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.
Data ingestion benchmark doesn't show memory usage difference between two approaches,
so let's use simpler approach in order to improve code readability and maintainability.
This is a follow-up for 77c597738c
This scheme was used for reducing memory usage when vmagent runs on a machine with big number of CPU cores
and the ingestion rate isn't too big. The scheme with channel-based pool could reduce memory usage,
since it minimizes the number of PushCtx structs in the pool in this case.
Performance tests didn't reveal significant difference in memory usage under both low and high ingestion rate
between plain sync.Pool and the current hybrid scheme, so replace the scheme with plain sync.Pool in order
to simplify the code.
- Automatically reload changed TLS root CA pointed by -remoteWrite.tlsCAFile command-line flag
- Automatically reload changed TLS root CA configured via oauth2.tsl_config.ca_file option at -promscrape.config
- Document the change as a feature instead of a bug at docs/CHANGELOG.md
- Simplify the code at lib/promauth, which is responsible for reloading changed TLS root CA files.
- Simplify the usage of lib/promauth.Config.NewRoundTripper() - now it accepts the base http.Transport
instead of a callback, which can change the internal http.Transport.
- Reuse the default tls config if lib/promauth.Config doesn't contain tls-specific configs.
This should reduce memory usage a bit when tls isn't used for scraping big number of targets.
- Do not re-read TLS root CA files on every processed request. Re-read them once per second.
This should reduce CPU usage when scraping big number of targets over https.
- Do not store cert.pem and key.pem files in TestTLSConfigWithCertificatesFilesUpdate, since they can be loaded
from byte slices via crypto/tls.X509KeyPair().
- Remove obsolete comparisons of string representations for authConfig and proxyAuthConfig at areEqualScrapeConfigs().
Updates https://github.com/VictoriaMetrics/VictoriaMetrics/pull/5725
Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/5526
Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/2171
- Make the configuration more clear by accepting the list of ignored labels during sharding
via a dedicated command-line flag - -remoteWrite.shardByURL.ignoreLabels.
This prevents from overloading the meaning of -remoteWrite.shardByURL.labels command-line flag.
- Removed superfluous memory allocation per each processed sample if sharding by remote storage is enabled.
Updates https://github.com/VictoriaMetrics/VictoriaMetrics/pull/5938
The remotewrite.Stop() expects that there are no pending calls to TryPush().
This means that the ingestionRateLimiter.Register() must be unblocked inside TryPush() when calling remotewrite.Stop().
Provide remotewrite.StopIngestionRateLimiter() function for unblocking the rate limiter before calling the remotewrite.Stop().
While at it, move the rate limiter into lib/ratelimiter package, since it has two users.
Also move the description of the feature to the correct place at docs/CHANGELOG.md.
Also cross-reference -remoteWrite.rateLimit and -maxIngestionRate command-line flags.
This is a follow-up for 02bccd1eb9
Updates https://github.com/VictoriaMetrics/VictoriaMetrics/pull/5900
To horizontally scale streaming aggregation, you might want to deploy a separate hashing tier
of vmagents that route to a separate aggregation tier. The hashing tier should shard by all labels
except the instance-level labels, to ensure the input metrics are routed correctly to the aggregator
instance responsible for those labels.
For this to achieve we introduce `remoteWrite.shardByURL.inverseLabels` flag to inverse logic of `remoteWrite.shardByURL.labels`
---------
Co-authored-by: Eugene Ma <eugene.ma@airbnb.com>
Co-authored-by: Roman Khavronenko <roman@victoriametrics.com>
* [vmagent] added ingestion rate limiting with new flag `-maxIngestionRate`. This flag can be used to limit the number of samples ingested by vmagent per second. If the limit is exceeded, the ingestion rate will be throttled.
* fix changelog
* fix review comment
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.
* app/{vmagent,vminsert}: adds /v1/metrics suffix for opentelemetry route path
it must fix compatibility with opentemetry-collector [spec](https://opentelemetry.io/docs/specs/otlp/\#otlphttp-request)
this suffix is hard-coded and cannot be changed with collector configuration
* Apply suggestions from code review
---------
Co-authored-by: Aliaksandr Valialkin <valyala@victoriametrics.com>
- Document the ability to read OpenTelemetry data from Amazon Firehose at docs/CHANGELOG.md
- Simplify parsing Firehose data. There is no need in trying to optimize the parsing with fastjson
and byte slice tricks, since OpenTelemetry protocol is really slooow because of over-engineering.
It is better to write clear code for better maintanability in the future.
- Move Firehose parser from /lib/protoparser/firehose to lib/protoparser/opentelemetry/firehose,
since it is used only by opentelemetry parser.
Updates https://github.com/VictoriaMetrics/VictoriaMetrics/pull/5893
There is no sense in running more than GOMAXPROCS concurrent marshalers,
since they are CPU-bound. More concurrent marshalers do not increase the marshaling bandwidth,
but they may result in more RAM usage.
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.
Examples:
1) -metricsAuthKey=file:///abs/path/to/file - reads flag value from the given absolute filepath
2) -metricsAuthKey=file://./relative/path/to/file - reads flag value from the given relative filepath
3) -metricsAuthKey=http://some-host/some/path?query_arg=abc - reads flag value from the given url
The flag value is automatically updated when the file contents changes.
The user may which to control the endpoint parameters for instance to
set the audience when requesting an access token. Exposing the
parameters as a map allows for additional use cases without requiring
modification.
