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# 8. Load Testing
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Date: 2022-12-21
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Initial date: 2022-12-21
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Updated: 2025-01-07
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## Decision
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Load Testing will assist in determining the performance of a system under
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real-life load conditions, both normal and extreme.
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In January 2025, we added the capability to run load tests automatically on a schedule in Azure.
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In the deployed load tests, we're hitting mock ReportStream endpoints. This allows us to 1) not
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bombard ReportStream with unexpected traffic and 2) identify performance issues that are specific
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to the Intermediary.
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## Impact
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### Positive
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-**Scalability:** Locust.io is highly scalable and can simulate millions of users, making it ideal for both small-scale and large-scale load tests.
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-**Scalability:** Locust.io is highly scalable and can simulate millions of users, making it ideal for both small-scale and large-scale load tests.
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-**Python-Based:** Writing tests in Python allows for flexibility and ease of use, especially for teams already familiar with the language.
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-**Cost Efficiency:** Locust.io is open-source, and doesn’t require licensing fees, which can reduce the overall cost of performance testing.
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-**Azure:** Running the load tests on a schedule in a more realistic enviornment gives us more consistent data
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### Negative
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-**Limited Features:** Compared to more feature-rich tools, Locust.io might lack advanced performance monitoring or detailed reporting features.
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-**Resource Usage:** Running large-scale tests using Locust.io may require significant system resources, which could impact cost and infrastructure planning.
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-**Locust Future in Azure is Uncertain:** We were able to create a Locust test in Azure in November 2024, but as of January 2025, were unable to create another one. We've submitted a bug report, but if we remain unable to create Locust tests in Azure, we won't be able to expand this test setup to other environments
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-**Azure Load Testing Cannot be Terraformed:** Since Azure Load Testing resources can't be created/managed in Terraform, they must be created manually. This is more work and more error prone
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