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.
wait groups are necessary with io.Pipes() to avoid
races when a blocking function may not be expected
and a Write() -> Close() before Read() races on each
other. We should avoid such situations..
Co-authored-by: Klaus Post <klauspost@gmail.com>
Current implementation heavily relies on readAllFileInfo
but with the advent of xl.meta inlined with data, we cannot
easily avoid reading data when we are only interested is
updating metadata, this leads to invariably write
amplification during metadata updates, repeatedly reading
data when we are only interested in updating metadata.
This PR ensures that we implement a metadata only update
API at storage layer, that handles updates to metadata alone
for any given version - given the version is valid and
present.
This helps reduce the chattiness for following calls..
- PutObjectTags
- DeleteObjectTags
- PutObjectLegalHold
- PutObjectRetention
- ReplicateObject (updates metadata on replication status)
In #11888 we observe a lot of running, WalkDir calls.
There doesn't appear to be any listerners for these calls, so they should be aborted.
Ensure that WalkDir aborts when upstream cancels the request.
Fixes#11888
ListObjectVersions would skip past the object in the marker when version id is specified.
Make `listPath` return the object with the marker and truncate it if not needed.
Avoid having to parse unintended objects to find a version marker.
store the cache in-memory instead of disks to avoid large
write amplifications for list heavy workloads, store in
memory instead and let it auto expire.
This commit adds a new package `etag` for dealing
with S3 ETags.
Even though ETag is often viewed as MD5 checksum of
an object, handling S3 ETags correctly is a surprisingly
complex task. While it is true that the ETag corresponds
to the MD5 for the most basic S3 API operations, there are
many exceptions in case of multipart uploads or encryption.
In worse, some S3 clients expect very specific behavior when
it comes to ETags. For example, some clients expect that the
ETag is a double-quoted string and fail otherwise.
Non-AWS compliant ETag handling has been a source of many bugs
in the past.
Therefore, this commit adds a dedicated `etag` package that provides
functionality for parsing, generating and converting S3 ETags.
Further, this commit removes the ETag computation from the `hash`
package. Instead, the `hash` package (i.e. `hash.Reader`) should
focus only on computing and verifying the content-sha256.
One core feature of this commit is to provide a mechanism to
communicate a computed ETag from a low-level `io.Reader` to
a high-level `io.Reader`.
This problem occurs when an S3 server receives a request and
has to compute the ETag of the content. However, the server
may also wrap the initial body with several other `io.Reader`,
e.g. when encrypting or compressing the content:
```
reader := Encrypt(Compress(ETag(content)))
```
In such a case, the ETag should be accessible by the high-level
`io.Reader`.
The `etag` provides a mechanism to wrap `io.Reader` implementations
such that the `ETag` can be accessed by a type-check.
This technique is applied to the PUT, COPY and Upload handlers.
This change moves away from a unified constructor for plaintext and encrypted
usage. NewPutObjReader is simplified for the plain-text reader use. For
encrypted reader use, WithEncryption should be called on an initialized PutObjReader.
Plaintext:
func NewPutObjReader(rawReader *hash.Reader) *PutObjReader
The hash.Reader is used to provide payload size and md5sum to the downstream
consumers. This is different from the previous version in that there is no need
to pass nil values for unused parameters.
Encrypted:
func WithEncryption(encReader *hash.Reader,
key *crypto.ObjectKey) (*PutObjReader, error)
This method sets up encrypted reader along with the key to seal the md5sum
produced by the plain-text reader (already setup when NewPutObjReader was
called).
Usage:
```
pReader := NewPutObjReader(rawReader)
// ... other object handler code goes here
// Prepare the encrypted hashed reader
pReader, err = pReader.WithEncryption(encReader, objEncKey)
```
```
mc admin config set alias/ storage_class standard=EC:3
```
should only succeed if parity ratio is valid for all
server pools, if not we should fail proactively.
This PR also needs to bring other changes now that
we need to cater for variadic drive counts per pool.
Bonus fixes also various bugs reproduced with
- GetObjectWithPartNumber()
- CopyObjectPartWithOffsets()
- CopyObjectWithMetadata()
- PutObjectPart,PutObject with truncated streams
parentDirIsObject is not using set level understanding
to check for parent objects, without this it can lead to
objects that can actually reside on a separate set as
objects and would conflict.
This PR refactors the way we use buffers for O_DIRECT and
to re-use those buffers for messagepack reader writer.
After some extensive benchmarking found that not all objects
have this benefit, and only objects smaller than 64KiB see
this benefit overall.
Benefits are seen from almost all objects from
1KiB - 32KiB
Beyond this no objects see benefit with bulk call approach
as the latency of bytes sent over the wire v/s streaming
content directly from disk negate each other with no
remarkable benefits.
All other optimizations include reuse of msgp.Reader,
msgp.Writer using sync.Pool's for all internode calls.
The only purpose of check-dir flag in
ReadVersion is to return 404 when
an object has xl.meta but without data.
This is causing an extract call to the disk
which can be penalizing in case of busy system
where disks receive many concurrent access.
Optimizations include
- do not write the metacache block if the size of the
block is '0' and it is the first block - where listing
is attempted for a transient prefix, this helps to
avoid creating lots of empty metacache entries for
`minioMetaBucket`
- avoid the entire initialization sequence of cacheCh
, metacacheBlockWriter if we are simply going to skip
them when discardResults is set to true.
- No need to hold write locks while writing metacache
blocks - each block is unique, per bucket, per prefix
and also is written by a single node.
current implementation was incorrect, it in-fact
assumed only read quorum number of disks. in-fact
that value is only meant for read quorum good entries
from all online disks.
This PR fixes this behavior properly.
Additional cases handled
- fix address situations where healing is not
triggered on failed writes and deletes.
- consider object exists during listing when
metadata can be successfully decoded.
When searching the caches don't copy the ids, instead inline the loop.
```
Benchmark_bucketMetacache_findCache-32 19200 63490 ns/op 8303 B/op 5 allocs/op
Benchmark_bucketMetacache_findCache-32 20338 58609 ns/op 111 B/op 4 allocs/op
```
Add a reasonable, but still the simplistic benchmark.
Bonus - make nicer zero alloc logging
With new refactor of bucket healing, healing bucket happens
automatically including its metadata, there is no need to
redundant heal buckets also in ListBucketsHeal remove
it.
Do listings with prefix filter when bloom filter is dirty.
This will forward the prefix filter to the lister which will make it
only scan the folders/objects with the specified prefix.
If we have a clean bloom filter we try to build a more generally
useful cache so in that case, we will list all objects/folders.
Add shortcut for `APN/1.0 Veeam/1.0 Backup/10.0`
It requests unique blocks with a specific prefix. We skip
scanning the parent directory for more objects matching the prefix.
Add trashcan that keeps recently updated lists after bucket deletion.
All caches were deleted once a bucket was deleted, so caches still running would report errors. Now they are canceled.
Fix `.minio.sys` not being transient.
* 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.
