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Remove docs and tracing in beta mention. Some additional improvements
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README.md

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[![Code Climate](https://api.codeclimate.com/v1/badges/c6b16c0bbcb0690c9c71/maintainability)]()
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[![GoDoc](https://godoc.org/github.com/timescale/promscale?status.svg)](https://pkg.go.dev/github.com/timescale/promscale)
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- **[Website](https://www.timescale.com/promscale)**
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- **[Website](https://www.timescale.com/promscale)*
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- **[Install](https://docs.timescale.com/promscale/latest/installation/)*
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- **[Docs](https://docs.timescale.com/promscale/latest/)**
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- **[Slack Community](https://timescaledb.slack.com/)** (join the #promscale channel)
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- **[Forum](https://www.timescale.com/forum/c/promscale-and-observability)**
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- **[Blog](https://blog.timescale.com/tag/observability/)**
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<img alt="Promscale" src="images/promscale-logo.png" width="600px">
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**Promscale is an open source observability backend for metrics and traces
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powered by SQL**.
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**Promscale is a unified observability backend for metrics and traces
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powered by SQL and built on PostgreSQL and TimescaleDB**.
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It's built on the robust and high-peformance foundation of
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**PostgreSQL and TimescaleDB**. It has native support for **Prometheus** metrics and
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It has native support for **Prometheus** metrics and
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**OpenTelemetry** traces as well as many other formats like StatsD, Jaeger and Zipkin
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through the OpenTelemetry Collector and is **100% PromQL compliant**.
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It's **full SQL** capabilities enable developers to **correlate metrics, traces and also
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each time (with no cross-cutting concerns) from PromLab's
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[PromQL Compliance Test Suite](https://promlabs.com/promql-compliance-tests/).
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For OpenTelemetry, Jaeger and Zipkin users, Promscale provides the ability to run deep analysis
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on traces using SQL. Install our lightweight [opentelemetry-demo](https://github.com/timescale/opentelemetry-demo)
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on your computer to quickly get a feel of what's possible. More details in
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[this blog post](https://www.timescale.com/blog/learn-opentelemetry-tracing-with-this-lightweight-microservices-demo/).
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<img src="docs/assets/promscale-arch.png" alt="Promscale Architecture Diagram" width="800"/>
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**Tracing** support is currently in beta. Read [the documentation](docs/tracing.md) to get started.
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This repository contains the source code of the Promscale Connector. Promscale also requires that the Promscale Extension
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who lives in this repository is installed in the database. The extension sets up and manages the database schemas
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and provides performance and SQL query experience improvements.
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For a detailed description of the initial architecture of Promscale for Prometheus metrics that covers
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some key design principles, please see [our design doc](https://tsdb.co/prom-design-doc).
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remote storage endpoints. For more information about prom-migrator, visit
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[prom-migrator's README](https://github.com/timescale/promscale/blob/master/migration-tool/cmd/prom-migrator/README.md).
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# Documentation
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* **[About TimescaleDB](#-about-timescaledb)**
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* **[Timescale cloud](#-timescale-cloud)**
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* **[Features](#-features)**
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* **[Installation](#-choose-your-own-installation-adventure)**
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* [The Observability Suite for Kubernetes](https://github.com/timescale/tobs)
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* [Docker](docs/docker.md)
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* [Binaries](docs/binary.md)
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* [Source](docs/binary.md#-building-from-source)
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* [Helm](deploy/helm-chart/README.md)
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* [Tutorial: Bare Metal Setup for a Monitoring Suite](docs/bare-metal-promscale-stack.md)
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* [Configuring Prometheus](docs/configuring_prometheus.md)
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* **[Analyzing Data Using SQL](docs/sql_schema.md)**
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* [Data Model](docs/sql_schema.md#data-model-schema)
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* [Filtering Series](docs/sql_schema.md#filtering-series)
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* [Data Retention](docs/sql_schema.md#data-retention)
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* [Roles and Permissions](docs/sql_permissions.md)
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* **[Official Promscale Docs](https://docs.timescale.com/promscale/latest/)**
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* **[Tracing (beta)](docs/tracing.md)**
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* **[High Availability](docs/high-availability/prometheus-HA.md)**
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* **[Multi-Node TimescaleDB](docs/multinode.md)**
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* **[Multi-tenancy](docs/multi_tenancy.md)**
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* **[Writing Metric Data](docs/writing_to_promscale.md)**
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* **[Alerting](docs/alerting.md)**
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* [Alerting for Promscale](docs/mixin/README.md)
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* **[Downsampling](docs/downsampling.md)**
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* **[Deleting Data](docs/metric_deletion_and_retention.md)**
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* **[Quick Tips](#-quick-tips)**
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* **[FAQ](docs/faq.md)**
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* **[Contributing](#-contributing)**
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## 🐯 About TimescaleDB
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**[TimescaleDB](https://github.com/timescale/timescaledb)** is a
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**distributed time-series database built on PostgreSQL** that scales to
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over 10 million of metrics per second, supports native compression,
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handles high cardinality, and offers native time-series capabilities,
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such as data retention policies, continuous aggregate views,
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downsampling, data gap-filling and interpolation.
