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IKV | Inlined Key-Value Store

IKV is a key-value store primarly designed for storing ML features. It is persistent, eventually-consistent and most importantly an embedded database. IKV is written in Rust, and provides SDKs (client libraries) in Python, Go and Java. This website provides detailed documentation for using IKV in your application.

About IKV

Cost-effective caching at sub-millisecond speed

Most key-value stores require you to allocate a dedicated database cluster which is costly (hardware + operations). IKV allows you to augment your existing application cluster with additional memory or Flash/SSD to store your data, without sacrificing on speed.

  • Sub-millisecond access latency when used with local (attached) Flash/SSD storage, which is still 2x faster than a remote all-in-memory Redis cluster
  • Automatic data-tiering and zero-copy disk reads.

Embedded & Blazing Fast

IKV can serve read requests without making any network calls, and provides single-digit microsecond P99 read latency from a client’s point-of-view. This is 100x faster than existing solutions like Redis. Read our benchmarks here.

IKV is heavily optimized for read performance (latency and throughput):

  • Zero network latency for reads (even on a cold start). No overhead of network hops, encryption and minimal ser-deserialization.
  • Frequently accessed data occupies local memory.
  • No need for index compaction (like LSM tree based stores), hence there is no resource saturation.
  • No Garbage Collection. IKV is written in Rust with client SDKs in Python, Go and Java.

Fully-managed, more than a library

Traditional embedded databases (like RocksDB or LevelDB) are just libraries, IKV does way more:

  • Data is fully persistent. Write once and read forever, without the need to perform any explicit backups or snapshots.
  • Geo-replicated. IKV's underlying data-plane (IKV Cloud or self-hosted) replicates your data globally across your application clusters.
  • Horizontal scaling - IKV scales horizontally for large datasets (with partitions) and high read/write traffic (with replication).

Usecases for IKV

IKV's primary usecase is for storing ML features for online retrieval - as part of ML inference/scoring for applications like recommendation engines, search, ecommerce, etc. It can also be used for fast general-purpose storage/caching - anything with tight latency requirements and doesn't need read-after-write consistency.