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 combine them whenever any tuple is updated to get the new value.
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?
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 :)
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.
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 finding whether some user exists in a set of users, and `SELECT DISTINCT` is the SQL way of representing a set.
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 anything. Logical decoding on the other hand is a producer of streaming data--it's basically a human readable replication log. So you could potentially stream the logically decoded log into PipelineDB and build some continuous views in front of it.
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. I don't think we'd be able to operate at the pace we want if we were pushing every change upstream.
However, we love Postgres and plan on actively merging upstream releases!