Hi, +1, I think it will be a very great tool for Flink, especially the creating new state part. On production, we’re really worried about the availability of the savepoints, because the generating logic is inside Flink and we don’t have a good way to validate it. But with this tool, we can construct a new state for our programs very soon even if the savepoints data is broken. It’s great, thanks!
Original Message Sender:Jamie grierjgr...@lyft.com Recipient:dev...@flink.apache.org Date:Saturday, Aug 18, 2018 02:32 Subject:Re: [Proposal] Utilities for reading, transforming and creatingStreaming savepoints This is great, Gyula! A colleague here at Lyft has also done some work around bootstrapping DataStream programs and we've also talked a bit about doing this by running DataSet programs. On Fri, Aug 17, 2018 at 3:28 AM, Gyula Fóra gyula.f...@gmail.com wrote: Hi All! I want to share with you a little project we have been working on at King (with some help from some dataArtisans folks). I think this would be a valuable addition to Flink and solve a bunch of outstanding production use-cases and headaches around state bootstrapping and state analytics. We have built a quick and dirty POC implementation on top of Flink 1.6, please check the README for some nice examples to get a quick idea: https://github.com/king/bravo *Short story* Bravo is a convenient state reader and writer library leveraging the Flink’s batch processing capabilities. It supports processing and writing Flink streaming savepoints. At the moment it only supports processing RocksDB savepoints but this can be extended in the future for other state backends and checkpoint types. Our goal is to cover a few basic features: - Converting keyed states to Flink DataSets for processing and analytics - Reading/Writing non-keyed operators states - Bootstrap keyed states from Flink DataSets and create new valid savepoints - Transform existing savepoints by replacing/changing some states Some example use-cases: - Point-in-time state analytics across all operators and keys - Bootstrap state of a streaming job from external resources such as reading from database/filesystem - Validate and potentially repair corrupted state of a streaming job - Change max parallelism of a job Our main goal is to start working together with other Flink production users and make this something useful that can be part of Flink. So if you have use-cases please talk to us :) I have also started a google doc which contains a little bit more info than the readme and could be a starting place for discussions: https://docs.google.com/document/d/103k6wPX20kMu5H3SOOXSg5PZIaYpw dhqBMr-ppkFL5E/edit?usp=sharing I know there are a bunch of rough edges and bugs (and no tests) but our motto is: If you are not embarrassed, you released too late :) Please let me know what you think! Cheers, Gyula
