User Manual

The goal of this documentation is to provide a brief introduction to the arrow data format, then provide a walk-through of the functionality provided in the Arrow.jl Julia package, with an aim to expose a little of the machinery "under the hood" to help explain how things work and how that influences real-world use-cases for the arrow data format.

The best place to learn about the Apache arrow project is the website itself, specifically the data format specification. Put briefly, the arrow project provides a formal speficiation for how columnar, "table" data can be laid out efficiently in memory to standardize and maximize the ability to share data across languages/platforms. In the current apache/arrow GitHub repository, language implementations exist for C++, Java, Go, Javascript, Rust, to name a few. Other database vendors and data processing frameworks/applications have also built support for the arrow format, allowing for a wide breadth of possibility for applications to "speak the data language" of arrow.

The Arrow.jl Julia package is another implementation, allowing the ability to both read and write data in the arrow format. As a data format, arrow specifies an exact memory layout to be used for columnar table data, and as such, "reading" involves custom Julia objects (Arrow.Table and Arrow.Stream), which read the metadata of an "arrow memory blob", then wrap the array data contained therein, having learned the type and size, amongst other properties, from the metadata. Let's take a closer look at what this "reading" of arrow memory really means/looks like.

Reading arrow data

After installing the Arrow.jl Julia package (via ] add Arrow), and if you have some arrow data, let's say a file named data.arrow generated from the pyarrow library (a Python library for interfacing with arrow data), you can then read that arrow data into a Julia session by doing:

using Arrow

table = Arrow.Table("data.arrow")

Arrow.Table

The type of table in this example will be an Arrow.Table. When "reading" the arrow data, Arrow.Table first "mmapped" the data.arrow file, which is an important technique for dealing with data larger than available RAM on a system. By "mmapping" a file, the OS doesn't actually load the entire file contents into RAM at the same time, but file contents are "swapped" into RAM as different regions of a file are requested. Once "mmapped", Arrow.Table then inspected the metadata in the file to determine the number of columns, their names and types, at which byte offset each column begins in the file data, and even how many "batches" are included in this file (arrow tables may be partitioned into one ore more "record batches" each containing portions of the data). Armed with all the appropriate metadata, Arrow.Table then created custom array objects (ArrowVector), which act as "views" into the raw arrow memory bytes. This is a significant point in that no extra memory is allocated for "data" when reading arrow data. This is in contrast to if we wanted to read the data of a csv file as columns into Julia structures; we would need to allocate those array structures ourselves, then parse the file, "filling in" each element of the array with the data we parsed from the file. Arrow data, on the other hand, is already laid out in memory or on disk in a binary format, and as long as we have the metadata to interpret the raw bytes, we can figure out whether to treat those bytes as a Vector{Float64}, etc. A sample of the kinds of arrow array types you might see when deserializing arrow data, include:

  • Arrow.Primitive: the most common array type for simple, fixed-size elements like integers, floats, time types, and decimals
  • Arrow.List: an array type where its own elements are also arrays of some kind, like string columns, where each element can be thought of as an array of characters
  • Arrow.FixedSizeList: similar to the List type, but where each array element has a fixed number of elements itself; you can think of this like a Vector{NTuple{N, T}}, where N is the fixed-size width
  • Arrow.Map: an array type where each element is like a Julia Dict; a list of key value pairs like a Vector{Dict}
  • Arrow.Struct: an array type where each element is an instance of a custom struct, i.e. an ordered collection of named & typed fields, kind of like a Vector{NamedTuple}
  • Arrow.DenseUnion: an array type where elements may be of several different types, stored compactly; can be thought of like Vector{Union{A, B}}
  • Arrow.SparseUnion: another array type where elements may be of several different types, but stored as if made up of identically lengthed child arrays for each possible type (less memory efficient than DenseUnion)
  • Arrow.DictEncoded: a special array type where values are "dictionary encoded", meaning the list of unique, possible values for an array are stored internally in an "encoding pool", whereas each stored element of the array is just an integer "code" to index into the encoding pool for the actual value.

And while these custom array types do subtype AbstractArray, there is only limited support for setindex!. Remember, these arrays are "views" into the raw arrow bytes, so for array types other than Arrow.Primitive, it gets pretty tricky to allow manipulating those raw arrow bytes. Nevetheless, it's as simple as calling copy(x) where x is any ArrowVector type, and a normal Julia Vector type will be fully materialized (which would then allow mutating/manipulating values).

So, what can you do with an Arrow.Table full of data? Quite a bit actually!

Because Arrow.Table implements the Tables.jl interface, it opens up a world of integrations for using arrow data. A few examples include:

  • df = DataFrame(Arrow.Table(file)): Build a DataFrame, using the arrow vectors themselves; this allows utilizing a host of DataFrames.jl functionality directly on arrow data; grouping, joining, selecting, etc.
  • Tables.datavaluerows(Arrow.Table(file)) |> @map(...) |> @filter(...) |> DataFrame: use Query.jl's row-processing utilities to map, group, filter, mutate, etc. directly over arrow data.
  • Arrow.Table(file) |> SQLite.load!(db, "arrow_table"): load arrow data directly into an sqlite database/table, where sql queries can be executed on the data
  • Arrow.Table(file) |> CSV.write("arrow.csv"): write arrow data out to a csv file

A full list of Julia packages leveraging the Tables.jl inteface can be found here.

