usman-m

18 ms·

1.
Show HN: Claudespace – Run isolated Claude Code instances locally(github.com)
3 points by usman-m 1y ago|1 comments
2.
by usman-m 9y ago
Curious how you guys use contexts?
3.
Redis-tdigest: a Redis module for online rank-based statistics estimation(github.com)
3 points by usman-m 10y ago|1 comments
4.
by usman-m 10y ago
Almost. Currently continuous views that contain an ORDER BY or LIMIT clause are not supported.
5.
by usman-m 10y ago
They work better for non-sliding window queries. Triggers on sliding window queries are much more resource intensive, both for CPU and memory. Essentially, the trigger process has to keep track of tuples for each step in the window and comb
6.
ContinuouSQL Triggers(pipelinedb.com)
24 points by usman-m 10y ago|7 comments
7.
PipelineDB Case Study: SmartNews(pipelinedb.com)
7 points by usman-m 10y ago|0 comments
8.
PipelineDB 0.9.0 – Streaming Topologies and PostgreSQL 9.5 Compatibility(pipelinedb.com)
8 points by usman-m 11y ago|0 comments
9.
Announcing PipelineDB Enterprise(pipelinedb.com)
3 points by usman-m 11y ago|1 comments
10.
by usman-m 11y ago
Great stuff! For now it seems like this leaves out the job of finding crowd workers. Could it be possible to do something like designing microtasks in MTurk and "stitching" the workflow together using Orchestra?
11.
SQL on Kafka(pipelinedb.com)
15 points by usman-m 11y ago|1 comments
12.
by usman-m 11y ago
Our native implementations of all probabilistic data structures use MurmurHash3, so this isn't a problem. The dumbloom implementation is in no way a good Bloom filter, as the name suggests :)
13.
by usman-m 11y ago
It probably uses a HyperLogLog--the 2% error rate kind of gives it away. Bloom filters approximate set membership queries, HyperLogLogs approximate set cardinality queries. COUNT DISTINCT is a set cardinality query. We actually support a Hy
14.
by usman-m 11y ago
Oops, it was meant to be: SELECT <user_id> FROM (SELECT DISTINCT user_id FROM user_actions); You're absolutely right that both those queries will give the same result. I guess I was trying to motivate the basic problem of
15.
by usman-m 11y ago
ahachete, I'm not sure if I totally understand your question. Continuous views are consumers for streams. You can think of them as high throughput real-time materialized views. The source of data for the stream can be practically anyth
16.
by usman-m 11y ago
Mostly that. I've also been thinking about how we could incorporate some machine learning algorithms, like online perceptrons.
17.
Making Postgres Bloom(pipelinedb.com)
69 points by usman-m 11y ago|23 comments
18.
PipelineDB: streaming Postgres(databasesoup.com)
21 points by usman-m 11y ago|0 comments
19.
by usman-m 11y ago
Not right now, but it's definitely a feature we're thinking about. Awesome--let us know what you think about it!
20.
by usman-m 11y ago
It's not so much as how different it is from an architectural standpoint as it is about the sheer magnitude of such a feature. All open-source communities have processes which help maintain high quality, but also add a bit of red tape.