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Badger was written with these design goals in mind:

  • Write a key-value database in pure Go.
  • Use latest research to build the fastest KV database for data sets spanning terabytes.
  • Optimize for SSDs.

Badger’s design is based on a paper titled WiscKey: Separating Keys from Values in SSD-conscious Storage.


Feature Badger RocksDB BoltDB
Design LSM tree with value log LSM tree only B+ tree
High Read throughput Yes No Yes
High Write throughput Yes Yes No
Designed for SSDs Yes (with latest research 1) Not specifically 2 No
Embeddable Yes Yes Yes
Sorted KV access Yes Yes Yes
Pure Go (no Cgo) Yes No Yes
Transactions Yes, ACID, concurrent with SSI3 Yes (but non-ACID) Yes, ACID
Snapshots Yes Yes Yes
TTL support Yes Yes No
3D access (key-value-version) Yes4 No No

1 The WISCKEY paper (on which Badger is based) saw big wins with separating values from keys, significantly reducing the write amplification compared to a typical LSM tree.

2 RocksDB is an SSD optimized version of LevelDB, which was designed specifically for rotating disks. As such RocksDB’s design isn’t aimed at SSDs.

3 SSI: Serializable Snapshot Isolation. For more details, see the blog post Concurrent ACID Transactions in Badger

4 Badger provides direct access to value versions via its Iterator API. Users can also specify how many versions to keep per key via Options.


We have run comprehensive benchmarks against RocksDB, Bolt and LMDB. The benchmarking code, and the detailed logs for the benchmarks can be found in the badger-bench repo. More explanation, including graphs can be found the blog posts (linked above).