somebol opened a new issue #1757:
URL: https://github.com/apache/hudi/issues/1757
Hi Team,
We are trying to load a very large dataset into hudi. The bulk insert job
took ~16.5 hours to complete. The job was run with vanilla settings without any
optimisations.
How can we tune the job to make it run faster?
**Dataset**
Data stored in HDFS / parquet
size: 5.5 TB
number of files: 27000
number of records: ~300 billion
**hudi options**
.option(RECORDKEY_FIELD_OPT_KEY(), "id")
.option(PARTITIONPATH_FIELD_OPT_KEY(), "<partition>")
.option(PRECOMBINE_FIELD_OPT_KEY(), "<ts>")
.option(HIVE_STYLE_PARTITIONING_OPT_KEY(), "true")
.option(TABLE_TYPE_OPT_KEY(), MOR_TABLE_TYPE_OPT_VAL())
.option(OPERATION_OPT_KEY(), BULK_INSERT_OPERATION_OPT_VAL())
.option(TABLE_NAME, "<name>")
**spark conf**
conf.set("spark.debug.maxToStringFields", "100");
conf.set("spark.sql.shuffle.partitions", "2001");
conf.set("spark.sql.warehouse.dir", "/user/hive/warehouse");
conf.set("spark.sql.autoBroadcastJoinThreshold", "31457280");
conf.set("spark.sql.hive.filesourcePartitionFileCacheSize", "2000000000");
conf.set("spark.sql.sources.partitionOverwriteMode", "dynamic");
conf.set("mapreduce.input.fileinputformat.input.dir.recursive", "true");
conf.set("spark.storage.replication.proactive", "true");
**spark submit**
SPARK_CMD="spark2-submit \
--files log4j.properties
--conf "spark.driver.extraJavaOptions=${log4j_setting}" \
--conf "spark.executor.extraJavaOptions=${log4j_setting}" \
--conf spark.kryoserializer.buffer.max=2040M \
--num-executors 25 \
--executor-cores 5 \
--driver-memory 8G \
--executor-memory 21G \
--master yarn \
--deploy-mode client
**Environment Description**
* Hudi version : 0.5.3
* Spark version : 2.40
* Cloudera version : 6.33
* Hadoop version : 3.0.0
* Storage (HDFS/S3/GCS..) : HDFS
* Running on Docker? (yes/no) : No
**screenshot of spark stages**
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