Achieve Multi-tenancy in Monitoring with Prometheus & Thanos Receiver

Hey there! If you are reading this blog post, then I guess you are
already aware of Prometheus and how it helps
us in monitoring distributed systems like Kubernetes. And if you are familiar with
Prometheus, then chances are that you have come across the tool called
Thanos. Thanos is a popular OSS that helps
enterprises achieve a HA Prometheus setup with long-term storage
capabilities. One of the common challenges of distributed monitoring is
to implement multi-tenancy. Thanos
receiver is a Thanos
component designed to address this common challenge.
Receiver was part of Thanos for a significant duration as an experimental feature. However, after some time, it reached the general availability stage and is now fully supported.

A few words on Thanos Receiver

Receiver is a Thanos component that can accept remote
write
requests from any Prometheus instance and store the data in its local
TSDB, optionally it can upload those TSDB blocks to an object
storage like S3 or GCS at
regular intervals. Receiver does this by implementing the Prometheus
Remote Write
API.
It builds on top of existing Prometheus TSDB and retains their
usefulness while extending their functionality with long-term-storage,
horizontal scalability, and down-sampling. It exposes the
StoreAPI
so that Thanos Queriers
can query received metrics in real-time.

Multi-tenancy in Thanos Receiver

Thanos receiver supports multi-tenancy. It accepts Prometheus remote
write requests, and writes these into a local instance of Prometheus
TSDB. The value of the HTTP header (“THANOS-TENANT”) of the incoming
request determines the id of the tenant Prometheus. To prevent data
leaking at the database level, each tenant has an individual TSDB
instance, meaning a single Thanos receiver may manage multiple TSDB
instances. Once the data is successfully committed to the tenant’s TSDB,
the requests return successfully. Thanos Receiver also supports
multi-tenancy by exposing labels which are similar to Prometheus
external
labels.

Hashring configuration file

If we want features like load-balancing and data replication, we can run
multiple instances of Thanos receiver as a part of a single hashring.
The receiver instances within the same hashring become aware of their
peers through a hashring configuration file. Following is an example of
a hashring configuration file.

[
   {
       "hashring": "tenant-a",
       "endpoints": ["tenant-a-1.metrics.local:19291/api/v1/receive", "tenant-a-2.metrics.local:19291/api/v1/receive"],
       "tenants": ["tenant-a"]
   },
   {
       "hashring": "tenants-b-c",
       "endpoints": ["tenant-b-c-1.metrics.local:19291/api/v1/receive", "tenant-b-c-2.metrics.local:19291/api/v1/receive"],
       "tenants": ["tenant-b", "tenant-c"]
   },
   {
       "hashring": "soft-tenants",
       "endpoints": ["http://soft-tenants-1.metrics.local:19291/api/v1/receive"]
   }
]

• Soft tenancy – If a hashring specifies no explicit tenants, then any tenant is considered a valid match; this allows for a cluster to provide soft-tenancy. Requests whose tenant ID matches no other hashring explicitly, will automatically land in this soft tenancy hashring. All incoming remote write requests which don’t set the tenant header in the HTTP request, fall under soft tenancy and default tenant ID (configurable through the flag –receive.default-tenant-id) is attached to their metrics.
• Hard tenancy – Hard tenants must set the tenant header in every HTTP request for remote write. Hard tenants in the Thanos receiver are configured in a hashring config file. Changes to this configuration must be orchestrated by a configuration management tool. When a remote write request is received by a Thanos receiver, it goes through the list of configured hard tenants. A hard tenant also has the number of associated receiver endpoints belonging to it.

P.S: A remote write request can be initially received by any receiver
instance, however, will only be dispatched to receiver endpoints that
correspond to that hard tenant.

Architecture

In this blog post, we are trying to implement the following
architecture. We will use Thanos v0.31.0 in this blog post.

A simple multi-tenancy model with Thanos Receiver

Brief overview on the above architecture:

• We have 3 Prometheuses running in namespaces: sre, tenant-a and
  tenant-b respectively.

• The Prometheus in sre namespace is demonstrated as a soft-tenant
  therefore it does not set any additional HTTP headers to the remote
  write requests.

