zhengruifeng created SPARK-29754:
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Summary: LoR/AFT/LiR/SVC use Summarizer instead of
MultivariateOnlineSummarizer
Key: SPARK-29754
URL: https://issues.apache.org/jira/browse/SPARK-29754
Project: Spark
Issue Type: Improvement
Components: ML
Affects Versions: 3.0.0
Reporter: zhengruifeng
Before iteration, LoR/AFT/LiR/SVC use MultivariateOnlineSummarizer to summarize
the input dataset, however, MultivariateOnlineSummarizer compute much more than
needed.
example:
bin/spark-shell --driver-memory=4G
{code:java}
import org.apache.spark.ml.feature._
import org.apache.spark.ml.regression._
import org.apache.spark.ml.classification._
scala> val df = spark.read.format("libsvm").load("/data1/Datasets/kdda/kdda.t")
19/11/05 13:47:02 WARN LibSVMFileFormat: 'numFeatures' option not specified,
determining the number of features by going though the input. If you know the
number in advance, please specify it via 'numFeatures' option to avoid the
extra scan.
df: org.apache.spark.sql.DataFrame = [label: double, features: vector]
scala> df.persist()
res0: org.apache.spark.sql.Dataset[org.apache.spark.sql.Row] = [label: double,
features: vector]
scala> df.count
res1: Long = 510302
scala> df.show(3)
+-----+--------------------+
|label| features|
+-----+--------------------+
| 1.0|(2014669,[0,1,2,3...|
| 1.0|(2014669,[1,2,3,4...|
| 0.0|(2014669,[1,2,3,4...|
+-----+--------------------+
val lr = new LogisticRegression().setMaxIter(1)
val tic = System.currentTimeMillis; val model = lr.fit(df); val toc =
System.currentTimeMillis; toc - tic {code}
The input dataset is here
([https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/binary.html#kdd2010%20(algebra))]
#instance=510302, #features=2014669
Above example will fail because of OOM:
{code:java}
Caused by: java.lang.OutOfMemoryError: Java heap space
at java.lang.Object.clone(Native Method)
at
org.apache.spark.mllib.stat.MultivariateOnlineSummarizer.merge(MultivariateOnlineSummarizer.scala:174)
at
org.apache.spark.ml.classification.LogisticRegression.$anonfun$train$3(LogisticRegression.scala:511)
at
org.apache.spark.ml.classification.LogisticRegression$$Lambda$4111/1818679131.apply(Unknown
Source)
at
org.apache.spark.rdd.PairRDDFunctions.$anonfun$foldByKey$3(PairRDDFunctions.scala:218)
at
org.apache.spark.rdd.PairRDDFunctions$$Lambda$4139/1537760275.apply(Unknown
Source)
at
org.apache.spark.util.collection.ExternalSorter.$anonfun$insertAll$1(ExternalSorter.scala:190)
at
org.apache.spark.util.collection.ExternalSorter.$anonfun$insertAll$1$adapted(ExternalSorter.scala:189)
at
org.apache.spark.util.collection.ExternalSorter$$Lambda$4180/1672153085.apply(Unknown
Source)
at
org.apache.spark.util.collection.AppendOnlyMap.changeValue(AppendOnlyMap.scala:144)
at
org.apache.spark.util.collection.SizeTrackingAppendOnlyMap.changeValue(SizeTrackingAppendOnlyMap.scala:32)
at
org.apache.spark.util.collection.ExternalSorter.insertAll(ExternalSorter.scala:195)
at
org.apache.spark.shuffle.sort.SortShuffleWriter.write(SortShuffleWriter.scala:62)
at
org.apache.spark.shuffle.ShuffleWriteProcessor.write(ShuffleWriteProcessor.scala:59)
at
org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:99)
at
org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:52)
at org.apache.spark.scheduler.Task.run(Task.scala:127)
at
org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$3(Executor.scala:462)
at
org.apache.spark.executor.Executor$TaskRunner$$Lambda$2799/542333665.apply(Unknown
Source)
at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1377)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:465)
at
java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
at
java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
at java.lang.Thread.run(Thread.java:748)
{code}
Here, if we use {{ml.Summarizer}} instead, only 3G memory is enough to fit this
LR model.
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