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Posted to github@arrow.apache.org by GitBox <gi...@apache.org> on 2021/01/07 21:50:34 UTC
[GitHub] [arrow] nealrichardson commented on a change in pull request #9121: ARROW-11158: [Julia] Implement Decimal256 support for Julia
nealrichardson commented on a change in pull request #9121:
URL: https://github.com/apache/arrow/pull/9121#discussion_r553604933
##########
File path: julia/Arrow/Project.toml
##########
@@ -1,9 +1,10 @@
name = "Arrow"
uuid = "69666777-d1a9-59fb-9406-91d4454c9d45"
authors = ["quinnj <qu...@gmail.com>"]
-version = "0.3.0"
+version = "1.1.0"
Review comment:
Apache Arrow libraries are currently at 2.0.0 and are about to release 3.0.0; should this version number track that as well?
##########
File path: julia/Arrow/README.md
##########
@@ -1,13 +1,38 @@
# Arrow
-[](https://travis-ci.com/JuliaData/Arrow.jl.svg?branch=master)
+[](https://arrow.juliadata.org/dev/)
+[](https://github.com/JuliaData/Arrow.jl/actions?query=workflow%3ACI)
[](https://codecov.io/gh/JuliaData/Arrow.jl)
+[](https://juliahub.com/ui/Packages/Arrow/QnF3w?t=2)
+[](https://juliahub.com/ui/Packages/Arrow/QnF3w)
+[](https://juliahub.com/ui/Packages/Arrow/QnF3w)
+
This is a pure Julia implementation of the [Apache Arrow](https://arrow.apache.org) 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](https://arrow.apache.org/docs/format/Columnar.html#physical-memory-layout) for a description of the Arrow memory layout.
+## Installation
+
+The package can be installed by typing in the following in a Julia REPL:
+
+```julia
+julia> using Pkg; Pkg.add(url="https://github.com/apache/arrow", subdir="julia/Arrow.jl")
+```
+
+or to use the non-official-apache code that will sometimes include bugfix patches between apache releases, you can do:
Review comment:
I believe "Apache" should be capitalized throughout
##########
File path: julia/Arrow/docs/src/manual.md
##########
@@ -55,10 +55,50 @@ In the arrow data format, specific logical types are supported, a list of which
* `Date`, `Time`, `Timestamp`, and `Duration` all have natural Julia defintions in `Dates.Date`, `Dates.Time`, `TimeZones.ZonedDateTime`, and `Dates.Period` subtypes, respectively.
* `Char` and `Symbol` Julia types are mapped to arrow string types, with additional metadata of the original Julia type; this allows deserializing directly to `Char` and `Symbol` in Julia, while other language implementations will see these columns as just strings
-* `Decimal128` has no corresponding builtin Julia type, so it's deserialized using a compatible type definition in Arrow.jl itself: `Arrow.Decimal`
+* `Decimal128` and `Decimal256` have no corresponding builtin Julia types, so they're deserialized using a compatible type definition in Arrow.jl itself: `Arrow.Decimal`
Note that when `convert=false` is passed, data will be returned in Arrow.jl-defined types that exactly match the arrow definitions of those types; the authoritative source for how each type represents its data can be found in the arrow [`Schema.fbs`](https://github.com/apache/arrow/blob/master/format/Schema.fbs) file.
+#### Custom types
+
+To support writing your custom Julia struct, Arrow.jl utilizes the format's mechanism for "extension types" by storing
+the Julia type name in the field metadata. To "hook in" to this machinery, custom types can just call
Review comment:
FWIW, both Pandas and R store custom metadata that only they recognize in the Schema's key-value metadata, not as extension types. By our understanding, extension types make most sense when it's a type definition that is to be supported in other languages/implementations of Arrow. That's often not the case when you're looking just to make sure you have high fidelity when write/reading data to Feather/Parquet/etc.
That's not to say that it is wrong to do what you're doing here: just sharing how we in python/R have worked through similar issues.
##########
File path: julia/Arrow/README.md
##########
@@ -1,13 +1,38 @@
# Arrow
-[](https://travis-ci.com/JuliaData/Arrow.jl.svg?branch=master)
+[](https://arrow.juliadata.org/dev/)
+[](https://github.com/JuliaData/Arrow.jl/actions?query=workflow%3ACI)
[](https://codecov.io/gh/JuliaData/Arrow.jl)
+[](https://juliahub.com/ui/Packages/Arrow/QnF3w?t=2)
+[](https://juliahub.com/ui/Packages/Arrow/QnF3w)
+[](https://juliahub.com/ui/Packages/Arrow/QnF3w)
+
This is a pure Julia implementation of the [Apache Arrow](https://arrow.apache.org) 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](https://arrow.apache.org/docs/format/Columnar.html#physical-memory-layout) for a description of the Arrow memory layout.
+## Installation
+
+The package can be installed by typing in the following in a Julia REPL:
+
+```julia
+julia> using Pkg; Pkg.add(url="https://github.com/apache/arrow", subdir="julia/Arrow.jl")
+```
+
+or to use the non-official-apache code that will sometimes include bugfix patches between apache releases, you can do:
+
+```julia
+julia> using Pkg; Pkg.add("Arrow")
+```
+
+## Difference between this code and the JuliaData/Arrow.jl repository
+
+This code is officially part of the apache/arrow repository and as such follows the regulated release cadence of the entire project, following standard community
+voting protocols. The JuliaData/Arrow.jl repository can be viewed as a sort of "dev" or "latest" branch of this code that may release more frequently, but without following
+official apache release guidelines. The two repositories are synced, however, so any bugfix patches in JuliaData will be upstreamed to apache/arrow for each release.
Review comment:
I would encourage you to consider inverting this workflow: do your issue tracking and bugfixes in apache/arrow, and if you need to cut a bugfix release, sync from here to JuliaData/Arrow.jl.
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