#ATAGTR2020 Presentation - GraphQL for CICD integration
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The document discusses the integration of GraphQL into CI/CD processes to address execution challenges such as lengthy test data preparation and frequent customer requirement changes. It outlines the implementation of GraphQL for performance testing, showcasing benefits like reduced test preparation time and improved code quality. Overall, it highlights the effectiveness of GraphQL in enhancing performance testing while minimizing manual intervention.
#ATAGTR2020 Presentation - GraphQL for CICD integration
- 1. Graph QL for CICD Integration Arunraj Karnam, Performance Test Engineer, Cognizant Technology Solutions. © 2020 Cognizant Authors: Vivek Rajagopal, Performance Test Engineer, Cognizant Technology Solutions.
- 2. © 2020 Cognizant Contents • Execution Challenges • GraphQL • Implementation • Major Challenges addressed • Other Benefits
- 3. © 2020 Cognizant Execution Challenges “Stand alone executions will have problem of losing track and make them accountable for either Shift Left or Shift Right processes” “Test data preparation took 1-2 hours per test based on complexity and requires manual updates into the CICD Pipeline.” “Customer requirement kept changing and ~6 load tests per day were executed. Manual intervention for test data has actually delayed the whole CICD process”
- 4. • A modern ways of building and querying APIs • It uses REST Technology • It is String interpreted by a Server that returns data in a specified Format • GraphQL server Expose single Endpoint • Enables Declarative Data Fetching GraphQL © 2020 Cognizant
- 5. Implementation • Create a GraphQL project • Deploy it into a Web server Via Repo • Verify the same through browser • Replicate the same and integrate in Jmeter • Once execution is started, Test data’s will be extracted from the GraphQL response © 2020 Cognizant
- 6. © 2020 Cognizant Major Challenges addressed Running Performance tests across various environments Externalizing the environment Clean up run generated time files Building scalable data-driven script without learning distributed systems Accountable in Shift Right or Shift Left model
- 7. Other Benefits • Increasing code quality and ensuring the performance is addressed at the component level itself • Reduction of 10-20% hours for test data preparation per test based on the script complexity • Using the Azure DevOps the nightly jobs can be executed • No Over fetching or under fetching the data • Number of JMeter code check-in's will be reduced, except script changes if Required © 2020 Cognizant
- 8. Thank You