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https://issues.apache.org/jira/browse/HIVE-18301?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16336946#comment-16336946
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liyunzhang edited comment on HIVE-18301 at 1/24/18 5:47 AM:
------------------------------------------------------------
In HIVE-18301.patch, it provides one solution to transfer the
{{IOContext::inputPath}}
{code:java}
inputRDD1 inputRDD2
|CopyFunction | CopyFunction
CopyRDD1 CopyRDD2
| |
MT_11 MT_12
| |
RT_1 RT_2
\ /
Union
{code}
MT_11 will call following stack to initialize IOContext::inputPath
{code:java}
CombineHiveRecordReader#init
->HiveContextAwareRecordReader.initIOContext
->IOContext.setInputPath
{code}
inputRDD1 and inputRDD2 are same table's rdd, so CopyRDD1 and CopyRDD2 are same
rdd if rdd cache is enabled. When MT_12 will not call
CombineHiveRecordReader#init to initialize {{IOContext::inputPath}} but
{{MapOperator#process(Writable value)}} still need this value. IOContext is
bound to single thread, so the value is different in different thread.
{{inputRDD1-CopyRDD1-MT_11-RT_1}} and {{inputRDD2-CopyRDD2-MT_12-RT_2}} is
called in different thread. So IOContext can not be shared between these two
threads.
For this issue, I gave following solution:
We save the inputPath in CopyRDD1 when {{inputRDD1-CopyRDD1-MT_11-RT_1}} is
executed. CopyRDD2 get the cached value and inputPath from CopyRDD1 which is
stored in spark cache manager. We reinitialized the {{IOContext::inputPath}} in
{{MapOperator#process(Writable value)}} in MT_12.
*where to setInputPath?*
MapInput#CopyFunction#call, save inputPath in the first element of returned
tuple
{code:java}
public MapInput(SparkPlan sparkPlan, JavaPairRDD<WritableComparable, Writable>
hadoopRDD) {
this(sparkPlan, hadoopRDD, false);
@@ -79,10 +83,19 @@ public void setToCache(boolean toCache) {
call(Tuple2<WritableComparable, Writable> tuple) throws Exception {
if (conf == null) {
conf = new Configuration();
+ conf.set("hive.execution.engine","spark");
}
-
- return new Tuple2<WritableComparable, Writable>(tuple._1(),
- WritableUtils.clone(tuple._2(), conf));
+ // CopyFunction MapFunction
+ // HADOOPRDD-----------------> RDD1-------------> RDD2.....
+ // these transformation are in one stage and will be executed by 1 spark
task(thread),
+ // IOContext.get(conf).getInputPath will not be null.
+ String inputPath = IOContextMap.get(conf).getInputPath().toString();
+ Text inputPathText = new Text(inputPath);
+ // save inputPath in the first element of returned tuple
+ // before we need not use tuple._1() in SparkMapRecordHandler#processRow
+ // so replace inputPathText with tuple._1().
+ return new Tuple2<WritableComparable, Writable>(inputPathText,
+ WritableUtils.clone(tuple._2(), conf));
}
}
{code}
*where to getInputPath?*
{code:java}
SparkMapRecordHandler#getInputPath
public void processRow(Object key, Object value) throws IOException {
....
+ if (HiveConf.getBoolVar(jc,
HiveConf.ConfVars.HIVE_SPARK_SHARED_WORK_OPTIMIZATION)) {
+ Path inputPath = IOContextMap.get(jc).getInputPath();
+ // when inputPath is null, it means the record is cached
+ if (inputPath == null) {
+ Text pathText = (Text) key;
+ IOContextMap.get(jc).setInputPath(new Path(pathText.toString()));
+ }
+ }
....
{code}
[~lirui], [~xuefuz], [~stakiar],[~csun], please give me your suggesions, thanks!
was (Author: kellyzly):
In HIVE-18301.patch, it provides one solution to transfer the
{{IOContext::inputPath}}
{code}
inputRDD1 inputRDD2
|CopyFunction | CopyFunction
CopyRDD1 CopyRDD2
| |
MT_11 MT_12
| |
RT_1 RT_2
\ /
Union
{code}
MT_11 will call following stack to initialize IOContext::inputPath
{code}
CombineHiveRecordReader#init
->HiveContextAwareRecordReader.initIOContext
->IOContext.setInputPath
{code}
inputRDD1 and inputRDD2 are same table's rdd, so CopyRDD1 and CopyRDD2 are same
rdd if rdd cache is enabled. When MT_12 will not call
CombineHiveRecordReader#init to initialize {{IOContext::inputPath}} but
{{MapOperator#process(Writable value)}} still need this value. IOContext is
bound to single thread, so the value is different in different thread.
{{inputRDD1-CopyRDD1-MT_11-RT_1}} and {{inputRDD2-CopyRDD2-MT_12-RT_2}} is
called in different thread. So IOContext can not be shared between these two
threads.
For this issue, I gave following solution:
We save the inputPath in CopyRDD1 when {{inputRDD1-CopyRDD1-MT_11-RT_1}} is
executed. CopyRDD2 get the cached value and inputPath from CopyRDD1 which is
stored in spark cache manager. We reinitialized the {{IOContext::inputPath}}
in {{MapOperator#process(Writable value)}} in MT_12.
