Performance Tuning, Cost Optimization / Internals, Research by Dmitry Tolpeko
Let’s review major REST API requests for uploading files to S3 (PutObject, CreateMultipartUpload, UploadPart and CompleteMultipartUpload) that you can observe in S3 access logs.
This can be helpful for monitoring S3 write performance. See also S3 Multipart Upload – S3 Access Log Messages.
Most applications writing data into S3 use the S3 multipart upload API to upload data in parts. First, you initiate the load, then upload parts and finally complete the multipart upload.
Let’s see how this operation is reflected in the S3 access log. My application uploaded the file data.gz into S3, and I can view it as follows:
One of our Flink streaming jobs had significant variance in the time spent on writing files to S3 by the same Task Manager process.
What settings do you need to check first?
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.
Apache Sqoop is widely used to import data from relational databases into cloud. One of our databases uses Amazon RDS for PostgreSQL to store sales data and its Sqoop import periodically failed with the following error:
Error: java.io.IOException: SQLException in nextKeyValue Caused by: org.postgresql.util.PSQLException: ERROR: canceling statement due to conflict with recovery Detail: User query might have needed to see row versions that must be removed.
In this article I will describe a solution that helped resolve the problem in our specific case.
I already wrote about recovering the ghost nodes in Amazon EMR clusters, but in this article I am going to extend this information for EMR clusters configured for auto-scaling.
In a Hadoop cluster besides Active nodes you may also have Unhealthy, Lost and Decommissioned nodes. Unhealthy nodes are running but just excluded from scheduling the tasks because they, for example, do not have enough disk space. Lost nodes are nodes that are not reachable anymore. The decommissioned nodes are nodes that successfully terminated and left the cluster.
But all these nodes are known to the Hadoop YARN cluster and you can see their details such as IP addresses, last health updates and so on.
At the same time there can be also Ghost nodes i.e. nodes that are running by Amazon EMR services but Hadoop itself does not know anything about their existence. Let’s see how you can find them.
Usually Hadoop is able to automatically recover cluster nodes from Unhealthy state by cleaning log and temporary directories. But sometimes nodes stay unhealthy for a long time and manual intervention is necessary to bring them back.
Amazon EMR allows you to define scale-out and scale-in rules to automatically add and remove instances based on the metrics you specify.
In this article I am going to explore the instance controller logs that can be very useful in monitoring the auto-scaling. The logs are located in /emr/instance-controller/log/ directory on the EMR master node.
When you run many Hadoop clusters it is useful to automatically collect metrics from all clusters in a single place (Hive table i.e.).
This allows you to perform any advanced and custom analysis of your clusters workload and not be limited to the features provided by Hadoop Administration UI tools that often offer only per cluster view so it is hard to see the whole picture of your data platform.