insta-integration - Integration Testing

Automated integration tests for any application/job.

• Spin up any external services
• Generate production-like data
• Run data validations to ensure application/job works as expected

Problems it can help with:

• Unreliable test environments
• Dependencies on other teams
• Simulate complex data flows

Usage

CLI

1. Install via npm install -g insta-integration

2. Create YAML file insta-integration.yaml to define your integration tests

   1. Examples can be found here.
   2. Use JSON schema to help guide you on available options

3. Run insta-integration

GitHub Action

1. Create YAML file .github/workflows/integration-test.yaml

   name: Integration Test
   on:
     push:
       branches:
         - *
   jobs:
     integration-test:
       name: Integration Test
       runs-on: ubuntu-latest
       steps:
         - name: Run integration tests
           uses: data-catering/insta-integration@v1

2. Create YAML file insta-integration.yaml to define your integration tests

   1. Examples can be found here.
   2. Use JSON schema to help guide you on available options

3. Push your code and the GitHub Action will run

Services

The following services are available to run alongside your application/job.

Click here

Service Type	Service	Supported
Change Data Capture	debezium	✅
Database	cassandra	✅
Database	cockroachdb	✅
Database	elasticsearch	✅
Database	mariadb	✅
Database	mongodb	✅
Database	mssql	✅
Database	mysql	✅
Database	neo4j	✅
Database	postgres	✅
Database	spanner	✅
Database	sqlite	✅
Database	opensearch	❌
Data Catalog	marquez	✅
Data Catalog	unitycatalog	✅
Data Catalog	amundsen	❌
Data Catalog	datahub	❌
Data Catalog	openmetadata	❌
Distributed Coordination	zookeeper	✅
Distributed Data Processing	flink	✅
HTTP	httpbin	✅
Identity Management	keycloak	✅
Job Orchestrator	airflow	✅
Job Orchestrator	dagster	✅
Job Orchestrator	mage-ai	✅
Job Orchestrator	prefect	✅
Messaging	activemq	✅
Messaging	kafka	✅
Messaging	rabbitmq	✅
Messaging	solace	✅
Notebook	jupyter	✅
Object Storage	minio	✅
Query Engine	duckdb	✅
Query Engine	flight-sql	✅
Query Engine	presto	✅
Query Engine	trino	✅
Real-time OLAP	clickhouse	✅
Real-time OLAP	doris	✅
Real-time OLAP	druid	✅
Real-time OLAP	pinot	✅
Test Data Management	data-caterer	✅
Workflow	temporal	✅

Generation and Validation

Since it uses data-caterer behind the scenes to help with data generation and validation, check the following pages for discovering what options are available.

• Data Generation
• Data Validation

Data Sources

The following data sources are available to generate/validate data.

Click here

Data Source Type	Data Source	Support
Cloud Storage	AWS S3	✅
Cloud Storage	Azure Blob Storage	✅
Cloud Storage	GCP Cloud Storage	✅
Database	Cassandra	✅
Database	MySQL	✅
Database	Postgres	✅
Database	Elasticsearch	❌
Database	MongoDB	❌
Database	Opensearch	❌
File	CSV	✅
File	Delta Lake	✅
File	JSON	✅
File	Iceberg	✅
File	ORC	✅
File	Parquet	✅
File	Hudi	❌
HTTP	REST API	✅
Messaging	Kafka	✅
Messaging	Solace	✅
Messaging	ActiveMQ	❌
Messaging	Pulsar	❌
Messaging	RabbitMQ	❌
Metadata	Data Contract CLI	✅
Metadata	Great Expectations	✅
Metadata	Marquez	✅
Metadata	OpenAPI/Swagger	✅
Metadata	OpenMetadata	✅
Metadata	Open Data Contract Standard (ODCS)	✅
Metadata	Amundsen	❌
Metadata	Datahub	❌
Metadata	Data Contract CLI	❌
Metadata	Solace Event Portal	❌

Examples

Simple Example

services: []
run:
  - command: ./my-app/run-app.sh
    test:
      generation:
        parquet:
          - options:
              path: /tmp/parquet/accounts
            schema:
              fields:
                - name: account_id
      validation:
        parquet:
          - options:
              path: /tmp/parquet/accounts
            validations:
              - expr: ISNOTNULL(account_id)
              - aggType: count
                aggExpr: count == 1000

