Previously, if we attempted to georeplicate storage from the existing location and, somehow, that existing location did not exist, we'd still mark the new location as invalid. This is a major problem for storage engines that are not consistent. Now, we first try a back off strategy to find the image in the existing storage and, as well, if the replication fails in any way, we log it.
Put worker to sleep for the duration of the default indexing interval
when an APIRequestFailure occurs, when the API request fails due to a
connection error, timeout, or other ambiguous errors, from
analyze_layer or get_layer_data .
before we were not catching the correct exception
[TESTING -> locally using docker]
Issue: https://www.pivotaltracker.com/story/show/144646649
- [ ] It works!
- [ ] Comments provide sufficient explanations for the next contributor
- [ ] Tests cover changes and corner cases
- [ ] Follows Quay syntax patterns and format
This stops notifications from firing over and over again if they are repeatedly failing.
[TESTING -> locally with docker compose, DATABASE MIGRATION -> there is a single migration]
Issue: https://www.pivotaltracker.com/story/show/b144646649n
- [ ] It works!
- [ ] Comments provide sufficient explanations for the next contributor
- [ ] Tests cover changes and corner cases
- [ ] Follows Quay syntax patterns and format
there should be two lines between functions and other code
Issue: https://www.pivotaltracker.com/story/show/b144646649n
- [ ] It works!
- [ ] Comments provide sufficient explanations for the next contributor
- [ ] Tests cover changes and corner cases
- [ ] Follows Quay syntax patterns and format
This will be used in a followup PR to order search results instead of the RAC join. Currently, the join with the RAC table in search results in a lookup of ~600K rows, which causes searching to take ~6s. This PR denormalizes the data we need, as well as allowing us to score based on a wider band (6 months vs the current 1 week).
This change adjusts our batch size to coerce to integer after all
floating point math in order to get a more accurate end result. In
addition, we handle the scenario when there are no longer any images in
the database to be scanned when finding the min id.
This ensures that even if security scanner pagination sends Old and New layer IDs on different pages, they will properly be handled across the entire notification.
Fixes https://www.pivotaltracker.com/story/show/136133657
Changes the security scanner code to raise exceptions now for non-successful operations. One of the new exceptions raised is MissingParentLayerException, which, when raised, will cause the security worker to perform a full rescan of all parent images for the current layer, before trying once more to scan the current layer. This should allow the system to be "self-healing" in the case where the security scanner engine somehow loses or corrupts a parent layer.
The FakeSecurityScanner mocks out all calls that Quay is expected to make to the security scanner API, and returns faked data that can be adjusted by the calling test case
With https://github.com/coreos/clair/pull/278 and https://github.com/coreos/clair/pull/279, performance of this API call has increased. It has been observed that querying 100 or 1000 layers page doesn't noticeably change the execution time. Therefore, doing significantly less calls will reduce the overall processing time for each notification.
- Switches database schema creation to alembic, which solves the MySQL issue (and makes sure we test migrations as well)
- Adds a few time.sleep(1) to work around MySQL's second-precision issue when adding items to queues and then immediately retrieving them
- Disables the storage proxy tests when running against non-SQLite databases, as it causes failures with the multiple process and multiple transactions
- Changes initdb to support only populating the database, as well as fixing a few small items around the test data when working with non-SQLite data
Instead of having the Swift storage engine try to delete the empty chunk(s) synchronously, we simply queue them and have a worker come along after 30s to delete the empty chunks. This has a few key benefits: it is async (doesn't slow down the push code), helps deal with Swift's eventual consistency (less retries necessary) and is generic for other storage engines if/when they need this as well
