In Memory Indexing With Rocksdb Persistence Layer

via “in-memory indexing with rocksdb persistence layer”

Instant search engine with vector support.

Unique: Combines in-memory primary indexes with RocksDB persistence via Store abstraction layer, enabling fast queries with crash recovery. Uses Jemalloc for efficient memory allocation, reducing fragmentation and improving cache locality.

vs others: Faster than Elasticsearch (which uses disk-based indexes with OS page cache) for typical workloads; more durable than pure in-memory systems like Redis; simpler operational model than distributed systems like Cassandra.