I am using Datasource Writer API to write 5000 records into Hudi copy-on-write table, each with 8 columns and the total size is less than 1Mb. Please refer to the below code.
Dataset<Row> ds1 = spark.read().json(jsc.parallelize(records, 2));
DataFrameWriter<Row> writer = ds1.write().format("org.apache.hudi")
.option("hoodie.insert.shuffle.parallelism", 2)
.option("hoodie.upsert.shuffle.parallelism", 2)
.option(DataSourceWriteOptions.OPERATION_OPT_KEY(), DataSourceWriteOptions.INSERT_OPERATION_OPT_VAL())
.option(DataSourceWriteOptions.TABLE_TYPE_OPT_KEY(), tableType)
.option(DataSourceWriteOptions.RECORDKEY_FIELD_OPT_KEY(), recordKey)
.option(DataSourceWriteOptions.PARTITIONPATH_FIELD_OPT_KEY(), partitionPath)
.option(DataSourceWriteOptions.PRECOMBINE_FIELD_OPT_KEY(), precombineKey)
.option(HoodieWriteConfig.TABLE_NAME, hudiTableName)
.option(DataSourceWriteOptions.HIVE_TABLE_OPT_KEY(), hudiTableName)
.option(DataSourceWriteOptions.HIVE_DATABASE_OPT_KEY(), hiveDatabase)
.option(DataSourceWriteOptions.HIVE_URL_OPT_KEY(), hiveServerUrl)
.option(DataSourceWriteOptions.HIVE_USER_OPT_KEY(), hiveUser)
.option(DataSourceWriteOptions.HIVE_PASS_OPT_KEY(), hivePassword)
.option(DataSourceWriteOptions.HIVE_SYNC_ENABLED_OPT_KEY(), hiveSyncEnabled)
.option(DataSourceWriteOptions.HIVE_PARTITION_FIELDS_OPT_KEY(), partitionPath)
.mode(SaveMode.Append);
writer.save(basePath);
Application is submitted to yarn through spark-submit. At the beginning, it only takes 3~4 seconds to finish the insert operation, but it gets longer and longer, say 30 seconds after 5 minutes. From the below spark logs, it shows that most of time is spent on HoodieSparkSqlWriter count task.
2020-05-25 16:36:37,851 | INFO | [dag-scheduler-event-loop] | Adding task set 185.0 with 1 tasks | org.apache.spark.internal.Logging$class.logInfo(Logging.scala:54)
2020-05-25 16:36:37,851 | INFO | [dispatcher-event-loop-0] | Starting task 0.0 in stage 185.0 (TID 190, node-ana-corepOlf, executor 2, partition 0, NODE_LOCAL, 7651 bytes) | org.apache.spark.internal.Logging$class.logInfo(Logging.scala:54)
2020-05-25 16:36:37,858 | INFO | [dispatcher-event-loop-1] | Added broadcast_124_piece0 in memory on node-ana-corepOlf:36554 (size: 138.1 KB, free: 29.2 GB) | org.apache.spark.internal.Logging$class.logInfo(Logging.scala:54)
2020-05-25 16:36:37,887 | INFO | [dispatcher-event-loop-1] | Asked to send map output locations for shuffle 53 to 10.155.114.97:32461 | org.apache.spark.internal.Logging$class.logInfo(Logging.scala:54)
2020-05-25 16:37:11,098 | INFO | [dispatcher-event-loop-0] | Added rdd_381_0 in memory on node-ana-corepOlf:36554 (size: 387.0 B, free: 29.2 GB) | org.apache.spark.internal.Logging$class.logInfo(Logging.scala:54)
2020-05-25 16:37:11,111 | INFO | [task-result-getter-2] | Finished task 0.0 in stage 185.0 (TID 190) in 33260 ms on node-ana-corepOlf (executor 2) (1/1) | org.apache.spark.internal.Logging$class.logInfo(Logging.scala:54)
2020-05-25 16:37:11,111 | INFO | [task-result-getter-2] | Removed TaskSet 185.0, whose tasks have all completed, from pool | org.apache.spark.internal.Logging$class.logInfo(Logging.scala:54)
2020-05-25 16:37:11,112 | INFO | [dag-scheduler-event-loop] | ResultStage 185 (count at HoodieSparkSqlWriter.scala:254) finished in 33.308 s | org.apache.spark.internal.Logging$class.logInfo(Logging.scala:54)
2020-05-25 16:37:11,113 | INFO | [Driver] | Job 70 finished: count at HoodieSparkSqlWriter.scala:254, took 33.438673 s | org.apache.spark.internal.Logging$class.logInfo(Logging.scala:54)
I tried to tune the parameter hoodie.insert.shuffle.parallelism to 20, but did not help. And the CPU/Heap usages are all normal.
Below is the setting for application. Any advice is appreciated.
Executor instance: 2
Executor memory: 55g
Executor cores: 4
Driver memory: 4g
you would ask for help in [email protected] ML or github issues.