vmselect passes query timeout to vmstorage in seconds.
The commit 20e9598254 treated it as timeout in nanoseconds.
Fix this in order to prevent from the following errors under vmstorage load:
cannot process vmselect request: cannot execute "search_v7": couldn't start executing the request in 0.000 seconds,
since -search.maxConcurrentRequests=... concurrent requests are already executed.
The per-series timestamps are usually shared among series, so it is unsafe modifying them.
The issue has been appeared after the optimization at 2f3ddd4884
* {lib/server, app/}: use `httpAuth.*` flag as fallback for `*AuthKey` if it is not set
* lib/ingestserver/opentsdbhttp: fix opentdb HTTP handler not respecting `httpAuth.*` flags
* Apply suggestions from code review
Co-authored-by: Aliaksandr Valialkin <valyala@victoriametrics.com>
* vmagent: add minimal scrape file exampe for vmagent quick start
Signed-off-by: Artem Navoiev <tenmozes@gmail.com>
* replace example with link to your prometheus.yml in docker
Signed-off-by: Artem Navoiev <tenmozes@gmail.com>
Signed-off-by: Artem Navoiev <tenmozes@gmail.com>
- Use promutils.Labels.GetLabels() instead of comparing promutils.Labels.Labels to nil.
This make the code more consistent with other places.
- Mention the release where the issue has been introduced at docs/CHANGELOG.md.
Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3624
Previously the selected time series were split evenly among available CPU cores
for further processing - e.g unpacking the data and applying the given rollup
function to the unpacked data.
Some time series could be processed slower than others.
This could result in uneven work distribution among available CPU cores,
e.g. some CPU cores could complete their work sooner than others.
This could slow down query execution.
The new algorithm allows stealing time series to process from other CPU cores
when all the local work is done. This should reduce the maximum time
needed for query execution (aka tail latency).
The new algorithm should also scale better on systems with many CPU cores,
since every CPU processes locally assigned time series without inter-CPU communications.
The inter-CPU communications are used only when all the local work is finished
and the pending work from other CPUs needs to be stealed.
Unpack time series with less than 400K samples in the currently running goroutine.
Previously a new goroutine was being started for unpacking the samples.
This was requiring additional memory allocations.
Usually the number of blocks returned per each time series during queries is around 4.
So it is a good idea to pre-allocate 4 block references per time series
in order to reduce the number of memory allocations.
See the list of configs supported by Prometheus at f88a0a7d83/discovery/nomad/nomad.go (L76-L84)
- Removed "token" option. In can be set either via NOMAD_TOKEN env var or via `bearer_token` config option.
- Removed "scheme" option. It is automatically detected depending on whether the `tls_config` is set.
- Removed "services" and "tags" options, since they aren't supported by Prometheus.
- Added "region" option. If it is missing, then the region is read from NOMAD_REGION env var.
If this var is empty, then it is set to "global" in the same way as Nomad client does.
See 865ee8d37c/api/api.go (L297)
and 865ee8d37c/api/api.go (L555-L556)
- If the "server" option is missing, then it is read from NOMAD_ADDR in the same way
as Nomad client does - see 865ee8d37c/api/api.go (L294-L296)
This is a follow-up for 8aee209c53
Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3367
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.
