Hi, I already went through it, that's one use case. I've a complex and very big pipeline of multiple jobs under one spark session. Not getting, on how to solve this, as it is happening over Logistic Regression and Random Forest models, which I'm just using from Spark ML package rather than doing anything by myself.
Thanks, Aakash. On Sun 17 Jun, 2018, 8:21 AM vaquar khan, <[email protected]> wrote: > Hi Akash, > > Please check stackoverflow. > > > https://stackoverflow.com/questions/41098953/codegen-grows-beyond-64-kb-error-when-normalizing-large-pyspark-dataframe > > Regards, > Vaquar khan > > On Sat, Jun 16, 2018 at 3:27 PM, Aakash Basu <[email protected]> > wrote: > >> Hi guys, >> >> I'm getting an error when I'm feature engineering on 30+ columns to >> create about 200+ columns. It is not failing the job, but the ERROR shows. >> I want to know how can I avoid this. >> >> Spark - 2.3.1 >> Python - 3.6 >> >> Cluster Config - >> 1 Master - 32 GB RAM, 16 Cores >> 4 Slaves - 16 GB RAM, 8 Cores >> >> >> Input data - 8 partitions of parquet file with snappy compression. >> >> My Spark-Submit -> spark-submit --master spark://192.168.60.20:7077 >> --num-executors 4 --executor-cores 5 --executor-memory 10G --driver-cores 5 >> --driver-memory 25G --conf spark.sql.shuffle.partitions=60 --conf >> spark.driver.maxResultSize=2G --conf >> "spark.executor.extraJavaOptions=-XX:+UseParallelGC" --conf >> spark.scheduler.listenerbus.eventqueue.capacity=20000 --conf >> spark.sql.codegen=true /appdata/bblite-codebase/pipeline_data_test_run.py > >> /appdata/bblite-data/logs/log_10_iter_pipeline_8_partitions_33_col.txt >> >> Stack-Trace below - >> >> ERROR CodeGenerator:91 - failed to compile: >>> org.codehaus.janino.InternalCompilerException: Compiling "GeneratedClass": >>> Code of method "processNext()V" of class >>> "org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage3426" >>> grows beyond 64 KB >>> org.codehaus.janino.InternalCompilerException: Compiling >>> "GeneratedClass": Code of method "processNext()V" of class >>> "org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage3426" >>> grows beyond 64 KB >>> at >>> org.codehaus.janino.UnitCompiler.compileUnit(UnitCompiler.java:361) >>> at org.codehaus.janino.SimpleCompiler.cook(SimpleCompiler.java:234) >>> at >>> org.codehaus.janino.SimpleCompiler.compileToClassLoader(SimpleCompiler.java:446) >>> at >>> org.codehaus.janino.ClassBodyEvaluator.compileToClass(ClassBodyEvaluator.java:313) >>> at >>> org.codehaus.janino.ClassBodyEvaluator.cook(ClassBodyEvaluator.java:235) >>> at org.codehaus.janino.SimpleCompiler.cook(SimpleCompiler.java:204) >>> at org.codehaus.commons.compiler.Cookable.cook(Cookable.java:80) >>> at >>> org.apache.spark.sql.catalyst.expressions.codegen.CodeGenerator$.org$apache$spark$sql$catalyst$expressions$codegen$CodeGenerator$$doCompile(CodeGenerator.scala:1417) >>> at >>> org.apache.spark.sql.catalyst.expressions.codegen.CodeGenerator$$anon$1.load(CodeGenerator.scala:1493) >>> at >>> org.apache.spark.sql.catalyst.expressions.codegen.CodeGenerator$$anon$1.load(CodeGenerator.scala:1490) >>> at >>> org.spark_project.guava.cache.LocalCache$LoadingValueReference.loadFuture(LocalCache.java:3599) >>> at >>> org.spark_project.guava.cache.LocalCache$Segment.loadSync(LocalCache.java:2379) >>> at >>> org.spark_project.guava.cache.LocalCache$Segment.lockedGetOrLoad(LocalCache.java:2342) >>> at >>> org.spark_project.guava.cache.LocalCache$Segment.get(LocalCache.java:2257) >>> at org.spark_project.guava.cache.LocalCache.get(LocalCache.java:4000) >>> at >>> org.spark_project.guava.cache.LocalCache.getOrLoad(LocalCache.java:4004) >>> at >>> org.spark_project.guava.cache.LocalCache$LocalLoadingCache.get(LocalCache.java:4874) >>> at >>> org.apache.spark.sql.catalyst.expressions.codegen.CodeGenerator$.compile(CodeGenerator.scala:1365) >>> at >>> org.apache.spark.sql.execution.WholeStageCodegenExec.liftedTree1$1(WholeStageCodegenExec.scala:579) >>> at >>> org.apache.spark.sql.execution.WholeStageCodegenExec.doExecute(WholeStageCodegenExec.scala:578) >>> at >>> org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:131) >>> at >>> org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:127) >>> at >>> org.apache.spark.sql.execution.SparkPlan$$anonfun$executeQuery$1.apply(SparkPlan.scala:155) >>> at >>> org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151) >>> at >>> org.apache.spark.sql.execution.SparkPlan.executeQuery(SparkPlan.scala:152) >>> at >>> org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:127) >>> at >>> org.apache.spark.sql.execution.exchange.ShuffleExchangeExec.prepareShuffleDependency(ShuffleExchangeExec.scala:92) >>> at >>> org.apache.spark.sql.execution.exchange.ShuffleExchangeExec$$anonfun$doExecute$1.apply(ShuffleExchangeExec.scala:128) >>> at >>> org.apache.spark.sql.execution.exchange.ShuffleExchangeExec$$anonfun$doExecute$1.apply(ShuffleExchangeExec.scala:119) >>> at >>> org.apache.spark.sql.catalyst.errors.package$.attachTree(package.scala:52) >>> at >>> org.apache.spark.sql.execution.exchange.ShuffleExchangeExec.doExecute(ShuffleExchangeExec.scala:119) >>> at >>> org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:131) >>> at >>> org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:127) >>> at >>> org.apache.spark.sql.execution.SparkPlan$$anonfun$executeQuery$1.apply(SparkPlan.scala:155) >>> at >>> org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151) >>> at >>> org.apache.spark.sql.execution.SparkPlan.executeQuery(SparkPlan.scala:152) >>> at >>> org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:127) >>> at >>> org.apache.spark.sql.execution.InputAdapter.inputRDDs(WholeStageCodegenExec.scala:371) >>> at >>> org.apache.spark.sql.execution.SortExec.inputRDDs(SortExec.scala:121) >>> at >>> org.apache.spark.sql.execution.WholeStageCodegenExec.doExecute(WholeStageCodegenExec.scala:605) >>> at >>> org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:131) >>> at >>> org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:127) >>> at >>> org.apache.spark.sql.execution.SparkPlan$$anonfun$executeQuery$1.apply(SparkPlan.scala:155) >>> at >>> org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151) >>> at >>> org.apache.spark.sql.execution.SparkPlan.executeQuery(SparkPlan.scala:152) >>> at >>> org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:127) >>> at >>> org.apache.spark.sql.execution.joins.SortMergeJoinExec.doExecute(SortMergeJoinExec.scala:150) >>> at >>> org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:131) >>> at >>> org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:127) >>> at >>> org.apache.spark.sql.execution.SparkPlan$$anonfun$executeQuery$1.apply(SparkPlan.scala:155) >>> at >>> org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151) >>> at >>> org.apache.spark.sql.execution.SparkPlan.executeQuery(SparkPlan.scala:152) >>> at >>> org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:127) >>> at >>> org.apache.spark.sql.execution.ProjectExec.doExecute(basicPhysicalOperators.scala:70) >>> at >>> org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:131) >>> at >>> org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:127) >>> at >>> org.apache.spark.sql.execution.SparkPlan$$anonfun$executeQuery$1.apply(SparkPlan.scala:155) >>> at >>> org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151) >>> at >>> org.apache.spark.sql.execution.SparkPlan.executeQuery(SparkPlan.scala:152) >>> at >>> org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:127) >>> at >>> org.apache.spark.sql.execution.joins.SortMergeJoinExec.doExecute(SortMergeJoinExec.scala:150) >>> at >>> org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:131) >>> at >>> org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:127) >>> at >>> org.apache.spark.sql.execution.SparkPlan$$anonfun$executeQuery$1.apply(SparkPlan.scala:155) >>> at >>> org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151) >>> at >>> org.apache.spark.sql.execution.SparkPlan.executeQuery(SparkPlan.scala:152) >>> at >>> org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:127) >>> at >>> org.apache.spark.sql.execution.ProjectExec.doExecute(basicPhysicalOperators.scala:70) >>> at >>> org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:131) >>> at >>> org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:127) >>> at >>> org.apache.spark.sql.execution.SparkPlan$$anonfun$executeQuery$1.apply(SparkPlan.scala:155) >>> at >>> org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151) >>> at >>> org.apache.spark.sql.execution.SparkPlan.executeQuery(SparkPlan.scala:152) >>> at >>> org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:127) >>> at >>> org.apache.spark.sql.execution.columnar.InMemoryRelation.buildBuffers(InMemoryRelation.scala:107) >>> at >>> org.apache.spark.sql.execution.columnar.InMemoryRelation.<init>(InMemoryRelation.scala:102) >>> at >>> org.apache.spark.sql.execution.columnar.InMemoryRelation$.apply(InMemoryRelation.scala:43) >>> at >>> org.apache.spark.sql.execution.CacheManager$$anonfun$cacheQuery$1.apply(CacheManager.scala:97) >>> at >>> org.apache.spark.sql.execution.CacheManager.writeLock(CacheManager.scala:67) >>> at >>> org.apache.spark.sql.execution.CacheManager.cacheQuery(CacheManager.scala:91) >>> at org.apache.spark.sql.Dataset.persist(Dataset.scala:2924) >>> at sun.reflect.GeneratedMethodAccessor78.invoke(Unknown Source) >>> at >>> sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43) >>> at java.lang.reflect.Method.invoke(Method.java:498) >>> at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244) >>> at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357) >>> at py4j.Gateway.invoke(Gateway.java:282) >>> at >>> py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132) >>> at py4j.commands.CallCommand.execute(CallCommand.java:79) >>> at py4j.GatewayConnection.run(GatewayConnection.java:238) >>> at java.lang.Thread.run(Thread.java:748) >>> Caused by: org.codehaus.janino.InternalCompilerException: Code of method >>> "processNext()V" of class >>> "org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage3426" >>> grows beyond 64 KB >>> >> >> Thanks, >> Aakash. >> > > > > -- > Regards, > Vaquar Khan > +1 -224-436-0783 > Greater Chicago >
