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Posted to issues@spark.apache.org by "James Conner (JIRA)" <ji...@apache.org> on 2017/07/27 20:32:00 UTC
[jira] [Comment Edited] (SPARK-18016) Code Generation: Constant
Pool Past Limit for Wide/Nested Dataset
[ https://issues.apache.org/jira/browse/SPARK-18016?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16103853#comment-16103853 ]
James Conner edited comment on SPARK-18016 at 7/27/17 8:31 PM:
---------------------------------------------------------------
The issue does not appear to be completely fixed. I downloaded the master today (last commit 2ff35a057efd36bd5c8a545a1ec3bc341432a904, Spark 2.3.0-SNAPSHOT), and attempted to perform a Gradient Boosted Tree (GBT) Regression on a dataframe which contains 2658 columns. I'm still getting the same constant pool exceeding JVM 0xFFFF error. The steps that I'm using to generate the error are:
{code:java}
// Imports
import org.apache.spark.sql.SparkSession
import org.apache.spark.sql.functions._
import org.apache.spark.ml.regression.{GBTRegressionModel, GBTRegressor}
import org.apache.spark.ml.feature.{VectorAssembler, Imputer, ImputerModel}
import org.apache.spark.ml.linalg.Vectors
import org.apache.spark.ml.Pipeline
import org.apache.spark.ml.evaluation.RegressionEvaluator
import org.apache.spark.ml.tuning.{ParamGridBuilder, CrossValidator}
import org.apache.spark.sql.{SQLContext, Row, DataFrame, Column}
// Load data
val mainDF = spark.read.parquet("/path/to/data/input/main_pqt").repartition(1024)
// Impute data
val inColsMain = mainDF.columns.filter(_ != "ID").filter(_ != "SCORE")
val outColsMain = inColsMain.map(i=>(i+"_imputed"))
val mainImputer = new Imputer().setInputCols(inColsMain).setOutputCols(outColsMain).setStrategy("mean")
val mainImputerBuild = mainImputer.fit(mainDF)
val imputedMainDF = mainImputerBuild.transform(mainDF)
// Drop original feature columns, retain imputed
val fullData = imputedMainDF.select(imputedMainDF.columns.filter(colName => !inColsMain.contains(colName)).map(colName => new Column(colName)): _*)
// Split data for testing & training
val Array(trainDF, testDF) = fullData.randomSplit(Array(0.80, 0.20),seed = 12345)
// Vector Assembler
val arrayName = fullData.columns.filter(_ != "ID").filter(_ != "SCORE")
val assembler = new VectorAssembler().setInputCols(arrayName).setOutputCol("features")
// GBT Object
val gbt = new GBTRegressor().setLabelCol("SCORE").setFeaturesCol("features").setMaxIter(5).setSeed(1993).setLossType("squared").setSubsamplingRate(1)
// Pipeline Object
val pipeline = new Pipeline().setStages(Array(assembler, gbt))
// Hyper Parameter Grid Object
val paramGrid = new ParamGridBuilder().addGrid(gbt.maxBins, Array(2, 4, 8)).addGrid(gbt.maxDepth, Array(1, 2, 4)).addGrid(gbt.stepSize, Array(0.1, 0.2)).build()
// Evaluator Object
val evaluator = new RegressionEvaluator().setLabelCol("SCORE").setPredictionCol("prediction").setMetricName("rmse")
// Cross Validation Object
val cv = new CrossValidator().setEstimator(pipeline).setEvaluator(evaluator).setEstimatorParamMaps(paramGrid).setNumFolds(5)
// Build the model
val gbtModel = cv.fit(trainDF)
{code}
Upon building the model, it errors out with the following cause:
{code:java}
java.lang.RuntimeException: Error while encoding: org.codehaus.janino.JaninoRuntimeException: failed to compile: org.codehaus.janino.JaninoRuntimeException: Constant pool for class org.apache.spark.sql.catalyst.expressions.GeneratedClass$SpecificUnsafeProjection$NestedClass2 has grown past JVM limit of 0xFFFF
{code}
was (Author: jamcon):
The issue does not appear to be completely fixed. I downloaded the master today (last commit 2ff35a057efd36bd5c8a545a1ec3bc341432a904, Spark 2.3.0-SNAPSHOT), and attempted to perform a Gradient Boosted Tree (GBT) Regression on a dataframe which contains 2658 columns. I'm still getting the same constant pool exceeding JVM 0xFFFF error. The steps that I'm using to generate the error are:
{code:scala}
// Imports
import org.apache.spark.sql.SparkSession
import org.apache.spark.sql.functions._
