* Fix caches having EOF marked as a failure.
* Simplify cache updates.
* Provide context for checkMetacacheState failures.
* Log 499 when the client disconnects.
Design: https://gist.github.com/klauspost/025c09b48ed4a1293c917cecfabdf21c
Gist of improvements:
* Cross-server caching and listing will use the same data across servers and requests.
* Lists can be arbitrarily resumed at a constant speed.
* Metadata for all files scanned is stored for streaming retrieval.
* The existing bloom filters controlled by the crawler is used for validating caches.
* Concurrent requests for the same data (or parts of it) will not spawn additional walkers.
* Listing a subdirectory of an existing recursive cache will use the cache.
* All listing operations are fully streamable so the number of objects in a bucket no
longer dictates the amount of memory.
* Listings can be handled by any server within the cluster.
* Caches are cleaned up when out of date or superseded by a more recent one.
After #10594 let's invalidate the bloom filters to force the next cycles to go through all data.
There is a small chance that the linked PR could have caused missing bloom filter data.
This will invalidate the current bloom filters and make the crawler go through everything.
Keep dataUpdateTracker while goroutine is starting.
This will ensure the object is updated one `start` returns
Tested with
```
λ go test -cpu=1,2,4,8 -test.run TestDataUpdateTracker -count=1000
PASS
ok github.com/minio/minio/cmd 8.913s
```
Fixes#10295
data usage tracker and crawler seem to be logging
non-actionable information on console, which is not
useful and is fixed on its own in almost all deployments,
lets keep this logging to minimal.
By monitoring PUT/DELETE and heal operations it is possible
to track changed paths and keep a bloom filter for this data.
This can help prioritize paths to scan. The bloom filter can identify
paths that have not changed, and the few collisions will only result
in a marginal extra workload. This can be implemented on either a
bucket+(1 prefix level) with reasonable performance.
The bloom filter is set to have a false positive rate at 1% at 1M
entries. A bloom table of this size is about ~2500 bytes when serialized.
To not force a full scan of all paths that have changed cycle bloom
filters would need to be kept, so we guarantee that dirty paths have
been scanned within cycle runs. Until cycle bloom filters have been
collected all paths are considered dirty.
