Re: Issue while consuming message in kafka using structured streaming
Hi Team,
I am facing this issue again.
I am using Spark 3.0.1 with Python.
Could you please suggest why it says the below error:
*Current Committed Offsets: {KafkaV2[Subscribe[my-topic]]:
{“my-topic”:{“1":1498,“0”:1410}}}Current Available Offsets:
{KafkaV2[Subscribe[my-topic]]: {“my-topic”:{“1”:1499,“0":1410}}}*
Kind Regards,
Sachit Murarka
On Fri, Mar 12, 2021 at 5:44 PM Gabor Somogyi
wrote:
> Please see that driver side for example resolved in 3.1.0...
>
> G
>
>
> On Fri, Mar 12, 2021 at 1:03 PM Sachit Murarka
> wrote:
>
>> Hi Gabor,
>>
>> Thanks a lot for the response. I am using Spark 3.0.1 and this is spark
>> structured streaming.
>>
>> Kind Regards,
>> Sachit Murarka
>>
>>
>> On Fri, Mar 12, 2021 at 5:30 PM Gabor Somogyi
>> wrote:
>>
>>> Since you've not provided any version I guess you're using 2.x and
>>> you're hitting this issue:
>>> https://issues.apache.org/jira/browse/SPARK-28367
>>> The executor side must be resolved out of the box in the latest Spark
>>> version however on driver side one must set "
>>> spark.sql.streaming.kafka.useDeprecatedOffsetFetching=false" to use the
>>> new way of fetching.
>>>
>>> If it doesn't solve your problem then Kafka side must be checked why
>>> it's not returning...
>>>
>>> Hope this helps!
>>>
>>> G
>>>
>>>
>>> On Fri, Mar 12, 2021 at 12:29 PM Sachit Murarka
>>> wrote:
>>>
Hi All,
I am getting following error in spark structured streaming while
connecting to Kakfa
Main issue from logs::
Caused by: org.apache.kafka.common.errors.TimeoutException: Timeout of
6ms expired before the position for partition my-topic-1 could be
determined
Current Committed Offsets: {KafkaV2[Subscribe[my-topic]]:
{“my-topic”:{“1":1498,“0”:1410}}}
Current Available Offsets: {KafkaV2[Subscribe[my-topic]]:
{“my-topic”:{“1”:1499,“0":1410}}}
Full logs::
21/03/12 11:04:35 ERROR TaskSetManager: Task 0 in stage 0.0 failed 4
times; aborting job
21/03/12 11:04:35 ERROR WriteToDataSourceV2Exec: Data source write
support
org.apache.spark.sql.execution.streaming.sources.MicroBatchWrite@1eff441c
is aborting.
21/03/12 11:04:35 ERROR WriteToDataSourceV2Exec: Data source write
support
org.apache.spark.sql.execution.streaming.sources.MicroBatchWrite@1eff441c
aborted.
21/03/12 11:04:35 ERROR MicroBatchExecution: Query [id =
2d788a3a-f0ee-4903-9679-0d13bc401e12, runId =
1b387c28-c8e3-4336-9c9f-57db16aa8132] terminated with error
org.apache.spark.SparkException: Writing job aborted.
