Reason for revert:
There are many statsd servers exist:
- https://github.com/statsd/statsd - classical statsd server
- https://docs.datadoghq.com/developers/dogstatsd/ - statsd server from DataDog built into DatDog Agent ( https://docs.datadoghq.com/agent/ )
- https://github.com/avito-tech/bioyino - high-performance statsd server
- https://github.com/atlassian/gostatsd - statsd server in Go
- https://github.com/prometheus/statsd_exporter - statsd server, which exposes the aggregated data as Prometheus metrics
These servers can be used for efficient aggregating of statsd data and sending it to VictoriaMetrics
according to https://docs.victoriametrics.com/#how-to-send-data-from-graphite-compatible-agents-such-as-statsd (
the https://github.com/prometheus/statsd_exporter can be scraped as usual Prometheus target
according to https://docs.victoriametrics.com/#how-to-scrape-prometheus-exporters-such-as-node-exporter ).
Adding support for statsd data ingestion protocol into VictoriaMetrics makes sense only if it provides
significant advantages over the existing statsd servers, while has no significant drawbacks comparing
to existing statsd servers.
The main advantage of statsd server built into VictoriaMetrics and vmagent - getting rid of additional statsd server.
The main drawback is non-trivial and inconvenient streaming aggregation configs, which must be used for the ingested statsd metrics (
see https://docs.victoriametrics.com/stream-aggregation/ ). These configs are incompatible with the configs for standalone statsd servers.
So you need to manually translate configs of the used statsd server to stream aggregation configs when migrating
from standalone statsd server to statsd server built into VictoriaMetrics (or vmagent).
Another important drawback is that it is very easy to shoot yourself in the foot when using built-in statsd server
with the -statsd.disableAggregationEnforcement command-line flag or with improperly configured streaming aggregation.
In this case the ingested statsd metrics will be stored to VictoriaMetrics as is without any aggregation.
This may result in high CPU usage during data ingestion, high disk space usage for storing all the unaggregated
statsd metrics and high CPU usage during querying, since all the unaggregated metrics must be read, unpacked and processed
during querying.
P.S. Built-in statsd server can be added to VictoriaMetrics and vmagent after figuring out more ergonomic
specialized configuration for aggregating of statsd metrics. The main requirements for this configuration:
- easy to write, read and update (ideally it should work out of the box for most cases without additional configuration)
- hard to misconfigure (e.g. hard to shoot yourself in the foot)
It would be great if this configuration will be compatible with the configuration of the most widely used statsd server.
In the mean time it is recommended continue using external statsd server.
Updates https://github.com/VictoriaMetrics/VictoriaMetrics/pull/6265
Updates https://github.com/VictoriaMetrics/VictoriaMetrics/pull/5053
Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/5052
Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/206
Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/4600
- Clarify docs for `Ignore aggregation intervals on start` feature.
- Make more clear the code dealing with ignoreFirstIntervals at aggregator.runFlusher() functions.
It is better from readability and maintainability PoV using distinct a.flush() calls
for distinct cases instead of merging them into a single a.flush() call.
- Take into account the first incomplete interval when tracking the number of skipped aggregation intervals,
since this behaviour is easier to understand by the end users.
Updates https://github.com/VictoriaMetrics/VictoriaMetrics/pull/6137
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>
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.
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.
- Reduce memory usage by up to 5x when de-duplicating samples across big number of time series.
- Reduce memory usage by up to 5x when aggregating across big number of output time series.
- Add lib/promutils.LabelsCompressor, which is going to be used by other VictoriaMetrics components
for reducing memory usage for marshaled []prompbmarshal.Label.
- Add `dedup_interval` option at aggregation config, which allows setting individual
deduplication intervals per each aggregation.
- Add `keep_metric_names` option at aggregation config, which allows keeping the original
metric names in the output samples.
- Add `unique_samples` output, which counts the number of unique sample values.
- Add `increase_prometheus` and `total_prometheus` outputs, which ignore the first sample
per each newly encountered time series.
- Use 64-bit hashes instead of marshaled labels as map keys when calculating `count_series` output.
This makes obsolete https://github.com/VictoriaMetrics/VictoriaMetrics/pull/5579
- Expose various metrics, which may help debugging stream aggregation:
- vm_streamaggr_dedup_state_size_bytes - the size of data structures responsible for deduplication
- vm_streamaggr_dedup_state_items_count - the number of items in the deduplication data structures
- vm_streamaggr_labels_compressor_size_bytes - the size of labels compressor data structures
- vm_streamaggr_labels_compressor_items_count - the number of entries in the labels compressor
- vm_streamaggr_flush_duration_seconds - a histogram, which shows the duration of stream aggregation flushes
- vm_streamaggr_dedup_flush_duration_seconds - a histogram, which shows the duration of deduplication flushes
- vm_streamaggr_flush_timeouts_total - counter for timed out stream aggregation flushes,
which took longer than the configured interval
- vm_streamaggr_dedup_flush_timeouts_total - counter for timed out deduplication flushes,
which took longer than the configured dedup_interval
- Actualize docs/stream-aggregation.md
The memory usage reduction increases CPU usage during stream aggregation by up to 30%.
This commit is based on https://github.com/VictoriaMetrics/VictoriaMetrics/pull/5850
Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/5898
* port un-synced changed from docs/readme to readme
* consistently use `sh` instead of `console` highlight, as it looks like
a more appropriate syntax highlight
* consistently use `sh` instead of `bash`, as it is shorter
* consistently use `yaml` instead of `yml`
See syntax codes here https://gohugo.io/content-management/syntax-highlighting/
Signed-off-by: hagen1778 <roman@victoriametrics.com>
Sending unfinished aggregate states tend to produce unexpected anomalies with lower values than expected.
The old behavior can be restored by specifying `flush_on_shutdown: true` setting in streaming aggregation config
Signed-off-by: hagen1778 <roman@victoriametrics.com>
vmagent: properly add extra labels before sending data to remote storage
labels from `remoteWrite.label` are now added to sent metrics just before they
are pushed to `remoteWrite.url` after all relabelings, including stream aggregation relabelings (#4247)
https://github.com/VictoriaMetrics/VictoriaMetrics/issues/4247
Signed-off-by: Alexander Marshalov <_@marshalov.org>
Co-authored-by: Roman Khavronenko <roman@victoriametrics.com>
- Use a byte slice instead of a map for tracking indexes for matching series.
This improves performance, since access by slice index is faster than access by map key.
- Re-use the byte slice for tracking indexes for matching series.
This removes unnecessary memory allocations and improves stream aggregation performance a bit.
- Add an ability to return to the previous behvaiour by specifying -remoteWrite.streamAggr.dropInput command-line flag.
In this case all the input samples are dropped when stream aggregation is enabled.
- Backport the new stream aggregation behaviour from vmagent to single-node VictoriaMetrics when -streamAggr.config
option is set.
- Improve docs regarding this change at docs/CHANGELOG.md
- Document the new behavior at docs/stream-aggregation.md
Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/4243
Updates https://github.com/VictoriaMetrics/VictoriaMetrics/pull/4575
