Paul Nordstrom

MillWheel: fault-tolerant stream processing at internet scale

MillWheel is a framework for building low-latency data-processing applications that is widely used at Google. Users specify a directed computation graph and application code for individual nodes, and the system manages persistent state and the continuous flow of records, all within the envelope of the frameworks fault-tolerance guarantees. This paper describes MillWheels programming model as well as its implementation. The case study of a continuous anomaly detector in use at Google serves to motivate how many of MillWheels features are used. MillWheels programming model provides a notion of logical time, making it simple to write time-based aggregations. MillWheel was designed from the outset with fault tolerance and scalability in mind. In practice, we find that MillWheels unique combination of scalability, fault tolerance, and a versatile programming model lends itself to a wide variety of problems at Google.