At its heart Thanos provides a global query view, data backup, and access historical data as core features. All three are well separated and Thanos deployments can be adapted to make use of each of them individually. This supports using just a subset of its features as well as a gradual rollout that immediately provides some of its benefits.
The following examples configure Thanos to work against a Google Cloud Storage bucket. However, any object storage (S3, HDFS, DigitalOcean Spaces, ...) can be used by using the equivalent flags to connect to the S3 API.
See this for up-to-date list of available object stores for Thanos.
- One or more Prometheus v2.0.0 installations
- An object storage bucket (optional)
Thanos has no official releases yet. With a working installation of the Go toolchain (GOPATH, PATH=${GOPATH}/bin:${PATH}), Thanos can be downloaded and built by running:
go get -d github.com/improbable-eng/thanos/...
make
The thanos binary should now be in your $PATH and is the only thing required to deploy any of its components.
Thanos integrates with existing Prometheus servers through a sidecar process, which runs on same machine/in the same pod as the Prometheus server itself. It only works with Prometheus instances of version 2.0.
The sidecar is responsible for backing up data into an object storage bucket and providing querying access to the underlying Prometheus instance for other Thanos components.
The following configures the sidecar to backup data into a Google Cloud Storage bucket.
thanos sidecar \
--prometheus.url http://localhost:9090 \ # Prometheus's HTTP address
--tsdb.path /var/prometheus \ # Data directory of Prometheus
--gcs.bucket example-bucket \ # Bucket to upload data to
Rolling this out has little to zero impact on the running Prometheus instance. It is a good start to ensure you are backing up your data while figuring out the other pieces of Thanos.
If you are not interested in backing up any data, the --gcs.bucket flag can simply be omitted.
Thanos comes with a highly efficient gRPC-based Store API for metric data access across all its components. The sidecar implements it in front of its connected Prometheus server. While it is ready to use with the above example, we must additionally configure the sidecar to join a Thanos cluster.
Components in a Thanos cluster are connected through a gossip protocol to advertise membership and propagate metadata about the data and services they provide.
We a set of initial peers addresses to the sidecar. They only must be valid on startup and subsequent cluster changes are automatically adapted to.
thanos sidecar \
--prometheus.url http://localhost:9090 \
--tsdb.path /var/prometheus \
--cluster.peers 10.9.1.4 \ # Static cluster peer (default port)
--cluster.peers 10.9.2.6:10900 \ # We can pass multiple of them (custom port)
--cluster.peers all.thanos.internal.org \ # Make Thanos lookup all addresses behind a domain
--cluster.peers all.thanos.internal.org:18088 \ # Specify a custom port
Configuration of initial peers is flexible and the argument can be repeated for Thanos to try different approaches.
Additional flags for cluster configuration exist but are typically not needed. Check the --help output for further information.
Prometheus allows to configure "external labels" for a Prometheus instance. Those are meant to globally identify the role of a given Prometheus instance. As Thanos aims to aggregate data across all Prometheus servers, providing a consistent of external labels for all Prometheus server becomes crucial!
Every Prometheus instance must have a globally unique set of identifying labels. For example, in Prometheus's configuration file:
global:
external_labels:
region: eu-west
monitor: infrastructure
replica: A
# ...
Now that we have setup the sidecar for one or more Prometheus servers, we want to use Thanos' global query layer to evaluate PromQL queries against all of them at once.
The query component is stateless and horizontally scalable and thus can be deployed with any required amount of replicas. Just like sidecars, it connects to the cluster via gossip protocol and automatically detects which Prometheus servers need to be contacted for a given PromQL query.
It implements Prometheus's official HTTP API and can thus seamlessly be used with external tools such as Grafana. Additionaly it servers a derviative of Prometheus's UI for ad-hoc querying.
thanos query \
--http-address "0.0.0.0:19092" \ # Endpoint for the UI
--cluster.peers all.thanos.internal.org # Discovery of initial cluster peers
The query component is also capable of merging data collected from Prometheus HA pairs. This requires a consistent choice of an external label for Prometheus servers that identifies replicas. Other external labels must be identical. A typical choice is simply the label name "replica" while its value is freely chosable.
Providing the label name to the query component will enable the deduplication.
thanos query \
--http-address "0.0.0.0:19092" \
--cluster.peers all.thanos.internal.org \
--query.replica-label replica
Go to the configured HTTP address that should now show a UI similar to that of Prometheus itself. If the cluster formed correctly you can now query data across all Prometheus servers within the cluster.
As the sidecar backs up data into the object storage of your choice, you can decrease Prometheus retention and store less locally. However we need a way to query all that historical data again. The store gateway does just that by implementing the same gRPC data API as the sidecars but backing it with data it can find in your object storage bucket. Just like sidecars and query nodes, the store gateway joins the gossip cluster and is automatically picked up by running query nodes as yet another data provider.
thanos store \
--tsdb.path /var/thanos/store \ # Disk space for local caches
--gcs.bucket example-bucket \ # Bucket to fetch data from
--cluster.peers all.thanos.internal.org
The store gateway occupies small amounts of disk space for caching basic information about data in the object storage. This will rarely exceed more than a few gigabytes and is used to improve restart times. It is not useful but not required to preserve it across restarts.
A local Prometheus installation periodically compacts older data to improve query efficieny. Since the sidecar backs up data as soon as possible, we need a way to apply the same process to data in the object storage.
The compactor component simple scans the object storage and processes compaction where required. At the same time it is responsible for creating downsampled copies of data to speed up queries.
thanos compact \
--data-dir /var/thanos/compact \ # Temporary workspace for data processing
--gcs-bucket example-bucket
The compactor is not in the critical path of querying or data backup. It can either be run as a periodic batch job or be left running to always compact data as soon as possible. It is recommended to provide 100-300GB of local disk space for data processing.
NOTE: The compactor must be run as a singleton and must not run when manually modifying data in the bucket.
You can find one-box example with minikube here.