A Rust binding for the Apache DataSketches library and command-line tool.
On the command-line, we provide
dsrs [--key] [--raw] [--merge]
for approximate distinct line-counting, anddsrs --hh k
for heavy hitters (approximate most frequent lines).For instance, the following experiment checks how many unique lines exist when you print all numbers up to 100M twice.
m100=$((100 * 1000 * 1000))
(seq $m100 && seq $m100) | \
/usr/bin/time -f "%e sec %M KB" dsrs
102055590
5.22 sec 4288 KB
(seq $m100 && seq $m100) | \
/usr/bin/time -f "%e sec %M KB" sort -u | wc -l
438.66 sec 12880 KB
100000000
(seq $m100 && seq $m100) | \
/usr/bin/time -f "%e sec %M KB" awk '{a[$0]=1}END{print length(a)}'
100000000
39.28 sec 898240 KB
Next, we can ask for the most popular lines from a stream (there is a topfew Rust package, but it does not support streams).
m10=$((10 * 1000 * 1000))
seq $m10 | sed 's/$/\n1\n2\n3/' | \
/usr/bin/time -f "%e sec %M KB" sort | \
uniq -c | sort -rn | head -3
54.88 sec 8968 KB
10000001 3
10000001 2
10000001 1
# exact hashmap solution, requires go
pushd /tmp && \
(test -d topfew || git clone git@github.com:timbray/topfew.git topfew) && \
pushd topfew && make && popd && popd
seq $m10 | sed 's/$/\n1\n2\n3/' | \
/usr/bin/time -f "%e sec %M KB" /tmp/topfew/bin/tf -f 1 -n 3
10000001 2
10000001 3
10000001 1
10.67 sec 1060332 KB
seq $m10 | sed 's/$/\n1\n2\n3/' | \
/usr/bin/time -f "%e sec %M KB" target/release/dsrs --hh 3
10000001 2
10000001 1
10000001 3
4.48 sec 4560 KB
Here's a sophisticated example of the tool in action, used to compute rolling average active reviewers for Amazon over a couple decades. The equivalent non-sketch based solution OOMs. Similarly, we can use dsrs --hh
to extract the most popular SciHub downloads using multiple orders of magnitude less memory than an exact solution.
Assumes a modern Rust cargo
is installed. The command line tool dsrs
can be installed with:
cargo install dsrs
The library may be used as a regular Rust dependency by adding it to your Cargo.toml
file.
This Rust library contains manually-copied header files from the header-only datasketches-cpp
library at commit 043b947f.
This was done by extracting all headers. Assuming you're in the datasketches-rs
directory, which has a sibling datasketches-cpp
:
# make all required directories
find ../datasketches-cpp/ -name "*.h" -or -name "*.hpp" | \
xargs dirname | \
sort -u |
cut -d/ -f2- | \
xargs mkdir -p
# copy over the actual headers
find ../datasketches-cpp/ -name "*.h" -or -name "*.hpp" | \
cut -d/ -f2- | \
xargs -I {} cp ../{} {}
# and the license info too
cp ../datasketches-cpp/{NOTICE,LICENSE} datasketches-cpp/
# some manual interventions were required for the heavy hitters
# implementation, which requires the C++ side to temporarily own
# keys from Rust, so additional management code needs to be injected.
git apply fi.patch
git grep -l "uint16_t DRIFT_LIMIT = [0-9]*;" | xargs sed -i 's/uint16_t DRIFT_LIMIT = [0-9]*;/uint32_t DRIFT_LIMIT = 1024 * 1024 * 1024;/'
This is all only possible thanks to the excellent dtolnay/cxx library!
There are quite a few crates containing HyperLogLog sketches. However, when I attempted to use them (as of 2021-06-20), I found that their APIs panicked on certain inputs (e.g., try amadeus_streaming::HyperLogLog::<u64>::new(0.0001);
), or did not have merge operations. A very rudimentary cargo criterion
on 1M unique keys finds that CPC has better accuracy anyway (for all of the below, the same nominal accuracy configuration was set, so these should be using roughly the same amount of memory):
repeat-ten/dsrs::CpcSketch/1000000
time: [144.95 ms 149.27 ms 155.42 ms]
repeat-ten/amadeus_streaming::HyperLogLog/1000000
time: [132.89 ms 134.01 ms 135.49 ms]
repeat-ten/probabilistic_collections::HyperLogLog/1000000
time: [159.99 ms 165.94 ms 172.32 ms]
repeat-ten/probably::HyperLogLog/1000000
time: [119.47 ms 123.95 ms 127.84 ms]
repeat-ten/hyperloglogplus::HyperLogLogPlus/1000000
time: [120.74 ms 121.32 ms 122.10 ms]
relative errors
size: 1000000
relerr: 1.1% name: dsrs::CpcSketch
relerr: 3.3% name: amadeus_streaming::HyperLogLog
relerr: 4.3% name: hyperloglogplus::HyperLogLogPlus
relerr: 50.7% name: probably::HyperLogLog
relerr: inf% name: probabilistic_collections::HyperLogLog
while overall update speed doesn't change too much between implementations.