Fast Filter: Fast approximate membership filter implementations (C++)
This is a research library currently. It is not meant for production use.
Developers might want to consider our Header-only Xor Filter library in C.
Reference: Xor Filters: Faster and Smaller Than Bloom and Cuckoo Filters, Journal of Experimental Algorithmics (to appear).
- A C++11 compiler such as GNU G++ or LLVM Clang++
- Though it should be possible to run this benchmark on any operating system, we expect Linux and use its performance counters to measure performance.
- We expect an x64 processor with AVX2 support though most filters work on any processor, if you compile on a machine that does not support AVX2 instructions, the corresponding filters that depend on AVX2 will be disabled.
Make sure to select the right GNU GCC compiler (e.g., via
export export CXX=g++-8).
You may want to disable hyperthreading and adjust page sizes. Run the benchmark
on a quiet machine.
git clone https://github.com/FastFilter/fastfilter_cpp.git cd fastfilter_cpp cd benchmarks make # there may be compiler warnings at this point, we compile with '-Wall' ./bulk-insert-and-query.exe 10000000 # collect the output (it is quite verbose) ./bulk-insert-and-query.exe 100000000
Your results will depend on the hardware, on the compiler and how the system is configured. A sample output is as follows:
$ ./bulk-insert-and-query.exe 10000000 find find find find find optimal wasted million add remove 0% 25% 50% 75% 100% ε bits/item bits/item space keys add cycles: 325.5/key, instructions: (303.2/key, 0.93/cycle) cache misses: 12.41/key branch misses: 1.17/key 0.00% cycles: 81.7/key, instructions: ( 48.0/key, 0.59/cycle) cache misses: 3.06/key branch misses: 0.00/key 0.25% cycles: 81.8/key, instructions: ( 48.0/key, 0.59/cycle) cache misses: 3.06/key branch misses: 0.00/key 0.50% cycles: 81.8/key, instructions: ( 48.0/key, 0.59/cycle) cache misses: 3.06/key branch misses: 0.00/key 0.75% cycles: 82.0/key, instructions: ( 48.0/key, 0.59/cycle) cache misses: 3.06/key branch misses: 0.00/key 1.00% cycles: 81.9/key, instructions: ( 48.0/key, 0.59/cycle) cache misses: 3.06/key branch misses: 0.00/key Xor8 106.79 0.00 25.92 25.88 25.86 25.94 25.98 0.3892% 9.84 8.01 22.9% 10.0 ... # many more lines omitted
add lines preceding the name of each algorithm gives you information regarding the construction time whereas
the other five lines give you information regarding the queries where a given percentage of elements are present
in the set. We use Linux performance counters to measure instructions, cache misses and branch misses.
As part of the benchmark, we check the correctness of the implementation.
The shell script
benchmark/benchmark.sh runs the benchmark 3 times for the most important algorithms,
with entry sizes of 10 million and 100 million keys.
It is much slower than the above, because each invocation runs only one algorithm
(to ensure running one algorithm doesn't influence benchmark results of other algorithms).
It stores the results in the file
To futher analyze the results, use the java tool
from the project https://github.com/FastFilter/fastfilter_java.
Requires GCC and Java 8.
To get a low error, it is best run on a Linux machine that is not otherwise in use.
Steps to run the tests and analyze the results:
git clone https://github.com/FastFilter/fastfilter_cpp.git git clone https://github.com/FastFilter/fastfilter_java.git cd fastfilter_cpp/benchmarks make clean ; make # this may take an hour to run ./benchmark.sh cd ../.. cd fastfilter_java/fastfilter mvn clean install java -cp target/test-classes org.fastfilter.analysis.AnalyzeResults ../../fastfilter_cpp/benchmarks/benchmark-results.txt
Where is your code?
The filter implementations are in
src/<type>/. Most implementations depend on
The cuckoo filter and the benchmark are derived from https://github.com/efficient/cuckoofilter by Bin Fan et al. The SIMD blocked Bloom filter is from https://github.com/apache/impala (via the cuckoo filter). The Morton filter is from https://github.com/AMDComputeLibraries/morton_filter. The Counting Quotient Filter (CQF) is from https://github.com/splatlab/cqf.