> 109_bucketizer_f22df23ef56f
> 110_bucketizer_bad04578bd20
> 111_bucketizer_35cfbde7e28f
> 112_bucketizer_cf89177a528b
> 113_bucketizer_183a0d393ef0
> 114_bucketizer_467c78156a67
> 115_bucketizer_380345e651ab
> 116_bucketizer_0f39f6de1625
> 117_bucketizer_d8500b2c0c2f
> 118_bucketizer_dc5f1fd09ff1
> 119_bucketizer_eeaf9e6cdaef
> 120_bucketizer_5614cd4533d7
> 121_bucketizer_2f1230e2871e
> 122_bucketizer_f8bf9d47e57e
> 123_bucketizer_2df774393575
> 124_bucketizer_259320b7fc86
> 125_bucketizer_e334afc63030
> 126_bucketizer_f17d4d6b4d94
> 127_bucketizer_da7834230ecd
> 128_bucketizer_8dbb503f658e
> 129_bucketizer_e09e2eb2b181
> 130_bucketizer_faa04fa16f3c
> 131_bucketizer_d0bd348a5613
> 132_bucketizer_de6da796e294
> 133_bucketizer_0395526346ce
> 134_bucketizer_ea3b5eb6058f
> 135_bucketizer_ad83472038f7
> 136_bucketizer_4a17c440fd16
> 137_bucketizer_d468637d4b86
> 138_bucketizer_4fc473a72f1d
> 139_vecAssembler_bd87cd105650
> 140_nb_f134e0890a0d
> 141_sql_a8590b83c826
> {code}
> There are 2 string columns that are converted to ints with
> StringIndexerModel. Then there are bucketizers that bin all the numeric
> columns into 2 or 3 mins each. Is there a way to bin many columns at once
> with a single stage? I did not see a way. Next there is a VectorAssembler to
> combine all the columns into one for the NaiveBayes classifier. Lastly, there
> is a simple SQLTransformer to cast one the prection column to an int.
> Here is what the metadata for the two StringIndexerModelss looks like:
> {code}
> {"class":"org.apache.spark.ml.feature.StringIndexerModel","timestamp":1492551461778,"sparkVersion":"2.1.1","uid":"strIdx_5708525b2b6c","paramMap":{"outputCol":"ADI_IDX__","handleInvalid":"skip","inputCol":"ADI_CLEANED__"}}
> {"class":"org.apache.spark.ml.feature.StringIndexerModel","timestamp":1492551462004,"sparkVersion":"2.1.1","uid":"strIdx_ec2296082913","paramMap":{"outputCol":"State_IDX__","inputCol":"State_CLEANED__","handleInvalid":"skip"}}
> {code}
> The bucketizers all look very similar. Here is what the meta data for few of
> them look like:
> {code}
> {"class":"org.apache.spark.ml.feature.Bucketizer","timestamp":1492551462636,"sparkVersion":"2.1.1","uid":"bucketizer_bd728fd89ba1","paramMap":{"outputCol":"HH_02_BINNED__","inputCol":"HH_02_CLEANED__","handleInvalid":"keep","splits":["-Inf",7521.0,12809.5,20299.0,"Inf"]}}
> {"class":"org.apache.spark.ml.feature.Bucketizer","timestamp":1492551462711,"sparkVersion":"2.1.1","uid":"bucketizer_e1e716f51796","paramMap":{"splits":["-Inf",6698.0,13690.5,"Inf"],"handleInvalid":"keep","outputCol":"HH_97_BINNED__","inputCol":"HH_97_CLEANED__"}}
> {"class":"org.apache.spark.ml.feature.Bucketizer","timestamp":1492551462784,"sparkVersion":"2.1.1","uid":"bucketizer_38be665993ba","paramMap":{"splits":["-Inf",4664.0,7242.5,11770.0,14947.0,"Inf"],"outputCol":"HH_90_BINNED__","handleInvalid":"keep","inputCol":"HH_90_CLEANED__"}}
> {"class":"org.apache.spark.ml.feature.Bucketizer","timestamp":1492551462858,"sparkVersion":"2.1.1","uid":"bucketizer_5a0e41e5e94f","paramMap":{"splits":["-Inf",6107.5,10728.5,"Inf"],"outputCol":"HH_80_BINNED__","inputCol":"HH_80_CLEANED__","handleInvalid":"keep"}}
> {"class":"org.apache.spark.ml.feature.Bucketizer","timestamp":1492551462931,"sparkVersion":"2.1.1","uid":"bucketizer_b5a3d5743aaa","paramMap":{"outputCol":"HHPG9702_BINNED__","splits":["-Inf",8.895000457763672,"Inf"],"handleInvalid":"keep","inputCol":"HHPG9702_CLEANED__"}}
> {"class":"org.apache.spark.ml.feature.Bucketizer","timestamp":1492551463004,"sparkVersion":"2.1.1","uid":"bucketizer_4420f98ff7ff","paramMap":{"splits":["-Inf",54980.5,"Inf"],"outputCol":"MEDHI97_BINNED__","handleInvalid":"keep","inputCol":"MEDHI97_CLEANED__"}}
> {code}
> Here is the metadata for the NaiveBayes model:
> {code}
> {"class":"org.apache.spark.ml.classification.NaiveBayesModel","timestamp":1492551472568,"sparkVersion":"2.1.1","uid":"nb_f134e0890a0d","paramMap":{"modelType":"multinomial","probabilityCol":"_class_probability_column__","smoothing":1.0,"predictionCol":"_prediction_column_","rawPredictionCol":"rawPrediction","featuresCol":"_features_column__","labelCol":"DAYPOP_BINNED__"}}
> {code}
> and for the final SQLTransformer
> {code}
> {"class":"org.apache.spark.ml.feature.SQLTransformer","timestamp":1492551472804,"sparkVersion":"2.1.1","uid":"sql_a8590b83c826","paramMap":{"statement":"SELECT
> *, CAST(_prediction_column_ AS INT) AS `_*_prediction_label_column_*__` FROM
> __THIS__"}}
> {code}
> Why is it that the duration gets extremely slow when more than a couple
> hundred columns (and only a few rows), but having millions of rows (with
> fewer columns) performs fine? In addition to it being slow when applying this
> pipeline, it is also slow to create it. The fit and evaluate steps take a few
> minutes each. Is there anything that can be done to make it faster?
> I get similar results using 2.1.1RC, 2.1.2(tip) and 2.2.0(tip). Spark 2.1.0
> gives a Janino 64k limit error when trying to build this pipeline (see
> https://issues.apache.org/jira/browse/SPARK-16845).
> I stepped through in the debugger when pipeline.fit was called and noticed
> that the queryPlan is a huge nested structure. I don't know how to interpret
> this plan, but it is likely related to the performance problem. It is
> attached.
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