* app/vmalert: autogenerate `ALERTS_FOR_STATE` time series for alerting rules with `for: 0`
Previously, `ALERTS_FOR_STATE` was generated only for alerts with `for > 0`.
This behavior differs from Prometheus behavior - it generates ALERTS_FOR_STATE
time series for alerting rules with `for: 0` as well. Such time series can
be useful for tracking the moment when alerting rule became active.
Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/5648https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3056
Signed-off-by: hagen1778 <roman@victoriametrics.com>
* app/vmalert: support ALERTS_FOR_STATE in `replay` mode
Signed-off-by: hagen1778 <roman@victoriametrics.com>
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Signed-off-by: hagen1778 <roman@victoriametrics.com>
The maxFileParts usage has been accidentally removed in fa566c68a6
While at it, add Count suffix to *AssistedMerges counter names in order to make them less misleading.
Previously their names were falsely suggesting that these are gauges, which show the number of concurrently
executed assisted merges.
This reverts cd4f641d32 , since it has been appeared that the disabled compression
for vmstorage->vmselect data increase network bandwidth usage by more than 10x on typical production workloads,
while it decreases CPU usage at vmstorage by up to 10% and improves query latency by up to 10%.
The 10x increase in network usage is too high price for 10% improvements on query latency and vmstorage CPU usage.
This may result in network bandwidth bottlenecks, which can reduce the overall performance and stability
of VictoriaMetrics cluster. That's why return back the vmstorage->vmselect data compression by default.
The vmstorage->vmselect compression can be disabled by passing -rpc.disableCompression command-line flag to vmstorage.
The vmselect->vmselect compression in multi-level cluster setup can be disabled by passing -clusternative.disableCompression command-line flag.
It has been appeared that the registration of new time series slows down linearly
with the number of indexdb parts, since VictoriaMetrics needs to check every indexdb part
when it searches for TSID by newly ingested metric name.
The number of in-memory parts grows when new time series are registered
at high rate. The number of in-memory parts grows faster on systems with big number
of CPU cores, because the mergeset maintains per-CPU buffers with newly added entries
for the indexdb, and every such entry is transformed eventually into a separate in-memory part.
The solution has been suggested in https://github.com/VictoriaMetrics/VictoriaMetrics/pull/5212
by @misutoth - to limit the number of in-memory parts with buffered channel.
This solution is implemented in this commit. Additionally, this commit merges per-CPU parts
into a single part before adding it to the list of in-memory parts. This reduces CPU load
when searching for TSID by newly ingested metric name.
The https://github.com/VictoriaMetrics/VictoriaMetrics/pull/5212 recommends setting the limit on the number
of in-memory parts to 100, but my internal testing shows that much lower limit 15 works with the same efficiency
on a system with 16 CPU cores while reducing memory usage for `indexdb/dataBlocks` cache by up to 50%.
Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/5190
This allows reducing the indexdb/tagFiltersToMetricIDs cache size by 8 on average.
The cache size can be checked via vm_cache_size_bytes{type="indexdb/tagFiltersToMetricIDs"} metric exposed at /metrics page.
Measuring read / write duration from / to in-memory buffers has little sense,
since it will be always fast. It is better to measure read / write duration from / to
real files at vm_filestream_write_duration_seconds_total and vm_filestream_read_duration_seconds_total metrics.
This also reduces overhead on time.Now() and Histogram.UpdateDuration() calls
per each filestream.Reader.Read() and filestream.Writer.Write() call when the data is read / written from / to in-memory buffers.
This is a follow-up for 2f63dec2e3
* lib/promscrape: respect `0` value for `series_limit` param
Respect `0` value for `series_limit` param in `scrape_config`
even if global limit was set via `-promscrape.seriesLimitPerTarget`.
Previously, `0` value will be ignored in favor of `-promscrape.seriesLimitPerTarget`.
This behavior aligns with possibility to override `series_limit` value via
relabeling with `__series_limit__` label.
Signed-off-by: hagen1778 <roman@victoriametrics.com>
* Update docs/CHANGELOG.md
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Signed-off-by: hagen1778 <roman@victoriametrics.com>
Co-authored-by: Aliaksandr Valialkin <valyala@victoriametrics.com>
Previously, it was not possible to determine which tenant sends metrics with excessive amount of labels of label values.
Signed-off-by: Zakhar Bessarab <z.bessarab@victoriametrics.com>
Co-authored-by: Aliaksandr Valialkin <valyala@victoriametrics.com>
* vmui: fix the logic of closing the popper #5470
* vmui: fix the logic of caching autocomplete results #5472
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Co-authored-by: Aliaksandr Valialkin <valyala@victoriametrics.com>
This reduces the number of memory allocations at the cost of possible memory usage increase,
since now different metric name strings may hold references to the previous byte slice.
This is good tradeoff, since ProcessSearchQuery is called in vmselect, and vmselect isn't usually limited by memory.
This change has been extracted from https://github.com/VictoriaMetrics/VictoriaMetrics/pull/5527
This should smooth CPU and RAM usage spikes related to these periodic tasks,
by reducing the probability that multiple concurrent periodic tasks are performed at the same time.
The dateMetricIDCache puts recently registered (date, metricID) entries into mutable cache protected by the mutex.
The dateMetricIDCache.Has() checks for the entry in the mutable cache when it isn't found in the immutable cache.
Access to the mutable cache is protected by the mutex. This means this access is slow on systems with many CPU cores.
The mutabe cache was merged into immutable cache every 10 seconds in order to avoid slow access to mutable cache.
This means that ingestion of new time series to VictoriaMetrics could result in significant slowdown for up to 10 seconds
because of bottleneck at the mutex.
Fix this by merging the mutable cache into immutable cache after len(cacheItems) / 2
cache hits under the mutex, e.g. when the entry is found in the mutable cache.
This should automatically adjust intervals between merges depending on the addition rate
for new time series (aka churn rate):
- The interval will be much smaller than 10 seconds under high churn rate.
This should reduce the mutex contention for mutable cache.
- The interval will be bigger than 10 seconds under low churn rate.
This should reduce the uneeded work on merging of mutable cache into immutable cache.
