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"...
When you use S3 for storing checkpoints it can easily become a bottleneck especially for your Flink application with a lot of subtasks. To overcome this problem FLINK-9061 introduced an entropy ingestion to the checkpoint path.
But the Flink documentation provides a misleading example (at least up to Flink 1.13) that actually destroys the value of the checkpoint entropy.
Amazon S3 is highly scalable distributed system that can handle extremely large volumes of data, can adapt to an increasing workload and provide quite good performance as a file storage.
But sometimes you have to tweak it to run faster that can be especially important for latency-sensitive applications.
It is a well known limitation that Amazon S3 multipart upload requires the part size to be between 5 MB and 5 GB with an exception that the last part can be less than 5 MB.
Does it mean that you cannot upload a single small file (< 5 MB) to S3 using the multipart upload?
You can use the Flink Web UI to monitor the checkpoint operations in Flink, but in some cases S3 access logs can provide more information, and can be especially useful if you run many Flink applications.
Although Amazon S3 can generate a lot of logs and it makes sense to have an ETL process to parse, combine and put the logs into Parquet or ORC format for better query performance, there is still an easy way to analyze logs using a Hive table created just on top of the raw S3 log directory.
For zero-downtime, large-scale systems you can have multiple compute clusters located in different availability zones.
The Kinesis KCL 2.x Consumer is very helpful to build highly scalable, elastic and fault-tolerant streaming data processing pipelines for Amazon Kinesis. Let’s review some of the KCL internals related to the load balancing and response to compute node/cluster failures and how you can tune and monitor such activities.
Let’s review how EC2 vCPUs correspond to YARN vCores in Amazon EMR and Qubole Hadoop clusters. As an example, I will choose m4.4xlarge, r4.4xlarge and c4.4xlarge EC2 instance types.
EC2 vCPU is a thread of a CPU core (typically, there are two threads per core). Does it mean that YARN vCores should be equal to the number of EC2 vCPU? That’s not always the case.