  We study the problem of load balancing in distributed stream processing
engines, which is exacerbated in the presence of skew. We introduce Partial Key
Grouping (PKG), a new stream partitioning scheme that adapts the classical
"power of two choices" to a distributed streaming setting by leveraging two
novel techniques: key splitting and local load estimation. In so doing, it
achieves better load balancing than key grouping while being more scalable than
shuffle grouping. We test PKG on several large datasets, both real-world and
synthetic. Compared to standard hashing, PKG reduces the load imbalance by up
to several orders of magnitude, and often achieves nearly-perfect load balance.
This result translates into an improvement of up to 60% in throughput and up to
45% in latency when deployed on a real Storm cluster.
