Blocking Shuffle
This documentation is for an unreleased version of Apache Flink. We recommend you use the latest stable version.

Blocking Shuffle #

Overview #

Flink supports a batch execution mode in both DataStream API and Table / SQL for jobs executing across bounded input. In this mode, network exchanges occur via a blocking shuffle. Unlike the pipeline shuffle used for streaming applications, blocking exchanges persists data to some storage. Downstream tasks then fetch these values via the network. Such an exchange reduces the resources required to execute the job as it does not need the upstream and downstream tasks to run simultaneously.

As a whole, Flink provides two different types of blocking shuffles; Hash shuffle and Sort shuffle.

They will be detailed in the following sections.

Hash Shuffle #

The default blocking shuffle implementation, Hash Shuffle, has each upstream task persist its results in a separate file for each downstream task on the local disk of the TaskManager. When the downstream tasks run, they will request partitions from the upstream TaskManager’s, which read the files and transmit data via the network.

Hash Shuffle provides different mechanisms for writing and reading files:

  • file: Writes files with the normal File IO, reads and transmits files with Netty FileRegion. FileRegion relies on sendfile system call to reduce the number of data copies and memory consumption.
  • mmap: Writes and reads files with mmap system call.
  • Auto: Writes files with the normal File IO, for file reading, it falls back to normal file option on 32 bit machine and use mmap on 64 bit machine. This is to avoid file size limitation of java mmap implementation on 32 bit machine.

The different mechanism could be chosen via TaskManager configurations.

This option is experimental and might be changed future.
If SSL is enabled, the file mechanism can not use FileRegion and instead uses an un-pooled buffer to cache data before transmitting. This might cause direct memory OOM. Additionally, since the synchronous file reading might block Netty threads for some time, the SSL handshake timeout needs to be increased to avoid connection reset errors.
The memory usage of mmap is not accounted for by configured memory limits, but some resource frameworks like Yarn will track this memory usage and kill the container if memory exceeds some threshold.

To further improve the performance, for most jobs we also recommend enabling compression unless the data is hard to compress.

Hash Shuffle works well for small scale jobs with SSD, but it also have some disadvantages:

  1. If the job scale is large, it might create too many files, and it requires a large write buffer to write these files at the same time.
  2. On HDD, when multiple downstream tasks fetch their data simultaneously, it might incur the issue of random IO.

Sort Shuffle #

Sort Shuffle is another blocking shuffle implementation introduced in version 1.13. Different from Hash Shuffle, sort shuffle writes only one file for each result partition. When the result partition is read by multiple downstream tasks concurrently, the data file is opened only once and shared by all readers. As a result, the cluster uses fewer resources like inode and file descriptors, which improves stability. Furthermore, by writing fewer files and making a best effort to read data sequentially, sort shuffle can achieve better performance than hash shuffle, especially on HDD. Additionally, sort shuffle uses extra managed memory as data reading buffer and does not rely on sendfile or mmap mechanism, thus it also works well with SSL. Please refer to FLINK-19582 and FLINK-19614 for more details about sort shuffle.

There are several config options that might need adjustment when using sort blocking shuffle:

Currently sort shuffle only sort records by partition index instead of the records themselves, that is to say, the sort is only used as a data clustering algorithm.

Choices of Blocking Shuffle #

As a summary,

  • For small scale jobs running on SSD, both implementation should work.
  • For large scale jobs or for jobs running on HDD, sort shuffle should be more suitable.
  • In both case, you may consider enabling compression to improve the performance unless the data is hard to compress.