at
org.apache.spark.sql.execution.datasources.v2.V2TableWriteExec.writeWithV2(WriteToDataSourceV2Exec.scala:413)
at
org.apache.spark.sql.execution.datasources.v2.V2TableWriteExec.writeWithV2$(WriteToDataSourceV2Exec.scala:361)
at
org.apache.spark.sql.execution.datasources.v2.WriteToDataSourceV2Exec.writeWithV2(WriteToDataSourceV2Exec.scala:322)
at
org.apache.spark.sql.execution.datasources.v2.WriteToDataSourceV2Exec.run(WriteToDataSourceV2Exec.scala:329)
at
org.apache.spark.sql.execution.datasources.v2.V2CommandExec.result$lzycompute(V2CommandExec.scala:39)
at
org.apache.spark.sql.execution.datasources.v2.V2CommandExec.result(V2CommandExec.scala:39)
at
org.apache.spark.sql.execution.datasources.v2.V2CommandExec.executeCollect(V2CommandExec.scala:45)
at org.apache.spark.sql.Dataset.collectFromPlan(Dataset.scala:3627)
at org.apache.spark.sql.Dataset.$anonfun$collect$1(Dataset.scala:2940)
at
org.apache.spark.sql.Dataset.$anonfun$withAction$1(Dataset.scala:3618)
at
org.apache.spark.sql.execution.SQLExecution$.$anonfun$withNewExecutionId$5(SQLExecution.scala:100)
at
org.apache.spark.sql.execution.SQLExecution$.withSQLConfPropagated(SQLExecution.scala:160)
at
org.apache.spark.sql.execution.SQLExecution$.$anonfun$withNewExecutionId$1(SQLExecution.scala:87)
at org.apache.spark.sql.SparkSession.withActive(SparkSession.scala:764)
at
org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:64)
at org.apache.spark.sql.Dataset.withAction(Dataset.scala:3616)
at org.apache.spark.sql.Dataset.collect(Dataset.scala:2940)
at
org.apache.spark.sql.execution.streaming.MicroBatchExecution.$anonfun$runBatch$16(MicroBatchExecution.scala:575)
at
org.apache.spark.sql.execution.SQLExecution$.$anonfun$withNewExecutionId$5(SQLExecution.scala:100)
at
org.apache.spark.sql.execution.SQLExecution$.withSQLConfPropagated(SQLExecution.scala:160)
at
org.apache.spark.sql.execution.SQLExecution$.$anonfun$withNewExecutionId$1(SQLExecution.scala:87)
at org.apache.spark.sql.SparkSession.withActive(SparkSession.scala:764)
at
org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:64)
at
Re: Issue while consuming message in kafka using structured streaming
Please see that driver side for example resolved in 3.1.0...
G
On Fri, Mar 12, 2021 at 1:03 PM Sachit Murarka
wrote:
> Hi Gabor,
>
> Thanks a lot for the response. I am using Spark 3.0.1 and this is spark
> structured streaming.
>
> Kind Regards,
> Sachit Murarka
>
>
> On Fri, Mar 12, 2021 at 5:30 PM Gabor Somogyi
> wrote:
>
>> Since you've not provided any version I guess you're using 2.x and you're
>> hitting this issue: https://issues.apache.org/jira/browse/SPARK-28367
>> The executor side must be resolved out of the box in the latest Spark
>> version however on driver side one must set "
>> spark.sql.streaming.kafka.useDeprecatedOffsetFetching=false" to use the
>> new way of fetching.
>>
>> If it doesn't solve your problem then Kafka side must be checked why it's
>> not returning...
>>
>> Hope this helps!
>>
>> G
>>
>>
>> On Fri, Mar 12, 2021 at 12:29 PM Sachit Murarka
>> wrote:
>>
>>> Hi All,
>>>
>>> I am getting following error in spark structured streaming while
>>> connecting to Kakfa
>>>
>>> Main issue from logs::
>>> Caused by: org.apache.kafka.common.errors.TimeoutException: Timeout of
>>> 6ms expired before the position for partition my-topic-1 could be
>>> determined
>>>
>>> Current Committed Offsets: {KafkaV2[Subscribe[my-topic]]:
>>> {“my-topic”:{“1":1498,“0”:1410}}}
>>> Current Available Offsets: {KafkaV2[Subscribe[my-topic]]:
>>> {“my-topic”:{“1”:1499,“0":1410}}}
>>>
>>>
>>> Full logs::
>>>
>>> 21/03/12 11:04:35 ERROR TaskSetManager: Task 0 in stage 0.0 failed 4
>>> times; aborting job
>>> 21/03/12 11:04:35 ERROR WriteToDataSourceV2Exec: Data source write
>>> support
>>> org.apache.spark.sql.execution.streaming.sources.MicroBatchWrite@1eff441c
>>> is aborting.
>>> 21/03/12 11:04:35 ERROR WriteToDataSourceV2Exec: Data source write
>>> support
>>> org.apache.spark.sql.execution.streaming.sources.MicroBatchWrite@1eff441c
>>> aborted.
