This reduces the number of memory allocations at the cost of possible memory usage increase,
since now different metric name strings may hold references to the previous byte slice.
This is good tradeoff, since ProcessSearchQuery is called in vmselect, and vmselect isn't usually limited by memory.
This change has been extracted from https://github.com/VictoriaMetrics/VictoriaMetrics/pull/5527
Previously, it was not possible to determine which tenant sends metrics with excessive amount of labels of label values.
Signed-off-by: Zakhar Bessarab <z.bessarab@victoriametrics.com>
Co-authored-by: Aliaksandr Valialkin <valyala@victoriametrics.com>
The change removes artificial delay before returning error, which sometimes
caused less retry events than expected.
Signed-off-by: hagen1778 <roman@victoriametrics.com>
This should smooth CPU and RAM usage spikes related to these periodic tasks,
by reducing the probability that multiple concurrent periodic tasks are performed at the same time.
The dateMetricIDCache puts recently registered (date, metricID) entries into mutable cache protected by the mutex.
The dateMetricIDCache.Has() checks for the entry in the mutable cache when it isn't found in the immutable cache.
Access to the mutable cache is protected by the mutex. This means this access is slow on systems with many CPU cores.
The mutabe cache was merged into immutable cache every 10 seconds in order to avoid slow access to mutable cache.
This means that ingestion of new time series to VictoriaMetrics could result in significant slowdown for up to 10 seconds
because of bottleneck at the mutex.
Fix this by merging the mutable cache into immutable cache after len(cacheItems) / 2
cache hits under the mutex, e.g. when the entry is found in the mutable cache.
This should automatically adjust intervals between merges depending on the addition rate
for new time series (aka churn rate):
- The interval will be much smaller than 10 seconds under high churn rate.
This should reduce the mutex contention for mutable cache.
- The interval will be bigger than 10 seconds under low churn rate.
This should reduce the uneeded work on merging of mutable cache into immutable cache.
* lib/promscrape/discovery/kubernetes: fix watcher start order for roles endpoints and endpointslice
Previously the groupWatcher could be mistakenly stopped when requests for pod or services resources take too long.
* remove mislead comment
* docs/sd_configs.md: mention -promscrape.kubernetes.attachNodeMetadataAll flag in the description for attach_metadata section
Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/4640
* wip
* lib/promscrape/kubernetes: prevent from stopping groupWatcher when there are in-flight apiWatcher.mustStart() calls
groupWatcher is stopped if it has zero registered apiWatchers during 14 seconds.
But such a groupWatcher can be still in use if apiWatcher for `role: endpoints` or `role: endpointslice`
is being registered and the discovery of the associated `pod` and/or `service` objects takes longer
than 14 seconds - see the beginning of groupWatcher.startWatchersForRole() function for details.
Track the number of in-flight calls to apiWatcher.mustStart() and prevent from stopping the associated groupWatcher
if the number of in-flight calls is non-zero.
P.S. postponing the discovery of `pod` and/or `service` objects associated with `endpoints` or `endpointslice` roles
isn't the best solution, since it slows down initial discovery of `endpoints` and `endpointslice` targets.
* typo fix
---------
Co-authored-by: Aliaksandr Valialkin <valyala@victoriametrics.com>
Examples:
1) -metricsAuthKey=file:///abs/path/to/file - reads flag value from the given absolute filepath
2) -metricsAuthKey=file://./relative/path/to/file - reads flag value from the given relative filepath
3) -metricsAuthKey=http://some-host/some/path?query_arg=abc - reads flag value from the given url
The flag value is automatically updated when the file contents changes.
* app/vmauth: adds metric_labels and backend_errors counter
it must improve observability for user requests with new metric - per user backend errors counter.
it's needed to calculate requests fail rate to the configured backends.
metric_labels configuration allows to perform additional aggregations on top of multiple users from configuration section.
It could be multiple clients or clients with separate read/write tokens
https://github.com/VictoriaMetrics/VictoriaMetrics/issues/5565
* wip
---------
Co-authored-by: Aliaksandr Valialkin <valyala@victoriametrics.com>
* app/vmselect/promql: properly handle possible negative results caused by float operations precision error in rollup functions like rate() or increase()
* fix test
- docs/sd_configs.md: moved hetzner_sd_configs docs to the correct place according to alphabetical order of SD names,
document missing __meta_hetzner_role label.
- lib/promscrape/config.go: added missing MustStop() call for Hetzner SD,
and moved the code to the correct place according to alphabetical order of SD names.
- lib/promscrape/discovery/hetzner: properly handle pagination for hloud API responses,
populate missing __meta_hetzner_role label like Prometheus does.
- Properly populate __meta_hetzner_public_ipv6_network label like Prometheus does.
- Remove unused SDConfig.Token.
- Remove "omitempty" annotation from SDConfig.Role field, since this field is mandatory.
Updates https://github.com/VictoriaMetrics/VictoriaMetrics/pull/5550
Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3154
Each Grafana dashboard has unique ID which can be used to fetch the dashboard
from grafana.com: https://grafana.com/grafana/dashboards/11176
The same dashboard can be accessed via URL with slug: https://grafana.com/grafana/dashboards/11176-victoriametrics-cluster/
But using slug implies that any change to dashboard name will break the link.
So it is better to just use ID, so the dashboard URL will never break.
This is follow-up for ff33e60a3d
Signed-off-by: hagen1778 <roman@victoriametrics.com>
It is likely this value was borrowed from panel `Slow inserts` panel
from Grafana dasbhoard for VM single/cluster installations. This is a mistake.
There is no such thing as "tolerable churn rate", as tolerancy depends on the amount
of allocated resources.
Although, it is unclear what is meant by 5%. If it refers to 5% of new time series per second,
then such churn rate is extremely high. It would mean that the avg life of a time series is 20s.
Signed-off-by: hagen1778 <roman@victoriametrics.com>
