Performance Tuning and Cost Optimization / Internals, Research, Consulting
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.
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.