Testing Flink Solution¶

As a generic test plan for Flink project, we propose to address the following test activities:

1. Unit tests
2. Integration tests with production data
3. Performance testing

Three main test activities¶

1. Unit Testing¶

• SQL Query Testing: For each DML flink statement defined in the project pipelines folder, Data Engineer may define test suites with test data to validate the logic of the SQL statement. The test harness tooling is used for that, as it authorized to test individual Flink SQL query in isolation, by creating temporary input and output tables for running test suites.

  • When window operators are used, special timing concerns need to be addressed
  • Unit test should be able to verify watermark handling and event time processing

• For User Define Function: The test need to support unit test of the function code, but also validate input/output data, error handling and edge cases.

2. Integration Testing¶

As Confluent Cloud for Flink is natively integrated with Kafka, the goal of integration testings is to isolate tests from the source/ replicated tables, down to sink to data lakehouse.

• Test end-to-end data flow from source to sink
• Verify message serialization/deserialization

• Validate schema evolution handling

• State Management is relevant for CP Flink or Open-source Flink. For Confluent cloud the state management is transparent for the users. The classical items are:

  • Test state backend operations
  • Verify checkpointing and savepoint functionality
  • Test state recovery scenarios

3. Performance Testing¶

The goal are:

1. To measure how it takes to deploy Flink Statements for a table, a pipeline, a data product
2. To assess how long it takes for a data created at the source is available for end consumption.
3. To assess how long it takes to process the lag of data in the first topics representing raw data

As an example, we will take a classical shift left processing architecture with a timing representation:

• Throughput Testing

  • Measure maximum processing rate (events/second)
  • Evaluate parallel processing capabilities

• Latency Testing

  • Measure end-to-end processing latency

• Scalability Testing

  • Test horizontal scaling by assessing CFU increase up to the limit, control those statements to assess if we need to split them.
  • Test with increasing data volumes

• Scalability Testing

  • Test horizontal scaling (adding/removing task managers)
  • Evaluate job manager performance
  • Test with increasing data volumes
  • Monitor resource utilization

Metrics to use:

• Get time stamp when the statement is created
• Time stamp when deploy is started and finished
• Time stamp when the LAG is processed
• Number of message per second processed per Flink statement
• Time stamp from source to destination for a given transaction

4. Blue/Green Deployment Testing¶

The classical blue-green deployment for ETL jobs change everything and once the batch is done, the consumer of the data product, switch to new content. The following figure illustrates the approach at a high level:

While in real-time processing the concept of blue-green deployment should be very limited and in the context of a pipeline, as presented in the pipeline management chapter

• Deployment Strategy

  • Test parallel deployment of new version
  • Verify zero-downtime deployment
  • Test rollback procedures
  • Validate state migration between versions

5. Pipeline Management Testing¶

• Monitoring and Alerting

  • Test metric collection and reporting
  • Verify alert thresholds
  • Test failure detection and notification
  • Validate logging mechanisms

• Operational Testing

  • Test job cancellation and restart
  • Verify savepoint creation and restoration
  • Test job scaling operations
  • Validate backup and recovery procedures

6. Confluent Cloud Specific Testing¶

• Cloud Integration

  • Test connectivity to Confluent Cloud
  • Verify authentication and authorization
  • Test network security and encryption
  • Validate cloud resource management

• Cost Optimization

  • Monitor resource utilization
  • Test auto-scaling configurations
  • Evaluate cost-effective configurations
  • Test resource cleanup procedures

7. Security Testing¶

• Authentication and Authorization

  • Test access control mechanisms
  • Verify encryption in transit and at rest
  • Test security configurations
  • Validate audit logging

8. Disaster Recovery Testing¶

• Failure Scenarios

  • Test node failures
  • Test network partition scenarios
  • Test data center failures
  • Validate recovery procedures

Test Environment Requirements¶

• Development Environment

• Staging Environment

  • Dedicated Confluent Cloud environment
  • Production-like configuration
  • Monitoring and logging setup

• Production Environment

  • Production Confluent Cloud setup
  • Production-grade monitoring
  • Backup and recovery systems

Test Tools and Frameworks¶

• Testing Tools

  • Flink Test Harness
  • Prometheus for metrics
  • Grafana for visualization

• CI/CD Integration

  • Automated test execution
  • Test result reporting
  • Deployment automation
  • Environment provisioning

Success Criteria¶

• Performance Metrics

  • Throughput: > X events/second
  • Latency: < Y milliseconds
  • Resource utilization: < Z%

• Reliability Metrics

  • Uptime: > 99.9%
  • Data consistency: 100%
  • Recovery time: < X minutes

• Operational Metrics

  • Deployment success rate: 100%
  • Rollback success rate: 100%
  • Alert accuracy: > 99%