Call runtime.Gosched() only when there is a work to steal from other workers.
Simplify the timeseriesWorker() and unpackWroker() code a bit by inlining stealTimeseriesWork() and stealUnpackWork().
This should reduce CPU usage when processing queries on systems with big number of CPU cores.
Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3966
* vmalert: support logs suppressing during config reloads
The change is mostly required for ENT version of vmalert,
since it supports object-storage for config files.
Reading data from object storage could be time-consuming,
so vmalert emits logs to track the progress.
However, these logs are mostly needed on start or on
manual config reload. Printing these logs each time
`rule.configCheckInterval` is triggered would too verbose.
So the change allows to control logs emitting during
config reloads.
Now, logs are emitted during start up or when SIGHUP is receieved.
For periodicall config checks logs emitted by config pkg are suppressed.
Signed-off-by: hagen1778 <roman@victoriametrics.com>
* vmalert: review fixes
Signed-off-by: hagen1778 <roman@victoriametrics.com>
---------
Signed-off-by: hagen1778 <roman@victoriametrics.com>
This commit changes background merge algorithm, so it becomes compatible with Windows file semantics.
The previous algorithm for background merge:
1. Merge source parts into a destination part inside tmp directory.
2. Create a file in txn directory with instructions on how to atomically
swap source parts with the destination part.
3. Perform instructions from the file.
4. Delete the file with instructions.
This algorithm guarantees that either source parts or destination part
is visible in the partition after unclean shutdown at any step above,
since the remaining files with instructions is replayed on the next restart,
after that the remaining contents of the tmp directory is deleted.
Unfortunately this algorithm doesn't work under Windows because
it disallows removing and moving files, which are in use.
So the new algorithm for background merge has been implemented:
1. Merge source parts into a destination part inside the partition directory itself.
E.g. now the partition directory may contain both complete and incomplete parts.
2. Atomically update the parts.json file with the new list of parts after the merge,
e.g. remove the source parts from the list and add the destination part to the list
before storing it to parts.json file.
3. Remove the source parts from disk when they are no longer used.
This algorithm guarantees that either source parts or destination part
is visible in the partition after unclean shutdown at any step above,
since incomplete partitions from step 1 or old source parts from step 3 are removed
on the next startup by inspecting parts.json file.
This algorithm should work under Windows, since it doesn't remove or move files in use.
This algorithm has also the following benefits:
- It should work better for NFS.
- It fits object storage semantics.
The new algorithm changes data storage format, so it is impossible to downgrade
to the previous versions of VictoriaMetrics after upgrading to this algorithm.
Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3236
Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3821
Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/70
* app/vmagent: allow vm proto for kafka consumer and producer
it should reduce network usage up to 50%.
According to benchmarks without any encoding at kafka topic, it reduces traffic up to 50%.
With enabled zstd at kafka topic, it shows no diffence in traffic. So it
doesn't make much sense to use it.
https://github.com/VictoriaMetrics/VictoriaMetrics/issues/1225
* mention eb61a7dd68b834b08d01727a918f207700348ada at changelog
* app/vmagent: bumps kafka lib version
it allows compiling vmagent for arm64 machines
fixes https://github.com/VictoriaMetrics/VictoriaMetrics/issues/2271
* mention d19b1a888248c96cfd7ccee00ba6f596d89be1d7 at change log
* app/vmagent: adds natural concurrency for kafka consumer
it should improve performance for data consumption
https://github.com/VictoriaMetrics/VictoriaMetrics/issues/1957
* mention change 0c143bb22ca2e7e0b7eec9bc84a94ee2b41626ca
* Update app/vmagent/kafka/consumer.go
Co-authored-by: Roman Khavronenko <roman@victoriametrics.com>
* Update app/vmagent/kafka/consumer_cgo.go
Co-authored-by: Roman Khavronenko <roman@victoriametrics.com>
---------
Co-authored-by: Aliaksandr Valialkin <valyala@victoriametrics.com>
Co-authored-by: Roman Khavronenko <roman@victoriametrics.com>
