A cache structure will be kept with a tree of usages.
The cache is a tree structure where each keeps track
of its children.
An uncompacted branch contains a count of the files
only directly at the branch level, and contains link to
children branches or leaves.
The leaves are "compacted" based on a number of properties.
A compacted leaf contains the totals of all files beneath it.
A leaf is only scanned once every dataUsageUpdateDirCycles,
rarer if the bloom filter for the path is clean and no lifecycles
are applied. Skipped leaves have their totals transferred from
the previous cycle.
A clean leaf will be included once every healFolderIncludeProb
for partial heal scans. When selected there is a one in
healObjectSelectProb that any object will be chosen for heal scan.
Compaction happens when either:
- The folder (and subfolders) contains less than dataScannerCompactLeastObject objects.
- The folder itself contains more than dataScannerCompactAtFolders folders.
- The folder only contains objects and no subfolders.
- A bucket root will never be compacted.
Furthermore, if a has more than dataScannerCompactAtChildren recursive
children (uncompacted folders) the tree will be recursively scanned and the
branches with the least number of objects will be compacted until the limit
is reached.
This ensures that any branch will never contain an unreasonable amount
of other branches, and also that small branches with few objects don't
take up unreasonable amounts of space.
Whenever a branch is scanned, it is assumed that it will be un-compacted
before it hits any of the above limits. This will make the branch rebalance
itself when scanned if the distribution of objects has changed.
TLDR; With current values: No bucket will ever have more than 10000
child nodes recursively. No single folder will have more than 2500 child
nodes by itself. All subfolders are compacted if they have less than 500
objects in them recursively.
We accumulate the (non-deletemarker) version count for paths as well,
since we are changing the structure anyway.
- collect real time replication metrics for prometheus.
- add pending_count, failed_count metric for total pending/failed replication operations.
- add API to get replication metrics
- add MRF worker to handle spill-over replication operations
- multiple issues found with replication
- fixes an issue when client sends a bucket
name with `/` at the end from SetRemoteTarget
API call make sure to trim the bucket name to
avoid any extra `/`.
- hold write locks in GetObjectNInfo during replication
to ensure that object version stack is not overwritten
while reading the content.
- add additional protection during WriteMetadata() to
ensure that we always write a valid FileInfo{} and avoid
ever writing empty FileInfo{} to the lowest layers.
Co-authored-by: Poorna Krishnamoorthy <poorna@minio.io>
Co-authored-by: Harshavardhana <harsha@minio.io>
* 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.
Klaus Post
Harshavardhana
Poorna Krishnamoorthy