HyperLogLog - Intersection Arithmetic

hyperloglog
hll
hyperloglogplus
streamlib
intersections
sets
estimation
algorithms

april 2014 by jm

'In general HLL intersection in StreamLib works. |A INTERSECT B|

= |A| + |B| - |A UNION B|. Timon's article on intersection is

important to read though. The usefulness of HLL intersection depends

on the features of the HLLs you are intersecting.'

april 2014 by jm

Druid | How We Scaled HyperLogLog: Three Real-World Optimizations

april 2014 by jm

3 optimizations Druid.io have made to the HLL algorithm to scale it up for production use in Metamarkets: compacting registers (fixes a bug with unions of multiple HLLs); a sparse storage format (to optimize space); faster lookups using a lookup table.

druid.io
metamarkets
scaling
hyperloglog
hll
algorithms
performance
optimization
counting
estimation
april 2014 by jm

Redis adds support for HyperLogLog

april 2014 by jm

good comment thread on HN, discussing hlld and bloomd as well

hll
bloom-filters
hyperloglog
redis
data-structures
estimation
cardinality
probabilistic
probability
hashing
random
april 2014 by jm

Recordinality

august 2013 by jm

a new, and interesting, sketching algorithm, with a Java implementation:

sketching
coding
algorithms
recordinality
cardinality
estimation
hll
hashing
murmurhash
java
Recordinality is unique in that it provides cardinality estimation like HLL, but also offers "distinct value sampling." This means that Recordinality can allow us to fetch a random sample of distinct elements in a stream, invariant to cardinality. Put more succinctly, given a stream of elements containing 1,000,000 occurrences of 'A' and one occurrence each of 'B' - 'Z', the probability of any letter appearing in our sample is equal. Moreover, we can also efficiently store the number of times elements in our distinct sample have been observed. This can help us to understand the distribution of occurrences of elements in our stream. With it, we can answer questions like "do the elements we've sampled present in a power law-like pattern, or is the distribution of occurrences relatively even across the set?"

august 2013 by jm

js-hll

june 2013 by jm

Good UI for exploration of HyperLogLog set intersections and unions.

javascript
ui
hll
hyperloglog
algorithms
sketching
js
sets
intersection
union
apache
open-source
One of the first things that we wanted to do with HyperLogLog when we first started playing with it was to support and expose it natively in the browser. The thought of allowing users to directly interact with these structures -- perform arbitrary unions and intersections on effectively unbounded sets all on the client -- was exhilarating to us. [...] we are pleased to announce the open-source release of AK’s HyperLogLog implementation for JavaScript, js-hll. We are releasing this code under the Apache License, Version 2.0.

We knew that we couldn’t just release a bunch of JavaScript code without allowing you to see it in action — that would be a crime. We passed a few ideas around and the one that kept bubbling to the top was a way to kill two birds with one stone. We wanted something that would showcase what you can do with HLL in the browser and give us a tool for explaining HLLs. It is typical for us to explain how HLL intersections work using a Venn diagram. You draw some overlapping circles with a border that represents the error and you talk about how if that border is close to or larger than the intersection then you can’t say much about the size of that intersection. This works just ok on a whiteboard but what you really want is to just build a visualization that allows you to select from some sets and see the overlap. Maybe even play with the precision a little bit to see how that changes the result. Well, we did just that!

june 2013 by jm

hlld

(via:cscotta)
hyper-log-log
hlld
hll
data-structures
memcached
daemons
sketching
estimation
big-data
cardinality
algorithms
via:cscotta

june 2013 by jm

a high-performance C server which is used to expose HyperLogLog sets and operations over them to networked clients. It uses a simple ASCII protocol which is human readable, and similar to memcached.

HyperLogLog's are a relatively new sketching data structure. They are used to estimate cardinality, i.e. the unique number of items in a set. They are based on the observation that any bit in a "good" hash function is indepedenent of any other bit and that the probability of getting a string of N bits all set to the same value is 1/(2^N). There is a lot more in the math, but that is the basic intuition. What is even more incredible is that the storage required to do the counting is log(log(N)). So with a 6 bit register, we can count well into the trillions. For more information, its best to read the papers referenced at the end. TL;DR: HyperLogLogs enable you to have a set with about 1.6% variance, using 3280 bytes, and estimate sizes in the trillions.

(via:cscotta)

june 2013 by jm

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