Hi Dikang, Cool stuff. 2 questions. Based on your presentation at ngcc, it seems like rocks db stores things in byte order. Does this mean that you have code that makes each of the existing types byte comparable, or is clustering order implementation dependent? Also, I don't see anything in the draft api that seems to support splitting the data set into arbitrary categories (ie repaired and unrepaired data living in the same token range). Is support for incremental repair planned for v1?
Thanks, Blake On October 4, 2017 at 1:28:01 PM, Dikang Gu (dikan...@gmail.com) wrote: Hello C* developers: In my previous email (https://www.mail-archive.com/dev@cassandra.apache.org/msg11024.html), I presented that Instagram was kicking off a project to make C*'s storage engine to be pluggable, as other modern databases, like mysql, mongoDB etc, so that users will be able to choose most suitable storage engine for different work load, or to use different features. In addition to that, a pluggable storage engine architecture will improve the modularity of the system, help to increase the testability and reliability of Cassandra. After months of development and testing, we'd like to share the work we have done, including the first(draft) version of the C* storage engine API, and the first version of the RocksDB based storage engine. For the C* storage engine API, here is the draft version we proposed, https://docs.google.com/document/d/1PxYm9oXW2jJtSDiZ-SR9O20jud_0jnA-mW7ttp2dVmk/edit. It contains the APIs for read/write requests, streaming, and table management. The storage engine related functionalities, like data encoding/decoding format, on-disk data read/write, compaction, etc, will be taken care by the storage engine implementation. Each storage engine is a class with each instance of the class is stored in the Keyspace instance. So all the column families within a keyspace will share one storage engine instance. Once a storage engine instance is created, Cassandra sever issues commands to the engine instance to performance data storage and retrieval tasks such as opening a column family, managing column families and streaming. How to config storage engine for different keyspaces? It's still open for discussion. One proposal is that we can add the storage engine option in the create keyspace cql command, and potentially we can overwrite the option per C* node in its config file. Under that API, we implemented a new storage engine, based on RocksDB, called RocksEngine. In long term, we want to support most of C* existing features in RocksEngine, and we want to build it in a progressive manner. For the first version of the RocksDBEngine, we support following features: Most of non-nested data types Table schema Point query Range query Mutations Timestamp TTL Deletions/Cell tombstones Streaming We do not supported following features in first version yet: Multi-partition query Nested data types Counters Range tombstone Materialized views Secondary indexes SASI Repair At this moment, we've implemented the V1 features, and deployed it to our shadow cluster. Using shadowing traffic of our production use cases, we saw ~3X P99 read latency drop, compared to our C* 2.2 prod clusters. Here are some detailed metrics: https://docs.google.com/document/d/1DojHPteDPSphO0_N2meZ3zkmqlidRwwe_cJpsXLcp10. So if you need the features in existing storage engine, please keep using the existing storage engine. If you want to have a more predictable and lower read latency, also the features supported by RocksEngine are enough for your use cases, then RocksEngine could be a fit for you. The work is 1% finished, and we want to work together with community to make it happen. We presented the work in NGCC last week, and also pushed the beta version of the pluggable storage engine to Instagram github Cassandra repo, rocks_3.0 branch (https://github.com/Instagram/cassandra/tree/rocks_3.0), which is based on C* 3.0.12, please feel free to play with it! You can download it and follow the instructions (https://github.com/Instagram/cassandra/blob/rocks_3.0/StorageEngine.md) to try it out in your test environment, your feedback will be very valuable to us. Thanks Dikang.
