'any' type is supported starting from Go1.18. Let's consistently use it
instead of 'interface{}' type across the code base, since `any` is easier to read than 'interface{}'.
This makes easier to read and debug these tests. This also reduces test lines count by 15% from 3K to 2.5K
See https://itnext.io/f-tests-as-a-replacement-for-table-driven-tests-in-go-8814a8b19e9e
While at it, consistently use t.Fatal* instead of t.Error*, since t.Error* usually leads
to more complicated and fragile tests, while it doesn't bring any practical benefits over t.Fatal*.
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
This reverts commit 5a3abfa041.
Reason for revert: exemplars aren't in wide use because they have numerous issues which prevent their adoption (see below).
Adding support for examplars into VictoriaMetrics introduces non-trivial code changes. These code changes need to be supported forever
once the release of VictoriaMetrics with exemplar support is published. That's why I don't think this is a good feature despite
that the source code of the reverted commit has an excellent quality. See https://docs.victoriametrics.com/goals/ .
Issues with Prometheus exemplars:
- Prometheus still has only experimental support for exemplars after more than three years since they were introduced.
It stores exemplars in memory, so they are lost after Prometheus restart. This doesn't look like production-ready feature.
See 0a2f3b3794/content/docs/instrumenting/exposition_formats.md (L153-L159)
and https://prometheus.io/docs/prometheus/latest/feature_flags/#exemplars-storage
- It is very non-trivial to expose exemplars alongside metrics in your application, since the official Prometheus SDKs
for metrics' exposition ( https://prometheus.io/docs/instrumenting/clientlibs/ ) either have very hard-to-use API
for exposing histograms or do not have this API at all. For example, try figuring out how to expose exemplars
via https://pkg.go.dev/github.com/prometheus/client_golang@v1.19.1/prometheus .
- It looks like exemplars are supported for Histogram metric types only -
see https://pkg.go.dev/github.com/prometheus/client_golang@v1.19.1/prometheus#Timer.ObserveDurationWithExemplar .
Exemplars aren't supported for Counter, Gauge and Summary metric types.
- Grafana has very poor support for Prometheus exemplars. It looks like it supports exemplars only when the query
contains histogram_quantile() function. It queries exemplars via special Prometheus API -
https://prometheus.io/docs/prometheus/latest/querying/api/#querying-exemplars - (which is still marked as experimental, btw.)
and then displays all the returned exemplars on the graph as special dots. The issue is that this doesn't work
in production in most cases when the histogram_quantile() is calculated over thousands of histogram buckets
exposed by big number of application instances. Every histogram bucket may expose an exemplar on every timestamp shown on the graph.
This makes the graph unusable, since it is litterally filled with thousands of exemplar dots.
Neither Prometheus API nor Grafana doesn't provide the ability to filter out unneeded exemplars.
- Exemplars are usually connected to traces. While traces are good for some
I doubt exemplars will become production-ready in the near future because of the issues outlined above.
Alternative to exemplars:
Exemplars are marketed as a silver bullet for the correlation between metrics, traces and logs -
just click the exemplar dot on some graph in Grafana and instantly see the corresponding trace or log entry!
This doesn't work as expected in production as shown above. Are there better solutions, which work in production?
Yes - just use time-based and label-based correlation between metrics, traces and logs. Assign the same `job`
and `instance` labels to metrics, logs and traces, so you can quickly find the needed trace or log entry
by these labes on the time range with the anomaly on metrics' graph.
### Describe Your Changes
Added flag to sanitize graphite metrics
fixes#6077
### Checklist
The following checks are **mandatory**:
- [ ] My change adheres [VictoriaMetrics contributing
guidelines](https://docs.victoriametrics.com/contributing/).
---------
Signed-off-by: hagen1778 <roman@victoriametrics.com>
Co-authored-by: hagen1778 <roman@victoriametrics.com>
* adds datadog extensions for statsd:
- multiple packed values (v1.1)
- additional types distribution, histogram
* adds type check and append metric type to the labels with special tag
name `__statsd_metric_type__`. It simplifies streaming aggregation
config.
