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Hi, I am trying a use case to use multi writer to write data into different partitions with version 0.14. I found this medium article https://medium.com/@simpsons/can-you-concurrently-write-data-to-apache-hudi-w-o-any-lock-provider-51ea55bf2dd6 which says I can do multi writing with writer 1 having in process lock which allows to perform services and writer 2 just writing the data with services turned off. I tried with configs given and one of the writes always fails with below error:
23/12/19 01:02:06 ERROR AppendDataExec: Data source write support org.apache.hudi.spark3.internal.HoodieDataSourceInternalBatchWrite@6db6a766 is aborting.
23/12/19 01:02:06 ERROR DataSourceInternalWriterHelper: Commit 20231219010014383 aborted
23/12/19 01:02:07 WARN BaseHoodieWriteClient: Cannot find instant 20231219010014383 in the timeline, for rollback
23/12/19 01:02:07 ERROR AppendDataExec: Data source write support org.apache.hudi.spark3.internal.HoodieDataSourceInternalBatchWrite@6db6a766 aborted.
Can someone help? Can this be done without using locks as per article or should I definitely use any recommended lock provider?\
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Describe the problem you faced Hi, I am trying a use case to use multi writer to write data into different partitions with version 0.14. I found this medium article https://medium.com/@simpsons/can-you-concurrently-write-data-to-apache-hudi-w-o-any-lock-provider-51ea55bf2dd6 which says I can do multi writing with writer 1 having in process lock which allows to perform services and writer 2 just writing the data with services turned off. I tried with configs given and one of the writes always fails with below error: 23/12/19 01:02:06 ERROR AppendDataExec: Data source write support org.apache.hudi.spark3.internal.HoodieDataSourceInternalBatchWrite@6db6a766 is aborting. 23/12/19 01:02:06 ERROR DataSourceInternalWriterHelper: Commit 20231219010014383 aborted 23/12/19 01:02:07 WARN BaseHoodieWriteClient: Cannot find instant 20231219010014383 in the timeline, for rollback 23/12/19 01:02:07 ERROR AppendDataExec: Data source write support org.apache.hudi.spark3.internal.HoodieDataSourceInternalBatchWrite@6db6a766 aborted.
Configs Used: load_df_1.write.format("org.apache.hudi"). option("hoodie.datasource.write.recordkey.field", "xxxxxxxxxxxx"). option("hoodie.datasource.write.partitionpath.field", "xxxxxxxxxxxx"). option("hoodie.datasource.write.precombine.field", "xxxxxxxxxxxx"). option("hoodie.datasource.write.operation", "bulk_insert"). option("hoodie.datasource.write.table.type", "COPY_ON_WRITE"). option("hoodie.datasource.query.type", "snapshot"). option("spark.serializer", "org.apache.spark.serializer.KryoSerializer"). option("hoodie.datasource.write.hive_style_partitioning", "true"). option("hoodie.cleaner.policy.failed.writes","LAZY"). option("hoodie.write.concurrency.mode","OPTIMISTIC_CONCURRENCY_CONTROL"). option("hoodie.write.lock.provider","org.apache.hudi.client.transaction.lock.InProcessLockProvider"). option("hoodie.metadata.enable","false"). option(HoodieWriteConfig.TABLE_NAME, "xxxxxxxxxxxx"). mode("Overwrite"). save("xxxxxxxxxxxx")
load_df_2.write.format("org.apache.hudi"). option("hoodie.datasource.write.recordkey.field", "xxxxxxxxxxxx"). option("hoodie.datasource.write.partitionpath.field", "xxxxxxxxxxxx"). option("hoodie.datasource.write.precombine.field", "xxxxxxxxxxxx"). option("hoodie.datasource.write.operation", "bulk_insert"). option("hoodie.datasource.write.table.type", "COPY_ON_WRITE"). option("hoodie.datasource.query.type", "snapshot"). option("spark.serializer", "org.apache.spark.serializer.KryoSerializer"). option("hoodie.datasource.write.hive_style_partitioning", "true"). option("hoodie.cleaner.policy.failed.writes","LAZY"). option("hoodie.metadata.enable","false"). option("hoodie.table.services.enabled","false"). option(HoodieWriteConfig.TABLE_NAME, "xxxxxxxxxxxx"). mode("Overwrite"). save("xxxxxxxxxxxx")
Can someone help? Can this be done without using locks as per article or should I definitely use any recommended lock provider?\ A clear and concise description of the problem.
To Reproduce
Steps to reproduce the behavior:
1. 2. 3. 4.
Expected behavior
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Environment Description
Hudi version :
Spark version :
Hive version :
Hadoop version :
Storage (HDFS/S3/GCS..) :
Running on Docker? (yes/no) :
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