Fast Redis Bloom Filters in Python
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Latest commit ee01ba2 May 8, 2016 @b4hand b4hand Merge pull request #22 from revictus/master
Make print statements compatible with Python 3

Python + Redis + Bloom Filter = pyreBloom

Build Status Status: Production Team: Big Data Scope: External Open Source: MIT Critical: Yes

One of Salvatore's suggestions for Redis' GETBIT and SETBIT commands is to implement bloom filters. There was an existing python project that we used for inspiration.


Important -- The most recent version uses different seed values from all previous releases. Previous releases were using srand and rand, though they are not guaranteed to yield the same values on different systems. For example, two clients compiled on different platforms with different C implementations may not necessarily agree on what's in the filters. This latest version fixes this, but will also be incompatible with filters constructed with any previous versions.


You will need hiredis installed, and a C compiler (probably GCC). You can optionally have Cython installed, which will generate the C extension code. With those things installed, it's pretty simple:

pip install -r requirements.txt
python install


In order to install hiredis, you can:

# From
# via
# On Mac:
brew install hiredis

# With Ubuntu:
apt-get install libhiredis-dev

# From source:
git clone
cd hiredis && make && sudo make install


There are serial and batch forms for both add and contains. The batch modes are about 4-5 times faster than their serial equivalents, so use them when you can. When you instantiate a pyreBloom, you should give it a redis key name, a capacity, and an error rate:

import pyreBloom
p = pyreBloom.pyreBloom('myBloomFilter', 100000, 0.01)
# You can find out how many bits this will theoretically consume
# And how many hashes are needed to satisfy the false positive rate

From that point, you can add elements quite easily:

tests = ['hello', 'how', 'are', 'you', 'today']

The batch mode of contains differs from the serial version in that it actually returns which elements are in the bloom filter:

# True
p.contains(['hello', 'whats', 'new', 'with', 'you'])
# ['hello', 'you']
'hello' in p
# True

The Story

We needed to keep track of sets of urls that we had seen when crawling web pages, and had previously been keeping track of them in redis sets. Redis sets are, after all, extremely fast. As you can see in the benchmarks, set insertions can handle about 500k 20-character insertions per second. That is performant.

However, these sets of urls got to be prohibitively large. But, since we didn't really need to know which urls we had seen but merely whether or not we had seen a given url, we started inserting hashes of urls into redis sets. Unfortunately, even these got to be prohibitively large. We tried a lot of things, including limiting the number of discovered urls, but we also thought about using bloom filters.

There was an existing library to use redis strings as bloom filters, but it wasn't inserting elements fast enough for our liking. By implementing our hash functions in pure C we were able to double our performance. Using the C bindings for redis (hiredis), we were able to squeeze another 5x performance boost, for a total of about 10x over the original implementation.

Rough Bench

Here are numbers from the benchmark script run on a 2011-ish MacBook Pro and Redis 2.4.0, inserting 10k 20-character psuedo-random words:

Generating 20000 random test words
Generated random test words in 0.365890s
Filter using 4 hash functions and 95850 bits
Batch insert : 0.209492s (47734.526951 words / second)
Serial insert: 0.770047s (12986.217154 words / second)
Batch test   : 0.170484s (58656.590137 words / second)
Serial test  : 0.728285s (13730.886920 words / second)
False positive rate: 0.012300 (0.100000 expected)
Redis set add  : 0.023647s (422885.373502 words / second)
Redis pipe chk : 0.244068s (40972.163611 words / second)
Redis pipe sadd: 0.241150s (41467.941791 words / second)
Redis pipe chk : 0.240877s (41514.979051 words / second)

While set insertions are much faster than our bloom filter insertions (this is mostly do to the fact that there's not a 'SETMBIT' command), the pipelined versions of 'sadd' and checking for membership in the set are actually a little slower than the bloom filter implementation. Win some, lose some.