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Posted to issues@spark.apache.org by "Roberto Mirizzi (JIRA)" <ji...@apache.org> on 2016/12/19 23:29:58 UTC
[jira] [Comment Edited] (SPARK-18492) GeneratedIterator grows
beyond 64 KB
[ https://issues.apache.org/jira/browse/SPARK-18492?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15762628#comment-15762628 ]
Roberto Mirizzi edited comment on SPARK-18492 at 12/19/16 11:29 PM:
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I'm having exactly the same issue on Spark 2.0.2. I had it also on Spark 2.0.0 and Spark 2.0.1.
My exception is:
{{JaninoRuntimeException: Code of method "()V" of class "org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIterator" grows beyond 64 KB}}
It happens when I try to do many transformations on a given dataset, like multiple {{.select(...).select(...)}} with multiple operations inside the select (like {{when(...).otherwise(...)}}, or {{regexp_extract}}).
The exception outputs about 15k lines of Java code, like:
{code:java}
16/12/19 07:16:43 ERROR CodeGenerator: failed to compile: org.codehaus.janino.JaninoRuntimeException: Code of method "()V" of class "org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIterator" grows beyond 64 KB
/* 001 */ public Object generate(Object[] references) {
/* 002 */ return new GeneratedIterator(references);
/* 003 */ }
/* 004 */
/* 005 */ final class GeneratedIterator extends org.apache.spark.sql.execution.BufferedRowIterator {
/* 006 */ private Object[] references;
/* 007 */ private boolean agg_initAgg;
/* 008 */ private org.apache.spark.sql.execution.aggregate.HashAggregateExec agg_plan;
/* 009 */ private org.apache.spark.sql.execution.UnsafeFixedWidthAggregationMap agg_hashMap;
/* 010 */ private org.apache.spark.sql.execution.UnsafeKVExternalSorter agg_sorter;
/* 011 */ private org.apache.spark.unsafe.KVIterator agg_mapIter;
/* 012 */ private org.apache.spark.sql.execution.metric.SQLMetric agg_peakMemory;
/* 013 */ private org.apache.spark.sql.execution.metric.SQLMetric agg_spillSize;
/* 014 */ private scala.collection.Iterator inputadapter_input;
/* 015 */ private UnsafeRow agg_result;
/* 016 */ private org.apache.spark.sql.catalyst.expressions.codegen.BufferHolder agg_holder;
/* 017 */ private org.apache.spark.sql.catalyst.expressions.codegen.UnsafeRowWriter agg_rowWriter;
/* 018 */ private UTF8String agg_lastRegex;
{code}
However, it looks like the execution continues successfully.
was (Author: roberto.mirizzi):
I'm having exactly the same issue on Spark 2.0.2. I had it also on Spark 2.0.0 and Spark 2.0.1.
My exception is:
bq. JaninoRuntimeException: Code of method "()V" of class "org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIterator" grows beyond 64 KB
It happens when I try to do many transformations on a given dataset, like multiple {{.select(...).select(...)}} with multiple operations inside the select (like {{when(...).otherwise(...)}}, or {{regexp_extract}}).
The exception outputs about 15k lines of Java code, like:
{code:java}
16/12/19 07:16:43 ERROR CodeGenerator: failed to compile: org.codehaus.janino.JaninoRuntimeException: Code of method "()V" of class "org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIterator" grows beyond 64 KB
/* 001 */ public Object generate(Object[] references) {
/* 002 */ return new GeneratedIterator(references);
/* 003 */ }
/* 004 */
/* 005 */ final class GeneratedIterator extends org.apache.spark.sql.execution.BufferedRowIterator {
/* 006 */ private Object[] references;
/* 007 */ private boolean agg_initAgg;
/* 008 */ private org.apache.spark.sql.execution.aggregate.HashAggregateExec agg_plan;
/* 009 */ private org.apache.spark.sql.execution.UnsafeFixedWidthAggregationMap agg_hashMap;
/* 010 */ private org.apache.spark.sql.execution.UnsafeKVExternalSorter agg_sorter;
/* 011 */ private org.apache.spark.unsafe.KVIterator agg_mapIter;
/* 012 */ private org.apache.spark.sql.execution.metric.SQLMetric agg_peakMemory;
/* 013 */ private org.apache.spark.sql.execution.metric.SQLMetric agg_spillSize;
/* 014 */ private scala.collection.Iterator inputadapter_input;
/* 015 */ private UnsafeRow agg_result;
/* 016 */ private org.apache.spark.sql.catalyst.expressions.codegen.BufferHolder agg_holder;
/* 017 */ private org.apache.spark.sql.catalyst.expressions.codegen.UnsafeRowWriter agg_rowWriter;
/* 018 */ private UTF8String agg_lastRegex;
{code}
However, it looks like the execution continues successfully.