Apart from letting other packages have all the fun, an Arrow.Table itself can be plenty useful. For example, with tbl = Arrow.Table(file):

  • tbl[1]: retrieve the first column via indexing; the number of columns can be queried via length(tbl)
  • tbl[:col1] or tbl.col1: retrieve the column named col1, either via indexing with the column name given as a Symbol, or via "dot-access"
  • for col in tbl: iterate through columns in the table
  • AbstractDict methods like haskey(tbl, :col1), get(tbl, :col1, nothing), keys(tbl), or values(tbl)

Arrow types

In the arrow data format, specific logical types are supported, a list of which can be found here. These include booleans, integers of various bit widths, floats, decimals, time types, and binary/string. While most of these map naturally to types builtin to Julia itself, there are a few cases where the definitions are slightly different, and in these cases, by default, they are converted to more "friendly" Julia types (this auto conversion can be avoided by passing convert=false to Arrow.Table, like Arrow.Table(file; convert=false)). Examples of arrow to julia type mappings include:

  • 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

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 file.

Arrow.Stream

In addition to Arrow.Table, the Arrow.jl package also provides Arrow.Stream for processing arrow data. While Arrow.Table will iterate all record batches in an arrow file/stream, concatenating columns, Arrow.Stream provides a way to iterate through record batches, one at a time. Each iteration yields an Arrow.Table instance, with columns/data for a single record batch. This allows, if so desired, "batch processing" of arrow data, one record batch at a time, instead of creating a single long table via Arrow.Table.

Table and column metadata

The arrow format allows attaching arbitrary metadata in the form of a Dict{String, String} to tables and individual columns. The Arrow.jl package supports retrieving serialized metadata by calling Arrow.getmetadata(table) or Arrow.getmetadata(column).

Writing arrow data

Ok, so that's a pretty good rundown of reading arrow data, but how do you produce arrow data? Enter Arrow.write.

Arrow.write

With Arrow.write, you provide either an io::IO argument or file::String to write the arrow data to, as well as a Tables.jl-compatible source that contains the data to be written.

What are some examples of Tables.jl-compatible sources? A few examples include:

  • Arrow.write(io, df::DataFrame): A DataFrame is a collection of indexable columns
  • Arrow.write(io, CSV.File(file)): read data from a csv file and write out to arrow format
  • Arrow.write(io, DBInterface.execute(db, sql_query)): Execute an SQL query against a database via the DBInterface.jl interface, and write the query resultset out directly in the arrow format. Packages that implement DBInterface include SQLite.jl, MySQL.jl, and ODBC.jl.
  • df |> @map(...) |> Arrow.write(io): Write the results of a Query.jl chain of operations directly out as arrow data
  • jsontable(json) |> Arrow.write(io): Treat a json array of objects or object of arrays as a "table" and write it out as arrow data using the JSONTables.jl package
  • Arrow.write(io, (col1=data1, col2=data2, ...)): a NamedTuple of AbstractVectors or an AbstractVector of NamedTuples are both considered tables by default, so they can be quickly constructed for easy writing of arrow data if you already have columns of data

And these are just a few examples of the numerous integrations.

In addition to just writing out a single "table" of data as a single arrow record batch, Arrow.write also supports writing out multiple record batches when the input supports the Tables.partitions functionality. One immediate, though perhaps not incredibly useful example, is Arrow.Stream. Arrow.Stream implements Tables.partitions in that it iterates "tables" (specifically Arrow.Table), and as such, Arrow.write will iterate an Arrow.Stream, and write out each Arrow.Table as a separate record batch. Another important point for why this example works is because an Arrow.Stream iterates Arrow.Tables that all have the same schema. This is important because when writing arrow data, a "schema" message is always written first, with all subsequent record batches written with data matching the initial schema.

In addition to inputs that support Tables.partitions, note that the Tables.jl itself provides the Tables.partitioner function, which allows providing your own separate instances of similarly-schema-ed tables as "partitions", like:

# treat 2 separate NamedTuples of vectors with same schema as 1 table, 2 partitions
tbl_parts = Tables.partitioner([(col1=data1, col2=data2), (col1=data3, col2=data4)])
Arrow.write(io, tbl_parts)

# treat an array of csv files with same schema where each file is a partition
# in this form, a function `CSV.File` is applied to each element of 2nd argument
csv_parts = Tables.partitioner(CSV.File, csv_files)
Arrow.write(io, csv_parts)

Multithreaded writing

By default, Arrow.write will use multiple threads to write multiple record batches simultaneously (e.g. if julia is started with julia -t 8 or the JULIA_NUM_THREADS environment variable is set).

Compression

Compression is supported when writing via the compress keyword argument. Possible values include :lz4, :zstd, or your own initialized LZ4FrameCompressor or ZstdCompressor objects; will cause all buffers in each record batch to use the respective compression encoding or compressor.