*where to setInputPath?*
MapInput#CopyFunction#call, save inputPath in the first element of returned
tuple
{code}
public MapInput(SparkPlan sparkPlan, JavaPairRDD<WritableComparable, Writable>
hadoopRDD) {
this(sparkPlan, hadoopRDD, false);
@@ -79,10 +83,19 @@ public void setToCache(boolean toCache) {
call(Tuple2<WritableComparable, Writable> tuple) throws Exception {
if (conf == null) {
conf = new Configuration();
+ conf.set("hive.execution.engine","spark");
}
-
- return new Tuple2<WritableComparable, Writable>(tuple._1(),
- WritableUtils.clone(tuple._2(), conf));
+ // CopyFunction MapFunction
+ // HADOOPRDD-----------------> RDD1-------------> RDD2.....
+ // these transformation are in one stage and will be executed by 1 spark
task(thread),
+ // IOContext.get(conf).getInputPath will not be null.
+ String inputPath = IOContextMap.get(conf).getInputPath().toString();
+ Text inputPathText = new Text(inputPath);
+ // save inputPath in the first element of returned tuple
+ return new Tuple2<WritableComparable, Writable>(inputPathText,
+ WritableUtils.clone(tuple._2(), conf));
}
}
{code}
*where to getInputPath?*
{code}
SparkMapRecordHandler#getInputPath
public void processRow(Object key, Object value) throws IOException {
....
+ if (HiveConf.getBoolVar(jc,
HiveConf.ConfVars.HIVE_SPARK_SHARED_WORK_OPTIMIZATION)) {
+ Path inputPath = IOContextMap.get(jc).getInputPath();
+ // when inputPath is null, it means the record is cached
+ if (inputPath == null) {
+ Text pathText = (Text) key;
+ IOContextMap.get(jc).setInputPath(new Path(pathText.toString()));
+ }
+ }
....
{code}
[~lirui], [~xuefuz], [~stakiar],[~csun], please give me your suggesions, thanks!
> Investigate to enable MapInput cache in Hive on Spark
> -----------------------------------------------------
>
> Key: HIVE-18301
> URL: https://issues.apache.org/jira/browse/HIVE-18301
> Project: Hive
> Issue Type: Bug
> Reporter: liyunzhang
> Assignee: liyunzhang
> Priority: Major
> Attachments: HIVE-18301.patch
>
>
> Before IOContext problem is found in MapTran when spark rdd cache is enabled
> in HIVE-8920.
> so we disabled rdd cache in MapTran at
> [SparkPlanGenerator|https://github.com/kellyzly/hive/blob/master/ql/src/java/org/apache/hadoop/hive/ql/exec/spark/SparkPlanGenerator.java#L202].
> The problem is IOContext seems not initialized correctly in the spark yarn
> client/cluster mode and caused the exception like
> {code}
> Job aborted due to stage failure: Task 93 in stage 0.0 failed 4 times, most
> recent failure: Lost task 93.3 in stage 0.0 (TID 616, bdpe48):
> java.lang.RuntimeException: Error processing row:
> java.lang.NullPointerException
> at
> org.apache.hadoop.hive.ql.exec.spark.SparkMapRecordHandler.processRow(SparkMapRecordHandler.java:165)
> at
> org.apache.hadoop.hive.ql.exec.spark.HiveMapFunctionResultList.processNextRecord(HiveMapFunctionResultList.java:48)
> at
> org.apache.hadoop.hive.ql.exec.spark.HiveMapFunctionResultList.processNextRecord(HiveMapFunctionResultList.java:27)
> at
> org.apache.hadoop.hive.ql.exec.spark.HiveBaseFunctionResultList.hasNext(HiveBaseFunctionResultList.java:85)
> at
> scala.collection.convert.Wrappers$JIteratorWrapper.hasNext(Wrappers.scala:42)
> at
> org.apache.spark.shuffle.sort.BypassMergeSortShuffleWriter.write(BypassMergeSortShuffleWriter.java:125)
> at
> org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:79)
> at
> org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:47)
> at org.apache.spark.scheduler.Task.run(Task.scala:85)
> at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:274)
> at
> java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
> at
> java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
> at java.lang.Thread.run(Thread.java:745)
> Caused by: java.lang.NullPointerException
> at
> org.apache.hadoop.hive.ql.exec.AbstractMapOperator.getNominalPath(AbstractMapOperator.java:101)
> at
> org.apache.hadoop.hive.ql.exec.MapOperator.cleanUpInputFileChangedOp(MapOperator.java:516)
> at
> org.apache.hadoop.hive.ql.exec.Operator.cleanUpInputFileChanged(Operator.java:1187)
> at
> org.apache.hadoop.hive.ql.exec.MapOperator.process(MapOperator.java:546)
> at
> org.apache.hadoop.hive.ql.exec.spark.SparkMapRecordHandler.processRow(SparkMapRecordHandler.java:152)
> ... 12 more
> Driver stacktrace:
> {code}
> in yarn client/cluster mode, sometimes
> [ExecMapperContext#currentInputPath|https://github.com/kellyzly/hive/blob/master/ql/src/java/org/apache/hadoop/hive/ql/exec/mr/ExecMapperContext.java#L109]
> is null when rdd cach is enabled.
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