On 01/12/14 02:02, Anant Patil wrote:
On GitHub:https://github.com/anantpatil/heat-convergence-poc

I'm trying to review this code at the moment, and finding some stuff I don't understand:

https://github.com/anantpatil/heat-convergence-poc/blob/master/heat/engine/stack.py#L911-L916

This appears to loop through all of the resources *prior* to kicking off any actual updates to check if the resource will change. This is impossible to do in general, since a resource may obtain a property value from an attribute of another resource and there is no way to know whether an update to said other resource would cause a change in the attribute value.

In addition, no attempt to catch UpdateReplace is made. Although that looks like a simple fix, I'm now worried about the level to which this code has been tested.


I'm also trying to wrap my head around how resources are cleaned up in dependency order. If I understand correctly, you store in the ResourceGraph table the dependencies between various resource names in the current template (presumably there could also be some left around from previous templates too?). For each resource name there may be a number of rows in the Resource table, each with an incrementing version. As far as I can tell though, there's nowhere that the dependency graph for _previous_ templates is persisted? So if the dependency order changes in the template we have no way of knowing the correct order to clean up in any more? (There's not even a mechanism to associate a resource version with a particular template, which might be one avenue by which to recover the dependencies.)

I think this is an important case we need to be able to handle, so I added a scenario to my test framework to exercise it and discovered that my implementation was also buggy. Here's the fix: https://github.com/zaneb/heat-convergence-prototype/commit/786f367210ca0acf9eb22bea78fd9d51941b0e40


It was difficult, for me personally, to completely understand Zane's PoC
and how it would lay the foundation for aforementioned design goals. It
would be very helpful to have Zane's understanding here. I could
understand that there are ideas like async message passing and notifying
the parent which we also subscribe to.

So I guess the thing to note is that there are essentially two parts to my Poc: 1) A simulation framework that takes what will be in the final implementation multiple tasks running in parallel in separate processes and talking to a database, and replaces it with an event loop that runs the tasks sequentially in a single process with an in-memory data store. I could have built a more realistic simulator using Celery or something, but I preferred this way as it offers deterministic tests.
2) A toy implementation of Heat on top of this framework.

The files map roughly to Heat something like this:

converge.engine       -> heat.engine.service
converge.stack        -> heat.engine.stack
converge.resource     -> heat.engine.resource
converge.template     -> heat.engine.template
converge.dependencies -> actually is heat.engine.dependencies
converge.sync_point   -> no equivalent
converge.converger    -> no equivalent (this is convergence "worker")
converge.reality      -> represents the actual OpenStack services

For convenience, I just use the @asynchronous decorator to turn an ordinary method call into a simulated message.

The concept is essentially as follows:
At the start of a stack update (creates and deletes are also just updates) we create any new resources in the DB calculate the dependency graph for the update from the data in the DB and template. This graph is the same one used by updates in Heat currently, so it contains both the forward and reverse (cleanup) dependencies. The stack update then kicks off checks of all the leaf nodes, passing the pre-calculated dependency graph.

Each resource check may result in a call to the create(), update() or delete() methods of a Resource plugin. The resource also reads any attributes that will be required from it. Once this is complete, it triggers any dependent resources that are ready, or updates a SyncPoint in the database if there are dependent resources that have multiple requirements. The message triggering the next resource will contain the dependency graph again, as well as the RefIds and required attributes of any resources it depends on.

The new dependencies thus created are added to the resource itself in the database at the time it is checked, allowing it to record the changes caused by a requirement being unexpectedly replaced without needing a global lock on anything.

When cleaning up resources, we also endeavour to remove any that are successfully deleted from the dependencies graph.

Each traversal has a unique ID that is both stored in the stack and passed down through the resource check triggers. (At present this is the template ID, but it may make more sense to have a unique ID since old template IDs can be resurrected in the case of a rollback.) As soon as these fail to match the resource checks stop propagating, so only an update of a single field is required (rather than locking an entire table) before beginning a new stack update.

Hopefully that helps a little. Please let me know if you have specific questions. I'm *very* happy to incorporate other ideas into it, since it's pretty quick to change, has tests to check for regressions, and is intended to be thrown away anyhow (so I genuinely don't care if some bits get thrown away earlier than others).


In retrospective, we had to struggle a lot to understand the existing
Heat engine. We couldn't have done justice by just creating another
project in GitHub and without any concrete understanding of existing
state-of-affairs.

I completely agree, and you guys did the right thing by starting out looking at Heat. But remember, the valuable thing isn't the code, it's what you learned. My concern is that now that you have Heat pretty well figured out, you won't be able to continue to learn nearly as fast trying to wrestle with the Heat codebase as you could with the simulator. We don't want to fall into the trap of just shipping whatever we have because it's too hard to explore the other options, we want to identify a promising design and iterate it as quickly as possible.

cheers,
Zane.

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