Blocking Shuffle #
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
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
sendfilesystem call to reduce the number of data copies and memory consumption.
mmap: Writes and reads files with
Auto: Writes files with the normal File IO, for file reading, it falls back to normal
fileoption on 32 bit machine and use
mmapon 64 bit machine. This is to avoid file size limitation of java
mmapimplementation 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
filemechanism can not use
FileRegionand 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
mmapis 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:
- 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.
- 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
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:
- taskmanager.network.blocking-shuffle.compression.enabled: Config option for shuffle data compression. it is suggested to enable it for most jobs except that the compression ratio of your data is low.
- taskmanager.network.sort-shuffle.min-parallelism: Config option to enable sort shuffle depending on the parallelism of downstream tasks. If parallelism is lower than the configured value,
hash shufflewill be used, otherwise
sort shufflewill be used.
- taskmanager.network.sort-shuffle.min-buffers: Config option to control data writing buffer size. For large scale jobs, you may need to increase this value, usually, several hundreds of megabytes memory is enough.
- taskmanager.memory.framework.off-heap.batch-shuffle.size: Config option to control data reading buffer size. For large scale jobs, you may need to increase this value, usually, several hundreds of megabytes memory is enough.
sort shuffleonly sort records by partition index instead of the records themselves, that is to say, the
sortis 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 shuffleshould be more suitable.
- In both case, you may consider enabling compression to improve the performance unless the data is hard to compress.