---------
Signed-off-by: hagen1778 <roman@victoriametrics.com>
Co-authored-by: hagen1778 <roman@victoriametrics.com>
This code adds Exemplars to VMagent and the promscrape parser adhering
to OpenMetrics Specifications. This will allow forwarding of exemplars
to Prometheus and other third party apps that support OpenMetrics specs.
---------
Signed-off-by: Ted Possible <ted_possible@cable.comcast.com>
Using plain sync.Pool simplifies the code without increasing memory usage and CPU usage.
So it is better to use plain sync.Pool from readability and maintainability PoV.
This is a follow-up for 8942f290eb
- Rename -opentelemetry.sanitizeMetrics command-line flag to more clear -opentelemetry.usePrometheusNaming
- Clarify the description of the change at docs/CHANGELOG.md
- Rename promrelabel.SanitizeLabelNameParts to more clear promrelabel.SplitMetricNameToTokens
- Properly split metric names at '_' char in promerlabel.SplitMetricNameToTokens.
- Add tests for various edge cases for Prometheus metric names' normalization
according to the code at b865505850/pkg/translator/prometheus/normalize_name.go
- Extract the code responsible for Prometheus metric names' normalization into a separate file (santize.go)
Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/6037
Updates https://github.com/VictoriaMetrics/VictoriaMetrics/pull/6035
- 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
* app/vmselect: adds milliseconds to the csv export response for rfc3339
* milliseconds is a standard prescion for VictoriaMetrics query request responses
https://github.com/VictoriaMetrics/VictoriaMetrics/issues/5837
* app/victoria-metrics: adds tests for csv export/import
follow-up after 3541a8d0cf96dd4f8563624c4aab6816615d0756
---------
Signed-off-by: hagen1778 <roman@victoriametrics.com>
Co-authored-by: hagen1778 <roman@victoriametrics.com>
Some clients may ingest samples via OpenTelemetry protocol without Resource labels.
Previously VictoriaMetrics was silently dropping such samples.
The commit 317834f876 added vm_protoparser_rows_dropped_total{type="opentelemetry",reason="resource_not_set"}
counter for tracking of such dropped samples. See https://github.com/VictoriaMetrics/VictoriaMetrics/pull/5459
It is better from usability PoV to accept such samples instead of dropping them and incrementing the corresponding counter.
Currently, it is impossible to understand why metrics are not ingested when resource is not set by OTEL exporter. Adding metric should simplify debugging and make it improve debuggability.
Signed-off-by: Zakhar Bessarab <z.bessarab@victoriametrics.com>
Co-authored-by: Roman Khavronenko <roman@victoriametrics.com>
- Add links to relevant docs into descriptions for every -kafka.* and -gcp.pubsub.* command-line flags.
- Wait until message processing goroutines are stopped before returning from gcppubsub.Stop().
- Prevent from multiple calls to Init() without Stop().
- Drop message if tenantID cannot be parsed properly.
- Take into account tenantID for all the supported message formats.
- Support gzip-compressed messages for graphite format.
- Use exponential backoff sleep when the message cannot be pushed to remote storage systems
because of disabled on-disk persistence - https://docs.victoriametrics.com/vmagent.html#disabling-on-disk-persistence
- Unblock from sleep as soon as Stop() is called. Previously the sleep could take up to 2 seconds after Stop() is called.
- Remove unused globalCtx and initContext from app/vmagent/remotewrite/gcppubsub
- Mention Google PubSub support at docs/enterprise.md
- Make Google PubSub docs more clear at docs/vmagent.md
This is a follow-up for commits 115245924a5f096c5a3383d6cc8e8b6fbd421984
and e6eab781ce42285a6a1750dc01eba6801dd35516 .
Updates https://github.com/VictoriaMetrics/VictoriaMetrics-enterprise/pull/717
Updates https://github.com/VictoriaMetrics/VictoriaMetrics-enterprise/pull/713
* prevent /api/v1 from panic on parsing rows
* add tests for Extract function for v1 and v2 api's
* separate request types in different pools to prevent different objects mixing
* add changelog line
543f218fe9
Signed-off-by: hagen1778 <roman@victoriametrics.com>
Co-authored-by: Andrew Chubatiuk <andrew.chubatiuk@motional.com>
Co-authored-by: Nikolay <https://github.com/f41gh7>
Co-authored-by: Roman Khavronenko <roman@victoriametrics.com>
Tests showed that importing a single line with 70MB size takes 5.3GiB
RSS memory for VictoriaMetrics single-node.
