Integration Testing¶

Our strategy for integration testing between Arrow implementations is:

Test datasets are specified in a custom human-readable, JSON-based format designed exclusively for Arrow’s integration tests
Each implementation provides a testing executable capable of converting between the JSON and the binary Arrow file representation
Each testing executable is used to generate binary Arrow file representations from the JSON-based test datasets. These results are then used to call the testing executable of each other implementation to validate the contents against the corresponding JSON file. - ie. the C++ testing executable generates binary arrow files from JSON specified datasets. The resulting files are then used as input to the Java testing executable for validation, confirming that the Java implementation can correctly read what the C++ implementation wrote.

Running integration tests¶

The integration test data generator and runner are implemented inside the Archery utility.

The integration tests are run using the archery integration command.

archery integration --help

In order to run integration tests, you’ll first need to build each component you want to include. See the respective developer docs for C++, Java, etc. for instructions on building those.

Some languages may require additional build options to enable integration testing. For C++, for example, you need to add -DARROW_BUILD_INTEGRATION=ON to your cmake command.

Depending on which components you have built, you can enable and add them to the archery test run. For example, if you only have the C++ project built, run:

archery integration --with-cpp=1

For Java, it may look like:

VERSION=0.11.0-SNAPSHOT
export ARROW_JAVA_INTEGRATION_JAR=$JAVA_DIR/tools/target/arrow-tools-$VERSION-jar-with-dependencies.jar
archery integration --with-cpp=1 --with-java=1

To run all tests, including Flight integration tests, do:

archery integration --with-all --run-flight

Note that we run these tests in continuous integration, and the CI job uses docker-compose. You may also run the docker-compose job locally, or at least refer to it if you have questions about how to build other languages or enable certain tests.

See Running Docker Builds for more information about the project’s docker-compose configuration.

JSON test data format¶

A JSON representation of Arrow columnar data is provided for cross-language integration testing purposes. This representation is not canonical but it provides a human-readable way of verifying language implementations.

See here for some examples of this JSON data.

The high level structure of a JSON integration test files is as follows:

Data file

{
  "schema": /*Schema*/,
  "batches": [ /*RecordBatch*/ ],
  "dictionaries": [ /*DictionaryBatch*/ ],
}

All files contain schema and batches, while dictionaries is only present if there are dictionary type fields in the schema.

Schema

{
  "fields" : [
    /* Field */
  ],
  "metadata" : /* Metadata */
}

Field

{
  "name" : "name_of_the_field",
  "nullable" : /* boolean */,
  "type" : /* Type */,
  "children" : [ /* Field */ ],
  "dictionary": {
    "id": /* integer */,
    "indexType": /* Type */,
    "isOrdered": /* boolean */
  },
  "metadata" : /* Metadata */
}

The dictionary attribute is present if and only if the Field corresponds to a dictionary type, and its id maps onto a column in the DictionaryBatch. In this case the type attribute describes the value type of the dictionary.

For primitive types, children is an empty array.

Metadata

null |
[ {
  "key": /* string */,
  "value": /* string */
} ]

A key-value mapping of custom metadata. It may be omitted or null, in which case it is considered equivalent to [] (no metadata). Duplicated keys are not forbidden here.

Type:

{
  "name" : "null|struct|list|largelist|fixedsizelist|union|int|floatingpoint|utf8|largeutf8|binary|largebinary|fixedsizebinary|bool|decimal|date|time|timestamp|interval|duration|map"
}

A Type will have other fields as defined in Schema.fbs depending on its name.

Int:

{
  "name" : "int",
  "bitWidth" : /* integer */,
  "isSigned" : /* boolean */
}

FloatingPoint:

{
  "name" : "floatingpoint",
  "precision" : "HALF|SINGLE|DOUBLE"
}

FixedSizeBinary:

{
  "name" : "fixedsizebinary",
  "byteWidth" : /* byte width */
}

Decimal:

{
  "name" : "decimal",
  "precision" : /* integer */,
  "scale" : /* integer */
}

Timestamp:

{
  "name" : "timestamp",
  "unit" : "$TIME_UNIT",
  "timezone": "$timezone"
}

$TIME_UNIT is one of "SECOND|MILLISECOND|MICROSECOND|NANOSECOND"

“timezone” is an optional string.

Duration:

{
  "name" : "duration",
  "unit" : "$TIME_UNIT"
}

Date:

{
  "name" : "date",
  "unit" : "DAY|MILLISECOND"
}

Time:

{
  "name" : "time",
  "unit" : "$TIME_UNIT",
  "bitWidth": /* integer: 32 or 64 */
}

Interval:

{
  "name" : "interval",
  "unit" : "YEAR_MONTH|DAY_TIME"
}

Union:

{
  "name" : "union",
  "mode" : "SPARSE|DENSE",
  "typeIds" : [ /* integer */ ]
}

The typeIds field in Union are the codes used to denote which member of the union is active in each array slot. Note that in general these discriminants are not identical to the index of the corresponding child array.

List:

{
  "name": "list"
}

The type that the list is a “list of” will be included in the Field’s “children” member, as a single Field there. For example, for a list of int32,

{
  "name": "list_nullable",
  "type": {
    "name": "list"
  },
  "nullable": true,
  "children": [
    {
      "name": "item",
      "type": {
        "name": "int",
        "isSigned": true,
        "bitWidth": 32
      },
      "nullable": true,
      "children": []
    }
  ]
}

FixedSizeList:

{
  "name": "fixedsizelist",
  "listSize": /* integer */
}

This type likewise comes with a length-1 “children” array.

Struct:

{
  "name": "struct"
}

The Field’s “children” contains an array of Fields with meaningful names and types.

