commit | ed66476434888bd9b49ed29d73ef5d2ea9cc7114 | [log] [tgz] |
---|---|---|
author | Jacob Quinn <quinn.jacobd@gmail.com> | Thu Oct 01 22:09:12 2020 -0600 |
committer | GitHub <noreply@github.com> | Thu Oct 01 22:09:12 2020 -0600 |
tree | 04efa73690bcad39fd54095f0006b6602c810116 | |
parent | ed780d9c9de9674101f9e53401916d317fa4fd3f [diff] |
Cleanup and actually run integration tests with testing suite (#29) * Cleanup and actually run integration tests with testing suite The initial integration tests were throw together before the latest big refactoring, and I didn't bother to see if they worked or not before merging since we weren't actually running the tests anyway. Here, ArrowJSON module is cleaned up and the integration tests are actually run to ensure everything passes/works. * fix test deps
This is a pure Julia implementation of the Apache Arrow data standard. This package provides Julia AbstractVector
objects for referencing data that conforms to the Arrow standard. This allows users to seamlessly interface Arrow formatted data with a great deal of existing Julia code.
Please see this document for a description of the Arrow memory layout.
] add Tables#master ] add https://github.com/JuliaData/Arrow.jl#master
Read from IO
, file, or byte vector directly. Arrow data can be in file or streaming format, Arrow.Table
will detect automatically.
using Arrow # read arrow table from file format tbl = Arrow.Table(file) # read arrow table from IO tbl = Arrow.Table(io) # read arrow table directly from bytes, like from an HTTP request resp = HTTP.get(url) tbl = Arrow.Table(resp.body)
Write any Tables.jl source as arrow formatted data. Can write directly to IO
or to a provided file name.
# write directly to any IO in streaming format Arrow.write(io, tbl) # write to a file in file format Arrow.write("data.arrow", tbl)