Paper notes: Stream Processing at Google with Millwheel
MillWheel: Fault-Tolerant Stream Processing at Internet Scale
Akidau et. al., VLDB 2013
The big idea: Streaming computations at scale are nothing new. Millwheel is a standard DAG stream processor, but one that runs at ‘Google’ scale. This paper really answers the following questions: what guarantees should be made about delivery and fault-tolerance to support most common use cases cheaply? What optimisations become available if you choose these guarantees carefully?
- A tight bound on the timestamp of all events still in the system, called the low watermark. This substitutes for in-order delivery by allowing processors to know when a time period has been fully processed (nice observation: you don’t necessarily care about receiving events in order, but you do care about knowing whether you’ve seen all the events before a certain time).
- Persistent storage available at all nodes in the stream graph
- Exactly once delivery semantics.
- Triggerable computations called timers that are executed in node context when either a low watermark or wall clock time is observed. This can be modelled as a separate source of null events that allows periodic roll-up to be done.
Each node in the graph receives input events, optionally computes some aggregate, and also optionally emits one or more output events as a result. In order to scale and do load balancing, each input record must have a key (the key extraction function is user defined and can change between nodes); events with identical keys are sent to the same node for processing.
The low watermark at A is defined as min(oldest event still in A, low watermark amongst all streams sending to A). Watermarks are tracked centrally. Note that the monotonic increasing property of watermarks requires that they enter the system in time order, and therefore we can’t track arbitrary functions as watermarks.
State is persisted centrally. To ensure atomicity, only one bulk write is permitted per event. To avoid zombie writers (where work has been moved elsewhere through failure detection or through load balancing), every writer has a lease or sequencer that ensures only they may write. The storage system allows for this to be atomically checked at the same time as a write (i.e. conditional atomic writes).
Emitted records are checkpointed before delivery so that if an acknowledgment is not received the record can be re-sent (duplicates are discarded by MillWheel at the recipient). The checkpoints allow fault-tolerance: if a processor crashes and is restarted somewhere else any intermediate computations can be recovered. When a delivery is ACKed the checkpoints can be garbage collected. The Checkpoint->Delivery->ACK->GC sequence is called a strong production. When a processor restarts, unacked productions are resent. The recipient de-duplicates.
In some cases, Millwheel users can optimise by allowing events to be sent before the checkpoint is committed to persistent storage – this is called a weak production. Weak productions are possible usually if the processing of an event is idempotent with respect to the persistent storage and event production (i.e. you always send the same event once you commit to sending it once (so aggregates over time don’t necessarily work) and / or receipt of those events multiple times doesn’t cause inconsistencies).