> GeneratedIterator grows beyond 64 KB
> ------------------------------------
>
> Key: SPARK-18492
> URL: https://issues.apache.org/jira/browse/SPARK-18492
> Project: Spark
> Issue Type: Bug
> Components: SQL
> Affects Versions: 2.0.1
> Environment: CentOS release 6.7 (Final)
> Reporter: Norris Merritt
>
> spark-submit fails with ERROR CodeGenerator: failed to compile: org.codehaus.janino.JaninoRuntimeException: Code of method "(I[Lscala/collection/Iterator;)V" of class "org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIterator" grows beyond 64 KB
> Error message is followed by a huge dump of generated source code.
> The generated code declares 1,454 field sequences like the following:
> /* 036 */ private org.apache.spark.sql.catalyst.expressions.ScalaUDF project_scalaUDF1;
> /* 037 */ private scala.Function1 project_catalystConverter1;
> /* 038 */ private scala.Function1 project_converter1;
> /* 039 */ private scala.Function1 project_converter2;
> /* 040 */ private scala.Function2 project_udf1;
> .... (many omitted lines) ...
> /* 6089 */ private org.apache.spark.sql.catalyst.expressions.ScalaUDF project_scalaUDF1454;
> /* 6090 */ private scala.Function1 project_catalystConverter1454;
> /* 6091 */ private scala.Function1 project_converter1695;
> /* 6092 */ private scala.Function1 project_udf1454;
> It then proceeds to emit code for several methods (init, processNext) each of which has totally repetitive sequences of statements pertaining to each of the sequences of variables declared in the class. For example:
> /* 6101 */ public void init(int index, scala.collection.Iterator inputs[]) {
> The reason that the 64KB JVM limit for code for a method is exceeded is because the code generator is using an incredibly naive strategy. It emits a sequence like the one shown below for each of the 1,454 groups of variables shown above, in
> /* 6132 */ this.project_udf = (scala.Function1)project_scalaUDF.userDefinedFunc();
> /* 6133 */ this.project_scalaUDF1 = (org.apache.spark.sql.catalyst.expressions.ScalaUDF) references[10];
> /* 6134 */ this.project_catalystConverter1 = (scala.Function1)org.apache.spark.sql.catalyst.CatalystTypeConverters$.MODULE$.createToCatalystConverter(project_scalaUDF1.dataType());
> /* 6135 */ this.project_converter1 = (scala.Function1)org.apache.spark.sql.catalyst.CatalystTypeConverters$.MODULE$.createToScalaConverter(((org.apache.spark.sql.catalyst.expressions.Expression)(((org.apache.spark.sql.catalyst.expressions.ScalaUDF)references[10]).getChildren().apply(0))).dataType());
> /* 6136 */ this.project_converter2 = (scala.Function1)org.apache.spark.sql.catalyst.CatalystTypeConverters$.MODULE$.createToScalaConverter(((org.apache.spark.sql.catalyst.expressions.Expression)(((org.apache.spark.sql.catalyst.expressions.ScalaUDF)references[10]).getChildren().apply(1))).dataType());
> It blows up after emitting 230 such sequences, while trying to emit the 231st:
> /* 7282 */ this.project_udf230 = (scala.Function2)project_scalaUDF230.userDefinedFunc();
> /* 7283 */ this.project_scalaUDF231 = (org.apache.spark.sql.catalyst.expressions.ScalaUDF) references[240];
> /* 7284 */ this.project_catalystConverter231 = (scala.Function1)org.apache.spark.sql.catalyst.CatalystTypeConverters$.MODULE$.createToCatalystConverter(project_scalaUDF231.dataType());
> .... many omitted lines ...
