Thanks for the thoughtful and constructive feedback — you're absolutely right that this isn't a strict apples-to-apples comparison. Our aim was to evaluate practical performance in edge workloads, especially for MQTT-style use cases on constrained devices like Raspberry Pi.
A few clarifications:
Indexing: AnuDB supports indexing via an explicit API — the user needs to define indexes manually. Internally, it's backed by RocksDB and uses a prefix extractor to optimize lookups. While it's not a full SQL-style index planner, it's efficient for our document-store model.
Parallel Writes: SQLite does well in many embedded use cases, but it struggles with highly parallel writes — even in WAL mode. RocksDB (and thus AnuDB) is built for concurrency and handles write-heavy parallel loads much better. That shows in our "Parallel" test.
Dataset Size: Agreed, 10K entries is small. We kept it modest to demonstrate behavior under low-latency edge conditions, but we’re planning larger-scale tests in follow-ups.
Hardware: The test was done on a Raspberry Pi 2 with 1GB RAM and microSD storage. Thanks for pointing out that CPU/peripheral differences could affect results — that’s something we’ll document better in future benchmarks.
Use Case Focus: You're spot on about the importance of use-case-driven evaluation. AnuDB was motivated by the need for a lightweight document database for IoT and edge scenarios with MQTT support — not as a direct SQLite replacement, but as an alternative where document flexibility and concurrent ingestion matter.
A few clarifications:
Indexing: AnuDB supports indexing via an explicit API — the user needs to define indexes manually. Internally, it's backed by RocksDB and uses a prefix extractor to optimize lookups. While it's not a full SQL-style index planner, it's efficient for our document-store model.
Parallel Writes: SQLite does well in many embedded use cases, but it struggles with highly parallel writes — even in WAL mode. RocksDB (and thus AnuDB) is built for concurrency and handles write-heavy parallel loads much better. That shows in our "Parallel" test.
Dataset Size: Agreed, 10K entries is small. We kept it modest to demonstrate behavior under low-latency edge conditions, but we’re planning larger-scale tests in follow-ups.
Hardware: The test was done on a Raspberry Pi 2 with 1GB RAM and microSD storage. Thanks for pointing out that CPU/peripheral differences could affect results — that’s something we’ll document better in future benchmarks.
Use Case Focus: You're spot on about the importance of use-case-driven evaluation. AnuDB was motivated by the need for a lightweight document database for IoT and edge scenarios with MQTT support — not as a direct SQLite replacement, but as an alternative where document flexibility and concurrent ingestion matter.