### Describe Your Changes
Replace VM- with VictoriaMetrics in QuickStart
Keep the previous anchors for backward compatibility
### Checklist
The following checks are **mandatory**:
- [ x] My change adheres [VictoriaMetrics contributing
guidelines](https://docs.victoriametrics.com/contributing/).
---------
Signed-off-by: Artem Navoiev <tenmozes@gmail.com>
### Describe Your Changes
In most cases histograms are exposed in sorted manner with lower buckets
being first. This means that during scraping buckets with lower bounds
have higher chance of being updated earlier than upper ones.
Previously, values were propagated from upper to lower bounds, which
means that in most cases that would produce results higher than expected
once all buckets will become updated.
Propagating from upper bound effectively limits highest value of
histogram to the value of previous scrape. Once the data will become
consistent in the subsequent evaluation this causes spikes in the
result.
Changing propagation to be from lower to higher buckets reduces value
spikes in most cases due to nature of the original inconsistency.
See: https://github.com/VictoriaMetrics/VictoriaMetrics/issues/4580
An example histogram with previous(red) and updated(blue) versions:
This also makes logic of filling nan values with lower buckets values: [1 2 3 nan nan nan] => [1 2 3 3 3 3] obsolete.
Since buckets are now fixed from lower ones to upper this happens in the main loop, so there is no need in a second one.
---------
Signed-off-by: Zakhar Bessarab <z.bessarab@victoriametrics.com>
Signed-off-by: hagen1778 <roman@victoriametrics.com>
Co-authored-by: Andrii Chubatiuk <andrew.chubatiuk@gmail.com>
Co-authored-by: hagen1778 <roman@victoriametrics.com>
vmbackup, vmrestore and vmbackupmanager use the same libs
for integrations with object storage. That means the auth can be configured
in the same way for all of them. So the docs should have either identical
config section for all 3 components, or we should cross-link to one source of truth.
This change removes incomplete auth options from vmrestore docs and adds link
to complete auth options in vmbackup instead.
Signed-off-by: hagen1778 <roman@victoriametrics.com>
### Describe Your Changes
These changes support using Azure Managed Identity for the `vmbackup`
utility. It adds two new environment variables:
* `AZURE_USE_DEFAULT_CREDENTIAL`: Instructs the `vmbackup` utility to
build a connection using the [Azure Default
Credential](https://pkg.go.dev/github.com/Azure/azure-sdk-for-go/sdk/azidentity@v1.5.2#NewDefaultAzureCredential)
mode. This causes the Azure SDK to check for a variety of environment
variables to try and make a connection. By default, it tries to use
managed identity if that is set up.
This will close
https://github.com/VictoriaMetrics/VictoriaMetrics/issues/5984
### Checklist
The following checks are **mandatory**:
- [x] My change adheres [VictoriaMetrics contributing
guidelines](https://docs.victoriametrics.com/contributing/).
### Testing
However you normally test the `vmbackup` utility using Azure Blob should
continue to work without any changes. The set up for that is environment
specific and not listed out here.
Once regression testing has been done you can set up [Azure Managed
Identity](https://learn.microsoft.com/en-us/entra/identity/managed-identities-azure-resources/overview)
so your resource (AKS, VM, etc), can use that credential method. Once it
is set up, update your environment variables according to the updated
documentation.
I added unit tests to the `FS.Init` function, then made my changes, then
updated the unit tests to capture the new branches.
I tested this in our environment, but with SAS token auth and managed
identity and it works as expected.
---------
Signed-off-by: Zakhar Bessarab <z.bessarab@victoriametrics.com>
Co-authored-by: Justin Rush <jarush@epic.com>
Co-authored-by: Zakhar Bessarab <z.bessarab@victoriametrics.com>
Co-authored-by: hagen1778 <roman@victoriametrics.com>
'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*.
* restore old anchor names to keep links compatibility.
See https://docs.victoriametrics.com/#documentation requirements
* consistently use the same format for commands `sh` as it makes it better
renderred and automatically adds `copy` button to fileds with commands
* simplify the text by removing extra points in the list
* add recommendations for installing the cluster setup
* explicitly mention the ports services are listening on
* add description for `storageNode` cmd-line flag to inform the reader what
values need to be put into it
* fix the incorrect vmui link in cluster installation recommendation
* rename component anchors to be more unique, because URL doesn't respect
hierarchy for the anchored links and may result into conflicts in future
Signed-off-by: hagen1778 <roman@victoriametrics.com>
### Describe Your Changes
Updated Quickstart guide for VIctoriaMetrics and VictoriaMetrics Cluster to include instructions for installing the binaries by hand
### Checklist
The following checks are **mandatory**:
- [ x] My change adheres [VictoriaMetrics contributing
guidelines](https://docs.victoriametrics.com/contributing/).
### Describe Your Changes
Fixed Custom Model guide according to newer `vmanomaly` versions
### Checklist
The following checks are **mandatory**:
- [x] My change adheres [VictoriaMetrics contributing
guidelines](https://docs.victoriametrics.com/contributing/).
### Describe Your Changes
docs: fix typos
### Checklist
The following checks are **mandatory**:
- [x] My change adheres [VictoriaMetrics contributing
guidelines](https://docs.victoriametrics.com/contributing/).
### Describe Your Changes
Please provide a brief description of the changes you made. Be as
specific as possible to help others understand the purpose and impact of
your modifications.
### Checklist
The following checks are **mandatory**:
- [ ] My change adheres [VictoriaMetrics contributing
guidelines](https://docs.victoriametrics.com/contributing/).
### Describe Your Changes
This PR is aimed to change the currently in place configuration of
running Go related jobs for code changes that don't contain actual Go
files ([example
1](https://github.com/VictoriaMetrics/VictoriaMetrics/pull/6517/checks)
- 2m32s , [example
2](https://github.com/VictoriaMetrics/VictoriaMetrics/pull/6543/checks)
- 4m11s).
In order to do that the `build` workflow was extracted from Go related
workflow (now it doesn't require lint as a `need` step -- let me know if
it's something we want to keep). It will run upon the same triggers as
before the change.
The `main` workflow now will be triggered by `**.go` pattern only and
contains lint/test steps that are relevant for Go file changes.
I expect this PR +
https://github.com/VictoriaMetrics/VictoriaMetrics/pull/6540 to improve
CI minutes usage.
### Checklist
The following checks are **mandatory**:
- [x] My change adheres [VictoriaMetrics contributing
guidelines](https://docs.victoriametrics.com/contributing/).
---------
Signed-off-by: Arkadii Yakovets <ark@victoriametrics.com>
The change adds a new docs section with examples on how source can be
overridden. It should address questions like
https://github.com/VictoriaMetrics/VictoriaMetrics/issues/6536
While there, fix the example in `external.alert.source` cmd-line flag
and docker-compose examples.
### 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>
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.
