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Project Gating

Project Gating

Traditionally, many software development projects merge changes from developers into the repository, and then identify regressions resulting from those changes (perhaps by running a test suite with a continuous integration system such as Jenkins), followed by more patches to fix those bugs. When the mainline of development is broken, it can be very frustrating for developers and can cause lost productivity, particularly so when the number of contributors or contributions is large.

The process of gating attempts to prevent changes that introduce regressions from being merged. This keeps the mainline of development open and working for all developers, and only when a change is confirmed to work without disruption is it merged.

Many projects practice an informal method of gating where developers with mainline commit access ensure that a test suite runs before merging a change. With more developers, more changes, and more comprehensive test suites, that process does not scale very well, and is not the best use of a developer's time. Zuul can help automate this process, with a particular emphasis on ensuring large numbers of changes are tested correctly.

Zuul was designed to handle the workflow of the OpenStack project, but can be used with any project.

A particular focus of Zuul is ensuring correctly ordered testing of changes in parallel. A gating system should always test each change applied to the tip of the branch exactly as it is going to be merged. A simple way to do that would be to test one change at a time, and merge it only if it passes tests. That works very well, but if changes take a long time to test, developers may have to wait a long time for their changes to make it into the repository. With some projects, it may take hours to test changes, and it is easy for developers to create changes at a rate faster than they can be tested and merged.

Zuul's DependentPipelineManager allows for parallel execution of test jobs for gating while ensuring changes are tested correctly, exactly as if they had been tested one at a time. It does this by performing speculative execution of test jobs; it assumes that all jobs will succeed and tests them in parallel accordingly. If they do succeed, they can all be merged. However, if one fails, then changes that were expecting it to succeed are re-tested without the failed change. In the best case, as many changes as execution contexts are available may be tested in parallel and merged at once. In the worst case, changes are tested one at a time (as each subsequent change fails, changes behind it start again). In practice, the OpenStack project observes something closer to the best case.

For example, if a core developer approves five changes in rapid succession:

A, B, C, D, E

Zuul queues those changes in the order they were approved, and notes that each subsequent change depends on the one ahead of it merging:

A <-- B <-- C <-- D <-- E

Zuul then starts immediately testing all of the changes in parallel. But in the case of changes that depend on others, it instructs the test system to include the changes ahead of it, with the assumption they pass. That means jobs testing change B include change A as well:

Jobs for A: merge change A, then test
Jobs for B: merge changes A and B, then test
Jobs for C: merge changes A, B and C, then test
Jobs for D: merge changes A, B, C and D, then test
Jobs for E: merge changes A, B, C, D and E, then test

If changes A and B pass tests, and C, D, and E fail:

A[pass] <-- B[pass] <-- C[fail] <-- D[fail] <-- E[fail]

Zuul will merge change A followed by change B, leaving this queue:

C[fail] <-- D[fail] <-- E[fail]

Since D was dependent on C, it is not clear whether D's failure is the result of a defect in D or C:

C[fail] <-- D[unknown] <-- E[unknown]

Since C failed, it will report the failure and drop C from the queue:

D[unknown] <-- E[unknown]

This queue is the same as if two new changes had just arrived, so Zuul starts the process again testing D against the tip of the branch, and E against D.