GitHub user danny0405 edited a discussion: RLI support for Flink streaming
## Background Flink does not support RLI while spark does, this caused inconsistency between engines, for tables migrated from Spark to flink streaming, the index type needs to be switched to either bucket or flink_state , this caused a overhead for users in production. Another reason is for multiple partition upserts, currently the only choice is flink_state index, but the flink_state actually costs a lot of memory and can not be shared between different workloads. ## Goals - Impl reliable and performant write and read support for RLI via Flink APIs [VC: list these out] - The RLI impl is engines compatible, for e.g, Flink can access and utilize the RLI written by Spark and vice versa - The RLI is global, upserts among partitions is supported; Also support partition level RLI for large fact tables. - Async compaction for MDT when RLI is enabled; in writer pipeline or table services background job. - Smart caching of RLI - Clearly set limits for the kind of write throughput supported by RLI (based on certain average response time for the RLI access, like from x0ms to x00ms) via empirical benchmarks - Ability to be expanded to arbitrary secondary indexing on different columns (or) at-least be compatible with how such secondary indexes can be built outside of Flink pipeline, in a consistent fashion. ## Non Goals [VC: what are these?] ## The Design The high-level ideas: - a RLI based index backend will be there to replace the flink_state index; - a cache of RLI would be introduced to speed the access - reuse the write function to write the RLI payloads for two reasons: a): less slots b): flink basically do append write, the cost for figuring out the RLI changes during data file write is super high, here the RLI changes can already be figured during the probe of the RLI before the stream write function - the MDT RLI files is written synchronously with the data table data files, the metadata is sent to the coordinator for a final commit to the MDT(after `FILES` partition is ready) - the MDT compaction is switched to be async and the data files compaction pipeline is reused for less take up of task slots. ## The Impl ### The Write #### The RLI Access In `BucketAssigner` operator, the RLI index metadata would be utilitized as the index backend, the `BucketAssigner` operator will probe the RLI with the incoming record keys to figure out whether msg is update or insert or delete. In other words, the RLI index metadata will served as the same role of the `flink_state` index. ##### The Cache of RLI Access We need fast access in streaming to have high throughput(ideally per record access should be < 10ms), thus a general hotspot cache is needed. We will build a in-memory LRU cache by the active upsert records keys, the cache items will be force evictted by a configured memory threshold. We also need a memory cache for the index mappings of current checkpoint because it is not committed to Hudi table yet so invisible. #### The Shuffle of RLI Payloads Then the index items are shuffled by `hash(record_key) % num_rli_shards`(the **same hashing algorithm** of the MDT `RLI index` partitioner), this is critical to avoid _N*M_ files to write to MDT partition(N is the RLI partition buckets number, M is the data table buckets involved in the current write). To elaborate, the `BucketAssigner` operator is responsible for tagging the incoming records as I/U/D, then outputs two kinds of msgs for each of the incoming record: A). index record B). data record The partitioning is customized such that index records are partitioned by `hash(record key)%num_rli_shards` AND data records are shuffled by `bucket/file group id`. #### The RLI Write In `StreamWrite` operator, the index items are buffered first and write to the MDT after the data items are flushed(triggered by Flink checkpoint), the write status metadata will be sent to the `coordinator` altogether with the data table write status metadata. The metadata sent to the coordinator includes two parts: - A: the written data file handles ; - B: the written MDT file handles(specifically under the RLI partition) #### The Commit of MDT(including RLI) And when commit to the data table, the MDT is committed firstly with the partial RLI write metadata list(the MDT RLI partition file handles info), the `RLI` partition file handles info would be commited altogether with the `FILES` partition. On Flink checkpoint : each index/data writing task flushes all its records to RLI and data files respectively. So the RLI and data files are always consistent. We commit both as we do now, from Coordinator into a single hudi commit. <img width="2582" height="1020" alt="image" src="https://github.com/user-attachments/assets/9cb080de-14b9-467d-a926-32f59fc8a1ab"; /> In order to keep exactly once semantics for job recovery, the write status metadata will also needs to be stored both in the `StreamWrite` operator and the `coordinator`, pretty much the same behaviors of the current maintainance of the data table metadata. ### The Compaction In order not take up too much task slot, we will reuse the current compaction sub-pipeline for scalable execution of the MDT compaction, it is auto applied when RLI is enabled. <img width="2558" height="620" alt="image" src="https://github.com/user-attachments/assets/4f539ae3-57ca-4e38-a0c6-e5bbe92e65ec"; /> ## Open Questions needs to benchmark the read perf of index items in BucketAssign op, to see if we need to introudce layered cache strategies similiar with RocksDB ; ## Appendix ### SI support Because SI needs to be figured out on the fly after data files are created, we generally needs another SI write op to handle the write of SI, the op parallelism is same with the SI partition buckets number, the shffle strategy should be in line with the MDT SI partitioner. The partial commit metadata of SI is written in to an auxilury file instead sending to the coordinator because the coordinator is just designed for one operator scope and can not be shared by multiple operators. This will increase the checkpoint time a lot and has risk of checkpoint timeout and backpressure for hight volumn workloads. ~~One solution is to build the SI async before queries but that is another story and not in the scope of this design.~~ <img width="2234" height="1102" alt="image" src="https://github.com/user-attachments/assets/05fce9bb-877b-41ea-9043-13493c085ac0"; /> GitHub link: https://github.com/apache/hudi/discussions/17452 ---- This is an automatically sent email for [email protected]. To unsubscribe, please send an email to: [email protected]
