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https://issues.apache.org/jira/browse/PIG-5029?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15500402#comment-15500402
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liyunzhang_intel commented on PIG-5029:
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[~knoguchi]: Thanks for your reply.
Here is a question about the example you provided in PIG-3257.
{code}
A = load ...
B = group A by UUID();
C = foreach B...
{code}
{quote}This job could successfully finish with output ranging from 0 to 2n
records.
For example, sequence of events can be,
mapper0_attempt0 finish with n outputs and say all n uuid keys were assigned to
reducer0.
reducer0_attempt0 pulls map outputs and produces n outputs.
reducer1_attempt0 tries to pull mapper0_attempt0 output and fail. (could be
fetch failure or node failure).
mapper0_attempt1 rerun. And this time, all n uuid keys were assigned to
reducer1.
reducer1_attempt0 pulls mapper0_attempt1 output and produces n outputs.
job finish successfully with 2n outputs.
This is certainly unexpected to users.
{quote}
My question is:
1. reducer0_attempt0 and reduce1_attempt0 both pull map outpout and ready to
produce n outputs because of speculative mechanism?
2. If because of speculative mechanism, after reduce0_attempt0 finishes to
produce n output, hadoop will cancel reduce1_attempt0 because reduce0_attempt0
success) so there is no possibility to generate 2n outputs.
> Optimize sort case when data is skewed
> --------------------------------------
>
> Key: PIG-5029
> URL: https://issues.apache.org/jira/browse/PIG-5029
> Project: Pig
> Issue Type: Sub-task
> Components: spark
> Reporter: liyunzhang_intel
> Assignee: liyunzhang_intel
> Fix For: spark-branch
>
> Attachments: PIG-5029.patch, SkewedData_L9.docx
>
>
> In PigMix L9.pig
> {code}
> register $PIGMIX_JAR
> A = load '$HDFS_ROOT/page_views' using
> org.apache.pig.test.pigmix.udf.PigPerformanceLoader()
> as (user, action, timespent, query_term, ip_addr, timestamp,
> estimated_revenue, page_info, page_links);
> B = order A by query_term parallel $PARALLEL;
> store B into '$PIGMIX_OUTPUT/L9out';
> {code}
> The pig physical plan will be changed to spark plan and to spark lineage:
> {code}
> [main] 2016-09-08 01:49:09,844 DEBUG converter.StoreConverter
> (StoreConverter.java:convert(110)) - RDD lineage: (23) MapPartitionsRDD[8] at
> map at StoreConverter.java:80 []
> | MapPartitionsRDD[7] at mapPartitions at SortConverter.java:58 []
> | ShuffledRDD[6] at sortByKey at SortConverter.java:56 []
> +-(23) MapPartitionsRDD[3] at map at SortConverter.java:49 []
> | MapPartitionsRDD[2] at mapPartitions at ForEachConverter.java:64 []
> | MapPartitionsRDD[1] at map at LoadConverter.java:127 []
> | NewHadoopRDD[0] at newAPIHadoopRDD at LoadConverter.java:102 []
> {code}
> We use
> [sortByKey|https://github.com/apache/pig/blob/spark/src/org/apache/pig/backend/hadoop/executionengine/spark/converter/SortConverter.java#L56]
> to implement the sort feature. Although
> [RangePartitioner|https://github.com/apache/spark/blob/d6dc12ef0146ae409834c78737c116050961f350/core/src/main/scala/org/apache/spark/Partitioner.scala#L106]
> is used by RDD.sortByKey and RangePartitiner will sample data and ranges the
> key roughly into equal range, the test result(attached document) shows that
> one partition will load most keys and take long time to finish.
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