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probabilistic data structures

SHOW HN2016

High-throughput probabilistic data structure service

Shown by seiflotfy on Mar 7, 2016. Click the card to turn it over.

53points

6comments

#699of 2,264 in 2016

Top 31%of 2016 by points

How HN took it

QuietFewer than 5 comments about the project

1 positive, 2 neutral or asking, 1 mixed, 0 critical, out of 4 comments about the project.

Idea: 1 for, 0 against

Each row is a top-level comment and its replies, in HN's order. Select a cell to open that comment on HN.

positive neutral or mixed critical about another commenter or something else the maker

Unclear how threshold and memory usage relate to error rate
From 1 comment
- I get the multiplexing and I think that's a great concept. One other thing that you might also want to consider (to reduce unneeded interaction with the server) is allow…
  alixaxel on HN

Support specifying counts per key, e.g. weighted heavy hitters
From 1 comment
- I get the multiplexing and I think that's a great concept. One other thing that you might also want to consider (to reduce unneeded interaction with the server) is allow…
  alixaxel on HN

How does it compare to the Bloom filter and HLL in Redis
From 1 comment
- Looks useful. However, how does the bloom filter and HLL implementation compare to the ones available in Redis?
  krat0sprakhar on HN
Memory requirements and threshold specification per domain
From 1 comment
- Great work on putting this together! Amazing idea really. I'm specially looking forward for the streaming metrics/statistics feature. PS: Could you go into a bit more into the…
  alixaxel on HN

Redis bitset-based Bloom filter and HyperLogLog
From 1 comment
- Looks useful. However, how does the bloom filter and HLL implementation compare to the ones available in Redis?
  krat0sprakhar on HN