* It must reduce memory usage for misbehaving clients. Since
VictoriaMetrics stores sparse index inmemory.
* Reduce disk space usage for indexdb.
* Prevent possible indexDB items drops.
* It may trigger slow insert and new timeseries registration due to
default value for flag change
https://github.com/VictoriaMetrics/VictoriaMetrics/issues/6176
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Co-authored-by: Aliaksandr Valialkin <valyala@victoriametrics.com>
Instead, log a sample of these long items once per 5 seconds into error log,
so users could notice and fix the issue with too long labels or too many labels.
Previously this panic could occur in production when ingesting samples with too long labels.
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
Previously bytesutil.Resize() was copying the original byte slice contents to a newly allocated slice.
This wasted CPU cycles and memory bandwidth in some places, where the original slice contents wasn't needed
after slize resizing. Switch such places to bytesutil.ResizeNoCopy().
Rename the original bytesutil.Resize() function to bytesutil.ResizeWithCopy() for the sake of improved readability.
Additionally, allocate new slice with `make()` instead of `append()`. This guarantees that the capacity of the allocated slice
exactly matches the requested size. The `append()` could return a slice with bigger capacity as an optimization for further `append()` calls.
This could result in excess memory usage when the returned byte slice was cached (for instance, in lib/blockcache).
Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/2007
This should improve lookup performance if the same `label=value` pair exists
in big number of time series.
This should also reduce memory usage for mergeset data cache, since `tag->metricIDs` rows
occupy less space than the original `tag->metricID` rows.
