Frames-streamly- v 0.1.0.2

This library contains some useful functions for using the Frames package with streamly.

Frames has some built-in dependencies on the Pipes package, a few of which--primarily file I/O-- require users of Frames to use the Pipes package explicitly. Streamly provides much of the same functionality as Pipes and may be some users preferred streaming interface.

This package replicates all the external-facing bits of Frames that rely on Pipes and uses streamly instead. It also fleshes out the Frames API in a couple of places:

1. It adds some flexibility to the functions to write CSV files.
   Frames supported formatting of fields for CSV via a typeclass ShowCSV. That is supported here as well. But this package also supports using the Show instance, and, for more customized formatting, creating field-by-field formatting on the fly via a Vinyl record of functions. Helpful combinators are provided for formatting any single field with a Show instance or a ShowCSV instance or a user provided function from the field type to Text.

2. It adds some (experimental) support for Frame transformations using Streamly streams as an intermediate state for transformations which may benefit from the concurrency available in streamly. Such transformations first make any foldable of Records (including a Frame) into a stream, apply a streamly transformation to a stream of some other records and then transforms those into a frame. So the result is a frame -> frame function but one that can take advantage of streamly's features at the cost of the transformation into a stream and then back in to a Frame. This allows use of the concurrent features of Streamly for functions like mapM or mapMaybeM.

3. It adds Streamly folds for the various stream to in-core transformations in case users want to use them directly in stream to Frame transformations. Frames exposed only the functions to transform an entire stream (a pipe producer) into Frames "AoS" structure. This library provides that functionality as well, in this case using streamly streams as the input. But here we also expose streamly folds from streams of Records to Frames so that more complex stream to Frame transformations can be done by the user. For example, suppose you are doing a map/reduce on a large data set and you want to store the grouped subsets as Frames for memory-efficiency. These folds make that simpler.

An example using some of the utilities is here.

LICENSE (BSD-3-Clause)

Copyright (c) 2020, Adam Conner-Sax, All rights reserved.

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