For this demo I generated a 100TB data set (50GB/min) and maintained sub-second query latencies

teamraft.com/resources/in...

The JVM internals series - #5 is out..

This episode lays out a good foundation for us to go into the internals of each of the amazing GCs and their tradeoffs.

Become a member to access the upcoming internals episode.. Meanwhile watch this video (free access until tomorrow). youtu.be/p1Yamdly0QE

With @apachepinot.bsky.social I can query a 100TB data set and using it’s Star-tree index can get 20ms query latency!

@startreedata.bsky.social Thank you and Merry Christmas! My community box arrived last night and with my winnings from the MI competition I recently purchased a pair of skis. #startree

@apachepinot.bsky.social It's a bit early for xmas, but thanks for all the features! youtu.be/gwcL7O0o6W4?...

@apachepinot.bsky.social Welcome!

Last night, I was exploring thread safety issues with ChatGPT. It only took two attempts to generate a test that exposed the lack of a synchronized block for a section that updates multiple member variables. #java

What does this program output? #java

90 days ago Bluesky had 6.18M users
30 days ago Bluesky had 10.85M users
Tomorrow Bluesky will have 15M users

This is absolutely wild, thanks for believing in us y'all

In which cases is performance more important than correctness?

Approximations. Like Pinot’s DISTINCTCOUNTHLL which returns an approximate distinct count using HyperLogLog.

Does anyone else have a good example?

Working on JSON predicate pushdown for the Trino-Pinot connector:

contains((1,2,3), json_extract_scalar(json, '$.key`)) -> json_match(json, '$.key in (1,2,3)')

json_array_contains(json_extract(json, '$.array'), value) -> json_match(json, '$.array[*] = value')

json_match uses Pinot's JSON index

It’s a good sign when a friend surprisingly welcomes you on your 1st day at a new job. Thanks @nbuesing.bsky.social !