Apache Avro VS RocksDB

Apache AVRO [1] is one but it has been largely replaced by Parquet [2] which is a hybrid row/columnar format

[1] https://avro.apache.org/

The most common format for describing schema in this scenario is Apache Avro.

I am testing a new schema registry which loads and retrieves different kinds of avro schemas. In the process of testing, I need to create a bunch of different types of avro schemas. As it involves a lot of permutations, I decided to create the schema programmatically.I am using the apache avro SchemaBuilder to do so.

Other serialization alternatives have a schema validation option: e.g., Avro, Kryo and Protocol Buffers. Interestingly enough, gRPC uses Protobuf to offer RPC across distributed components:

Apache Avro is a data serialization system, for more information visit Apache Avro

Once things like JSON became more popular Apache Avro appeared. You can define Avro files which can then be generated into Python, Java C, Ruby, etc.. classes.

Avro, a data serialization system based on JSON schemas.

Supporting multiple versions of an event schema is a solved problem. Apache Avro with a published schema hash in a message header is one solution.

https://avro.apache.org/

RocksDB: A high-performance embedded database optimized for multi-core CPUs and fast storage like SSDs. Its use of a log-structured merge-tree (LSM tree) makes it suitable for applications requiring high throughput and efficient storage, such as streaming data processing.

[RocksDB](https://rocksdb.org/) isn’t a distributed storage system, fwiw. It’s an embedded KV engine similar to LevelDB, LMDB, or really sqlite (though that’s full SQL, not just KV)

To output the top 3 rocks, our engine has to first store all the rocks in some sorted way. To do this, we of course picked RocksDB, an embedded lexicographically sorted key-value store, which acts as the sorting operation's persistent state. In our RocksDB state, the diffs are keyed by the value of weight, and since RocksDB is sorted, our stored diffs are automatically sorted by their weight.

The in-memory version of Memgraph uses Delta storage to support multi-version concurrency control (MVCC). However, for larger-than-memory storage, we decided to use the Optimistic Concurrency Control Protocol (OCC) since we assumed conflicts would rarely happen, and we could make use of RocksDB’s transactions without dealing with the custom layer of complexity like in the case of Delta storage.

I was looking at https://github.com/facebook/rocksdb/ but it seems to not allow queries by value, as my last requirmenet.

Tuning RocksDB well is a very very hard challenge, and one that I am happy to not do day to day anymore. RocksDB is very powerful but it comes with other very sharp edges. Compaction is one of those, and all answers are likely workload dependent.

If you are worried about write amplification then leveled compactions are sub-optimal. I would try the universal compaction.

- https://github.com/facebook/rocksdb/wiki/Universal-Compactio...

It's fairly challenging to write a KV database, and takes several years of development to get the balance right between performance and reliability and avoiding data loss. Maybe read through the documentation for RocksDB https://github.com/facebook/rocksdb/wiki/RocksDB-Overview and watch the video on why it was developed and that may give you an impression of what is involved.

LMDB is much more sain in the sense that it supports real ACID transactions instead of savepoints for RocksDB. The latter is heavy and consumes a lot more memory for a lot less read throughput. However, RocksDB has a much better parallel and concurrent write story, where you can merge entries with merge functions and therefore write from multiple CPUs.

https://github.com/facebook/rocksdb/issues?q=is%3Aissue+clic...

Here are some bugs in JeMalloc: