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>
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.
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.
Stress the importance of specifying of all Alertmanager
URLs in vmalert's `-notifier.url` or `notifier.config`
if it runs in cluster mode.
See https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3547
Signed-off-by: hagen1778 <roman@victoriametrics.com>
- Show in the line tooltip the number of the query which generates the given line.
This simplifies comparison of lines generated by multiple queries.
- Show metric name as __name__ label in the line tooltip in the same way as other labels are shown there.
This makes the label information in the tooltip more consistent.
- Properly quote label values with JSON.stringify(). This prevents from improper formatting
when label values contain doublequote chars.
- Remove double curly braces artifact at graph legend for lines without names and labels.
- Properly use modifier for regular expressions across the code.
There is no need to manually call `queryDuration.UpdateDuration(startTime)`, because `defer queryDuration.UpdateDuration(startTime)` is executed at the beginning of the function(L660).
Allow configuring the default number of stored rule's update states in memory
via global `-rule.updateEntriesLimit` command-line flag or per-rule via rule's
`update_entries_limit` configuration param.
Signed-off-by: hagen1778 <roman@victoriametrics.com>
This simplifies manual usage of the APIs. For example, the following query
would return the results over the 2022 year.
/api/v1/query_range?start=2022&end=2023&step=1d&query=...
This is equivalent to:
/api/v1/query_range?start=2022-01-01T00:00:00Z&end=2023-01-01T00:00:00Z&step=1d&query=...
