AnuDB– Backed on RocksDB, 279x Faster Than SQLite in Parallel Workloads
We recently benchmarked AnuDB, a lightweight embedded database built on top of RocksDB, against SQLite on a Raspberry Pi. The performance difference, especially for parallel operations, was dramatic. GitHub Links: AnuDBBenchmark: https://github.com/hash-anu/AnuDBBenchmark AnuDB (Core): https://github.com/hash-anu/AnuDB Why Compare AnuDB and SQLite? SQLite is excellent for many embedded use cases — it’s simple, battle-tested, and extremely reliable. But it doesn't scale well when parallelism or concurrent writes are required. AnuDB, built over RocksDB,…
In plain words
AnuDB is a lightweight embedded database built on RocksDB that handles concurrent operations and parallel workloads more efficiently than SQLite. It uses RocksDB and MsgPack serialization for improved performance in scenarios requiring multiple simultaneous writes and queries. AnuDB is designed for developers building applications on resource-constrained devices like Raspberry Pi who need better concurrency support than traditional SQLite implementations provide.
written from the facts on this page · September 2026
From the sources
In the maker’s words, at launch
We recently benchmarked AnuDB, a lightweight embedded database built on top of RocksDB, against SQLite on a Raspberry Pi. The performance difference, especially for parallel operations, was dramatic. GitHub Links: AnuDBBenchmark: https://github.com/hash-anu/AnuDBBenchmark AnuDB (Core): https://github.com/hash-anu/AnuDB Why Compare AnuDB and SQLite? SQLite is excellent for many embedded use cases — it’s simple, battle-tested, and extremely reliable. But it doesn't scale well when parallelism or concurrent writes are required. AnuDB, built over RocksDB, offers better concurrency out of the box. We wanted to measure the practical differences using real benchmarks on a Raspberry Pi. Benchmark Setup Platform: Raspberry Pi 2 (ARMv7) Benchmarked operations: Insert, Query, Update, Delete, Parallel AnuDB uses RocksDB and MsgPack serialization SQLite uses raw data, with WAL mode enabled for fairness Key Results Insert: AnuDB: 448 ops/sec SQLite: 838 ops/sec Query: AnuDB: 54 ops/sec SQLite: 30 ops/sec Update: AnuDB: 408 ops/sec SQLite: 600 ops/sec Delete: AnuDB: 555 ops/sec SQLite: 1942 ops/sec Parallel (10 threads): AnuDB: 412 ops/sec SQLite: 1.4 ops/sec (!) In the parallel case, AnuDB was over 279x faster than SQLite. Why the Huge Parallel Difference? SQLite, even with WAL mode, uses global database-level locks. It’s not designed for high-concurrency scenarios. RocksDB (used in AnuDB) supports: Fine-grained locking Concurrent readers/writers Better parallelism using LSM-tree architecture This explains why AnuDB significantly outperforms SQLite under threaded workloads. Try It Yourself Clone the repo: git clone https://github.com/hash-anu/AnuDBBenchmark cd AnuDBBenchmark ./build.sh /path/to/AnuDB /path/to/sqlite ./benchmark Results are saved to benchmark_results.csv. When to Use AnuDB Use AnuDB if: You need embedded storage with high concurrency You’re dealing with telemetry, sensor data, or parallel workloads You want something lightweight and faster than SQLite under load Stick with SQLite if: You need SQL compatibility You value mature ecosystem/tooling Feedback Welcome This is an early experiment. We’re actively developing AnuDB and would love feedback: Is our benchmark fair? Where could we optimize further? Would this be useful in your embedded project?
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