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Posted to issues@flink.apache.org by "Timo Walther (JIRA)" <ji...@apache.org> on 2019/06/26 14:06:00 UTC
[jira] [Commented] (FLINK-12898) FlinkSQL job fails when rowTime
field encounters dirty data
[ https://issues.apache.org/jira/browse/FLINK-12898?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16873363#comment-16873363 ]
Timo Walther commented on FLINK-12898:
--------------------------------------
This seems like acceptable behavior to me. In those cases, you should implement a custom rowtime extractor ({{timestampsFromExtractor(TimestampExtractor extractor)}}) that might wrap the existing one and corrects those mistakes.
> FlinkSQL job fails when rowTime field encounters dirty data
> -----------------------------------------------------------
>
> Key: FLINK-12898
> URL: https://issues.apache.org/jira/browse/FLINK-12898
> Project: Flink
> Issue Type: Bug
> Components: Table SQL / Runtime
> Affects Versions: 1.7.2
> Reporter: Mai
> Assignee: Mai
> Priority: Minor
>
> I use FlinkSQL to process Kafka data in the following format：
>
> | id| server_time|
> | 1 |2019-05-15 10:00:00|
> | 2 |2019-05-15 10:00:00|
> .......
>
> and I define rowtime from the server_time field:
> new Schema()
> .field("rowtime", Types.SQL_TIMESTAMP)
> .rowtime(new Rowtime().timestampsFromField("server_time"))
> .field("id", Types.String)
> .field("server_time", Types.String)
>
> when dirty data arrives, such as :
> | id | server_time|
> | 99 |11.22.33.44 |
>
> My FlinkSQL job fails with exception：
> {code:java}
> java.lang.NumberFormatException: For input string: "11.22.33.44"
> at java.lang.NumberFormatException.forInputString(NumberFormatException.java:65)
> at java.lang.Integer.parseInt(Integer.java:580)
> at java.lang.Integer.parseInt(Integer.java:615)
> at org.apache.calcite.avatica.util.DateTimeUtils.dateStringToUnixDate(DateTimeUtils.java:625)
> at org.apache.calcite.avatica.util.DateTimeUtils.timestampStringToUnixDate(DateTimeUtils.java:715)
> at DataStreamSourceConversion$288.processElement(Unknown Source)
> at org.apache.flink.table.runtime.CRowOutputProcessRunner.processElement(CRowOutputProcessRunner.scala:70)
> at org.apache.flink.streaming.api.operators.ProcessOperator.processElement(ProcessOperator.java:66)
> at org.apache.flink.streaming.runtime.tasks.OperatorChain$CopyingChainingOutput.pushToOperator(OperatorChain.java:579)
> at org.apache.flink.streaming.runtime.tasks.OperatorChain$CopyingChainingOutput.collect(OperatorChain.java:554)
> at org.apache.flink.streaming.runtime.tasks.OperatorChain$CopyingChainingOutput.collect(OperatorChain.java:534)
> at org.apache.flink.streaming.api.operators.AbstractStreamOperator$CountingOutput.collect(AbstractStreamOperator.java:718)
> at org.apache.flink.streaming.api.operators.AbstractStreamOperator$CountingOutput.collect(AbstractStreamOperator.java:696)
> at org.apache.flink.streaming.api.operators.StreamSourceContexts$ManualWatermarkContext.processAndCollectWithTimestamp(StreamSourceContexts.java:310)
> at org.apache.flink.streaming.api.operators.StreamSourceContexts$WatermarkContext.collectWithTimestamp(StreamSourceContexts.java:409)
> at org.apache.flink.streaming.connectors.kafka.internals.AbstractFetcher.emitRecordWithTimestamp(AbstractFetcher.java:398)
> at org.apache.flink.streaming.connectors.kafka.internal.KafkaFetcher.emitRecord(KafkaFetcher.java:187)
> at org.apache.flink.streaming.connectors.kafka.internal.KafkaFetcher.runFetchLoop(KafkaFetcher.java:152)
> at org.apache.flink.streaming.connectors.kafka.FlinkKafkaConsumerBase.run(FlinkKafkaConsumerBase.java:665)
> at org.apache.flink.streaming.api.operators.StreamSource.run(StreamSource.java:94)
> at org.apache.flink.streaming.api.operators.StreamSource.run(StreamSource.java:58)
> at org.apache.flink.streaming.runtime.tasks.SourceStreamTask.run(SourceStreamTask.java:99)
> at org.apache.flink.streaming.runtime.tasks.StreamTask.invoke(StreamTask.java:300)
> at org.apache.flink.runtime.taskmanager.Task.run(Task.java:704)
> at java.lang.Thread.run(Thread.java:748){code}
>
> Because my flink job use EXACTLY_ONCE, so the job is re-executed from the last checkpoint, consumes dirty data again, fails again, and keeps looping like this.
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