CSV files are the de-facto standard in many cases of data transfer, particularly when dealing with enterprise application or disparate database systems.
While there are a number of csv libraries in Haskell, at the time of this project's start, there wasn't one that provided all of the following:
- Full flexibility in quote characters, separators, input/output
- Constant space operation
- Robust parsing and error resiliency
- Battle-tested reliability in real-world datasets
- Fast operation
- Convenient interface that supports a variety of use cases
Over time, people created other plausible CSV packages like cassava. The major benefit from this library remains to be:
- Direct participation in the conduit ecosystem, which is now quite large, and all the benefits that come with it.
- Flexibility in CSV format definition.
- Resiliency to errors in the input data.
csv-conduit is a conduit-based CSV parsing library that is easy to use, flexible and fast. It leverages the conduit infrastructure to provide constant-space operation, which is quite critical in many real world use cases.
For example, you can use http-conduit to download a CSV file from the internet and plug its Source into intoCSV to stream-convert the download into the Row data type and do something with it as the data streams, that is without having to download the entire file to disk first.
- Ozgun Ataman (@ozataman)
- Daniel Bergey (@bergey)
- BJTerry (@BJTerry)
- Mike Craig (@mkscrg)
- Daniel Corson (@dancor)
- Dmitry Dzhus (@dzhus)
- Niklas Hambüchen (@nh2)
- The CSVeable typeclass implements the key operations.
- CSVeable is parameterized on both a stream type and a target CSV row type.
- There are 2 basic row types and they implement exactly the same operations,
so you can chose the right one for the job at hand:
- type MapRow t = Map t t
- type Row t = [t]
- You basically use the Conduits defined in this library to do the parsing from a CSV stream and rendering back into a CSV stream.
- Use the full flexibility and modularity of conduits for sources and sinks.
While fast operation is of concern, I have so far cared more about correct operation and a flexible API. Please let me know if you notice any performance regressions or optimization opportunities.
{-# LANGUAGE OverloadedStrings #-}
import Data.Conduit
import Data.Conduit.Binary
import Data.Conduit.List as CL
import Data.CSV.Conduit
import Data.Text (Text)
-- Just reverse te columns
myProcessor :: Monad m => Conduit (Row Text) m (Row Text)
myProcessor = CL.map reverse
test :: IO ()
test = runResourceT $
transformCSV defCSVSettings
(sourceFile "input.csv")
myProcessor
(sinkFile "output.csv")
{-# LANGUAGE OverloadedStrings #-}
import Data.Conduit
import Data.Conduit.Binary
import Data.CSV.Conduit
import Data.Text (Text)
myProcessor :: Monad m => Conduit (Row Text) m (Row Text)
myProcessor = awaitForever $ yield
-- Let's simply stream from a file, parse the CSV, reserialize it
-- and push back into another file.
test :: IO ()
test = runResourceT $
sourceFile "test/BigFile.csv" $=
intoCSV defCSVSettings $=
myProcessor $=
fromCSV defCSVSettings $$
sinkFile "test/BigFileOut.csv"