Unbelievably fast streaming DSV file parser that reads based on succinct data structures.
This library will use support for some BMI2 or AVX2 CPU instructions on some x86 based
CPUs if compiled with the appropriate flags on ghc-8.4.1
or later.
Pre-requisites:
cabal-install-3.0.0.0
ghc-8.4.4
or higher
It is sufficient to build, test and benchmark the library as follows for basic performance. The library will be compiled to use broadword implementation of rank & select, which has reasonable performance.
cabal v2-configure --enable-tests --enable-benchmarks --disable-documentation
cabal v2-build
cabal v2-test
cabal v2-bench
cabal v2-install --overwrite-policy=always --installdir="$HOME/.local/bin"
Ensure that $HOME/.local/bin
is in your path if you are using intending to
use the hw-dsv
binary.
For best performance, add the bmi2
and avx2
flags to target the BMI2 and
AVX2 instruction are specified in the cabal.project
file.
For slightly older CPUs, remove avx2
flags from the cabal.project
file to
target only the BMI2 instruction set.
It should be possible to install hw-dsv
via stack:
stack install --flag bits-extra:bmi2 --flag hw-rankselect-base:bmi2 --flag hw-rankselect:bmi2 --flag hw-simd:bmi2 --flag hw-simd:avx2 --flag hw-dsv:bmi2 --flag hw-dsv:avx2
Although your mileage may vary depending on which snapshot you are using.
The flags should be adjusted for the CPU you are targetting.
The following benchmark shows the kinds of performance gain that can be expected from enabling the BMI2 instruction set for CPU targets that support them. Benchmarks were run on 2.9 GHz Intel Core i7, macOS High Sierra.
With BMI2 disabled:
$ cat 7g.csv | pv -t -e -b -a | hw-dsv query-lazy -k 1 -k 2 -d , -e '|' > /dev/null
7.08GiB 0:07:25 [16.3MiB/s]
With BMI2 and AVX2 enabled:
$ cat 7gb.csv | pv -t -e -b -a | hw-dsv query-lazy -k 1 -k 2 -d , -e '|' > /dev/null
7.08GiB 0:00:39 [ 181MiB/s]
With only BMI2 enabled:
$ cat 7gb.csv | pv -t -e -b -a | hw-dsv query-lazy -k 1 -k 2 -d , -e '|' > /dev/null
7.08GiB 0:00:43 [ 165MiB/s]
The hw-dsv
application accepts 1-based column indexes rather than 0-based. The library is 0-based.
{-# LANGUAGE ScopedTypeVariables #-}
module Example where
import qualified Data.ByteString.Lazy as LBS
import qualified Data.Vector as DV
import qualified HaskellWorks.Data.Dsv.Lazy.Cursor as SVL
import qualified HaskellWorks.Data.Dsv.Lazy.Cursor.Lazy as SVL
example :: IO ()
example = do
bs <- LBS.readFile "sample.csv"
let c = SVL.makeCursor ',' bs
let rows :: [DV.Vector LBS.ByteString] = SVL.toListVector c
return ()