EC-Shuffle: Dynamic Erasure Coding Optimization for Efficient and Reliable Shuffle in Spark

Xin Yao, Cho-Li Wang, Mingzhe Zhang EC-Shuffle: Dynamic Erasure Coding Optimization for Efficient and Reliable Shuffle in Spark

Download paper here

Abstract: Fault-tolerance capabilities attract increasing attention from existing data processing frameworks, such as Apache Spark. To avoid replaying costly distributed computation, like shuffle, local checkpoint and remote replication are two popular approaches. They incur significant runtime overhead, such as extra storage cost or network traffic. Erasure coding is another emerging technology, which also enables data resilience. It is perceived as capable of replacing the checkpoint and replication mechanisms for its high storage efficiency. However, it suffers heavy network traffic due to distributing data partitions to different locations. In this paper, we propose EC-Shuffle with two encoding schemes and optimize the shuffle-based operations in Spark or MapReduce-like frameworks. Specifically, our encoding schemes concentrate on optimizing the data traffic during the execution of shuffle operations. They only transfer the parity chunks generated via erasure coding, instead of a whole copy of all data chunks. EC-Shuffle also provides a strategy, which can dynamically select the per-shuffle biased encoding scheme according to the number of senders and receivers in each shuffle. Our analyses indicate that this dynamic encoding selection can minimize the total size of parity chunks. The extensive experimental results using BigDataBench with hundreds of mappers and reducers shows this optimization can reduce up to 50% network traffic and achieve up to 38% performance improvement.