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Apache Spark代码生成阶段超过64 KB

apache-sparkpysparkcodegenjanino
24

当我在对30多列进行特征工程以创建约200多列时，出现了错误。它没有使作业失败，但是会显示ERROR。 我想知道如何避免这种情况。

Spark-2.3.1 Python-3.6

集群配置-1主控节点-32 GB RAM，16个内核，4个从节点-16 GB RAM，8个内核

输入数据-具有snappy压缩的parquet文件的8个分区。

我的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

以下是堆栈跟踪：

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
1
你正在进行什么类型的操作？ - Kaushal
1
spark.conf.set("spark.sql.codegen.wholeStage", false) 尝试使用这个属性。 - Kaushal
5个回答
21

问题在于，使用DataFrame和Dataset编写的程序通过Catalyst生成的Java程序编译成Java字节码时，一个方法的字节码大小不能达到64 KB或更多。这与Java类文件的限制冲突，会导致异常错误。

隐藏错误：

spark.sql.codegen.wholeStage= "false"

解决方法：

为了避免Spark中出现由于上述限制而导致异常的情况，解决方案是在Catalyst生成Java程序时将编译并生成可能超过64 KB的Java字节码的方法拆分成多个方法。

在管道中使用persist或任何其他逻辑分隔。

出现相同的错误，但persist/cache对我有效。 - Minh Thai
Spark 2.3.2版本中是否可用此配置？ - Sreenath Chothar
3
修改这个配置会影响性能，因为它会禁用一些底层的优化，详细解释请参考这里。 - mirkhosro
6
如果您正在使用pyspark 2.3+版本，请尝试以下方法：
spark = SparkSession.builder.master('local').appName('tow-way')\
        .config('spark.sql.codegen.wholeStage', 'false')\ ## <-- add this line
        .getOrCreate()
3

正如vaquar所述，引入管道中的逻辑分离应该有所帮助。

一种切断血统并在计划中引入中断的方法似乎是进行DF -> RDD -> DF往返转换：

df = spark_session.sparkContext.createDataFrame(df.rdd, schema=df.schema)

在书籍《高性能Spark》中，它进一步提到使用底层的Java RDD更好（更快）地完成此操作，即使用 j_rdd = df._jdf.toJavaRDD() 和其模式 j_schema = df._jdf.schema() 构建一个新的Java DataFrame，最后将其转换回PySpark DataFrame。请保留HTML标记。
sql_ctx = df.sql_ctx
java_sql_context = sql_ctx._jsqlContext
new_java_df = java_sql_context.createDataFrame(j_rdd, j_schema)
new_df = DataFrame(new_java_df, sql_ctx)
3
0
我们遇到了一个使用案例，需要从HBase中读取约10K个带有前缀过滤器的键。
这个错误是因为我们需要生成10K个startWith()条件，并在它们之间使用OR，而生成的代码超过了64K。
最终的解决方案是为每个数据框生成一组键，并合并结果。
这将生成的代码分成了更小的部分。
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