stringlabels stores all time series labels as a single string using this format: <length><name><length><value>[<length><name><length><value> ...] So a label set for my_metric{job=foo, instance="bar", env="prod", blank=""} would be encoded as: [8]__name__[9]my_metric[3]job[3]foo[8]instance[3]bar[3]env[4]prod[5]blank[0] This is a huge improvement over 'classic' labels implementation that stores all label names & values as seperate strings. There is some room for improvement though since some string are present more often than others. For example __name__ will be present for all label sets of every time series we store in HEAD, eating 1+8=9 bytes. Since __name__ is well known string we can try to use a single byte to store it in our encoded string, rather than repeat it in full each time. To be able to store strings that are short cut into a single byte we need to somehow signal that to the reader of the encoded string, for that we use the fact that zero length strings are rare and generaly not stored on time series. If we have an encoded string with zero length then this will now signal that it represents a mapped value - to learn the true value of this string we need to read the next byte which gives us index in a static mapping. That mapping must include empty string, so that we can still encode empty strings using this scheme. Example of our mapping (minimal version): 0: "" 1: "__name__" 2: "instance" 3: "job" With that mapping our example label set would be encoded as: [0]1[9]mymetric[0]3[3]foo[0]2[3]bar[3]env[4]prod[5]blank[0]0 The tricky bit is how to populate this mapping with useful strings that will result in measurable memory savings. This is further complicated by the fact that the mapping must remain static and cannot be modified during Prometheus lifetime. We can use all the 255 slots we have inside our mapping byte with well known generic strings and that will provide some measurable savings for all Prometheus users, and is essentially a slightly more compact stringlabels variant. We could also allow users to pass in a list of well know strings via flags, which will allow Prometheus operators to reduce memory usage for any labels if they know those are popular. Third option is to discover most popular strings from TSDB or WAL on startup, but that's more complicated and we might pick a list that would be the best set of mapped strings on startup, but after some time is no longer the best set. Benchmark results: goos: linux goarch: amd64 pkg: github.com/prometheus/prometheus/model/labels cpu: 13th Gen Intel(R) Core(TM) i7-13800H │ main.txt │ new1.txt │ │ sec/op │ sec/op vs base │ String-20 863.8n ± 4% 873.0n ± 4% ~ (p=0.353 n=10) Labels_Get/with_5_labels/first_label/get-20 4.763n ± 1% 5.035n ± 0% +5.72% (p=0.000 n=10) Labels_Get/with_5_labels/first_label/has-20 3.439n ± 0% 3.967n ± 0% +15.37% (p=0.000 