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https://issues.apache.org/jira/browse/FLINK-13738?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Robert Metzger updated FLINK-13738:
-----------------------------------
Issue Type: Bug (was: Task)
> NegativeArraySizeException in LongHybridHashTable
> -------------------------------------------------
>
> Key: FLINK-13738
> URL: https://issues.apache.org/jira/browse/FLINK-13738
> Project: Flink
> Issue Type: Bug
> Components: Table SQL / Runtime
> Affects Versions: 1.9.0
> Reporter: Robert Metzger
> Priority: Major
>
> Executing this (meaningless) query:
> {code:java}
> INSERT INTO sinkTable ( SELECT CONCAT( CAST( id AS VARCHAR), CAST( COUNT(*)
> AS VARCHAR)) as something, 'const' FROM CsvTable, table1 WHERE sometxt LIKE
> 'a%' AND id = key GROUP BY id ) {code}
> leads to the following exception:
> {code:java}
> Caused by: java.lang.NegativeArraySizeException
> at
> org.apache.flink.table.runtime.hashtable.LongHybridHashTable.tryDenseMode(LongHybridHashTable.java:216)
> at
> org.apache.flink.table.runtime.hashtable.LongHybridHashTable.endBuild(LongHybridHashTable.java:105)
> at LongHashJoinOperator$36.endInput1$(Unknown Source)
> at LongHashJoinOperator$36.endInput(Unknown Source)
> at
> org.apache.flink.streaming.runtime.tasks.OperatorChain.endInput(OperatorChain.java:256)
> at
> org.apache.flink.streaming.runtime.io.StreamTwoInputSelectableProcessor.checkFinished(StreamTwoInputSelectableProcessor.java:359)
> at
> org.apache.flink.streaming.runtime.io.StreamTwoInputSelectableProcessor.processInput(StreamTwoInputSelectableProcessor.java:193)
> at
> org.apache.flink.streaming.runtime.tasks.StreamTask.performDefaultAction(StreamTask.java:276)
> at
> org.apache.flink.streaming.runtime.tasks.StreamTask.run(StreamTask.java:298)
> at
> org.apache.flink.streaming.runtime.tasks.StreamTask.invoke(StreamTask.java:403)
> at org.apache.flink.runtime.taskmanager.Task.doRun(Task.java:687)
> at org.apache.flink.runtime.taskmanager.Task.run(Task.java:517)
> at java.lang.Thread.run(Thread.java:748){code}
> This is the plan:
>
> {code:java}
> == Abstract Syntax Tree ==
> LogicalSink(name=[sinkTable], fields=[f0, f1])
> +- LogicalProject(something=[CONCAT(CAST($0):VARCHAR(2147483647) CHARACTER
> SET "UTF-16LE", CAST($1):VARCHAR(2147483647) CHARACTER SET "UTF-16LE" NOT
> NULL)], EXPR$1=[_UTF-16LE'const'])
> +- LogicalAggregate(group=[{0}], agg#0=[COUNT()])
> +- LogicalProject(id=[$1])
> +- LogicalFilter(condition=[AND(LIKE($0, _UTF-16LE'a%'), =($1,
> CAST($2):BIGINT))])
> +- LogicalJoin(condition=[true], joinType=[inner])
> :- LogicalTableScan(table=[[default_catalog, default_database,
> CsvTable, source: [CsvTableSource(read fields: sometxt, id)]]])
> +- LogicalTableScan(table=[[default_catalog, default_database,
> table1, source: [GeneratorTableSource(key, rowtime, payload)]]])
> == Optimized Logical Plan ==
> Sink(name=[sinkTable], fields=[f0, f1]): rowcount = 1498810.6659336376,
> cumulative cost = {4.459964319978008E8 rows, 1.879799762133187E10 cpu, 4.8E9
> io, 8.4E8 network, 1.799524266373455E8 memory}
> +- Calc(select=[CONCAT(CAST(id), CAST($f1)) AS something, _UTF-16LE'const' AS
> EXPR$1]): rowcount = 1498810.6659336376, cumulative cost =
> {4.444976213318672E8 rows, 1.8796498810665936E10 cpu, 4.8E9 io, 8.4E8
> network, 1.799524266373455E8 memory}
