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 4M 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.
* {app/vmstorage,app/vmselect}: add API to get list of existing tenants
* {app/vmstorage,app/vmselect}: add API to get list of existing tenants
* app/vmselect: fix error message
* {app/vmstorage,app/vmselect}: fix error messages
* app/vmselect: change log level for error handling
* wip
Co-authored-by: Aliaksandr Valialkin <valyala@victoriametrics.com>
Previously netstorage.MustStop() call didn't free up all the resources,
so the subsequent call to nestorage.Init() would panic.
This allows writing tests, which call nestorage.Init() + nestorage.MustStop() in a loop.
The panic may trigger during data blocks' processing received
from vmstorage nodes when some of vmstorage nodes return an error
or when `-replicationFactor` is set to values higher than 2 at `vmselect`.
Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3058
The bug results in `duplicate output time series` error
because the same time series is added two times into the orderedMetricNames list
inside the tmpBlocksFileWrapper.Finalize().
While at it, properly release all the tmpBlocksFile structs on tbf.Finalize() error.
Previously only the remaining tbf entries were released. This could result in resource leak.
This should improve vmselect performance scalability on systems with many CPU cores.
The following tasks were done:
- Use separate temporary files for storing the data read from each vmstorage node.
This may result in the following potential issues:
- Up to N times higher memory usage for performing each query where N is the number
of vmstorage nodes known to vmselect.
This issue shouldn't increase chances of out of memory errors in most cases,
since per-query memory overhead is quite low comparing to the overall vmselect memory usage.
- Up to N times higher number of open temporary files where N is the number
of vmstorage nodes known to vmselect.
This issue should be fixed by increasing the limit on the number of open files.
- Use separate counters per each vmstorage node for various stats calculation
when reading the data from vmstorage nodes.
Previously a single syncwg.WaitGroup was used for tracking the lifetime of processBlock callbacks
across all the per-vmstorage goroutines. This could be slow on systems with many CPU cores
because of inter-CPU synchronization overhead.
Use a separate per-vmstorage sync.WaitGroup instead in order to reduce inter-CPU synchronization overhead.
This should imrpove performance for heavy queries over big number of blocks on multi-CPU systems.
Reduce inter-CPU communications when processing the query over big number of time series.
This should improve performance for queries over big number of time series
on systems with many CPU cores.
Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/2896
Based on b596ac3745
Thanks to @zqyzyq for the idea.
- Use binary search instead of linear scan when locating the run of smallest timestamps
in blocks with intersected time ranges. This should improve performance
when merging blocks with big number of samples
- Skip samples with duplicate timestamps. This should increase query performance
in cluster version of VictoriaMetrics with the enabled replication.
Previously SearchMetricNames was returning unmarshaled metric names.
This wasn't great for vmstorage, which should spend additional CPU time
for marshaling the metric names before sending them to vmselect.
While at it, remove possible duplicate metric names, which could occur when
multiple samples for new time series are ingested via concurrent requests.
Also sort the metric names before returning them to the client.
This simplifies debugging of the returned metric names across repeated requests to /api/v1/series
Metrics `vm_partial_results_total` and `vm_requests_total` serving
the similar purpose, but contain inconsistent set of labels.
This change updates `vm_partial_results_total` labels to be consistent
with `vm_requests_total`.
The change breaks backward compatibility with assumption that
`vm_partial_results_total` wasn't widely used, since it is
not documented and absent in the alerts and dashboards.
Signed-off-by: hagen1778 <roman@victoriametrics.com>
