[epic] v1.0.0 Performance and Scale

[joelanford]

Epic Goal

• Measure and implement any necessary improvements to ensure OLM v1.0.0 meets or exceeds OCP guidelines around performance and scalability.

Why is this important?

• OLM v1.0.0 will be a payload component that always runs in OCP clusters. In order to reduce SD and customer costs, we need to minimize this overhead.
• OLM v1.0.0 is intended to be used on a wide variety of clusters, ranging from single node clusters with just a few namespaces to clusters 2-3 orders of magnitude larger. We need make sure that it runs just as well on a small cluster as it does a large cluster.
• In order to reduce user frustration, we need to provide a responsive user experience. Reconciliation needs to be fast and non-blocking to ensure users receive the experience they have come to expect from OCP. To the extent possible, long-running tasks (e.g. catalog fetching/caching and image pulling) should be performed asynchronously.

Scenarios

1. Collect pprof profiles for CPU and memory when running standard user flows around installing, upgrading, and removing operators from public catalogs (e.g. operatorhub)
2. Find the most resource intensive code paths. Provide documentation and recommendations related to making improvements in those areas.
3. Coordinate with OLM maintainers to make improvements in areas deemed to provide the most significant performance and scale gain.
4. Implement automated performance and scale regression tests in the existing upstream CI test suite.

Examples of known areas for improvement include:

• When reconciling a ClusterExtension to resolve a bundle from the criteria provided by a user, the reconciler should return a desired bundle within 100ms and allocate no more memory than the size of the catalog metadata for the named spec.packageName.
• When the ClusterExtension reconciler does not have the contents of a resolved image bundle available, it does not block waiting for the image to be pulled and processed. Rather, it starts an asychronous job, reports the pending image pull via the ClusterExtension status, and returns from reconcile.

[OchiengEd]

/assign

[joelanford]

I think I've found one unexpected slowdown: the bundle handler that converts a registry+v1 bundle to plain and then to helm. It takes 5s on my machine in the "Force upgrade" e2e test.

[joelanford]

@OchiengEd just wanted to check to make sure you didn't find any critical (as in "must fix for 1.0.0") issues in your performance and scale research?

If not, can we move the remaining scope of this epic to v1.x?

[everettraven]

Item to include in performance and scale (although maybe more of a release blocker based on other discussions):

• (Provided ServiceAccount) Follow-up: update dynamic cache to re-use cache.Cache instead of creating a new one every time #1025 (optimization)

EDIT: After discussion in the community meeting, this issue is not in scope for this epic.

[OchiengEd]

No critical issues were identified. This epic was slated to be moved to 1.x

[LalatenduMohanty]

Lets re-access this and identify the acceptance criteria for this epic.

[grokspawn]

We should remodel this issue to design the assessment/reporting infrastructure, with MVP implementation. However, it should include the scope of the new query catalogd web API discussed in #1607.
We can create subsequent epics to give us measurable progress and continue to refine the implementation.

[LalatenduMohanty]

Next step is to call a meeting and agree on the things we want to achieve with this epic. cc @dtfranz

[grokspawn]

From the committee meeting, this is analogous to work that we're doing w.r.t. feature-gates, where we have

1. a general framework which provides for measurement/assessment/detection capabilities in a standardized approach; and
2. features which hook into the framework to inform their measurement/assessment/detection functionality, starting with catalogd web api benchmarking #1607