In the scenario when user exports and imports data from one VM to another,
it could possibly lead to OOM exception for destination VM.
Importing a single line with 16MB size taks 1.3GiB RSS memory.
Hence, the limit for `import.maxLineLen` was decreased from 100MB to 10MB
to improve reliability of VictoriaMetrics during imports.
Signed-off-by: hagen1778 <roman@victoriametrics.com>
Co-authored-by: Aliaksandr Valialkin <valyala@victoriametrics.com>
This case is possible after the following steps:
1. vmagent successfully performed handshake with the -remoteWrite.url and the remote storage supports zstd-compressed data.
2. remote storage became unavailable or slow to ingest data, vmagent compressed the collected data into blocks with zstd and puts these blocks to persistent queue on disk.
3. vmagent restarts and the remote storage is unavailable during the handshake, then vmagent falls back to Snappy compression.
4. vmagent starts sending zstd-compressed data from persistent queue to the remote storage, while falsely advertizing it sends Snappy-compressed data.
5. The remote storage receives zstd-compressed data and fails unpacking it with Snappy.
The solution is the same as 12cd32fd75, just fall back to zstd decompression if Snappy decompression fails.
This case is possible after the following steps:
1. vmagent tries to perform handshake with the -remoteWrite.url in order to determine whether
the remote storage supports zstd-compressed data.
2. The remote storage is unavailable during the handshake. In this case vmagent falls back to Snappy compression
for the data sent to the remote storage.
3. vmagent compresses the collected data into blocks with Snappy and puts these blocks to persistent queue on disk.
4. The remote storage becomes available.
5. vmagent restarts, performs the handshake with the remote storage and detects that it supports zstd-compressed data.
6. vmagent starts sending Snappy-compressed data from persistent queue to the remote storage,
while falsely advertizing it sends zstd-compressed data.
7. The remote storage receives Snappy-compressed data and fails unpacking it with zstd.
The solution is to just fall back to Snappy decompression if zstd decompression fails.
Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/5301
- Make sure that invalid/missing TLS CA file or TLS client certificate files at vmagent startup
don't prevent from processing the corresponding scrape targets after the file becomes correct,
without the need to restart vmagent.
Previously scrape targets with invalid TLS CA file or TLS client certificate files
were permanently dropped after the first attempt to initialize them, and they didn't
appear until the next vmagent reload or the next change in other places of the loaded scrape configs.
- Make sure that TLS CA is properly re-loaded from file after it changes without the need to restart vmagent.
Previously the old TLS CA was used until vmagent restart.
- Properly handle errors during http request creation for the second attempt to send data to remote system
at vmagent and vmalert. Previously failed request creation could result in nil pointer dereferencing,
since the returned request is nil on error.
- Add more context to the logged error during AWS sigv4 request signing before sending the data to -remoteWrite.url at vmagent.
Previously it could miss details on the source of the request.
- Do not create a new HTTP client per second when generating OAuth2 token needed to put in Authorization header
of every http request issued by vmagent during service discovery or target scraping.
Re-use the HTTP client instead until the corresponding scrape config changes.
- Cache error at lib/promauth.Config.GetAuthHeader() in the same way as the auth header is cached,
e.g. the error is cached for a second now. This should reduce load on CPU and OAuth2 server
when auth header cannot be obtained because of temporary error.
- Share tls.Config.GetClientCertificate function among multiple scrape targets with the same tls_config.
Cache the loaded certificate and the error for one second. This should significantly reduce CPU load
when scraping big number of targets with the same tls_config.
- Allow loading TLS certificates from HTTP and HTTPs urls by specifying these urls at `tls_config->cert_file` and `tls_config->key_file`.
- Improve test coverage at lib/promauth
- Skip unreachable or invalid files specified at `scrape_config_files` during vmagent startup, since these files may become valid later.
Previously vmagent was exitting in this case.
Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/4959
