Eliminate Redundant Downloads of Uncached Images¶
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Multiple requests for an image that is not yet cached on the Glance API node handling the request currently results in multiple download requests for the same image from the backend store. For example, 1000 concurrent build requests based off an uncached image can result in 1000 download requests from the backend store.
Feature: Elasticity In order to briefly leverage the power of the Cloud to do some work, As an OpenStack Powered Cloud customer leveraging Glance caching I want to quickly provision a large number of servers, perform some work, and then destroy them. Scenario Outline: Concurrent Requests for Uncached Image Given a single Glance API node And the requested image exists in the backend store And the requested image is uncached on the Glance API node When <n> concurrent request(s) for the image is/are made Then the image will be downloaded from the backend store <m> times And the image will be cached on the Glance API node And every request for the image will succeed Examples: Concurrent Requests | n | m | | 1 | 1 | | 2 | 1 | Scenario: Concurrent Requests for Uncached Image Fails Given a single Glance API node And the requested image exists in the backend store And the requested image is uncached on the Glance API node When 2 concurrent requests for the image are made And mid-download the client closes the first connection Then only the first download request will fail And the image will be cached on the Glance API node Scenario: Stream to all requests while caching Given a Glance API node (1) with this feature deployed And a Glance API node (2) without this feature deployed And the requested image exists in the backend store And the requested image is uncached on Glance API node 1 And the requested image is uncached on Glance API node 2 When 2 concurrent requests are made to API node 1 And 2 concurrent requests are made to API node 2 Then the 2 requests to API node 1 will succeed And the 2 requests to API node 2 will succeed And the image will be cached on Glance API node 1 And the image will be cached on Glance API node 2 And the request completion time between the 2 requests to node 1 will be statistically less than or equal to the request completion time between the 2 requests to node 2
Currently, the Glance caching middleware returns an iterator that downloads from the cache only if the image is already cached. If the image is uncached, the request is passed onto the API to obtain an iterator that will download directly from the store. The response from the API containing this direct iterator is returned back through the caching middleware. If the image is completely uncached when the middleware processes the response, it will wrap the direct download iterator from the API in an another iterator that will tee to the cache (i.e. read from the store and write to both the client and the cache via a split pipe).
Therefore, depending on the state of the cache, one of three iterators can be returned: an iterator to the cache (if the image is completely cached), an iterator to the store (if the image is partially cached), or a teeing iterator that streams from the store and writes to the cache (if the image is completely uncached). This approach is racey and can result in many responses downloading directly from the store and a subset of those teeing data to the same location on the filesystem.
The proposed solution is for the first download request to instead of writing the whole file to the cache, we write the file to cache in chunks. Then, the subsequent download requests read from the chunks that have been written. Once the subsequent request finishes reading all the available chunks in the cache, it will wait for the next available chunk written to the cache by the first request. It will keep doing this until the first request finishes all the chunks.
For the first request:
if the cache entry does not exist: mark the image "caching" create a new folder in the cache directory with the image id take the iterator from the download (like we are doing now) write the data in 1GB chunks to cache upon finish, mark the image "cached"
For the subsequent request:
if the image is marked "caching" or "cached": read the chunk from the cache until we get all the expected chunks if a chunk is not available: wait for it to be written by the first request
Note: The hit count of cached image should not be increased for each chunk read, instead it should be increased once per actual request to read the image from cache.
Add a configuration option,
eliminate_duplicate_downloads, to enable this feature. The addition of a configuration option to control how the caching middleware behaves puts unnecessary burden on the operator. The caching middleware should meet the expected behaviors as outlined in the problem description without introducing a new configuration option. The only value of such option is to allow a phased roll-out of the feature. If the consensus is to introduce such an option, being defaulted to disabled, it should then be deprecated and defaulted to enabled in the next release.
To avoid streaming partial image to multiple clients in case of the initial caching request failing we could block all the subsequent requests until the image is fully in cache and serve those only from cache.
This approach would cause significant delay serving the rest of the clients with a benefit of saved bandwidth in those rare cases where the caching gets interrupted by the image or store going unavailable. Due to possible very long delays on large images this would complicate the download process as some kind of keepalive for the client connection would be needed to avoid timeouts.
Create a lock within the middleware request handler: This prevents requests from reaching the root app and establishing a download iterator in a race to be the first to initiate the download in the cache middleware response handler. However, it comes at a reliability and complexity cost. Logic would have to be implemented in the request handler to recover from failures between the request and response. That’s a lot of squeeze for not a lot of juice.
Move the cache out of the middleware into the root app and provide a locking mechanism around caching and downloading. There are architectural benefits to this. However, it is a serious undertaking, and I believe that any conversations around this should be had completely outside the context of this change.
Move cache out of Glance API: This requires client side logic and new / external caching code.
Data model impact¶
REST API impact¶
Other end user impact¶
See Other deployer impact
Image request time for concurrent requests will decrease.
Bandwidth consumed between Glance API nodes and backend store will decrease.
Other deployer impact¶
Every request being served from the cache will impact the reliability and performance profile. The bottleneck between the backend store and Glance will be removed for the thundering herd problem. However, there could still be a bottleneck between the hypervisors and the Glance API nodes.
Primary assignee: Mridula Joshi
Core reviewer(s): Erno Kuvaja
Update the cache methods in the drivers
Update the cache request handler
Update the cache response handler
Update the docs
Document any new configuration options, if any.