>>> 21/03/12 11:04:35 ERROR MicroBatchExecution: Query [id =
>>> 2d788a3a-f0ee-4903-9679-0d13bc401e12, runId =
>>> 1b387c28-c8e3-4336-9c9f-57db16aa8132] terminated with error
>>> org.apache.spark.SparkException: Writing job aborted.
>>> at
>>> org.apache.spark.sql.execution.datasources.v2.V2TableWriteExec.writeWithV2(WriteToDataSourceV2Exec.scala:413)
>>> at
>>> org.apache.spark.sql.execution.datasources.v2.V2TableWriteExec.writeWithV2$(WriteToDataSourceV2Exec.scala:361)
>>> at
>>> org.apache.spark.sql.execution.datasources.v2.WriteToDataSourceV2Exec.writeWithV2(WriteToDataSourceV2Exec.scala:322)
>>> at
>>> org.apache.spark.sql.execution.datasources.v2.WriteToDataSourceV2Exec.run(WriteToDataSourceV2Exec.scala:329)
>>> at
>>> org.apache.spark.sql.execution.datasources.v2.V2CommandExec.result$lzycompute(V2CommandExec.scala:39)
>>> at
>>> org.apache.spark.sql.execution.datasources.v2.V2CommandExec.result(V2CommandExec.scala:39)
>>> at
>>> org.apache.spark.sql.execution.datasources.v2.V2CommandExec.executeCollect(V2CommandExec.scala:45)
>>> at org.apache.spark.sql.Dataset.collectFromPlan(Dataset.scala:3627)
>>> at org.apache.spark.sql.Dataset.$anonfun$collect$1(Dataset.scala:2940)
>>> at org.apache.spark.sql.Dataset.$anonfun$withAction$1(Dataset.scala:3618)
>>> at
>>> org.apache.spark.sql.execution.SQLExecution$.$anonfun$withNewExecutionId$5(SQLExecution.scala:100)
>>> at
>>> org.apache.spark.sql.execution.SQLExecution$.withSQLConfPropagated(SQLExecution.scala:160)
>>> at
>>> org.apache.spark.sql.execution.SQLExecution$.$anonfun$withNewExecutionId$1(SQLExecution.scala:87)
>>> at org.apache.spark.sql.SparkSession.withActive(SparkSession.scala:764)
>>> at
>>> org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:64)
>>> at org.apache.spark.sql.Dataset.withAction(Dataset.scala:3616)
>>> at org.apache.spark.sql.Dataset.collect(Dataset.scala:2940)
>>> at
>>> org.apache.spark.sql.execution.streaming.MicroBatchExecution.$anonfun$runBatch$16(MicroBatchExecution.scala:575)
>>> at
>>> org.apache.spark.sql.execution.SQLExecution$.$anonfun$withNewExecutionId$5(SQLExecution.scala:100)
>>> at
>>> org.apache.spark.sql.execution.SQLExecution$.withSQLConfPropagated(SQLExecution.scala:160)
>>> at
>>> org.apache.spark.sql.execution.SQLExecution$.$anonfun$withNewExecutionId$1(SQLExecution.scala:87)
>>> at org.apache.spark.sql.SparkSession.withActive(SparkSession.scala:764)
>>> at
>>> org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:64)
>>> at
>>> org.apache.spark.sql.execution.streaming.MicroBatchExecution.$anonfun$runBatch$15(MicroBatchExecution.scala:570)
>>> at
>>> org.apache.spark.sql.execution.streaming.ProgressReporter.reportTimeTaken(ProgressReporter.scala:352)
>>> at
>>> org.apache.spark.sql.execution.streaming.ProgressReporter.reportTimeTaken$(ProgressReporter.scala:350)
>>> at
>>> org.apache.spark.sql.execution.streaming.StreamExecution.reportTimeTaken(StreamExecution.scala:69)
>>> at
>>> org.apache.spark.sql.execution.streaming.MicroBatchExecution.runBatch(MicroBatchExecution.scala:570)
>>> at
>>>
Re: Issue while consuming message in kafka using structured streaming
Hi Gabor,
Thanks a lot for the response. I am using Spark 3.0.1 and this is spark
structured streaming.
