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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 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:

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

Packages

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Languages

  • JavaScript 96.0%
  • Dockerfile 2.2%
  • Shell 1.8%