fast-sqlite3-inserts VS dqlite

I am experimenting with SQLite, where I try inserting 1B rows in under a minute. The current best is inserting 100M rows at 23s. I cut many corners to get performance, but the tweaks might suit your workload.

I have explained my rationale and approach here - https://avi.im/blag/2021/fast-sqlite-inserts/

the repo link - https://github.com/avinassh/fast-sqlite3-inserts

I am working on a project [0] to generate 1 billion rows in SQLite under a minute and inserted 100M rows inserts in 33 seconds. First, I generate the rows and insert them in an in-memory database, then flush them to the disk at the end. To flush it to disk it takes only 2 seconds, so 99% of the time is being spent generating and adding rows to the in-memory B Tree.

For Python optimisation, have you tried PyPy? I ran my same code (zero changes) using PyPy, and I got 3.5x better speed.

I published my findings here [1].

[0] - https://github.com/avinassh/fast-sqlite3-inserts

[1] - https://avi.im/blag/2021/fast-sqlite-inserts/

I have a side project where I tried to insert one billion rows in SQLite. I was able to insert 100 million rows using Python under 210 seconds. The same thing with PyPy took 120 seconds. I am wondering what kind of speed boost I would get with Cython

Measure, measure, measure! There is a PR which made really minor changes, but it got 2x speed boost with CPython version

[somewhat related] Recently, I was benchmarking SQLite inserts and I managed to insert 3.3M records per second (100M in 33 ish seconds) on my local machine - https://github.com/avinassh/fast-sqlite3-inserts Ofcourse the comparison is not apples to apples, but sharing here if anyone finds it interesting

If you're interested in this, here are some related projects that all take slightly different approaches:

- LiteSync directly competes with Marmot and supports DDL sync, but is closed source commercial (similar to SQLite EE): https://litesync.io

- dqlite is Canonical's distributed SQLite that depends on c-raft and kernel-level async I/O: https://dqlite.io

- cr-sqlite is a Rust-based loadable extension that adds CRDT changeset generation and reconciliation to SQLite: https://github.com/vlcn-io/cr-sqlite

Slightly related but not really (no multi writer, no C-level SQLite API or other restrictions):

- comdb2 (Bloombergs multi-homed RDMS using SQLite as the frontend)

- rqlite: RDMS with HTTP API and SQLite as the storage engine, used for replication and strong consistency (does not scale writes)

- litestream/LiteFS: disaster recovery replication

- liteserver: active read-only replication (predecessor of LiteSync)

I'd be curious for a similar tuning with Dqlite: https://github.com/canonical/dqlite

Anyone knows how the DB this is about, https://rqlite.io/, compares with https://dqlite.io/ by Canonical (both seem to be distributed versions of sqlite)?

For folks' context, the new tool that's being discussed in the thread mentioned by the parent here is litefs [0], as well as which you can also look at rqlite [1] and dqlite [2], which all provide different trade-offs (e.g. rqlite is 'more strongly consistent' than litefs).

[0]: https://github.com/superfly/litefs

[1]: https://github.com/rqlite/rqlite

[2]: https://github.com/canonical/dqlite

I presume you're familiar with https://github.com/canonical/dqlite (made by my employer) and https://github.com/rqlite/rqlite (unrelated)? How will mvsqlite compare to those?

Check out dqlite, it's sqlite but with a raft consensus to distribute changes through a log: https://dqlite.io/ You can link it in as a library too, it sounds like exactly what you want.