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https://issues.apache.org/jira/browse/SPARK-11330?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Davies Liu closed SPARK-11330.
------------------------------
Resolution: Duplicate
Assignee: Davies Liu
Fix Version/s: 1.5.1
1.6.0
> Filter operation on StringType after groupBy PERSISTED brings no results
> ------------------------------------------------------------------------
>
> Key: SPARK-11330
> URL: https://issues.apache.org/jira/browse/SPARK-11330
> Project: Spark
> Issue Type: Bug
> Components: Spark Core
> Affects Versions: 1.5.1
> Environment: Stand alone Cluster of five servers (happens as well in
> local mode). sqlContext instance of HiveContext (happens as well with
> SQLContext)
> No special options other than driver memory and executor memory.
> Parquet partitions are 512 where there are 160 cores. Happens as well with
> other partitioning
> Data is nearly 2 billion rows.
> Reporter: Saif Addin Ellafi
> Assignee: Davies Liu
> Priority: Blocker
> Fix For: 1.6.0, 1.5.1
>
> Attachments: bug_reproduce.zip, bug_reproduce_50k.zip
>
>
> ONLY HAPPENS WHEN PERSIST() IS CALLED
> val data = sqlContext.read.parquet("/var/data/Saif/ppnr_pqt")
> data.groupBy("vintages").count.select("vintages").filter("vintages =
> '2007-01-01'").first
> >>> res9: org.apache.spark.sql.Row = [2007-01-01]
> data.groupBy("vintages").count.persist.select("vintages").filter("vintages =
> '2007-01-01'").first
> >>> Exception on empty iterator stuff
> This does not happen if using another type of field, eg IntType
> data.groupBy("yyyymm").count.persist.select("yyyymm").filter("yyyymm =
> 200805").first >>> res13: org.apache.spark.sql.Row = [200805]
> NOTE: I have no idea whether I used KRYO serializer when stored this parquet.
> NOTE2: If setting the persist after the filtering, it works fine. But this is
> not a good enough workaround since any filter operation afterwards will break
> results.
> NOTE3: I have reproduced the issue with several different datasets.
> NOTE4: The only real workaround is to store the groupBy dataframe in database
> and reload it as a new dataframe.
> Query to raw-data works fine:
> data.select("vintages").filter("vintages = '2007-01-01'").first >>> res4:
> org.apache.spark.sql.Row = [2007-01-01]
> Originally, the issue happened with a larger aggregation operation, the
> result was that data was inconsistent bringing different results every call.
> Reducing the operation step by step, I got into this issue.
> In any case, the original operation was:
> val data = sqlContext.read.parquet("/var/Saif/data_pqt")
> val res = data.groupBy("product", "band", "age", "vint", "mb",
> "yyyymm").agg(count($"account_id").as("N"),
> sum($"balance").as("balance_eom"), sum($"balancec").as("balance_eoc"),
> sum($"spend").as("spend"), sum($"payment").as("payment"),
> sum($"feoc").as("feoc"), sum($"cfintbal").as("cfintbal"), count($"newacct"
> === 1).as("newacct")).persist()
> val z = res.select("vint", "yyyymm").filter("vint =
> '2007-01-01'").select("yyyymm").distinct.collect
> z.length
> >>> res0: Int = 102
> res.unpersist()
> val z = res.select("vint", "yyyymm").filter("vint =
> '2007-01-01'").select("yyyymm").distinct.collect
> z.length
> >>> res1: Int = 103
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