n=10) Labels_Get/with_5_labels/middle_label/get-20 7.077n ± 1% 9.588n ± 1% +35.47% (p=0.000 n=10) Labels_Get/with_5_labels/middle_label/has-20 5.166n ± 0% 6.990n ± 1% +35.30% (p=0.000 n=10) Labels_Get/with_5_labels/last_label/get-20 9.181n ± 1% 12.970n ± 1% +41.26% (p=0.000 n=10) Labels_Get/with_5_labels/last_label/has-20 8.101n ± 1% 11.640n ± 1% +43.69% (p=0.000 n=10) Labels_Get/with_5_labels/not-found_label/get-20 3.974n ± 0% 4.768n ± 0% +19.98% (p=0.000 n=10) Labels_Get/with_5_labels/not-found_label/has-20 3.974n ± 0% 5.033n ± 0% +26.65% (p=0.000 n=10) Labels_Get/with_10_labels/first_label/get-20 4.761n ± 0% 5.042n ± 0% +5.90% (p=0.000 n=10) Labels_Get/with_10_labels/first_label/has-20 3.442n ± 0% 3.972n ± 0% +15.40% (p=0.000 n=10) Labels_Get/with_10_labels/middle_label/get-20 10.62n ± 1% 14.85n ± 1% +39.83% (p=0.000 n=10) Labels_Get/with_10_labels/middle_label/has-20 9.360n ± 1% 13.375n ± 0% +42.90% (p=0.000 n=10) Labels_Get/with_10_labels/last_label/get-20 18.19n ± 1% 22.00n ± 0% +20.97% (p=0.000 n=10) Labels_Get/with_10_labels/last_label/has-20 16.51n ± 0% 20.50n ± 1% +24.14% (p=0.000 n=10) Labels_Get/with_10_labels/not-found_label/get-20 3.985n ± 0% 4.768n ± 0% +19.62% (p=0.000 n=10) Labels_Get/with_10_labels/not-found_label/has-20 3.973n ± 0% 5.045n ± 0% +26.97% (p=0.000 n=10) Labels_Get/with_30_labels/first_label/get-20 4.773n ± 0% 5.050n ± 1% +5.80% (p=0.000 n=10) Labels_Get/with_30_labels/first_label/has-20 3.443n ± 1% 3.976n ± 2% +15.50% (p=0.000 n=10) Labels_Get/with_30_labels/middle_label/get-20 31.93n ± 0% 43.50n ± 1% +36.21% (p=0.000 n=10) Labels_Get/with_30_labels/middle_label/has-20 30.53n ± 0% 41.75n ± 1% +36.75% (p=0.000 n=10) Labels_Get/with_30_labels/last_label/get-20 106.55n ± 0% 71.17n ± 0% -33.21% (p=0.000 n=10) Labels_Get/with_30_labels/last_label/has-20 104.70n ± 0% 69.21n ± 1% -33.90% (p=0.000 n=10) Labels_Get/with_30_labels/not-found_label/get-20 3.976n ± 1% 4.772n ± 0% +20.03% (p=0.000 n=10) Labels_Get/with_30_labels/not-found_label/has-20 3.974n ± 0% 5.032n ± 0% +26.64% (p=0.000 n=10) Labels_Equals/equal-20 2.382n ± 0% 2.446n ± 0% +2.67% (p=0.000 n=10) Labels_Equals/not_equal-20 0.2741n ± 2% 0.2662n ± 2% -2.88% (p=0.001 n=10) Labels_Equals/different_sizes-20 0.2762n ± 3% 0.2652n ± 0% -3.95% (p=0.000 n=10) Labels_Equals/lots-20 2.381n ± 0% 2.386n ± 1% +0.23% (p=0.011 n=10) Labels_Equals/real_long_equal-20 6.087n ± 1% 5.558n ± 1% -8.70% (p=0.000 n=10) Labels_Equals/real_long_different_end-20 5.030n ± 0% 4.699n ± 0% -6.57% (p=0.000 n=10) Labels_Compare/equal-20 4.814n ± 1% 4.777n ± 0% -0.77% (p=0.000 n=10) Labels_Compare/not_equal-20 17.55n ± 8% 20.92n ± 1% +19.24% (p=0.000 n=10) Labels_Compare/different_sizes-20 3.711n ± 1% 3.707n ± 0% ~ (p=0.224 n=10) Labels_Compare/lots-20 27.09n ± 3% 28.73n ± 2% +6.05% (p=0.000 n=10) Labels_Compare/real_long_equal-20 27.91n ± 3% 15.67n ± 1% -43.86% (p=0.000 