MATLAB Interface to Apache Arrow

Status

Warning The MATLAB interface is under active development and should be considered experimental.

This is a very early stage MATLAB interface to the Apache Arrow C++ libraries.

Currently, the MATLAB interface supports:

Converting between a subset of Arrow Array types and MATLAB array types (see table below)
Converting between MATLAB tables and arrow.tabular.RecordBatchs
Creating Arrow Fields, Schemas, and Types
Reading and writing Feather V1 files

Supported arrow.array.Array types are included in the table below.

NOTE: All Arrow Array classes listed below are part of the arrow.array package (e.g. arrow.array.Float64Array).

MATLAB Array Type	Arrow Array Type
`uint8`	`UInt8Array`
`uint16`	`UInt16Array`
`uint32`	`UInt32Array`
`uint64`	`UInt64Array`
`int8`	`Int8Array`
`int16`	`Int16Array`
`int32`	`Int32Array`
`int64`	`Int64Array`
`single`	`Float32Array`
`double`	`Float64Array`
`logical`	`BooleanArray`
`string`	`StringArray`
`datetime`	`TimestampArray`
`datetime`	`Date32Array`
`datetime`	`Date64Array`
`duration`	`Time32Array`
`duration`	`Time64Array`
`cell`	`ListArray`
`table`	`StructArray`

Prerequisites

To build the MATLAB Interface to Apache Arrow from source, the following software must be installed on the target machine:

MATLAB
CMake
C++ compiler which supports C++17 (e.g. gcc on Linux, Xcode on macOS, or Visual Studio on Windows)
Git

Setup

To set up a local working copy of the source code, start by cloning the apache/arrow GitHub repository using Git:

$ git clone https://github.com/apache/arrow.git

After cloning, change the working directory to the matlab subdirectory:

$ cd arrow/matlab

Build

To build the MATLAB interface, use CMake:

$ cmake -S . -B build
$ cmake --build build --config Release

Install

To install the MATLAB interface to the default software installation location for the target machine (e.g. /usr/local on Linux or C:\Program Files on Windows), pass the --target install flag to CMake.

$ cmake --build build --config Release --target install

As part of the install step, the installation directory is added to the MATLAB Search Path.

Note: This step may fail if the current user is lacking necessary filesystem permissions. If the install step fails, the installation directory can be manually added to the MATLAB Search Path using the addpath command.

Test

To run the MATLAB tests, start MATLAB in the arrow/matlab directory and call the runtests command on the test directory with IncludeSubFolders=true:

>> runtests("test", IncludeSubFolders=true);

Refer to Testing Guidelines for more information.

Usage

Included below are some example code snippets that illustrate how to use the MATLAB interface.

Arrow `Array` classes (i.e. `arrow.array.<Array>`)

Create an Arrow `Float64Array` from a MATLAB `double` array

>> matlabArray = double([1, 2, 3])

matlabArray =

     1     2     3

>> arrowArray = arrow.array(matlabArray)

arrowArray = 

  Float64Array with 3 elements and 0 null values:

    1 | 2 | 3

Create a MATLAB `logical` array from an Arrow `BooleanArray`

>> arrowArray = arrow.array([true, false, true])

arrowArray = 

  BooleanArray with 3 elements and 0 null values:

    true | false | true

>> matlabArray = toMATLAB(arrowArray)

matlabArray =

  3×1 logical array

   1
   0
   1

Specify `Null` Values when constructing an `arrow.array.Int8Array`

>> matlabArray = int8([122, -1, 456, -10, 789])

matlabArray =

  1×5 int8 row vector

    122     -1    127    -10    127

% Treat all negative array elements as Null
>> validElements = matlabArray > 0

validElements =

  1×5 logical array

   1   0   1   0   1

% Specify which values are Null/Valid by supplying a logical validity "mask"
>> arrowArray = arrow.array(matlabArray, Valid=validElements)

arrowArray = 

  Int8Array with 5 elements and 2 null values:

    122 | null | 127 | null | 127

Arrow `RecordBatch` class

Create an Arrow `RecordBatch` from a MATLAB `table`

>> matlabTable = table(["A"; "B"; "C"], [1; 2; 3], [true; false; true])

matlabTable =

  3x3 table

    Var1    Var2    Var3
    ____    ____    _____

    "A"      1      true
    "B"      2      false
    "C"      3      true

>> arrowRecordBatch = arrow.recordBatch(matlabTable)

arrowRecordBatch = 

  Arrow RecordBatch with 3 rows and 3 columns:

    Schema:

        Var1: String | Var2: Float64 | Var3: Boolean

    First Row:

        "A" | 1 | true

Create a MATLAB `table` from an Arrow `RecordBatch`

>> arrowRecordBatch

arrowRecordBatch = 

  Arrow RecordBatch with 3 rows and 3 columns:

    Schema:

        Var1: String | Var2: Float64 | Var3: Boolean

    First Row:

        "A" | 1 | true

>> matlabTable = table(arrowRecordBatch)

matlabTable =

  3x3 table

    Var1    Var2    Var3
    ____    ____    _____

    "A"      1      true
    "B"      2      false
    "C"      3      true

Create an Arrow `RecordBatch` from multiple Arrow `Array`s

>> stringArray = arrow.array(["A", "B", "C"])

stringArray = 

  StringArray with 3 elements and 0 null values:

    "A" | "B" | "C"

>> timestampArray = arrow.array([datetime(1997, 01, 01), datetime(1998, 01, 01), datetime(1999, 01, 01)])

timestampArray = 

  TimestampArray with 3 elements and 0 null values:

    1997-01-01 00:00:00.000000 | 1998-01-01 00:00:00.000000 | 1999-01-01 00:00:00.000000

>> booleanArray = arrow.array([true, false, true])

booleanArray = 

  BooleanArray with 3 elements and 0 null values:

    true | false | true

>> arrowRecordBatch = arrow.tabular.RecordBatch.fromArrays(stringArray, timestampArray, booleanArray)

arrowRecordBatch = 

  Arrow RecordBatch with 3 rows and 3 columns:

    Schema:

        Column1: String | Column2: Timestamp | Column3: Boolean

    First Row:

        "A" | 1997-01-01 00:00:00.000000 | true

Extract a column from a `RecordBatch` by index

>> arrowRecordBatch = arrow.tabular.RecordBatch.fromArrays(stringArray, timestampArray, booleanArray)

arrowRecordBatch = 

  Arrow RecordBatch with 3 rows and 3 columns:

    Schema:

        Column1: String | Column2: Timestamp | Column3: Boolean

    First Row:

        "A" | 1997-01-01 00:00:00.000000 | true

>> timestampArray = arrowRecordBatch.column(2)

timestampArray = 

  TimestampArray with 3 elements and 0 null values:

    1997-01-01 00:00:00.000000 | 1998-01-01 00:00:00.000000 | 1999-01-01 00:00:00.000000

Arrow `Type` classes (i.e. `arrow.type.<Type>`)

Create an Arrow `Int8Type` object

>> type = arrow.int8()

type =

  Int8Type with properties:

    ID: Int8

Create an Arrow `TimestampType` object with a specific `TimeUnit` and `TimeZone`

>> type = arrow.timestamp(TimeUnit="Second", TimeZone="Asia/Kolkata")

type =

  TimestampType with properties:

          ID: Timestamp
    TimeUnit: Second
    TimeZone: "Asia/Kolkata"

Get the type enumeration `ID` for an Arrow `Type` object

>> type.ID

ans =

  ID enumeration

    Timestamp

>> type = arrow.string()

type =

  StringType with properties:

    ID: String

>> type.ID

ans =

  ID enumeration

    String

Arrow `Field` class

Create an Arrow `Field` with type `Int8Type`

>> field = arrow.field("Number", arrow.int8())

field = 

  Field with properties:

    Name: "Number"
    Type: [1x1 arrow.type.Int8Type]

>> field.Name

ans =

    "Number"

>> field.Type

ans =

  Int8Type with properties:

    ID: Int8

Create an Arrow `Field` with type `StringType`

>> field = arrow.field("Letter", arrow.string())

field = 

  Field with properties:

    Name: "Letter"
    Type: [1x1 arrow.type.StringType]

>> field.Name

ans =

    "Letter"

>> field.Type

ans =

  StringType with properties:

    ID: String

Extract an Arrow `Field` from an Arrow `Schema` by index

>> arrowSchema

arrowSchema = 

  Arrow Schema with 2 fields:

    Letter: String | Number: Int8

% Specify the field to extract by its index (i.e. 2)
>> field = arrowSchema.field(2)

field = 

  Field with properties:

    Name: "Number"
    Type: [1x1 arrow.type.Int8Type]

Extract an Arrow `Field` from an Arrow `Schema` by name

>> arrowSchema

arrowSchema = 

  Arrow Schema with 2 fields:

    Letter: String | Number: Int8

% Specify the field to extract by its name (i.e. "Letter")
>> field = arrowSchema.field("Letter")

field = 

  Field with properties:

    Name: "Letter"
    Type: [1x1 arrow.type.StringType]

Arrow `Schema` class

Create an Arrow `Schema` from multiple Arrow `Field`s

>> letter = arrow.field("Letter", arrow.string())

letter = 

  Field with properties:

    Name: "Letter"
    Type: [1x1 arrow.type.StringType]

>> number = arrow.field("Number", arrow.int8())

number = 

  Field with properties:

    Name: "Number"
    Type: [1x1 arrow.type.Int8Type]

>> schema = arrow.schema([letter, number])

schema = 

  Arrow Schema with 2 fields:

    Letter: String | Number: Int8

Get the `Schema` of an Arrow `RecordBatch`

>> matlabTable = table(["A"; "B"; "C"], [1; 2; 3], VariableNames=["Letter", "Number"])

matlabTable =

  3x2 table

    Letter    Number
    ______    ______

     "A"        1
     "B"        2
     "C"        3

>> arrowRecordBatch = arrow.recordBatch(matlabTable)

arrowRecordBatch = 

  Arrow RecordBatch with 3 rows and 2 columns:

    Schema:

        Letter: String | Number: Float64

    First Row:

        "A" | 1

>> arrowSchema = arrowRecordBatch.Schema

arrowSchema = 

  Arrow Schema with 2 fields:

    Letter: String | Number: Float64

Feather V1

Write a MATLAB table to a Feather V1 file

>> t = table(["A"; "B"; "C"], [1; 2; 3], [true; false; true])

t =

  3×3 table

    Var1    Var2    Var3
    ____    ____    _____

    "A"      1      true
    "B"      2      false
    "C"      3      true

>> filename = "table.feather";

>> featherwrite(filename, t)

Read a Feather V1 file into a MATLAB table

>> filename = "table.feather";

>> t = featherread(filename)

t =

  3×3 table

    Var1    Var2    Var3
    ____    ____    _____

    "A"      1      true
    "B"      2      false
    "C"      3      true

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