import org.apache.spark.ml.regression.{GBTRegressionModel, GBTRegressor}
import org.apache.spark.ml.feature.{VectorAssembler, Imputer, ImputerModel}
import org.apache.spark.ml.linalg.Vectors
import org.apache.spark.ml.Pipeline
import org.apache.spark.ml.evaluation.RegressionEvaluator
import org.apache.spark.ml.tuning.{ParamGridBuilder, CrossValidator}
import org.apache.spark.sql.{SQLContext, Row, DataFrame, Column}
// Load data
val mainDF = spark.read.parquet("/path/to/data/input/main_pqt").repartition(1024)
// Impute data
val inColsMain = mainDF.columns.filter(_ != "ID").filter(_ != "SCORE")
val outColsMain = inColsMain.map(i=>(i+"_imputed"))
val mainImputer = new Imputer().setInputCols(inColsMain).setOutputCols(outColsMain).setStrategy("mean")
val mainImputerBuild = mainImputer.fit(mainDF)
val imputedMainDF = mainImputerBuild.transform(mainDF)
// Drop original feature columns, retain imputed
val fullData = imputedMainDF.select(imputedMainDF.columns.filter(colName => !inColsMain.contains(colName)).map(colName => new Column(colName)): _*)
// Split data for testing & training
val Array(trainDF, testDF) = fullData.randomSplit(Array(0.80, 0.20),seed = 12345)
// Vector Assembler
val arrayName = fullData.columns.filter(_ != "ID").filter(_ != "SCORE")
val assembler = new VectorAssembler().setInputCols(arrayName).setOutputCol("features")
// GBT Object
val gbt = new GBTRegressor().setLabelCol("SCORE").setFeaturesCol("features").setMaxIter(5).setSeed(1993).setLossType("squared").setSubsamplingRate(1)
// Pipeline Object
val pipeline = new Pipeline().setStages(Array(assembler, gbt))
// Hyper Parameter Grid Object
val paramGrid = new ParamGridBuilder().addGrid(gbt.maxBins, Array(2, 4, 8)).addGrid(gbt.maxDepth, Array(1, 2, 4)).addGrid(gbt.stepSize, Array(0.1, 0.2)).build()
// Evaluator Object
val evaluator = new RegressionEvaluator().setLabelCol("SCORE").setPredictionCol("prediction").setMetricName("rmse")
// Cross Validation Object
val cv = new CrossValidator().setEstimator(pipeline).setEvaluator(evaluator).setEstimatorParamMaps(paramGrid).setNumFolds(5)
// Build the model
val gbtModel = cv.fit(trainDF)
{code}
Upon building the model, it errors out with the following cause:
{code:java}
java.lang.RuntimeException: Error while encoding: org.codehaus.janino.JaninoRuntimeException: failed to compile: org.codehaus.janino.JaninoRuntimeException: Constant pool for class org.apache.spark.sql.catalyst.expressions.GeneratedClass$SpecificUnsafeProjection$NestedClass2 has grown past JVM limit of 0xFFFF
{code}
> Code Generation: Constant Pool Past Limit for Wide/Nested Dataset
> -----------------------------------------------------------------
>
> Key: SPARK-18016
> URL: https://issues.apache.org/jira/browse/SPARK-18016
> Project: Spark
> Issue Type: Bug
> Components: SQL
> Affects Versions: 2.1.0
> Reporter: Aleksander Eskilson
> Assignee: Aleksander Eskilson
> Fix For: 2.3.0
>
>
> When attempting to encode collections of large Java objects to Datasets having very wide or deeply nested schemas, code generation can fail, yielding:
> {code}
> Caused by: org.codehaus.janino.JaninoRuntimeException: Constant pool for class org.apache.spark.sql.catalyst.expressions.GeneratedClass$SpecificUnsafeProjection has grown past JVM limit of 0xFFFF
> at org.codehaus.janino.util.ClassFile.addToConstantPool(ClassFile.java:499)
> at org.codehaus.janino.util.ClassFile.addConstantNameAndTypeInfo(ClassFile.java:439)
> at org.codehaus.janino.util.ClassFile.addConstantMethodrefInfo(ClassFile.java:358)
> at org.codehaus.janino.UnitCompiler.writeConstantMethodrefInfo(UnitCompiler.java:11114)
> at org.codehaus.janino.UnitCompiler.compileGet2(UnitCompiler.java:4547)
> at org.codehaus.janino.UnitCompiler.access$7500(UnitCompiler.java:206)
> at org.codehaus.janino.UnitCompiler$12.visitMethodInvocation(UnitCompiler.java:3774)
> at org.codehaus.janino.UnitCompiler$12.visitMethodInvocation(UnitCompiler.java:3762)
> at org.codehaus.janino.Java$MethodInvocation.accept(Java.java:4328)
