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>
Using plain sync.Pool simplifies the code without increasing memory usage and CPU usage.
So it is better to use plain sync.Pool from readability and maintainability PoV.
This is a follow-up for 8942f290eb
* lib/promscrape: make concurrency control optional
Before, `-maxConcurrentInserts` was limiting all calls to `promscrape.Parse`
function: during ingestion and scraping. This behavior is incorrect.
Cmd-line flag `-maxConcurrentInserts` should have effect onl on ingestion.
Since both pipelines use the same `promscrape.Parse` function, we extend it
to make concurrency limiter optional. So caller can decide whether concurrency
should be limited or not.
This commit makes c53b5788b4
obsolete.
Signed-off-by: hagen1778 <roman@victoriametrics.com>
* Revert "dashboards: move `Concurrent inserts` panel to Troubleshooting section"
This reverts commit c53b5788b4.
---------
Signed-off-by: hagen1778 <roman@victoriametrics.com>
`lib/protoparser/prometheus` is used by various applications,
such as `app/vmalert`. The recent change to the
`lib/protoparser/prometheus` package introduced a new dependency
of `lib/writeconcurrencylimiter` which exposes some metrics.
Because of the dependency, now all applications which have this
dependency also expose these metrics.
Creating a new `lib/protoparser/prometheus/stream` package helps
to remove these metrics from apps which use `lib/protoparser/prometheus`
as dependency.
See https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3761
Signed-off-by: hagen1778 <roman@victoriametrics.com>
Previously the -maxConcurrentInserts was limiting the number of established client connections,
which write data to VictoriaMetrics. Some of these connections could be idle.
Such connections do not consume big amounts of CPU and RAM, so there is a little sense in limiting
the number of such connections. So now the -maxConcurrentInserts command-line option
limits the number of concurrently executed insert requests, not including idle connections.
It is recommended removing -maxConcurrentInserts command-line option, since the default value
for this option should work good for most cases.
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.
OpenMetrics timestamps are floating-point numbers, that represent Unix timestamp in seconds.
This differs from Prometheus exposition format, where timestamps are integer numbers representing Unix timestamp in milliseconds.
Previously certain errors in timestamps and/or values could be silently skipped,
which could lead to samples with zero values stored in the database.
Updates https://github.com/VictoriaMetrics/vmctl/issues/25
* add vm_protoparser_rows_read_total metrics to promscrape
move vm_protoparser_rows_read_total for promscrape to better place
move vm_protoparser_rows_read_total for promscrape to better place
* remove possibility of infinity loop at prometheus parser
