Keydb Eng

RocksDB uses Log-Structured Merge-trees (LSM). Writes are first appended to an in-memory buffer (MemTable) and a write-ahead log (WAL).

Traditional Redis Architecture (Single-Threaded Event Loop) [ Network I/O ] ──> [ Single Thread (CPU Core 0) ] ──> [ In-Memory Dataset ] KeyDB Engine Architecture (Multithreaded Architecture) [ Network I/O ] ──┬──> [ Worker Thread 1 ] ──┬──> [ Shared MVCC Dataset ] ├──> [ Worker Thread 2 ] ──┤ ├──> [ Worker Thread 3 ] ──┤ └──> [ Worker Thread 4 ] ──┘ 1. True Multithreading

Because it can use multiple CPU cores, a single KeyDB instance can scale vertically, eliminating the immediate need to set up complex clustering for higher throughput. Key Features and Capabilities

: The database is typically distributed as keydb_eng.zip from community-maintained sites like FindVUK Online . keydb eng

Traditional Redis Model: Client 1 ----\ Client 2 ------> [ Network I/O Threads ] --> [ Single-Threaded Core Event Loop ] --> Database Client 3 ----/ The KeyDB Multithreaded Engine Model

: Unlike Redis, which uses a single thread for network I/O and command execution, KeyDB uses multiple threads to handle these tasks. This allows it to scale vertically as you add more CPU cores.

This design trades off perfect scalability for 80% of workloads (single-key or same-shard operations) while remaining correct for the rest. RocksDB uses Log-Structured Merge-trees (LSM)

Whether you're building a real-time leaderboard, a high-speed cache, or a complex messaging system using KeyDB Streams , it provides the low-latency and high-throughput modern apps demand.

: You don't need to rewrite your code. KeyDB remains fully compatible with the Redis protocol, modules, and scripts. You can literally swap your redis.conf for a keydb.conf and get started.

KeyDB launched as a high-performance, multithreaded fork of Redis. It dramatically improves throughput by utilizing multiple CPU cores. However, raw speed requires efficient data architecture. The heart of this architecture is the . True Multithreading Because it can use multiple CPU

Multi-threading introduces slightly higher memory usage per connection (~2–3x than Redis) because each thread maintains its own client state. For many small connections, this matters; for persistent, long-lived connections, it’s negligible.

: It supports different levels of on-disk persistence, including RDB and AOF, to ensure data durability. 3. Performance Metrics In benchmarking tests (often conducted using the

Here is a proposal for a new feature: .