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The linear interpolation (assuming that observations are uniformly distributed within a bucket) is a solid and simple assumption in lack of any other information. However, the exponential bucketing used by standard schemas of native histograms has been chosen to cover the whole range of observations in a way that bucket populations are spread out over buckets in a reasonably way for typical distributions encountered in real-world scenarios. This is the origin of the idea implemented here: If we divide a given bucket into two (or more) smaller exponential buckets, we "most naturally" expect that the samples in the original buckets will split among those smaller buckets in a more or less uniform fashion. With this assumption, we end up with an "exponential interpolation", which therefore appears to be a better match for histograms with exponential bucketing. This commit leaves the linear interpolation in place for NHCB, but changes the interpolation for exponential native histograms to exponential. This affects `histogram_quantile` and `histogram_fraction` (because the latter is more or less the inverse of the former). The zero bucket has to be treated specially because the assumption above would lead to an "interpolation to zero" (the bucket density approaches infinity around zero, and with the postulated uniform usage of buckets, we would end up with an estimate of zero for all quantiles ending up in the zero bucket). We simply fall back to linear interpolation within the zero bucket. At the same time, this commit makes the call to stick with the assumption that the zero bucket only contains positive observations for native histograms without negative buckets (and vice versa). (This is an assumption relevant for interpolation. It is a mostly academic point, as the zero bucket is supposed to be very small anyway. However, in cases where it _is_ relevantly broad, the assumption helps a lot in practice.) This commit also updates and completes the documentation to match both details about interpolation. As a more high level note: The approach here attempts to strike a balance between a more simplistic approach without any assumption, and a more involved approach with more sophisticated assumptions. I will shortly describe both for reference: The "zero assumption" approach would be to not interpolate at all, but _always_ return the harmonic mean of the bucket boundaries of the bucket the quantile ends up in. This has the advantage of minimizing the maximum possible relative error of the quantile estimation. (Depending on the exact definition of the relative error of an estimation, there is also an argument to return the arithmetic mean of the bucket boundaries.) While limiting the maximum possible relative error is a good property, this approach would throw away the information if a quantile is closer to the upper or lower end of the population within a bucket. This can be valuable trending information in a dashboard. With any kind of interpolation, the maximum possible error of a quantile estimation increases to the full width of a bucket (i.e. it more than doubles for the harmonic mean approach, and precisely doubles for the arithmetic mean approach). However, in return the _expectation value_ of the error decreases. The increase of the theoretical maximum only has practical relevance for pathologic distributions. For example, if there are thousand observations within a bucket, they could _all_ be at the upper bound of the bucket. If the quantile calculation picks the 1st observation in the bucket as the relevant one, an interpolation will yield a value close to the lower bucket boundary, while the true quantile value is close to the upper boundary. The "fancy interpolation" approach would be one that analyses the _actual_ distribution of samples in the histogram. A lot of statistics could be applied based on the information we have available in the histogram. This would include the population of neighboring (or even all) buckets in the histogram. In general, the resolution of a native histogram should be quite high, and therefore, those "fancy" approaches would increase the computational cost quite a bit with very little practical benefits (i.e. just tiny corrections of the estimated quantile value). The results are also much harder to reason with. Signed-off-by: beorn7 <beorn@grafana.com> |
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| notifier | ||
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| prompb | ||
| promql | ||
| rules | ||
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| template | ||
| tracing | ||
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| util | ||
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| .golangci.yml | ||
| .promu.yml | ||
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| 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 | ||
| 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 1.17 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
prometheusandpromtoolbinaries (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 v2.y.z releases, we are publishing 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, v2.x.y will be displayed as v0.x.y.
- 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.