DataSketches in Rust

A Rust binding for the Apache DataSketches library.

At this point, this package only wraps the count-distinct CPC sketch and provides a command-line tool, dsrs, for approximate distinct line-counting functionality (I personally find this useful when collecting statistics from logs). Examples require GNU Parallel. The command-line part probably doesn't work on Windows.

(seq $((100 * 1000 * 1000)) && seq $((100 * 1000 * 1000))) | \
  /usr/bin/time -f "%e sec %M KB" dsrs
102055590
5.22 sec 4288 KB

(seq $((100 * 1000 * 1000)) && seq $((100 * 1000 * 1000))) | \
  /usr/bin/time -f "%e sec %M KB" sort -u | wc -l
438.66 sec 12880 KB
100000000

(seq $((100 * 1000 * 1000)) && seq $((100 * 1000 * 1000))) | \
  /usr/bin/time -f "%e sec %M KB" awk '{a[$0]=1}END{print length(a)}'
100000000
39.28 sec 898240 KB

TODO: some really fast keyed approx count in parallel, compare memory and time

Installation

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.

Embedded C++ Library

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/

This is all only possible thanks to the excellent dtolnay/cxx library!

Why DataSketches in Rust?

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.