Map:

{
  "name": "map",
  "keysSorted": /* boolean */
}

The Field’s “children” contains a single struct field, which itself contains 2 children, named “key” and “value”.

Null:

{
  "name": "null"
}

RunEndEncoded:

{
  "name": "runendencoded"
}

The Field’s “children” should be exactly two child fields. The first child must be named “run_ends”, be non-nullable and be either an int16, int32, or int64 type field. The second child must be named “values”, but can be of any type.

Extension types are, as in the IPC format, represented as their underlying storage type plus some dedicated field metadata to reconstruct the extension type. For example, assuming a “uuid” extension type backed by a FixedSizeBinary(16) storage, here is how a “uuid” field would be represented:

{
  "name" : "name_of_the_field",
  "nullable" : /* boolean */,
  "type" : {
     "name" : "fixedsizebinary",
     "byteWidth" : 16
  },
  "children" : [],
  "metadata" : [
     {"key": "ARROW:extension:name", "value": "uuid"},
     {"key": "ARROW:extension:metadata", "value": "uuid-serialized"}
  ]
}

RecordBatch:

{
  "count": /* integer number of rows */,
  "columns": [ /* FieldData */ ]
}

DictionaryBatch:

{
  "id": /* integer */,
  "data": [ /* RecordBatch */ ]
}

FieldData:

{
  "name": "field_name",
  "count" "field_length",
  "$BUFFER_TYPE": /* BufferData */
  ...
  "$BUFFER_TYPE": /* BufferData */
  "children": [ /* FieldData */ ]
}

The “name” member of a Field in the Schema corresponds to the “name” of a FieldData contained in the “columns” of a RecordBatch. For nested types (list, struct, etc.), Field’s “children” each have a “name” that corresponds to the “name” of a FieldData inside the “children” of that FieldData. For FieldData inside of a DictionaryBatch, the “name” field does not correspond to anything.

Here $BUFFER_TYPE is one of VALIDITY, OFFSET (for variable-length types, such as strings and lists), TYPE_ID (for unions), or DATA.

BufferData is encoded based on the type of buffer:

VALIDITY: a JSON array of 1 (valid) and 0 (null). Data for non-nullable Field still has a VALIDITY array, even though all values are 1.
OFFSET: a JSON array of integers for 32-bit offsets or string-formatted integers for 64-bit offsets
TYPE_ID: a JSON array of integers
DATA: a JSON array of encoded values

The value encoding for DATA is different depending on the logical type:

For boolean type: an array of 1 (true) and 0 (false).
For integer-based types (including timestamps): an array of JSON numbers.
For 64-bit integers: an array of integers formatted as JSON strings, so as to avoid loss of precision.
For floating point types: an array of JSON numbers. Values are limited to 3 decimal places to avoid loss of precision.
For binary types, an array of uppercase hex-encoded strings, so as to represent arbitrary binary data.
For UTF-8 string types, an array of JSON strings.

For “list” and “largelist” types, BufferData has VALIDITY and OFFSET, and the rest of the data is inside “children”. These child FieldData contain all of the same attributes as non-child data, so in the example of a list of int32, the child data has VALIDITY and DATA.

For “fixedsizelist”, there is no OFFSET member because the offsets are implied by the field’s “listSize”.

Note that the “count” for these child data may not match the parent “count”. For example, if a RecordBatch has 7 rows and contains a FixedSizeList of listSize 4, then the data inside the “children” of that FieldData will have count 28.

For “null” type, BufferData does not contain any buffers.

Archery Integration Test Cases¶

This list can make it easier to understand what manual testing may need to be done for any future Arrow Format changes by knowing what cases the automated integration testing actually tests.

There are two types of integration test cases: the ones populated on the fly by the data generator in the Archery utility, and gold files that exist in the arrow-testing <https://github.com/apache/arrow-testing/tree/master/data/arrow-ipc-stream/integration> repository.

Data Generator Tests¶

This is the high-level description of the cases which are generated and tested using the archery integration command (see get_generated_json_files in datagen.py):

Primitive Types - No Batches - Various Primitive Values - Batches with Zero Length - String and Binary Large offset cases
Null Type * Trivial Null batches
Decimal128
Decimal256
DateTime with various units
Durations with various units
Intervals - MonthDayNano interval is a separate case
Map Types - Non-Canonical Maps
Nested Types - Lists - Structs - Lists with Large Offsets
Unions
Custom Metadata
Schemas with Duplicate Field Names
Dictionary Types - Signed indices - Unsigned indices - Nested dictionaries
Extension Types

Gold File Integration Tests¶

Pre-generated json and arrow IPC files (both file and stream format) exist in the arrow-testing repository in the data/arrow-ipc-stream/integration directory. These serve as gold files that are assumed to be correct for use in testing. They are referenced by runner.py in the code for the Archery utility. Below are the test cases which are covered by them:

Backwards Compatibility
- The following cases are tested using the 0.14.1 format:
  - datetime
  - decimals
  - dictionaries
  - intervals
  - maps
  - nested types (list, struct)
  - primitives
  - primitive with no batches
  - primitive with zero length batches
- The following is tested for 0.17.1 format:
  - unions
Endianness
- The following cases are tested with both Little Endian and Big Endian versions for auto conversion
  - custom metadata
  - datetime
  - decimals
  - decimal256
  - dictionaries
  - dictionaries with unsigned indices
  - record batches with duplicate fieldnames
  - extension types
  - interval types
  - map types
  - non-canonical map data
  - nested types (lists, structs)
  - nested dictionaries
  - nested large offset types
  - nulls
  - primitive data
  - large offset binary and strings
  - primitives with no batches included
  - primitive batches with zero length
  - recursive nested types
  - union types
Compression tests
- LZ4
- ZSTD
Batches with Shared Dictionaries

Arrow Flight SQL

The Arrow C data interface