alessandro pontis created SPARK-47842:
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Summary: Spark job relying over Hudi are blocked after one or zero
commit
Key: SPARK-47842
URL: https://issues.apache.org/jira/browse/SPARK-47842
Project: Spark
Issue Type: Bug
Components: PySpark, Structured Streaming
Affects Versions: 3.3.0
Environment: Hudi version : 0.12.1-amzn-0
Spark version : 3.3.0
Hive version : 3.1.3
Hadoop version : 3.3.3 amz
Storage (HDFS/S3/GCS..) : S3
Running on Docker? (yes/no) : no (EMR 6.9.0)
Additional context
Reporter: alessandro pontis
Attachments: Screenshot_20 1.png
Hello, we are facing the fact that some pyspark job that rely on Hudi seems to
be blocked, in fact if we go over the spark console we can see the following
situation:
!image-2024-04-13-15-57-56-605.png|width=1040,height=558!
we can see that we have 71 completed jobs but those are CDC process that should
read from Kafka topic continuously. We verified yet that there are messages
queued over the kafka topic. If you kill the application and then restart in
some cases the job will act normally and other times the job still remain
stacked.
Our deploy condition are the following:
We read INSERT, UPDATE and DELETE operation from a Kafka topic and we replicate
them in a target hudi table stored on Hive via a pyspark job running 24/7
PYSPARK WRITE
df_source.writeStream.foreachBatch(foreach_batch_write_function)
{{ FOR EACH BATCH FUNCTION:
#management of delete messages
batchDF_deletes.write.format('hudi') \
.option('hoodie.datasource.write.operation', 'delete') \
.options(**hudiOptions_table) \
.mode('append') \
.save(S3_OUTPUT_PATH)
#management of update and insert messages
batchDF_upserts.write.format('org.apache.hudi') \
.option('hoodie.datasource.write.operation', 'upsert') \
.options(**hudiOptions_table) \
.mode('append') \
.save(S3_OUTPUT_PATH)}}
SPARK SUBMIT
spark-submit --master yarn --deploy-mode cluster --num-executors 1
--executor-memory 1G --executor-cores 2 --conf
spark.dynamicAllocation.enabled=false --packages
org.apache.spark:spark-sql-kafka-0-10_2.12:3.1.2 --conf
spark.serializer=org.apache.spark.serializer.KryoSerializer --conf
spark.sql.hive.convertMetastoreParquet=false --jars
/usr/lib/hudi/hudi-spark-bundle.jar <path_to_script>
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