Description
This started as a question on stack overflow, 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:
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
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:
{"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"}}
The bucketizers all look very similar. Here is what the meta data for few of them look like:
{"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__"}}
Here is the metadata for the NaiveBayes model:
{"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__"}}
and for the final SQLTransformer
{"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__"}}
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.
Attachments
Attachments
Issue Links
- incorporates
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SPARK-24865 Remove AnalysisBarrier
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- Resolved
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- relates to
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SPARK-20542 Add an API into Bucketizer that can bin a lot of columns all at once
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- Resolved
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- links to
