Remove api.expiration_workers config setting which was inadvertently left behind. Per review comment
https://github.com/minio/minio/pull/18926, expiration_workers can be configured via ilm.expiration_workers.
- Use a shared worker pool for all ILM expiry tasks
- Free version cleanup executes in a separate goroutine
- Add a free version only if removing the remote object fails
- Add ILM expiry metrics to the node namespace
- Move tier journal tasks to expiryState
- Remove unused on-disk journal for tiered objects pending deletion
- Distribute expiry tasks across workers such that the expiry of versions of
the same object serialized
- Ability to resize worker pool without server restart
- Make scaling down of expiryState workers' concurrency safe; Thanks
@klauspost
- Add error logs when expiryState and transition state are not
initialized (yet)
* metrics: Add missed tier journal entry tasks
* Initialize the ILM worker pool after the object layer
With the current asynchronous behaviour in sending notification events
to the targets, we can't provide guaranteed delivery as the systems
might go for restarts.
For such event-driven use-cases, we can provide an option to enable
synchronous events where the APIs wait until the event is successfully
sent or persisted.
This commit adds 'MINIO_API_SYNC_EVENTS' env which when set to 'on'
will enable sending/persisting events to targets synchronously.
Main motivation is move towards a common backend format
for all different types of modes in MinIO, allowing for
a simpler code and predictable behavior across all features.
This PR also brings features such as versioning, replication,
transitioning to single drive setups.
When reloading a dynamic config allow the request pool to scale both ways.
Existing requests hold on to the previous pool, so they will pop the elements from that.
Enabled with `mc admin config set alias/ api gzip_objects=on`
Standard filtering applies (1K response minimum, not compressed content
type, not range request, gzip accepted by client).
Some users running MinIO claim that their system became slow. One
way to investigate is to look at this Prometheus history of the number of
the requests reaching the server. The existing current S3 requests metric
is not enough because it can increase of the system really becomes slow,
due to disk issues for example.
repeated reads on single large objects in HPC like
workloads, need the following option to disable
O_DIRECT for a more effective usage of the kernel
page-cache.
However this optional should be used in very specific
situations only, and shouldn't be enabled on all
servers.
NVMe servers benefit always from keeping O_DIRECT on.
Go's atomic.Value does not support `nil` type,
concrete type is necessary to avoid any panics with
the current implementation.
Also remove boolean to turn-off tracking of freezeCount.
an active running speedTest will reject all
new S3 requests to the server, until speedTest
is complete.
this is to ensure that speedTest results are
accurate and trusted.
Co-authored-by: Klaus Post <klauspost@gmail.com>
container limits would not be properly honored in
our current implementation, mem.VirtualMemory()
function only reads /proc/meminfo which points to
the host system information inside the container.
This feature also changes the default port where
the browser is running, now the port has moved
to 9001 and it can be configured with
```
--console-address ":9001"
```
This is to ensure that there are no projects
that try to import `minio/minio/pkg` into
their own repo. Any such common packages should
go to `https://github.com/minio/pkg`
https://github.com/minio/console takes over the functionality for the
future object browser development
Signed-off-by: Harshavardhana <harsha@minio.io>
just like replication workers, allow failed replication
workers to be configurable in situations like DR failures
etc to catch up on replication sooner when DR is back
online.
Signed-off-by: Harshavardhana <harsha@minio.io>
major performance improvements in range GETs to avoid large
read amplification when ranges are tiny and random
```
-------------------
Operation: GET
Operations: 142014 -> 339421
Duration: 4m50s -> 4m56s
* Average: +139.41% (+1177.3 MiB/s) throughput, +139.11% (+658.4) obj/s
* Fastest: +125.24% (+1207.4 MiB/s) throughput, +132.32% (+612.9) obj/s
* 50% Median: +139.06% (+1175.7 MiB/s) throughput, +133.46% (+660.9) obj/s
* Slowest: +203.40% (+1267.9 MiB/s) throughput, +198.59% (+753.5) obj/s
```
TTFB from 10MiB BlockSize
```
* First Access TTFB: Avg: 81ms, Median: 61ms, Best: 20ms, Worst: 2.056s
```
TTFB from 1MiB BlockSize
```
* First Access TTFB: Avg: 22ms, Median: 21ms, Best: 8ms, Worst: 91ms
```
Full object reads however do see a slight change which won't be
noticeable in real world, so not doing any comparisons
TTFB still had improvements with full object reads with 1MiB
```
* First Access TTFB: Avg: 68ms, Median: 35ms, Best: 11ms, Worst: 1.16s
```
v/s
TTFB with 10MiB
```
* First Access TTFB: Avg: 388ms, Median: 98ms, Best: 20ms, Worst: 4.156s
```
This change should affect all new uploads, previous uploads should
continue to work with business as usual. But dramatic improvements can
be seen with these changes.
We can use this metric to check if there are too many S3 clients in the
queue and could explain why some of those S3 clients are timing out.
```
minio_s3_requests_waiting_total{server="127.0.0.1:9000"} 9981
```
If max_requests is 10000 then there is a strong possibility that clients
are timing out because of the queue deadline.
Skip notifications on objects that might have had
an error during deletion, this also avoids unnecessary
replication attempt on such objects.
Refactor some places to make sure that we have notified
the client before we
- notify
- schedule for replication
- lifecycle etc.