Kind Regards,
Sachit Murarka
On Fri, Mar 12, 2021 at 5:30 PM Gabor Somogyi
wrote:
> Since you've not provided any version I guess you're using 2.x and you're
> hitting this issue: https://issues.apache.org/jira/browse/SPARK-28367
> The executor side must be resolved out of the box in the latest Spark
> version however on driver side one must set "
> spark.sql.streaming.kafka.useDeprecatedOffsetFetching=false" to use the
> new way of fetching.
>
> If it doesn't solve your problem then Kafka side must be checked why it's
> not returning...
>
> Hope this helps!
>
> G
>
>
> On Fri, Mar 12, 2021 at 12:29 PM Sachit Murarka
> wrote:
>
>> Hi All,
>>
>> I am getting following error in spark structured streaming while
>> connecting to Kakfa
>>
>> Main issue from logs::
>> Caused by: org.apache.kafka.common.errors.TimeoutException: Timeout of
>> 6ms expired before the position for partition my-topic-1 could be
>> determined
>>
>> Current Committed Offsets: {KafkaV2[Subscribe[my-topic]]:
>> {“my-topic”:{“1":1498,“0”:1410}}}
>> Current Available Offsets: {KafkaV2[Subscribe[my-topic]]:
>> {“my-topic”:{“1”:1499,“0":1410}}}
>>
>>
>> Full logs::
>>
>> 21/03/12 11:04:35 ERROR TaskSetManager: Task 0 in stage 0.0 failed 4
>> times; aborting job
>> 21/03/12 11:04:35 ERROR WriteToDataSourceV2Exec: Data source write
>> support
>> org.apache.spark.sql.execution.streaming.sources.MicroBatchWrite@1eff441c
>> is aborting.
>> 21/03/12 11:04:35 ERROR WriteToDataSourceV2Exec: Data source write
>> support
>> org.apache.spark.sql.execution.streaming.sources.MicroBatchWrite@1eff441c
>> aborted.
>> 21/03/12 11:04:35 ERROR MicroBatchExecution: Query [id =
>> 2d788a3a-f0ee-4903-9679-0d13bc401e12, runId =
>> 1b387c28-c8e3-4336-9c9f-57db16aa8132] terminated with error
>> org.apache.spark.SparkException: Writing job aborted.
>> at
>> org.apache.spark.sql.execution.datasources.v2.V2TableWriteExec.writeWithV2(WriteToDataSourceV2Exec.scala:413)
>> at
>> org.apache.spark.sql.execution.datasources.v2.V2TableWriteExec.writeWithV2$(WriteToDataSourceV2Exec.scala:361)
>> at
>> org.apache.spark.sql.execution.datasources.v2.WriteToDataSourceV2Exec.writeWithV2(WriteToDataSourceV2Exec.scala:322)
>> at
>> org.apache.spark.sql.execution.datasources.v2.WriteToDataSourceV2Exec.run(WriteToDataSourceV2Exec.scala:329)
>> at
>> org.apache.spark.sql.execution.datasources.v2.V2CommandExec.result$lzycompute(V2CommandExec.scala:39)
>> at
>> org.apache.spark.sql.execution.datasources.v2.V2CommandExec.result(V2CommandExec.scala:39)
>> at
>> org.apache.spark.sql.execution.datasources.v2.V2CommandExec.executeCollect(V2CommandExec.scala:45)
>> at org.apache.spark.sql.Dataset.collectFromPlan(Dataset.scala:3627)
>> at org.apache.spark.sql.Dataset.$anonfun$collect$1(Dataset.scala:2940)
>> at org.apache.spark.sql.Dataset.$anonfun$withAction$1(Dataset.scala:3618)
>> at
>> org.apache.spark.sql.execution.SQLExecution$.$anonfun$withNewExecutionId$5(SQLExecution.scala:100)
>> at
>> org.apache.spark.sql.execution.SQLExecution$.withSQLConfPropagated(SQLExecution.scala:160)
>> at
>> org.apache.spark.sql.execution.SQLExecution$.$anonfun$withNewExecutionId$1(SQLExecution.scala:87)
>> at org.apache.spark.sql.SparkSession.withActive(SparkSession.scala:764)
>> at
