Chaining Exactly-Once Operations With Kafka

Ben Stopford shows how you can use Kafka to chain together services while maintaining exactly-once guarantees:

Any service-based architecture is itself a distributed system, a field renowned for being difficult, particularly when things go wrong. We have thought experiments like The Two Generals Problemand proofs like FLP which highlight that these systems are difficult to work with.

In practice we make compromises. We rely on timeouts. If one service calls another service and gets an error, or no response at all, it retries that call in the knowledge that it will get there in the end.

The problem is that retries can result in duplicate processing—which can cause very real problems. Taking a payment, twice, from someone’s account will lead to an incorrect balance. Adding duplicate tweets to a user’s feed will lead to a poor user experience.  The list goes on.

I just had a discussion at SQL Saturday Albany about this exact thing, and the pain of rolling your own solutions.

Related Posts

Page Ranking With Kafka Streams

Hunter Kelly walks through a page ranking algorithm: Once you have the adjacency matrix, you perform some straightforward matrix calculations to calculate a vector of Hub scores and a vector of Authority scores as follows: Sum across the columns and normalize, this becomes your Hub vector Multiply the Hub vector element-wise across the adjacency matrix […]

Read More

Stateful Processing In Spark Streaming

Bill Chambers and Jules Damji look at a couple of stateful scenarios within Spark Streaming: No streaming events are free of duplicate entries. Dropping duplicate entries in record-at-a-time systems is imperative—and often a cumbersome operation for a couple of reasons. First, you’ll have to process small or large batches of records at time to discard […]

Read More

Categories

July 2017
MTWTFSS
« Jun Aug »
 12
3456789
10111213141516
17181920212223
24252627282930
31