tverdokhlebd edited a comment on issue #1491: [SUPPORT] OutOfMemoryError during upsert 53M records URL: https://github.com/apache/incubator-hudi/issues/1491#issuecomment-610564674 Code: sparkSession .read .jdbc( url = jdbcConfig.url, table = table, columnName = "partition", lowerBound = 0, upperBound = jdbcConfig.partitionsCount.toInt, numPartitions = jdbcConfig.partitionsCount.toInt, connectionProperties = new Properties() { put("driver", jdbcConfig.driver) put("user", jdbcConfig.user) put("password", jdbcConfig.password) } ) .withColumn("year", substring(col(jdbcConfig.dateColumnName), 0, 4)) .withColumn("month", substring(col(jdbcConfig.dateColumnName), 6, 2)) .withColumn("day", substring(col(jdbcConfig.dateColumnName), 9, 2)) .write .option(HoodieWriteConfig.TABLE_NAME, hudiConfig.tableName) .option(DataSourceWriteOptions.RECORDKEY_FIELD_OPT_KEY, hudiConfig.recordKey) .option(DataSourceWriteOptions.PRECOMBINE_FIELD_OPT_KEY, hudiConfig.precombineKey) .option(DataSourceWriteOptions.PARTITIONPATH_FIELD_OPT_KEY, hudiConfig.partitionPathKey) .option(DataSourceWriteOptions.KEYGENERATOR_CLASS_OPT_KEY, classOf[ComplexKeyGenerator].getName) .option(DataSourceWriteOptions.HIVE_STYLE_PARTITIONING_OPT_KEY, "true") .option("hoodie.datasource.write.operation", writeOperation) .option("hoodie.bulkinsert.shuffle.parallelism", hudiConfig.bulkInsertParallelism) .option("hoodie.insert.shuffle.parallelism", hudiConfig.parallelism) .option("hoodie.upsert.shuffle.parallelism", hudiConfig.parallelism) .option("hoodie.cleaner.policy", HoodieCleaningPolicy.KEEP_LATEST_FILE_VERSIONS.name()) .option("hoodie.cleaner.fileversions.retained", "1") .option("hoodie.metrics.graphite.host", hudiConfig.graphiteHost) .option("hoodie.metrics.graphite.port", hudiConfig.graphitePort) .option("hoodie.metrics.graphite.metric.prefix", hudiConfig.graphiteMetricPrefix) .format("org.apache.hudi") .mode(SaveMode.Append) .save(outputPath) This code is executing on Jenkins, with next parameters: docker run --rm -v ${PWD}:${PWD} -v /mnt/ml_data:/mnt/ml_data bde2020/spark-master:2.4.5-hadoop2.7 \ bash ./spark/bin/spark-submit \ --master "local[2]" \ --packages org.apache.hudi:hudi-spark-bundle_2.11:0.5.2-incubating,org.apache.hadoop:hadoop-aws:2.7.3,org.apache.spark:spark-avro_2.11:2.4.4 \ --conf spark.local.dir=/mnt/ml_data \ --conf spark.ui.enabled=false \ --conf spark.driver.memory=4g \ --conf spark.driver.memoryOverhead=1024 \ --conf spark.driver.maxResultSize=2g \ --conf spark.kryoserializer.buffer.max=512m \ --conf spark.serializer=org.apache.spark.serializer.KryoSerializer \ --conf spark.rdd.compress=true \ --conf spark.shuffle.service.enabled=true \ --conf spark.sql.hive.convertMetastoreParquet=false \ --conf spark.hadoop.fs.defaultFS=s3a://ir-mtu-ml-bucket/ml_hudi \ --conf spark.hadoop.fs.s3a.impl=org.apache.hadoop.fs.s3a.S3AFileSystem \ --conf spark.hadoop.fs.s3a.access.key=${AWS_ACCESS_KEY_ID} \ --conf spark.hadoop.fs.s3a.secret.key=${AWS_SECRET_ACCESS_KEY} \ --conf spark.executorEnv.period.startDate=${date} \ --conf spark.executorEnv.period.numDays=${numDays} \ --conf spark.executorEnv.jdbc.url=${VERTICA_URL} \ --conf spark.executorEnv.jdbc.user=${VERTICA_USER} \ --conf spark.executorEnv.jdbc.password=${VERTICA_PWD} \ --conf spark.executorEnv.jdbc.driver=${VERTICA_DRIVER}\ --conf spark.executorEnv.jdbc.schemaName=mtu_owner \ --conf spark.executorEnv.jdbc.tableName=ext_ml_data \ --conf spark.executorEnv.jdbc.dateColumnName=hit_date \ --conf spark.executorEnv.jdbc.partitionColumnName=hit_timestamp \ --conf spark.executorEnv.jdbc.partitionsCount=8 \ --conf spark.executorEnv.hudi.outputPath=s3a://ir-mtu-ml-bucket/ml_hudi \ --conf spark.executorEnv.hudi.tableName=ext_ml_data \ --conf spark.executorEnv.hudi.recordKey=tds_cid \ --conf spark.executorEnv.hudi.precombineKey=hit_timestamp \ --conf spark.executorEnv.hudi.parallelism=8 \ --conf spark.executorEnv.hudi.bulkInsertParallelism=8 \ --class mtu.spark.analytics.ExtMLDataToS3 \ ${PWD}/ml-vertica-to-s3-hudi.jar I try to move 53 million records (The table contains 48 columns) from the Vertica database to s3 storage. Operation "bulk_insert" successfully completes and take about 40-50 minutes. Operation "upsert" on the same records throws exceptions with OOM.
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