This reverts commit 5a3abfa041.
Reason for revert: exemplars aren't in wide use because they have numerous issues which prevent their adoption (see below).
Adding support for examplars into VictoriaMetrics introduces non-trivial code changes. These code changes need to be supported forever
once the release of VictoriaMetrics with exemplar support is published. That's why I don't think this is a good feature despite
that the source code of the reverted commit has an excellent quality. See https://docs.victoriametrics.com/goals/ .
Issues with Prometheus exemplars:
- Prometheus still has only experimental support for exemplars after more than three years since they were introduced.
It stores exemplars in memory, so they are lost after Prometheus restart. This doesn't look like production-ready feature.
See 0a2f3b3794/content/docs/instrumenting/exposition_formats.md (L153-L159)
and https://prometheus.io/docs/prometheus/latest/feature_flags/#exemplars-storage
- It is very non-trivial to expose exemplars alongside metrics in your application, since the official Prometheus SDKs
for metrics' exposition ( https://prometheus.io/docs/instrumenting/clientlibs/ ) either have very hard-to-use API
for exposing histograms or do not have this API at all. For example, try figuring out how to expose exemplars
via https://pkg.go.dev/github.com/prometheus/client_golang@v1.19.1/prometheus .
- It looks like exemplars are supported for Histogram metric types only -
see https://pkg.go.dev/github.com/prometheus/client_golang@v1.19.1/prometheus#Timer.ObserveDurationWithExemplar .
Exemplars aren't supported for Counter, Gauge and Summary metric types.
- Grafana has very poor support for Prometheus exemplars. It looks like it supports exemplars only when the query
contains histogram_quantile() function. It queries exemplars via special Prometheus API -
https://prometheus.io/docs/prometheus/latest/querying/api/#querying-exemplars - (which is still marked as experimental, btw.)
and then displays all the returned exemplars on the graph as special dots. The issue is that this doesn't work
in production in most cases when the histogram_quantile() is calculated over thousands of histogram buckets
exposed by big number of application instances. Every histogram bucket may expose an exemplar on every timestamp shown on the graph.
This makes the graph unusable, since it is litterally filled with thousands of exemplar dots.
Neither Prometheus API nor Grafana doesn't provide the ability to filter out unneeded exemplars.
- Exemplars are usually connected to traces. While traces are good for some
I doubt exemplars will become production-ready in the near future because of the issues outlined above.
Alternative to exemplars:
Exemplars are marketed as a silver bullet for the correlation between metrics, traces and logs -
just click the exemplar dot on some graph in Grafana and instantly see the corresponding trace or log entry!
This doesn't work as expected in production as shown above. Are there better solutions, which work in production?
Yes - just use time-based and label-based correlation between metrics, traces and logs. Assign the same `job`
and `instance` labels to metrics, logs and traces, so you can quickly find the needed trace or log entry
by these labes on the time range with the anomaly on metrics' graph.
This code adds Exemplars to VMagent and the promscrape parser adhering
to OpenMetrics Specifications. This will allow forwarding of exemplars
to Prometheus and other third party apps that support OpenMetrics specs.
---------
Signed-off-by: Ted Possible <ted_possible@cable.comcast.com>
Stale series are sent when there is a difference between current
and previous scrapes. Those series which disappeared in the current scrape
are marked as stale and sent to the remote storage.
Sending stale series requires memory allocation and in case when too many
series disappear in the same it could result in noticeable memory spike.
For example, re-deploy of a big fleet of service can result into
excessive memory usage for vmagent, because all the series with old
pod name will be marked as stale and sent to the remote write storage.
This change limits the number of stale series which can be sent at once,
so memory usage remains steady.
https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3668https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3675
Signed-off-by: hagen1778 <roman@victoriametrics.com>
Signed-off-by: hagen1778 <roman@victoriametrics.com>
- Return meta-labels for the discovered targets via promutils.Labels
instead of map[string]string. This improves the speed of generating
meta-labels for discovered targets by up to 5x.
- Remove memory allocations in hot paths during ScrapeWork generation.
The ScrapeWork contains scrape settings for a single discovered target.
This improves the service discovery speed by up to 2x.
The issue was in the `labels := dst[offset:]` line in the beginning of appendExtraLabels() function.
The `dst` may be re-allocated when adding extra labels to it. In this case the addition of `exported_`
prefix to labels inside `labels` slice become invisible in the returned `dst` labels.
While at it, properly handle some corner cases:
- Add additional `exported_` prefix to clashing metric labels with already existing `exported_` prefix.
- Store scraped metric names in `exported___name__` label if scrape target contains `__name__` label.
Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3278
Thanks to @jplanckeel for the initial attempt to fix this issue
at https://github.com/VictoriaMetrics/VictoriaMetrics/pull/3281
Also reduce CPU usage when applying `series_limit` to scrape targets with constant set of metrics.
The main idea is to perform the calculations on scrape_series_added and series_limit
only if the set of metrics exposed by the target has been changed.
Scrape targets rarely change the set of exposed metrics,
so this optimization should reduce CPU usage in general case.
The number of series per target can be limited with the following options:
* Global limit with `-promscrape.maxSeriesPerTarget` command-line option.
* Per-target limit with `max_series: N` option in `scrape_config` section.
Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/1561
* feature: Add multitenant for vmagent
* Minor fix
* Fix rcs index out of range
* Minor fix
* Fix multi Init
* Fix multi Init
* Fix multi Init
* Add default multi
* Adjust naming
* Add TenantInserted metrics
* Add TenantInserted metrics
* fix: remove unused metrics for vmagent
* fix: remove unused metrics for vmagent
Co-authored-by: mghader <marc.ghader@ubisoft.com>
Co-authored-by: Sebastian YEPES <syepes@gmail.com>
See the description for `sample_limit` option from Prometheus docs:
Per-scrape limit on number of scraped samples that will be accepted.
If more than this number of samples are present after metric relabeling
the entire scrape will be treated as failed. 0 means no limit.
https://prometheus.io/docs/prometheus/latest/configuration/configuration/#scrape_config