> at org.codehaus.janino.UnitCompiler.compileGet(UnitCompiler.java:3762)
> at org.codehaus.janino.UnitCompiler.compileGetValue(UnitCompiler.java:4933)
> at org.codehaus.janino.UnitCompiler.compile2(UnitCompiler.java:3180)
> at org.codehaus.janino.UnitCompiler.access$5000(UnitCompiler.java:206)
> at org.codehaus.janino.UnitCompiler$9.visitMethodInvocation(UnitCompiler.java:3151)
> at org.codehaus.janino.UnitCompiler$9.visitMethodInvocation(UnitCompiler.java:3139)
> at org.codehaus.janino.Java$MethodInvocation.accept(Java.java:4328)
> at org.codehaus.janino.UnitCompiler.compile(UnitCompiler.java:3139)
> at org.codehaus.janino.UnitCompiler.compile2(UnitCompiler.java:2112)
> at org.codehaus.janino.UnitCompiler.access$1700(UnitCompiler.java:206)
> at org.codehaus.janino.UnitCompiler$6.visitExpressionStatement(UnitCompiler.java:1377)
> at org.codehaus.janino.UnitCompiler$6.visitExpressionStatement(UnitCompiler.java:1370)
> at org.codehaus.janino.Java$ExpressionStatement.accept(Java.java:2558)
> at org.codehaus.janino.UnitCompiler.compile(UnitCompiler.java:1370)
> at org.codehaus.janino.UnitCompiler.compileStatements(UnitCompiler.java:1450)
> at org.codehaus.janino.UnitCompiler.compile(UnitCompiler.java:2811)
> at org.codehaus.janino.UnitCompiler.compileDeclaredMethods(UnitCompiler.java:1262)
> at org.codehaus.janino.UnitCompiler.compileDeclaredMethods(UnitCompiler.java:1234)
> at org.codehaus.janino.UnitCompiler.compile2(UnitCompiler.java:538)
> at org.codehaus.janino.UnitCompiler.compile2(UnitCompiler.java:890)
> at org.codehaus.janino.UnitCompiler.compile2(UnitCompiler.java:894)
> at org.codehaus.janino.UnitCompiler.access$600(UnitCompiler.java:206)
> at org.codehaus.janino.UnitCompiler$2.visitMemberClassDeclaration(UnitCompiler.java:377)
> at org.codehaus.janino.UnitCompiler$2.visitMemberClassDeclaration(UnitCompiler.java:369)
> at org.codehaus.janino.Java$MemberClassDeclaration.accept(Java.java:1128)
> at org.codehaus.janino.UnitCompiler.compile(UnitCompiler.java:369)
> at org.codehaus.janino.UnitCompiler.compileDeclaredMemberTypes(UnitCompiler.java:1209)
> at org.codehaus.janino.UnitCompiler.compile2(UnitCompiler.java:564)
> at org.codehaus.janino.UnitCompiler.compile2(UnitCompiler.java:420)
> at org.codehaus.janino.UnitCompiler.access$400(UnitCompiler.java:206)
> at org.codehaus.janino.UnitCompiler$2.visitPackageMemberClassDeclaration(UnitCompiler.java:374)
> at org.codehaus.janino.UnitCompiler$2.visitPackageMemberClassDeclaration(UnitCompiler.java:369)
> at org.codehaus.janino.Java$AbstractPackageMemberClassDeclaration.accept(Java.java:1309)
> at org.codehaus.janino.UnitCompiler.compile(UnitCompiler.java:369)
> at org.codehaus.janino.UnitCompiler.compileUnit(UnitCompiler.java:345)
> at org.codehaus.janino.SimpleCompiler.compileToClassLoader(SimpleCompiler.java:396)
> at org.codehaus.janino.ClassBodyEvaluator.compileToClass(ClassBodyEvaluator.java:311)
> at org.codehaus.janino.ClassBodyEvaluator.cook(ClassBodyEvaluator.java:229)
> at org.codehaus.janino.SimpleCompiler.cook(SimpleCompiler.java:196)
> at org.codehaus.commons.compiler.Cookable.cook(Cookable.java:91)
> at org.apache.spark.sql.catalyst.expressions.codegen.CodeGenerator$.org$apache$spark$sql$catalyst$expressions$codegen$CodeGenerator$$doCompile(CodeGenerator.scala:905)
> ... 35 more
> {code}
> During generation of the code for SpecificUnsafeProjection, all the mutable variables are declared up front. If there are too many, it seems it perhaps exceeds some type of resource limit.
> This issue seems related to (but is not fixed by) SPARK-17702, which itself was about the size of individual methods growing beyond the 64 KB limit. SPARK-17702 was resolved by breaking extractions into smaller methods, but does not seem to have resolved this issue.
> I've created a small project [1] where I declare a list of "wide" and "nested" Bean objects that I attempt to encode to a Dataset. This code can trigger the failure for Spark 2.1.0-SNAPSHOT.
> [1] - https://github.com/bdrillard/spark-codegen-error
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