• The Prometheuses in tenant-a and tenant-b are demonstrated as
  hard tenants. The NGINX servers in those respective namespaces are
  used for setting tenant header for the tenant Prometheus.

• From security point of view we are only exposing the Thanos receiver
  statefulset responsible for the soft-tenant (sre Prometheus).

• For both Thanos receiver statefulsets (soft and hard) we are setting
  a replication
  factor=2.
  This would ensure that the incoming data get replicated between two
  receiver pods.

• The remote write request which is received by the soft tenant
  receiver
  instance is forwarded to the hard tenant thanos
  receiver.
  This routing is based on the hashring config.

The above architecture obviously misses few features that one would also
expect from a multi-tenant architecture, e.g: tenant isolation,
authentication, etc. This blog post only focuses how we can use the
Thanos Receiver to store time-series from multiple Prometheus(es) to
achieve multi-tenancy. Also the idea behind this setup is to show how we
can make the prometheus on the tenant side nearly stateless yet maintain
data resiliency.

We will improve this architecture, in the upcoming posts. So, stay
tuned.

Prerequisites

• KIND / managed cluster / minikube (We will be using Kind)
• kubectl
• helm
• jq(optional)

Cluster setup

Clone the repo:

 git clone https://github.com/infracloudio/thanos-receiver-demo.git 

Setup a local KIND cluster

1. Move to the local-cluster directory:
   

   cd local-cluster/
   

2. Create the cluster with calico, ingress and extra-port mappings:
   

   ./create-cluster.sh cluster-1 kind-calico-cluster-1.yaml
   

3. Deploy the nginx ingress controller:
   

   helm repo add ingress-nginx https://kubernetes.github.io/ingress-nginx
   helm repo update
   helm install nginx-controller ingress-nginx/ingress-nginx -n ingress-nginx --create-namespace
   

4. Now, move back to the root directory of the repo:
   

   cd -
   

Install minio as object storage

1. helm repo add bitnami https://charts.bitnami.com/bitnami

2. Install minio in the cluster:
   

   helm upgrade --install my-minio bitnami/minio \
     --set ingress.enabled=true --set auth.rootUser=minio --set auth.rootPassword=minio123 \
     --namespace minio --create-namespace
   

3.

    kubectl port-forward svc/my-minio 9001:9001 -n minio
    ```
{% endraw %}

4.  if you face  **E0528 13:02:43.145873   44832 portforward.go:346] error creating error stream for port 9001 -> 9001: Timeout occurred** issue while doing port-forward for minio then execute above port-forward command in one terminal and then open another terminal and execute below command which will keep connection to minio pods keep running.
{% raw %}

    ```sh
    while true ; do wget 127.0.0.1:9001 ; sleep 10 ; done
    ```
{% endraw %}

5.  then Login to minio by opening http://localhost:9001/ in browser with credentials username minio and password minio123
6.  Create a bucket with name **thanos** from UI