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TimescaleDB also supports full SQL, a variety of data types (numerics,
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text, arrays, JSON, booleans), and ACID semantics. Operationally mature
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capabilities include high-availability, streaming backups, upgrades over
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time, roles and permissions, and security.
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TimescaleDB has a **large and active user community** (tens of millions
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of downloads, hundreds of thousands of active deployments, Slack channel
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with 7,000+ members). Users include Comcast, Fujitsu,
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Schneider Electric, Siemens, Walmart, Warner Music, and thousands of
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others.
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Developers and organizations around the world trust TimescaleDB with their
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time-series data. AppDynamics (now part of Cisco Systems and one of the
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largest application performance monitoring providers) relies on TimescaleDB
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as its main metrics database. TimescaleDB is also the preferred (recommended)
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backend datasource for Zabbix users and is natively supported in Grafana.
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## 🚀 Timescale Cloud
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**[Timescale cloud](https://www.timescale.com/cloud)** offers you an unrivalled experience of TimescaleDB.
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All you have to do to integrate with Timescale cloud is configure the db uri using `-db-uri` flag in Promscale to database
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instance running in Timescale cloud.
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**Note:** Timescale cloud offers a 30-day free trial.
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## 🌟 Features
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Click the video below for an overview of Promscale:
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<p align="center">
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<a href="https://www.youtube.com/watch?v=FWZju1De5lc&amp;list=PLsceB9ac9MHTrmU-q7WCEvies-o7"> <img src="https://media.giphy.com/media/aLIuNJwflN425UvYjR/giphy.gif"> </a>
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</p>
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* **Analysis in PromQL and SQL** get the ability to analyze data in both query languages. Use PromQL for monitoring and alerting and
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SQL for deeper analytics and compatibility with a huge ecosystem of data visualization, analysis, and AI/ML tools.
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* **Rock-solid stability** due to being built on top of PostgreSQL, with 30+ years of development work.
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* **Support for backfilling** to ingest data from the past.
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* **Native support for Multi-Tenancy**. Ingest data from multiple tenants and write queries across [more than one tenant](docs/multi_tenancy.md)
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easily, using PromQL or SQL.
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* **High-Availability** support for [Prometheus HA deployments](docs/high-availability/prometheus-HA.md) as well as
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high-availability deployments of [TimescaleDB itself](https://blog.timescale.com/blog/high-availability-timescaledb-postgresql-patroni-a4572264a831/).
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* **Simple architecture**. Unlike some other long-term stores, our architecture consists of only three components: Prometheus, Promscale, and TimescaleDB.
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* **ACID compliance** to ensure consistency of your data.
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* **Horizontal scalability** using [multinode support](https://blog.timescale.com/blog/timescaledb-2-0-a-multi-node-petabyte-scale-completely-free-relational-database-for-time-series/) with TimescaleDB version 2.0.
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* **Operationally mature**. Built on top of Postgres, data written via Promscale is safe, reliable and offers
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all of the advantages of an operationally mature platform.