> +- HashAggregate(isMerge=[false], groupBy=[id], select=[id, COUNT(*) AS
> $f1]): rowcount = 1498810.6659336376, cumulative cost = {4.429988106659336E8
> rows, 1.8795E10 cpu, 4.8E9 io, 8.4E8 network, 1.799524266373455E8 memory}
> +- Calc(select=[id]): rowcount = 1.575E7, cumulative cost = {4.415E8
> rows, 1.848E10 cpu, 4.8E9 io, 8.4E8 network, 1.2E8 memory}
> +- HashJoin(joinType=[InnerJoin], where=[=(id, key0)], select=[id,
> key0], build=[left]): rowcount = 1.575E7, cumulative cost = {4.2575E8 rows,
> 1.848E10 cpu, 4.8E9 io, 8.4E8 network, 1.2E8 memory}
> :- Exchange(distribution=[hash[id]]): rowcount = 5000000.0,
> cumulative cost = {1.1E8 rows, 8.4E8 cpu, 2.0E9 io, 4.0E7 network, 0.0 memory}
> : +- Calc(select=[id], where=[LIKE(sometxt, _UTF-16LE'a%')]):
> rowcount = 5000000.0, cumulative cost = {1.05E8 rows, 0.0 cpu, 2.0E9 io, 0.0
> network, 0.0 memory}
> : +- TableSourceScan(table=[[default_catalog,
> default_database, CsvTable, source: [CsvTableSource(read fields: sometxt,
> id)]]], fields=[sometxt, id]): rowcount = 1.0E8, cumulative cost = {1.0E8
> rows, 0.0 cpu, 2.0E9 io, 0.0 network, 0.0 memory}
> +- Exchange(distribution=[hash[key0]]): rowcount = 1.0E8,
> cumulative cost = {3.0E8 rows, 1.68E10 cpu, 2.8E9 io, 8.0E8 network, 0.0
> memory}
> +- Calc(select=[CAST(key) AS key0]): rowcount = 1.0E8,
> cumulative cost = {2.0E8 rows, 0.0 cpu, 2.8E9 io, 0.0 network, 0.0 memory}
> +- TableSourceScan(table=[[default_catalog,
> default_database, table1, source: [GeneratorTableSource(key, rowtime,
> payload)]]], fields=[key, rowtime, payload]): rowcount = 1.0E8, cumulative
> cost = {1.0E8 rows, 0.0 cpu, 2.8E9 io, 0.0 network, 0.0 memory}
> == Physical Execution Plan ==
> Stage 1 : Data Source
> content : collect elements with CollectionInputFormat
> Stage 2 : Operator
> content : CsvTableSource(read fields: sometxt, id)
> ship_strategy : REBALANCE
> Stage 3 : Operator
> content :
> SourceConversion(table=[default_catalog.default_database.CsvTable, source:
> [CsvTableSource(read fields: sometxt, id)]], fields=[sometxt, id])
> ship_strategy : FORWARD
> Stage 4 : Operator
> content : Calc(select=[id], where=[(sometxt
> LIKE _UTF-16LE'a%')])
> ship_strategy : FORWARD
> Stage 6 : Data Source
> content : collect elements with CollectionInputFormat
> Stage 7 : Operator
> content :
> SourceConversion(table=[default_catalog.default_database.table1, source:
> [GeneratorTableSource(key, rowtime, payload)]], fields=[key, rowtime,
> payload])
> ship_strategy : FORWARD
> Stage 8 : Operator
> content : Calc(select=[CAST(key) AS key0])
> ship_strategy : FORWARD
> Stage 10 : Operator
> content : HashJoin(joinType=[InnerJoin],
> where=[(id = key0)], select=[id, key0], build=[left])
> ship_strategy : HASH[id]
> Stage 11 : Operator
> content : Calc(select=[id])
> ship_strategy : FORWARD
> Stage 12 : Operator
> content :
> HashAggregate(isMerge=[false], groupBy=[id], select=[id, COUNT(*) AS $f1])
> ship_strategy : FORWARD
> Stage 13 : Operator
> content :
> Calc(select=[(CAST(id) CONCAT CAST($f1)) AS something, _UTF-16LE'const' AS
> EXPR$1])
> ship_strategy : FORWARD
> Stage 14 : Operator
> content :
> SinkConversionToRow
> ship_strategy :
> FORWARD
> Stage 15 :
> Operator
> content
> : Map
>
> ship_strategy : FORWARD
> Stage
> 16 : Data Sink
>
> content : Sink: CsvTableSink(f0, f1)
>
> ship_strategy : FORWARD
> {code}
>
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