`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>
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>
- Document the fix at docs/CHANGELOG.md
- Limit the concurrency for sendStaleMarkers() function in order to limit its memory usage
when big number of targets disappear and staleness markers are sent
for all the metrics exposed by these targets.
- Make sure that the writeRequestCtx is returned to the pool
when there is no need to send staleness markers.
Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3668
This should reduce the maximum memory usage for processScrapedData() function by 2x.
The only part, which can be IO-bound in the processScrapedData() is pushData() call,
when it buffers data to persistent queue if the remote storage cannot keep up
with the data ingestion speed. In this case it is OK if the scrape pace will be limited.
This should reduce memory usage when scraping big number of targets,
since this limits the summary memory usage during concurrent parsing and relabeling
by the number of available CPU cores.
This fixes handling of values bigger than 2GiB for the following command-line flags:
- -storage.minFreeDiskSpaceBytes
- -remoteWrite.maxDiskUsagePerURL
- 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
Sort labels explicitly after calling the ParsedConfigs.Apply() when needed.
This reduces CPU usage when performing metric-level relabeling, where labels' sorting isn't needed.
Previously the `__address__` label could contain only `host:port` part of the target url,
while the scheme and metrics path were obtained from `__scheme__` and `__metrics_path__`
labels. Now it is possible to set the full url in `__address__` label.
This makes valid the following scrape config, which is frequently used by novice users:
scrape_configs:
- job_name: foo
static_configs:
- targets:
- http://host1/metrics1
- https://host2/metrics2
This reduces scrape duration for targets returning big responses.
The response body was already read into memory in stream parsing mode before this change,
so this commit shouldn't increase memory usage.
ioutil.ReadAll is deprecated since Go1.16 - see https://tip.golang.org/doc/go1.16#ioutil
VictoriaMetrics requires at least Go1.18, so it is OK to switch from ioutil.ReadAll to io.ReadAll.
This is a follow-up for 02ca2342ab
- Remove unused js bloatware from /targets page. This strips down binary size by more than 100Kb
- Add /service-discovery page for API compatibility with Prometheus
- Properly load bootstrap.min.css from /prometheus/targets
- Serve static contents for /targets page from app/vminsert instead of app/vmselect, because /targets page is served from there
This should handle the case when the original job_name has been changed in -promscrape.config ,
while the resulting job label remains the same because it is overriden via relabeling.
Previously only the lower part of 64-bit hash was used for calculating the offset.
This may give uneven distribution in some cases. So let's use all the available 64 bits from the hash
for calculating the offset.
Do not store in memory the response from the last scrape per each target if -promscrape.noStaleMarkers option is enabled.
This should reduce memory usage when the scraped targets return large responses.
This allows sending staleness marks and properly calculate scrape_series_added metric in stream parsing mode
at the cost of the increased memory usage, since now the potentially big response is kept
in the lastScrape byte slice per each scrapeWork.
In practice the memory usage increase shouldn't be big, since the response size
is usually much smaller than the parsed metrics from this response after the relabeling,
which usually adds a big pile of target-specific labels per each metric.
