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numberlabs-developers / hudi

Upserts, Deletes And Incremental Processing on Big Data.
https://hudi.apache.org/
Apache License 2.0
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[SUPPORT] Hi, I am trying a use case to use multi writer to write data into different partitions with version 0 #67

Open torvalds-dev-testbot[bot] opened 10 months ago

torvalds-dev-testbot[bot] commented 10 months ago

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? (edited)

torvalds-dev-testbot[bot] commented 10 months ago

torvalds.dev is analyzing the ticket

torvalds-dev-testbot[bot] commented 10 months ago

I would be glad to help, but to fully determine the compatibility, I need you to provide more details about the environment you are using. Specifically, could you please provide the following version information:

  1. Spark version
  2. Hive version
  3. Hadoop version
  4. Storage type
  5. Are you running this on Docker?

Without this information, it would be hard to confirm the compatibility among the different versions you are using.