Can Grafana and Prometheus resources be scaled up and down?

Note:
This topic has been translated from a Chinese forum by GPT and might contain errors.

Original topic: 请问下grafana和prometheus资源都可以扩容缩容吧

| username: TiDBer_Y2d2kiJh

[TiDB Usage Environment] Production Environment / Testing / PoC
[TiDB Version] v5.4.0
[Reproduction Path] As mentioned, the TiDB cluster can scale Grafana and Prometheus resources up and down, right?
[Encountered Issues: Issue Phenomenon and Impact]
[Resource Configuration] Go to TiDB Dashboard - Cluster Info - Hosts and take a screenshot of this page
[Attachments: Screenshots/Logs/Monitoring]

| username: 胡杨树旁 | Original post link

Sure.

| username: Kongdom | Original post link

It can be scaled up

| username: ljluestc | Original post link

Scaling Grafana:

Scaling up Grafana:

You can scale up Grafana by adding more compute and memory resources to the server where Grafana is running.

If using containerized Grafana, you can scale up by increasing the resource limits of the container.

You can also consider performance optimization in Grafana’s configuration, such as reducing the number of charts or decreasing the query interval of the charts.

Scaling down Grafana:

If Grafana usage is low, you can consider reducing the compute and memory resources of the server where Grafana is running.

For containerized Grafana, you can lower the resource limits of the container.

Scaling Prometheus:

Scaling up Prometheus:

Prometheus performance is usually affected by storage and the query engine, so you can consider using a faster storage backend like Thanos.

Allocate more memory and storage resources to Prometheus to accommodate more data and metrics.

You can also horizontally scale Prometheus by adding more Prometheus instances to share the load.

Scaling down Prometheus:

If Prometheus usage is low, you can consider reducing the compute and memory resources of the server where Prometheus is running.

For containerized Prometheus, you can lower the resource limits of the container.

Additionally, you can regularly clean up old data and metrics that are no longer needed to free up storage space.

Before performing scaling operations, be sure to back up your configurations and data, and ensure you understand the resource usage and load patterns of your cluster. Monitoring tools typically provide information on resource usage and performance metrics to help make appropriate decisions.

| username: zhanggame1 | Original post link

Sure, just like with other components, modify the configuration file for scaling in and out, then scale out or in.

| username: Fly-bird | Original post link

Absolutely.