py-caskdb VS fast-sqlite3-inserts

self plug: I wanted to learn how databases work internally, like how they store and retrieve data, build indexes, etc.

I built an educational KV store to teach someone to write a database from scratch. I have set up this project in TDD fashion with the tests. So, you start with simple functions, pass the tests, and the difficulty level goes up. There are hints if you get stuck. When all the tests pass, you would have written a persistent key-value store in the end.

link: https://github.com/avinassh/py-caskdb

link: https://github.com/avinassh/py-caskdb

If you like databases, you start exploring the internals and start writing one! There is no going back once you dig deep into storage internals, KV stores, and distributed systems.

plug: I made an educational project which can help you write a database in python, from scratch - https://github.com/avinassh/py-caskdb

Build your own disk based KV store

link - https://github.com/avinassh/py-caskdb

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