Performance Tuning, Cost Optimization / Internals, Research by Dmitry Tolpeko
By default, Spark EMR clusters have spark.dynamicAllocation.enabled set to true meaning that the cluster will dynamically allocate resources to scale the executors up and down whenever required.
But what is the initial number of executors when you start your Spark job?
Starting from EMR 5.32 and EMR 6.2 you can notice that Spark can launch much larger executors that you request in your job settings. For example, EMR created my cluster with the following default settings (it depends on the instance type and maximizeResourceAllocation classification option):
spark.executor.memory 18971M spark.executor.cores 4 spark.yarn.executor.memoryOverheadFactor 0.1875
But when I start a Spark session (pyspark command) I see the following:
I have a partitioned Hive table created by an open-source version of Hadoop that uses S3A scheme as the location for every partition. The table has more than 10,000 partitions and every partition has about 8,000 Parquet files:
$ hive -e "show partitions events"; ... dateint=20220419/hour=11 dateint=20220419/hour=12 dateint=20220419/hour=13 $ hive -e "describe formatted events partition (dateint=20220419, hour='11')" | grep Location Location: s3a://cloudsqale/events/dateint=20220419/hour=11 $ hive -e "describe formatted events partition (dateint=20220419, hour='12')" | grep Location Location: s3a://cloudsqale/events/dateint=20220419/hour=12 $ hive -e "describe formatted events partition (dateint=20220419, hour='13')" | grep Location Location: s3a://cloudsqale/events/dateint=20220419/hour=13
S3A:// is specified for every partition in this table.
Reading a Partition in Amazon EMR Spark
When I made an attempt to read data from a single partition using Spark SQL:
$ spark-sql --master yarn -e "select count(*) from events where dateint=20220419 and hour='11'"
The Spark driver failed with:
# java.lang.OutOfMemoryError: GC overhead limit exceeded # -XX:OnOutOfMemoryError="kill -9 %p" # Executing /bin/sh -c "kill -9 4847"...
A row group is a unit of work for reading from Parquet that cannot be split into smaller parts, and you expect that the number of tasks created by Spark is no more than the total number of row groups in your Parquet data source.
But Spark still can create much more tasks than the number of row groups. Let’s see how this is possible.
A Parquet file contains MIN/MAX statistics for every column for every row group that allows Spark applications to skip reading unnecessary data chunks depending on the query predicate. Let’s see how this works with LIKE pattern matching filter.
For my tests I will use a Parquet file with 4 row groups and the following MIN/MAX statistics for product column:
I have a Spark job that transforms incoming data from compressed text files into Parquet format and loads them into a daily partition of a Hive table. This is a typical job in a data lake, it is quite simple but in my case it was very slow.
Initially it took about 4 hours to convert ~2,100 input .gz files (~1.9 TB of data) into Parquet, while the actual Spark job took just 38 minutes to run and the remaining time was spent on loading data into a Hive partition.
Let’s see what is the reason of such behavior and how we can improve the performance.
After creating an Amazon EMR cluster with Spark support, and running a spark application you can notice that the Spark job creates too many tasks to process even a very small data set.
For example, I have a small table country_iso_codes having 249 rows and stored in a comma-delimited text file with the length of 10,657 bytes.
When running the following application on Amazon EMR 5.7 cluster with Spark 2.1.1 with the default settings I can see the large number of partitions generated: