Barry Becker created SPARK-20392:
------------------------------------
Summary: 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
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.
--
This message was sent by Atlassian JIRA
(v6.3.15#6346)
---------------------------------------------------------------------
To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org
For additional commands, e-mail: issues-h...@spark.apache.org