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## 🔧 Choose your own (installation) adventure
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We have four main ways to set up Promscale:
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#### tobs (recommended for Kubernetes environments)
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[The Observability Suite for Kubernetes](https://github.com/timescale/tobs) is a
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CLI tool and Helm chart that makes installing a full observability suite into your
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Kubernetes cluster really easy. Tobs includes Prometheus, TimescaleDB,
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Promscale Connector, and Grafana.
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To get started, run the following in your terminal, then follow the on-screen instructions.
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```bash
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curl --proto '=https' --tlsv1.2 -sSLf https://tsdb.co/install-tobs-sh |sh
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```
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Or visit the [tobs GitHub repo](https://github.com/timescale/tobs) for more information and instructions
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on advanced configuration.
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#### 🐳 Docker
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We provide [docker images](https://github.com/timescale/promscale/releases) with every release.
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Instructions on how to use our docker images are available [here](docs/docker.md).
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You may also want to check [tobs](https://github.com/timescale/tobs) which makes it very easy to deploy a complete
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observability stack built on Prometheus, OpenTelemetry and Promscale in Kubernetes via cli or helm.
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#### 🔟 Binaries
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We have [pre-packaged binaries](https://github.com/timescale/promscale/releases) available for MacOS and Linux on both the x86_64 and i386 architectures.
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Instructions on how to use our prepackaged binaries are available [here](docs/binary.md).
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You can also [build binaries from source](docs/binary.md#-building-from-source).
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#### ⎈ Helm (sub)chart for Promscale Connector only
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A Helm chart for only the Promscale Connector is available in
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the [helm-chart directory](deploy/helm-chart/README.md) of this repository.
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This is used as a Helm dependency from the `tobs`
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[Helm chart](https://github.com/timescale/tobs/tree/master/chart) and can be used as a dependency in your own custom Helm chart as well.
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## 🔍 Analyzing the data using SQL
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We describe how to use our pre-defined views and functions to work with the prometheus data in [the SQL schema doc](docs/sql_schema.md).
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A Reference for our SQL API is [available here](docs/sql_api.md).
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## 💡 Quick tips
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### Configuring Data Retention
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By default, data is stored for 90 days and then deleted. This
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default can be changed globally or overridden for individual
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metrics.
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If using tobs, these settings can be changed using the
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`tobs metrics retention` command (use
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`tobs metrics retention -h` to see all options).
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These setting can also be changed using the appropriate
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[SQL commands](docs/sql_schema.md#data-retention).
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### 🌐 Prometheus HTTP API
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The Promscale Connector can be used directly as a Prometheus Data
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Source in Grafana, or other software.
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The connector implements some endpoints of the currently stable (V1) [Prometheus HTTP
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API](https://prometheus.io/docs/prometheus/latest/querying/api). The API is
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accessible at `http://<promscale_connector_address>:9201/api/v1` and can be
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used to execute instant or range PromQL queries against the data in
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TimescaleDB, as well as retrieve the metadata for series, label names and
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label values.
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A Reference for the implemented endpoints of the Prometheus HTTP API is [available here](docs/prometheus_api.md)
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### Configuring Prometheus to filter which metrics are sent (optional)
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You can limit the metrics being sent to the adapter (and thus being
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stored in your long-term storage) by setting up `write_relabel_configs`
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in Prometheus, via the `prometheus.yml` file.
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Doing this can reduce the amount of space used by your database and thus
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increase query performance.
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The example below drops all metrics starting with the prefix `go_`,
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which matches Golang process information exposed by exporters like
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`node_exporter`:
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```
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remote_write:
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- url: "http://promscale_connector:9201/write"
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write_relabel_configs:
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- source_labels: [__name__]
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regex: 'go_.*'
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action: drop
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```
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# Documentation
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Additional information about setting up relabel configs, the
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`source_labels` field, and the possible actions can be found in the
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[Prometheus Docs](https://prometheus.io/docs/prometheus/latest/configuration/configuration/#remote_write).
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Complete user documentation is available in https://docs.timescale.com/promscale/latest/
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## ✎ Contributing
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