Full Example

services:
  - name: postgres #define external services
    data: my-data/sql #initial service setup (i.e. schema/tables, topics, queues)
run:
  - command: ./my-app/run-postgres-extract-app.sh #how to run your application/job
    env: #environment variables for your application/job
      POSTGRES_URL: jdbc:postgresql://postgres:5432/docker
    test:
      env: #environment variables for data generation/validation
        POSTGRES_URL: jdbc:postgresql://postgres:5432/docker
      mount: #volume mount for data validation
        - ${PWD}/example/my-app/shared/generated:/opt/app/shared/generated
      relationship: #generate data with same values used across different data sources
        postgres_balance.account_number: #ensure account_number in balance table exists when transaction created
          - postgres_transaction.account_number
      generation: #define data sources for data generation
        postgres:
          - name: postgres_transaction #give it a name to use in relationship definition
            options: #configuration on specific data source
              dbtable: account.transactions
            count: #how many records to generate (1,000 by default)
              perColumn: #generate 5 records per account_number
                columnNames: [account_number]
                count: 5
            schema: #schema of the data source
              fields:
                - name: account_number
                  type: string
                - name: create_time
                  type: timestamp
                - name: transaction_id
                  type: string
                - name: amount
                  type: double
          - name: postgres_balance
            options:
              dbtable: account.balances
            schema:
              fields:
                - name: account_number
                  options: #additional metadata for data generation
                    isUnique: true
                    regex: ACC[0-9]{10}
                - name: create_time
                  type: timestamp
                - name: account_status
                  type: string
                  options:
                    oneOf: [open, closed]
                - name: balance
                  type: double
      validation:
        csv: #define data source for data validations
          - options:
              path: /opt/app/shared/generated/balances.csv
              header: true
            validations: #list of validation to run, can be basic SQL, aggregations, upstream data source or column name validations
              - expr: ISNOTNULL(account_number)
              - aggType: count
                aggExpr: count == 1000
          - options:
              path: /opt/app/shared/generated/transactions.csv
              header: true
            validations:
              - expr: ISNOTNULL(account_number)
              - aggType: count
                aggExpr: count == 5000
              - groupByCols: [account_number]
                aggType: count
                aggExpr: count == 5

GitHub Action Options

Input

Optional configurations to alter the files and folders used by the GitHub Action can be found below.

Name	Description	Default
configuration_file	File path to configuration file	insta-integration.yaml
insta_infra_folder	Folder path to insta-infra (this repository)	${HOME}/.insta-integration/insta-infra
base_folder	Folder path to use for execution files	${HOME}/.insta-integration
data_caterer_version	Version of data-caterer Docker image	0.12.1

To use these configurations, alter your .github/workflows/integration-test.yaml.

name: Integration Test
on:
  push:
    branches:
      - *
jobs:
  integration-test:
    name: Integration Test
    runs-on: ubuntu-latest
    steps:
      - name: Run integration tests
        uses: data-catering/insta-integration@v1
        with:
          configuration_file: my/custom/folder/insta-integration.yaml

Output

If you want to use the output of the GitHub Action, the following attributes are available:

Name	Description
num_records_generated	Total number of records generated.
num_success_validations	Total number of successful validations.
num_failed_validations	Total number of failed validations.
num_validations	Total number of validations.
validation_success_rate	Success rate of validations (i.e. 0.75 = 75% success rate).
full_result	All result details as JSON (data generation and validation).

For example, you can print out the results like below:

- name: Run integration tests
  id: test-action
  uses: data-catering/insta-integration@v1
- name: Print Output
  id: output
  run: |
    echo "Records generated:         ${{ steps.test-action.outputs.num_records_generated }}"
    echo "Successful validations:    ${{ steps.test-action.outputs.num_success_validations }}"
    echo "Failed validations:        ${{ steps.test-action.outputs.num_failed_validations }}"
    echo "Number of validations:     ${{ steps.test-action.outputs.num_validations }}"
    echo "Validation success rate:   ${{ steps.test-action.outputs.validation_success_rate }}"

JSON Schema for insta-integration.yaml

A JSON Schema has been created to help guide users on what is possible in the insta-integration.yaml. The links below show how you can import the schema in your favourite IDE:

• IntelliJ
• Visual Studio Code

Validate JSON Schema

Using the following tool ajv.

Validate the JSON Schema:

ajv compile --spec=draft2019 -s schema/insta-integration-config-latest.json

Validate a insta-integration.yaml file:

ajv validate --spec=draft2019 -s schema/insta-integration-config-latest.json -d example/postgres-to-csv.yaml

Example Flows

Examples can be found here.

Why Integration Test?

It is the closest you get to simulating production. This involves:

• Test your application/job end-to-end
• Connect to data sources
• Production-like data processed