n=10) Labels_Compare/real_long_different_end-20 33.92n ± 1% 35.35n ± 1% +4.22% (p=0.000 n=10) Labels_Hash/typical_labels_under_1KB-20 59.63n ± 0% 59.67n ± 0% ~ (p=0.897 n=10) Labels_Hash/bigger_labels_over_1KB-20 73.42n ± 1% 73.81n ± 1% ~ (p=0.342 n=10) Labels_Hash/extremely_large_label_value_10MB-20 720.3µ ± 2% 715.2µ ± 3% ~ (p=0.971 n=10) Builder-20 371.6n ± 4% 1191.0n ± 3% +220.46% (p=0.000 n=10) Labels_Copy-20 85.52n ± 4% 53.90n ± 48% -36.97% (p=0.000 n=10) geomean 13.26n 14.68n +10.71% │ main.txt │ new1.txt │ │ B/op │ B/op vs base │ String-20 240.0 ± 0% 240.0 ± 0% ~ (p=1.000 n=10) ¹ Labels_Get/with_5_labels/first_label/get-20 0.000 ± 0% 0.000 ± 0% ~ (p=1.000 n=10) ¹ Labels_Get/with_5_labels/first_label/has-20 0.000 ± 0% 0.000 ± 0% ~ (p=1.000 n=10) ¹ Labels_Get/with_5_labels/middle_label/get-20 0.000 ± 0% 0.000 ± 0% ~ (p=1.000 n=10) ¹ Labels_Get/with_5_labels/middle_label/has-20 0.000 ± 0% 0.000 ± 0% ~ (p=1.000 n=10) ¹ Labels_Get/with_5_labels/last_label/get-20 0.000 ± 0% 0.000 ± 0% ~ (p=1.000 n=10) ¹ Labels_Get/with_5_labels/last_label/has-20 0.000 ± 0% 0.000 ± 0% ~ (p=1.000 n=10) ¹ Labels_Get/with_5_labels/not-found_label/get-20 0.000 ± 0% 0.000 ± 0% ~ (p=1.000 n=10) ¹ Labels_Get/with_5_labels/not-found_label/has-20 0.000 ± 0% 0.000 ± 0% ~ (p=1.000 n=10) ¹ Labels_Get/with_10_labels/first_label/get-20 0.000 ± 0% 0.000 ± 0% ~ (p=1.000 n=10) ¹ Labels_Get/with_10_labels/first_label/has-20 0.000 ± 0% 0.000 ± 0% ~ (p=1.000 n=10) ¹ Labels_Get/with_10_labels/middle_label/get-20 0.000 ± 0% 0.000 ± 0% ~ (p=1.000 n=10) ¹ Labels_Get/with_10_labels/middle_label/has-20 0.000 ± 0% 0.000 ± 0% ~ (p=1.000 n=10) ¹ Labels_Get/with_10_labels/last_label/get-20 0.000 ± 0% 0.000 ± 0% ~ (p=1.000 n=10) ¹ Labels_Get/with_10_labels/last_label/has-20 0.000 ± 0% 0.000 ± 0% ~ (p=1.000 n=10) ¹ Labels_Get/with_10_labels/not-found_label/get-20 0.000 ± 0% 0.000 ± 0% ~ (p=1.000 n=10) ¹ Labels_Get/with_10_labels/not-found_label/has-20 0.000 ± 0% 0.000 ± 0% ~ (p=1.000 n=10) ¹ Labels_Get/with_30_labels/first_label/get-20 0.000 ± 0% 0.000 ± 0% ~ (p=1.000 n=10) ¹ Labels_Get/with_30_labels/first_label/has-20 0.000 ± 0% 0.000 ± 0% ~ (p=1.000 n=10) ¹ Labels_Get/with_30_labels/middle_label/get-20 0.000 ± 0% 0.000 ± 0% ~ (p=1.000 n=10) ¹ Labels_Get/with_30_labels/middle_label/has-20 0.000 ± 0% 0.000 ± 0% ~ (p=1.000 n=10) ¹ Labels_Get/with_30_labels/last_label/get-20 0.000 ± 0% 0.000 ± 0% ~ (p=1.000 n=10) ¹ Labels_Get/with_30_labels/last_label/has-20 0.000 ± 0% 0.000 ± 0% ~ (p=1.000 n=10) ¹ Labels_Get/with_30_labels/not-found_label/get-20 0.000 ± 0% 0.000 ± 0% ~ (p=1.000 n=10) ¹ Labels_Get/with_30_labels/not-found_label/has-20 0.000 ± 0% 0.000 ± 0% ~ (p=1.000 n=10) ¹ Labels_Equals/equal-20 0.000 ± 0% 0.000 ± 0% ~ (p=1.000 n=10) ¹ Labels_Equals/not_equal-20 0.000 ± 0% 0.000 ± 0% ~ (p=1.000 n=10) ¹ Labels_Equals/different_sizes-20 0.000 ± 0% 0.000 ± 0% ~ (p=1.000 n=10) ¹ Labels_Equals/lots-20 0.000 ± 0% 0.000 ± 0% ~ (p=1.000 n=10) ¹ Labels_Equals/real_long_equal-20 0.000 ± 0% 0.000 ± 0% ~ (p=1.000 n=10) ¹ Labels_Equals/real_long_different_end-20 0.000 ± 0% 0.000 ± 0% ~ (p=1.000 n=10) ¹ Labels_Compare/equal-20 0.000 ± 0% 0.000 ± 0% ~ (p=1.000 n=10) ¹ Labels_Compare/not_equal-20 0.000 ± 0% 0.000 ± 0% ~ (p=1.000 n=10) ¹ Labels_Compare/different_sizes-20 0.000 ± 0% 0.000 ± 0% ~ (p=1.000 n=10) ¹ Labels_Compare/lots-20 0.000 ± 0% 0.000 ± 0% ~ (p=1.000 n=10) ¹ Labels_Compare/real_long_equal-20 0.000 ± 0% 0.000 ± 0% ~ (p=1.000 n=10) ¹ Labels_Compare/real_long_different_end-20 0.000 ± 0% 0.000 ± 0% ~ (p=1.000 n=10) ¹ Labels_Hash/typical_labels_under_1KB-20 0.000 ± 0% 0.000 ± 0% ~ (p=1.000 n=10) ¹ Labels_Hash/bigger_labels_over_1KB-20 0.000 ± 0% 0.000 ± 0% ~ (p=1.000 n=10) ¹ Labels_Hash/extremely_large_label_value_10MB-20 0.000 ± 0% 0.000 ± 0% ~ (p=1.000 n=10) ¹ Builder-20 224.0 ± 0% 192.0 ± 0% -14.29% (p=0.000 n=10) Labels_Copy-20 224.0 ± 0% 192.0 ± 0% -14.29% (p=0.000 n=10) geomean ² -0.73% ² ¹ all samples are equal ² summaries must be >0 to compute geomean │ main.txt │ new1.txt │ │ allocs/op │ allocs/op vs base │ String-20 1.000 ± 0% 1.000 ± 0% ~ (p=1.000 n=10) ¹ Labels_Get/with_5_labels/first_label/get-20 0.000 ± 0% 0.000 ± 0% ~ (p=1.000 n=10) ¹ Labels_Get/with_5_labels/first_label/has-20 0.000 ± 0% 0.000 ± 0% ~ (p=1.000 n=10) ¹ Labels_Get/with_5_labels/middle_label/get-20 0.000 ± 0% 0.000 ± 0% ~ (p=1.000 n=10) ¹ Labels_Get/with_5_labels/middle_label/has-20 0.000 ± 0% 0.000 ± 0% ~ (p=1.000 n=10) ¹ Labels_Get/with_5_labels/last_label/get-20 0.000 ± 0% 0.000 ± 0% ~ (p=1.000 n=10) ¹ Labels_Get/with_5_labels/last_label/has-20 0.000 ± 0% 0.000 ± 0% ~ (p=1.000 n=10) ¹ Labels_Get/with_5_labels/not-found_label/get-20 0.000 ± 0% 0.000 ± 0% ~ (p=1.000 n=10) ¹ Labels_Get/with_5_labels/not-found_label/has-20 0.000 ± 0% 0.000 ± 0% ~ (p=1.000 n=10) ¹ Labels_Get/with_10_labels/first_label/get-20 0.000 ± 0% 0.000 ± 0% ~ (p=1.000 n=10) ¹ Labels_Get/with_10_labels/first_label/has-20 0.000 ± 0% 0.000 ± 0% ~ (p=1.000 n=10) ¹ Labels_Get/with_10_labels/middle_label/get-20 0.000 ± 0% 0.000 ± 0% ~ (p=1.000 n=10) ¹ Labels_Get/with_10_labels/middle_label/has-20 0.000 ± 0% 0.000 ± 0% ~ (p=1.000 n=10) ¹ Labels_Get/with_10_labels/last_label/get-20 0.000 ± 0% 0.000 ± 0% ~ (p=1.000 n=10) ¹ Labels_Get/with_10_labels/last_label/has-20 0.000 ± 0% 0.000 ± 0% ~ (p=1.000 n=10) ¹ Labels_Get/with_10_labels/not-found_label/get-20 0.000 ± 0% 0.000 ± 0% ~ (p=1.000 n=10) ¹ Labels_Get/with_10_labels/not-found_label/has-20 0.000 ± 0% 0.000 ± 0% ~ (p=1.000 n=10) ¹ Labels_Get/with_30_labels/first_label/get-20 0.000 ± 0% 0.000 ± 0% ~ (p=1.000 n=10) ¹ Labels_Get/with_30_labels/first_label/has-20 0.000 ± 0% 0.000 ± 0% ~ (p=1.000 n=10) ¹ Labels_Get/with_30_labels/middle_label/get-20 0.000 ± 0% 0.000 ± 0% ~ (p=1.000 n=10) ¹ Labels_Get/with_30_labels/middle_label/has-20 0.000 ± 0% 0.000 ± 0% ~ (p=1.000 n=10) ¹ Labels_Get/with_30_labels/last_label/get-20 0.000 ± 0% 0.000 ± 0% ~ (p=1.000 n=10) ¹ Labels_Get/with_30_labels/last_label/has-20 0.000 ± 0% 0.000 ± 0% ~ (p=1.000 n=10) ¹ Labels_Get/with_30_labels/not-found_label/get-20 0.000 ± 0% 0.000 ± 0% ~ (p=1.000 n=10) ¹ Labels_Get/with_30_labels/not-found_label/has-20 0.000 ± 0% 0.000 ± 0% ~ (p=1.000 n=10) ¹ Labels_Equals/equal-20 0.000 ± 0% 0.000 ± 0% ~ (p=1.000 n=10) ¹ Labels_Equals/not_equal-20 0.000 ± 0% 0.000 ± 0% ~ (p=1.000 n=10) ¹ Labels_Equals/different_sizes-20 0.000 ± 0% 0.000 ± 0% ~ (p=1.000 n=10) ¹ Labels_Equals/lots-20 0.000 ± 0% 0.000 ± 0% ~ (p=1.000 n=10) ¹ Labels_Equals/real_long_equal-20 0.000 ± 0% 0.000 ± 0% ~ (p=1.000 n=10) ¹ Labels_Equals/real_long_different_end-20 0.000 ± 0% 0.000 ± 0% ~ (p=1.000 n=10) ¹ Labels_Compare/equal-20 0.000 ± 0% 0.000 ± 0% ~ (p=1.000 n=10) ¹ Labels_Compare/not_equal-20 0.000 ± 0% 0.000 ± 0% ~ (p=1.000 n=10) ¹ Labels_Compare/different_sizes-20 0.000 ± 0% 0.000 ± 0% ~ (p=1.000 n=10) ¹ Labels_Compare/lots-20 0.000 ± 0% 0.000 ± 0% ~ (p=1.000 n=10) ¹ Labels_Compare/real_long_equal-20 0.000 ± 0% 0.000 ± 0% ~ (p=1.000 n=10) ¹ Labels_Compare/real_long_different_end-20 0.000 ± 0% 0.000 ± 0% ~ (p=1.000 n=10) ¹ Labels_Hash/typical_labels_under_1KB-20 0.000 ± 0% 0.000 ± 0% ~ (p=1.000 n=10) ¹ Labels_Hash/bigger_labels_over_1KB-20 0.000 ± 0% 0.000 ± 0% ~ (p=1.000 n=10) ¹ Labels_Hash/extremely_large_label_value_10MB-20 0.000 ± 0% 0.000 ± 0% ~ (p=1.000 n=10) ¹ Builder-20 1.000 ± 0% 1.000 ± 0% ~ (p=1.000 n=10) ¹ Labels_Copy-20 1.000 ± 0% 1.000 ± 0% ~ (p=1.000 n=10) ¹ geomean ² +0.00% ² ¹ all samples are equal ² summaries must be >0 to compute geomean Signed-off-by: Lukasz Mierzwa <l.mierzwa@gmail.com> |
||
---|---|---|
.circleci | ||
.github | ||
cmd | ||
config | ||
discovery | ||
docs | ||
documentation | ||
model | ||
notifier | ||
plugins | ||
prompb | ||
promql | ||
rules | ||
scrape | ||
scripts | ||
storage | ||
template | ||
tracing | ||
tsdb | ||
util | ||
web | ||
.dockerignore | ||
.gitignore | ||
.gitpod.Dockerfile | ||
.gitpod.yml | ||
.golangci.yml | ||
.promu.yml | ||
.yamllint | ||
CHANGELOG.md | ||
CODE_OF_CONDUCT.md | ||
CONTRIBUTING.md | ||
Dockerfile | ||
go.mod | ||
go.sum | ||
LICENSE | ||
MAINTAINERS.md | ||
Makefile | ||
Makefile.common | ||
NOTICE | ||
plugins.yml | ||
README.md | ||
RELEASE.md | ||
SECURITY-INSIGHTS.yml | ||
SECURITY.md | ||
ui-commits | ||
VERSION |

Prometheus
Visit prometheus.io for the full documentation, examples and guides.
Prometheus, a Cloud Native Computing Foundation project, is a systems and service monitoring system. It collects metrics from configured targets at given intervals, evaluates rule expressions, displays the results, and can trigger alerts when specified conditions are observed.
