### Describe Your Changes
* check if `lastValue` was seen at least twice with different
timestamps. Otherwise, the difference between last timestamp and
previous timestamp could be `0` and will result into `NaN` calculation
* check if there items left in lastValue map after staleness cleanup.
Otherwise, `rate_avg` could have produce `NaN` result.
### Checklist
The following checks are **mandatory**:
- [x] My change adheres [VictoriaMetrics contributing
guidelines](https://docs.victoriametrics.com/contributing/).
---------
Signed-off-by: hagen1778 <roman@victoriametrics.com>
These functions are called every time `/metrics` page is scraped, so it would be great
if they could be sped up for the cases when dedupAggr tracks tens of millions of active time series.
- Use bytesutil.InternString() instead of strings.Clone() for inputKey and outputKey in aggregatorpushSamples().
This should reduce string allocation rate, since strings can be re-used between aggrState flushes.
- Reduce memory allocations at dedupAggrShard by storing dedupAggrSample by value in the active series map.
- Remove duplicate call to bytesutil.InternBytes() at Deduplicator, since it is already called inside dedupAggr.pushSamples().
- Add missing string interning at rateAggrState.pushSamples().
Updates https://github.com/VictoriaMetrics/VictoriaMetrics/pull/6402
The main change is getting rid of interning of sample key. It was
discovered that for cases with many unique time series aggregated by
vmagent interned keys could grow up to hundreds of millions of objects.
This has negative impact on the following aspects:
1. It slows down garbage collection cycles, as GC has to scan all inuse
objects periodically. The higher is the number of inuse objects, the
longer it takes/the more CPU it takes.
2. It slows down the hot path of samples aggregation where each key
needs to be looked up in the map first.
The change makes code more fragile, but suppose to provide performance
optimization for heavy-loaded vmagents with stream aggregation enabled.
---------
Signed-off-by: hagen1778 <roman@victoriametrics.com>
Co-authored-by: Aliaksandr Valialkin <valyala@victoriametrics.com>
### Describe Your Changes
Added streamaggr metrics to:
- `vm_streamaggr_samples_lag_seconds` - samples lag
- `vm_streamaggr_ignored_samples_total{reason="nan"}` - ignored NaN
samples
- `vm_streamaggr_ignored_samples_total{reason="too_old"}` - ignored old
samples
Prevent excessive resource usage when stream aggregation config file
contains no matchers by prevent pushing data into Aggregators object.
Before this change a lot of extra work was invoked without reason.
Signed-off-by: hagen1778 <roman@victoriametrics.com>
Change the return values for these functions - now they return the unmarshaled result plus
the size of the unmarshaled result in bytes, so the caller could re-slice the src for further unmarshaling.
This improves performance of these functions in hot loops of VictoriaLogs a bit.
Added `rate` and `rate_avg` output
Resource usage is the same as for increase output, tested on a benchmark
---------
Signed-off-by: hagen1778 <roman@victoriametrics.com>
Co-authored-by: hagen1778 <roman@victoriametrics.com>
Set correct suffix `<output>_prometheus` for aggregation outputs
`increase_prometheus` and `total_prometheus`
Before, outputs `total` and `total_prometheus` or `increase` and
`increase_prometheus` had the same suffix.
Signed-off-by: hagen1778 <roman@victoriametrics.com>
Though labels compressor is quite resource intensive, each aggregator
and deduplicator instance has it's own compressor. Made it shared across
all aggregators to consume less resources while using multiple
aggregators.
Co-authored-by: Roman Khavronenko <hagen1778@gmail.com>
Stream aggregation may yield inaccurate results if it processes incomplete data.
This issue can arise when data is sourced from clients that maintain a queue of unsent data, such as Prometheus or vmagent.
If the queue isn't fully cleared within the aggregation interval, only a portion of the time series may be included in that period, leading to distorted calculations.
To mitigate this we add an option to ignore first N aggregation intervals. It is expected, that client queues
will be cleared during the time while aggregation ignores first N intervals and all subsequent aggregations
will be correct.
This reverts commit eb40395a1c.
Reason for revert: it has been appeared that the performance gain on multiple CPU cores
wasn't visible because the benchmark was generating incorrect pushSample.key.
See a207e0bf687d65f5198207477248d70c69284296
Previously samples were dropped on the first incomplete interval and the next complete interval.
Also make sure that the de-duplication is performed just before flushing the aggregate state.
This should help the case then dedup_interval = interval.
For example, if `interval: 1m`, then data flush occurs at the end of every minute,
while `interval: 1h` leads to data flush at the end of every hour.
Add `no_align_flush_to_interval` option, which can be used for disabling the alignment.
