Load Testing PUT, PATCH, and DELETE Endpoints

Most load testing tutorials focus on GET requests. Maybe POST. But real APIs have PUT, PATCH, and DELETE operations too, and they behave differently under load.

Update and delete operations modify data. That changes everything about how you test them. The API load testing basics cover general setup — this guide focuses on the specifics of mutation operations.

Why these are different

GET requests are read-only. You can fire thousands of them at the same endpoint and nothing changes. Easy to test, easy to repeat.

PUT, PATCH, and DELETE modify state. Each request changes something. Run the same DELETE twice and the second one fails because the resource is already gone. Run a thousand PUTs against the same resource and you might create lock contention.

This makes test setup more complex. You need data to modify. You need to handle the fact that your test changes that data.

The data problem

To test DELETE, you need things to delete. To test PUT, you need things to update. And after your test runs, that data is changed.

Options:

Reset data between test runs. Restore from a snapshot, truncate tables, whatever gets you back to a known state.

Create data as part of the test. POST to create resources, then PUT/DELETE them. This tests the full lifecycle but makes the test more complex.

Use a pool of test resources. Create 10,000 test records. Each test run uses some of them. Replenish periodically.

Lock contention

Multiple concurrent updates to the same resource cause lock contention. The database has to serialize those writes. Throughput suffers.

This is realistic — it happens in production too. But you need to know if your test is measuring application performance or lock contention.

Test with updates to different resources (no contention) and updates to the same resource (maximum contention). The difference tells you how much locking affects your performance.

The database bottlenecks guide covers lock-related issues.

Idempotency

PUT is supposed to be idempotent — calling it twice with the same data should have the same result as calling it once. PATCH and DELETE should also be idempotent in most implementations.

Load testing can verify this. Send the same PUT request multiple times. Does it behave correctly? Or do you get errors, duplicate records, or corrupted state?

This matters because retries happen. Network glitches, timeouts, client bugs — requests get sent twice. Your API needs to handle that.

Testing PATCH specifically

PATCH updates partial resources. Send only the fields you want to change.

Test with different patch sizes. Updating one field might be fast. Updating twenty fields might trigger more validation, more database writes, more work.

Test concurrent patches to the same resource. What happens when two clients try to update different fields simultaneously? Does one overwrite the other? Do you have proper conflict handling?

Testing DELETE

DELETE seems simple. Resource exists, then it doesn't. But there are subtleties.

Soft delete vs hard delete — soft delete is usually faster (just flipping a flag) but might affect query performance over time as deleted records accumulate.

Cascade deletes — deleting a parent might delete children. A user delete might cascade to posts, comments, likes. That's a lot of database work.

Test cascade scenarios specifically. Deleting a user with 10,000 posts is very different from deleting a user with 10.

Webhooks and side effects

Update and delete operations often trigger side effects. Webhooks fire. Caches invalidate. Search indexes update. Audit logs write.

These side effects take time and resources. Under load, they can become bottlenecks.

Test with side effects enabled. If your production system sends webhooks on every update, your load test should too. The webhook testing guide covers this.

Realistic scenarios

Real update patterns aren't uniform. Some records get updated frequently, others rarely. Some users delete lots of data, others never delete anything.

Match your test patterns to production. If 90% of updates are to recent records, test that distribution. If deletes are rare, don't over-weight them in your load test. The realistic load patterns guide covers designing tests that reflect actual usage.

Practical approach

Set up test data that you can modify. Plan for data reset between runs.

Test updates to different resources to measure baseline performance. Test updates to the same resource to measure contention.

Verify idempotency — send duplicate requests and check behavior.

Include side effects in your tests. Watch for cascade operations that might be expensive. The load testing pagination guide covers testing list endpoints that often accompany CRUD operations.

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