### Install Thanos components

**Create shared components**
{% raw %}


```shell
kubectl create ns thanos

## Create a file _thanos-s3.yaml_ containing the minio object storage config for tenant-a:
cat << EOF > thanos-s3.yaml
type: S3
config:
  bucket: "thanos"
  endpoint: "my-minio.minio.svc.cluster.local:9000"
  access_key: "minio"
  secret_key: "minio123"
  insecure: true
EOF

## Create secret from the file created above to be used with the thanos components e.g store, receiver
kubectl -n thanos create secret generic thanos-objectstorage --from-file=thanos-s3.yaml
kubectl -n thanos label secrets thanos-objectstorage part-of=thanos

## go to manifests directory
cd ../manifests/

Install Thanos Receive Controller

• Deploy a thanos-receiver-controller to auto-update the hashring
  configmap when the thanos receiver statefulset scales:
  

  kubectl apply -f thanos-receiver-hashring-configmap-base.yaml
  kubectl apply -f thanos-receive-controller.yaml
  

  The deployment above would generate a new configmap
  thanos-receive-generated and keep it updated with a list of
  endpoints when a statefulset with label:
  controller.receive.thanos.io/hashring=hashring-0 and/or
  controller.receive.thanos.io/hashring=default get created or
  updated. The thanos receiver pods would load the
  thanos-receive-generatedconfigmaps in them.

  **NOTE: The **default* and hashring-0 hashrings would be
  responsible for the soft-tenancy and hard-tenancy respectively.*

Install Thanos Receiver

1. Create the thanos-receiver statefulsets and headless services for
   soft and hard tenants.

   We are not using persistent volumes just for this demo.
   

   kubectl apply -f thanos-receive-default.yaml 
   kubectl apply -f thanos-receive-hashring-0.yaml
   

   The receiver pods are configured to store 15d of data and with replication factor of 2

2. Create a service in front of the thanos receiver statefulset for the
   soft tenants.
   

   kubectl apply -f thanos-receive-service.yaml
   

   The pods of **thanos-receive-default* statefulset would
   load-balance the incoming requests to other receiver pods based on
   the hashring config maintained by the thanos receiver controller.*

Install Thanos Store

Create a thanos store statefulsets.

  kubectl apply -f thanos-store-shard-0.yaml

We have configured it such that the thanos querier fans out queries to the store only for data older than 2w. Data earlier than 15d are to be provided by the receiver pods. P.S: There is a overlap of 1d between the two time windows is intentional for data-resiliency.

Install Thanos Querier

Create a thanos querier deployment, expose it through service and ingress.

  kubectl apply -f thanos-query.yaml

We configure the thanos query to connect to receiver(s) and store(s) for fanning out queries.

Install Prometheus(es)

Create shared resource

kubectl create ns sre
kubectl create ns tenant-a
kubectl create ns tenant-b

Install kube-prometheus-stack

We install the
kube-prometheus-stack

helm repo add prometheus-community https://prometheus-community.github.io/helm-charts
helm upgrade --namespace sre --debug --install cluster-monitor prometheus-community/kube-prometheus-stack \
  --set prometheus.ingress.enabled=true \
  --set prometheus.ingress.hosts[0]="cluster.prometheus.local" \
  --set prometheus.prometheusSpec.remoteWrite[0].url="http://thanos-receive.thanos.svc.cluster.local:19291/api/v1/receive" \
  --set alertmanager.ingress.enabled=true \
  --set alertmanager.ingress.hosts[0]="cluster.alertmanager.local" \
  --set grafana.ingress.enabled=true \
  --set grafana.ingress.hosts[0]="grafana.local"

Install Prometheus and ServiceMonitor for tenant-a

In tenant-a namespace:

• Deploy a nginx proxy to forward the requests from prometheus to thanos-receive service in thanos namespace. It also sets the tenant header of the outgoing request
  

    kubectl apply -f nginx-proxy-a.yaml
  

• Create a
  prometheus and a servicemonitor to monitor itself
  

    kubectl apply -f prometheus-tenant-a.yaml
  

Install Prometheus and ServiceMonitor for tenant-b

In tenant-b namespace:

• Deploy a nginx proxy to forward the requests from prometheus to
  thanos-receive service in thanos namespace. It also sets the
  tenant header of the outgoing request
  

    kubectl apply -f nginx-proxy-b.yaml
  

• Create a prometheus and a servicemonitor to monitor itself
  

    kubectl apply -f prometheus-tenant-b.yaml
  

Test the setup

Access the thanos querier by port-forwarding thanos-query service

kubectl port-forward svc/thanos-query 9090:9090 -n thanos

and open the thanos query UI by opening http://localhost:9090/ in the browser,
execute the query count(up) by (tenant_id).

Otherwise, if we have jq
installed, you can run the following command:

curl -s http://localhost:9090/api/v1/query?query="count(up)by("tenant_id")"|jq -r '.data.result[]|"\(.metric) \(.value[1])"'
{"tenant_id":"a"} 1
{"tenant_id":"b"} 1
{"tenant_id":"cluster"} 17

Either of the above outputs show that, cluster, a and b prometheus
tenants are respectively having 17, 1 and 1 scrape targets up and
running. All these data are getting stored in thanos-receiver in real
time by prometheus’ remote write
queue.
This model creates an opportunity for the tenant side prometheus to be
nearly stateless yet maintain data resiliency.

In our next post, we would improve this architecture to enforce tenant
isolation on thanos-querier side.

I hope you found this blog informative and engaging. If you encounter

References:

• Thanos Receiver
• Github thanos-receive-controller
• Prometheus consulting and enterprise support capabilities