The features that distinguish Prometheus from other metrics and monitoring systems are:
- A multi-dimensional data model (time series defined by metric name and set of key/value dimensions)
- PromQL, a powerful and flexible query language to leverage this dimensionality
- No dependency on distributed storage; single server nodes are autonomous
- An HTTP pull model for time series collection
- Pushing time series is supported via an intermediary gateway for batch jobs
- Targets are discovered via service discovery or static configuration
- Multiple modes of graphing and dashboarding support
- Support for hierarchical and horizontal federation
Architecture overview
Install
There are various ways of installing Prometheus.
Precompiled binaries
Precompiled binaries for released versions are available in the download section on prometheus.io. Using the latest production release binary is the recommended way of installing Prometheus. See the Installing chapter in the documentation for all the details.
Docker images
Docker images are available on Quay.io or Docker Hub.
You can launch a Prometheus container for trying it out with
docker run --name prometheus -d -p 127.0.0.1:9090:9090 prom/prometheus
Prometheus will now be reachable at http://localhost:9090/.
Building from source
To build Prometheus from source code, You need:
- Go version 1.22 or greater.
- NodeJS version 16 or greater.
- npm version 7 or greater.
Start by cloning the repository:
git clone https://github.com/prometheus/prometheus.git
cd prometheus
You can use the go
tool to build and install the prometheus
and promtool
binaries into your GOPATH
:
GO111MODULE=on go install github.com/prometheus/prometheus/cmd/...
prometheus --config.file=your_config.yml
However, when using go install
to build Prometheus, Prometheus will expect to be able to
read its web assets from local filesystem directories under web/ui/static
and
web/ui/templates
. In order for these assets to be found, you will have to run Prometheus
from the root of the cloned repository. Note also that these directories do not include the
React UI unless it has been built explicitly using make assets
or make build
.
An example of the above configuration file can be found here.
You can also build using make build
, which will compile in the web assets so that
Prometheus can be run from anywhere:
make build
./prometheus --config.file=your_config.yml
The Makefile provides several targets:
- build: build the
prometheus
andpromtool
binaries (includes building and compiling in web assets) - test: run the tests
- test-short: run the short tests
- format: format the source code
- vet: check the source code for common errors
- assets: build the React UI
Service discovery plugins
Prometheus is bundled with many service discovery plugins. When building Prometheus from source, you can edit the plugins.yml file to disable some service discoveries. The file is a yaml-formatted list of go import path that will be built into the Prometheus binary.
After you have changed the file, you
need to run make build
again.
If you are using another method to compile Prometheus, make plugins
will
generate the plugins file accordingly.
If you add out-of-tree plugins, which we do not endorse at the moment,
additional steps might be needed to adjust the go.mod
and go.sum
files. As
always, be extra careful when loading third party code.
Building the Docker image
The make docker
target is designed for use in our CI system.
You can build a docker image locally with the following commands:
make promu
promu crossbuild -p linux/amd64
make npm_licenses
make common-docker-amd64
Using Prometheus as a Go Library
Remote Write
We are publishing our Remote Write protobuf independently at buf.build.
You can use that as a library:
go get buf.build/gen/go/prometheus/prometheus/protocolbuffers/go@latest
This is experimental.
Prometheus code base
In order to comply with go mod rules, Prometheus release number do not exactly match Go module releases.
For the Prometheus v3.y.z releases, we are publishing equivalent v0.3y.z tags. The y in v0.3y.z is always padded to two digits, with a leading zero if needed.
Therefore, a user that would want to use Prometheus v3.0.0 as a library could do:
go get github.com/prometheus/prometheus@v0.300.0
For the Prometheus v2.y.z releases, we published the equivalent v0.y.z tags.
Therefore, a user that would want to use Prometheus v2.35.0 as a library could do:
go get github.com/prometheus/prometheus@v0.35.0
This solution makes it clear that we might break our internal Go APIs between minor user-facing releases, as breaking changes are allowed in major version zero.
React UI Development
For more information on building, running, and developing on the React-based UI, see the React app's README.md.
More information
- Godoc documentation is available via pkg.go.dev. Due to peculiarities of Go Modules, v3.y.z will be displayed as v0.3y.z (the y in v0.3y.z is always padded to two digits, with a leading zero if needed), while v2.y.z will be displayed as v0.y.z.
- See the Community page for how to reach the Prometheus developers and users on various communication channels.
Contributing
Refer to CONTRIBUTING.md
License
Apache License 2.0, see LICENSE.