fast-sqlite3-inserts VS dynamic-dns

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