> Example of repetitive code sequences emitted for processNext method:
> /* 12253 */ boolean project_isNull247 = project_result244 == null;
> /* 12254 */ MapData project_value247 = null;
> /* 12255 */ if (!project_isNull247) {
> /* 12256 */ project_value247 = project_result244;
> /* 12257 */ }
> /* 12258 */ Object project_arg = sort_isNull5 ? null : project_converter489.apply(sort_value5);
> /* 12259 */
> /* 12260 */ ArrayData project_result249 = null;
> /* 12261 */ try {
> /* 12262 */ project_result249 = (ArrayData)project_catalystConverter248.apply(project_udf248.apply(project_arg));
> /* 12263 */ } catch (Exception e) {
> /* 12264 */ throw new org.apache.spark.SparkException(project_scalaUDF248.udfErrorMessage(), e);
> /* 12265 */ }
> /* 12266 */
> /* 12267 */ boolean project_isNull252 = project_result249 == null;
> /* 12268 */ ArrayData project_value252 = null;
> /* 12269 */ if (!project_isNull252) {
> /* 12270 */ project_value252 = project_result249;
> /* 12271 */ }
> /* 12272 */ Object project_arg1 = project_isNull252 ? null : project_converter488.apply(project_value252);
> /* 12273 */
> /* 12274 */ ArrayData project_result248 = null;
> /* 12275 */ try {
> /* 12276 */ project_result248 = (ArrayData)project_catalystConverter247.apply(project_udf247.apply(project_arg1));
> /* 12277 */ } catch (Exception e) {
> /* 12278 */ throw new org.apache.spark.SparkException(project_scalaUDF247.udfErrorMessage(), e);
> /* 12279 */ }
> /* 12280 */
> /* 12281 */ boolean project_isNull251 = project_result248 == null;
> /* 12282 */ ArrayData project_value251 = null;
> /* 12283 */ if (!project_isNull251) {
> /* 12284 */ project_value251 = project_result248;
> /* 12285 */ }
> /* 12286 */ Object project_arg2 = project_isNull251 ? null : project_converter487.apply(project_value251);
> /* 12287 */
> /* 12288 */ InternalRow project_result247 = null;
> /* 12289 */ try {
> /* 12290 */ project_result247 = (InternalRow)project_catalystConverter246.apply(project_udf246.apply(project_arg2));
> /* 12291 */ } catch (Exception e) {
> /* 12292 */ throw new org.apache.spark.SparkException(project_scalaUDF246.udfErrorMessage(), e);
> /* 12293 */ }
> /* 12294 */
> /* 12295 */ boolean project_isNull250 = project_result247 == null;
> /* 12296 */ InternalRow project_value250 = null;
> /* 12297 */ if (!project_isNull250) {
> /* 12298 */ project_value250 = project_result247;
> /* 12299 */ }
> /* 12300 */ Object project_arg3 = project_isNull250 ? null : project_converter486.apply(project_value250);
> /* 12301 */
> /* 12302 */ InternalRow project_result246 = null;
> /* 12303 */ try {
> /* 12304 */ project_result246 = (InternalRow)project_catalystConverter245.apply(project_udf245.apply(project_arg3));
> /* 12305 */ } catch (Exception e) {
> /* 12306 */ throw new org.apache.spark.SparkException(project_scalaUDF245.udfErrorMessage(), e);
> /* 12307 */ }
> /* 12308 */
> It is pretty clear that the code generation strategy is naive. The code generator should use arrays and loops instead of emitting all these repetitive code sequences which only differ by a few numerical digits used to generate the name of the variables.
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