>> org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:64)
>> at org.apache.spark.sql.Dataset.withAction(Dataset.scala:3616)
>> at org.apache.spark.sql.Dataset.collect(Dataset.scala:2940)
>> at
>> org.apache.spark.sql.execution.streaming.MicroBatchExecution.$anonfun$runBatch$16(MicroBatchExecution.scala:575)
>> at
>> org.apache.spark.sql.execution.SQLExecution$.$anonfun$withNewExecutionId$5(SQLExecution.scala:100)
>> at
>> org.apache.spark.sql.execution.SQLExecution$.withSQLConfPropagated(SQLExecution.scala:160)
>> at
>> org.apache.spark.sql.execution.SQLExecution$.$anonfun$withNewExecutionId$1(SQLExecution.scala:87)
>> at org.apache.spark.sql.SparkSession.withActive(SparkSession.scala:764)
>> at
>> org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:64)
>> at
>> org.apache.spark.sql.execution.streaming.MicroBatchExecution.$anonfun$runBatch$15(MicroBatchExecution.scala:570)
>> at
>> org.apache.spark.sql.execution.streaming.ProgressReporter.reportTimeTaken(ProgressReporter.scala:352)
>> at
>> org.apache.spark.sql.execution.streaming.ProgressReporter.reportTimeTaken$(ProgressReporter.scala:350)
>> at
>> org.apache.spark.sql.execution.streaming.StreamExecution.reportTimeTaken(StreamExecution.scala:69)
>> at
>> org.apache.spark.sql.execution.streaming.MicroBatchExecution.runBatch(MicroBatchExecution.scala:570)
>> at
>> org.apache.spark.sql.execution.streaming.MicroBatchExecution.$anonfun$runActivatedStream$2(MicroBatchExecution.scala:223)
>> at scala.runtime.java8.JFunction0$mcV$sp.apply(JFunction0$mcV$sp.java:23)
>> at
>>
Re: Issue while consuming message in kafka using structured streaming
Since you've not provided any version I guess you're using 2.x and you're
hitting this issue: https://issues.apache.org/jira/browse/SPARK-28367
The executor side must be resolved out of the box in the latest Spark
version however on driver side one must set "
spark.sql.streaming.kafka.useDeprecatedOffsetFetching=false" to use the new
way of fetching.
If it doesn't solve your problem then Kafka side must be checked why it's
not returning...
Hope this helps!
G
On Fri, Mar 12, 2021 at 12:29 PM Sachit Murarka
wrote:
> Hi All,
>
> I am getting following error in spark structured streaming while
> connecting to Kakfa
>
> Main issue from logs::
> Caused by: org.apache.kafka.common.errors.TimeoutException: Timeout of
> 6ms expired before the position for partition my-topic-1 could be
> determined
>
> Current Committed Offsets: {KafkaV2[Subscribe[my-topic]]:
> {“my-topic”:{“1":1498,“0”:1410}}}
> Current Available Offsets: {KafkaV2[Subscribe[my-topic]]:
> {“my-topic”:{“1”:1499,“0":1410}}}
>
>
> Full logs::
>
> 21/03/12 11:04:35 ERROR TaskSetManager: Task 0 in stage 0.0 failed 4
> times; aborting job
> 21/03/12 11:04:35 ERROR WriteToDataSourceV2Exec: Data source write support
> org.apache.spark.sql.execution.streaming.sources.MicroBatchWrite@1eff441c
> is aborting.
> 21/03/12 11:04:35 ERROR WriteToDataSourceV2Exec: Data source write support
> org.apache.spark.sql.execution.streaming.sources.MicroBatchWrite@1eff441c
> aborted.
> 21/03/12 11:04:35 ERROR MicroBatchExecution: Query [id =
> 2d788a3a-f0ee-4903-9679-0d13bc401e12, runId =
> 1b387c28-c8e3-4336-9c9f-57db16aa8132] terminated with error
> org.apache.spark.SparkException: Writing job aborted.
