pontisa95 opened a new issue, #11011: URL: https://github.com/apache/hudi/issues/11011
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: ![Screenshot_20 1](https://github.com/apache/hudi/assets/166909082/3117f307-37c1-4295-8cbc-cccdf8f4fdfd) 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 **Expected behavior** We would like to know if there is a way to reduce, or at least to keep constant, the writing latency on the hudi table and understand if there is something we can improve in the deploy condition or in other configuration described below. **Environment Description** 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 HOODIE TABLE PROPERTIES: 'hoodie.datasource.write.table.type': 'COPY_ON_WRITE', 'hoodie.datasource.write.keygenerator.class': 'org.apache.hudi.keygen.ComplexKeyGenerator', 'hoodie.datasource.write.hive_style_partitioning':'true', 'hoodie.index.type':'GLOBAL_BLOOM', 'hoodie.simple.index.update.partition.path':'true', 'hoodie.datasource.hive_sync.enable': 'true', 'hoodie.datasource.hive_sync.partition_extractor_class': 'org.apache.hudi.hive.MultiPartKeysValueExtractor', 'hoodie.datasource.hive_sync.use_jdbc': 'false', 'hoodie.datasource.hive_sync.mode': 'hms', 'hoodie.copyonwrite.record.size.estimate':285, 'hoodie.parquet.small.file.limit': 104857600, 'hoodie.parquet.max.file.size': 120000000, 'hoodie.cleaner.commits.retained': 1 KAFKA READ CONFIG: .readStream .format("kafka") .option("kafka.security.protocol", "SSL") .option("kafka.ssl.enabled.protocols", "TLSv1.2, TLSv1.1, TLSv1") .option("kafka.ssl.protocol", "TLS") .option("startingOffsets", "latest") .option("failOnDataLoss", "true") .option("maxOffsetsPerTrigger", 2000) .option("kafka.group.id",CG_NAME) .load() 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> -- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. To unsubscribe, e-mail: commits-unsubscr...@hudi.apache.org.apache.org For queries about this service, please contact Infrastructure at: us...@infra.apache.org