> at
> org.apache.spark.sql.execution.datasources.v2.V2TableWriteExec.writeWithV2(WriteToDataSourceV2Exec.scala:413)
> at
> org.apache.spark.sql.execution.datasources.v2.V2TableWriteExec.writeWithV2$(WriteToDataSourceV2Exec.scala:361)
> at
> org.apache.spark.sql.execution.datasources.v2.WriteToDataSourceV2Exec.writeWithV2(WriteToDataSourceV2Exec.scala:322)
> at
> org.apache.spark.sql.execution.datasources.v2.WriteToDataSourceV2Exec.run(WriteToDataSourceV2Exec.scala:329)
> at
> org.apache.spark.sql.execution.datasources.v2.V2CommandExec.result$lzycompute(V2CommandExec.scala:39)
> at
> org.apache.spark.sql.execution.datasources.v2.V2CommandExec.result(V2CommandExec.scala:39)
> at
> org.apache.spark.sql.execution.datasources.v2.V2CommandExec.executeCollect(V2CommandExec.scala:45)
> at org.apache.spark.sql.Dataset.collectFromPlan(Dataset.scala:3627)
> at org.apache.spark.sql.Dataset.$anonfun$collect$1(Dataset.scala:2940)
> at org.apache.spark.sql.Dataset.$anonfun$withAction$1(Dataset.scala:3618)
> at
> org.apache.spark.sql.execution.SQLExecution$.$anonfun$withNewExecutionId$5(SQLExecution.scala:100)
> at
> org.apache.spark.sql.execution.SQLExecution$.withSQLConfPropagated(SQLExecution.scala:160)
> at
> org.apache.spark.sql.execution.SQLExecution$.$anonfun$withNewExecutionId$1(SQLExecution.scala:87)
> at org.apache.spark.sql.SparkSession.withActive(SparkSession.scala:764)
> at
> org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:64)
> at org.apache.spark.sql.Dataset.withAction(Dataset.scala:3616)
> at org.apache.spark.sql.Dataset.collect(Dataset.scala:2940)
> at
> org.apache.spark.sql.execution.streaming.MicroBatchExecution.$anonfun$runBatch$16(MicroBatchExecution.scala:575)
> at
> org.apache.spark.sql.execution.SQLExecution$.$anonfun$withNewExecutionId$5(SQLExecution.scala:100)
> at
> org.apache.spark.sql.execution.SQLExecution$.withSQLConfPropagated(SQLExecution.scala:160)
> at
> org.apache.spark.sql.execution.SQLExecution$.$anonfun$withNewExecutionId$1(SQLExecution.scala:87)
> at org.apache.spark.sql.SparkSession.withActive(SparkSession.scala:764)
> at
> org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:64)
> at
> org.apache.spark.sql.execution.streaming.MicroBatchExecution.$anonfun$runBatch$15(MicroBatchExecution.scala:570)
> at
> org.apache.spark.sql.execution.streaming.ProgressReporter.reportTimeTaken(ProgressReporter.scala:352)
> at
> org.apache.spark.sql.execution.streaming.ProgressReporter.reportTimeTaken$(ProgressReporter.scala:350)
> at
> org.apache.spark.sql.execution.streaming.StreamExecution.reportTimeTaken(StreamExecution.scala:69)
> at
> org.apache.spark.sql.execution.streaming.MicroBatchExecution.runBatch(MicroBatchExecution.scala:570)
> at
> org.apache.spark.sql.execution.streaming.MicroBatchExecution.$anonfun$runActivatedStream$2(MicroBatchExecution.scala:223)
> at scala.runtime.java8.JFunction0$mcV$sp.apply(JFunction0$mcV$sp.java:23)
> at
> org.apache.spark.sql.execution.streaming.ProgressReporter.reportTimeTaken(ProgressReporter.scala:352)
> at
> org.apache.spark.sql.execution.streaming.ProgressReporter.reportTimeTaken$(ProgressReporter.scala:350)
> at
> org.apache.spark.sql.execution.streaming.StreamExecution.reportTimeTaken(StreamExecution.scala:69)
> at
>
