Grafana

This guide walks you through the steps required to monitor the performance and overall health of your Materialize region using Grafana.

Before you begin

To make Materialize metadata available to Grafana, you must configure and run the following additional services:

• A Prometheus SQL Exporter.
• A metrics scraper: Grafana Agent for Grafana Cloud users, and Prometheus for self-hosted Grafana.

Step 1. Set up a Prometheus SQL Exporter

To export metrics from Materialize and expose them in a format that Grafana can consume, you need to configure and run a Prometheus SQL Exporter. This service will run SQL queries against Materialize at specified intervals, and export the resulting metrics to a Prometheus endpoint.

We recommend using justwatchcom/sql_exporter, which has been tried and tested in production environments.

1. In the host that will run the Prometheus SQL Exporter, create a configuration file (config.yml) to hold the Exporter configuration.

   💡 Tip: You can use this sample config.yml.example as guidance to bootstrap your monitoring with some key Materialize metrics and indicators.

2. In the configuration file, define the connection to your Materialize region under connections using the credentials provided in the Materialize console.

   NOTE: You must escape the special @ character in USER for a successful connection. Example: instead of name@email.com, use name%40email.com.

   Filename: config.yml

   ---
   jobs:
   - name: "materialize"
     # Interval between the runs of the job
     interval: '1m'
     # Materialize connection string
     connections:
     - "postgres://<USER>:<PASSWORD>@<HOST>:6875/materialize?application_name=mz_Grafana_integration&sslmode=require"
     ...
   

   To specify different configurations for different sets of metrics, like a different interval, use additional jobs with a dedicated connection.

   ...
   - name: "materialize"
     interval: '1h'
     connections:
     - "postgres://<USER>:<PASSWORD>@<HOST>:6875/materialize?application_name=mz_Grafana_integration&sslmode=require"
     ...
   

3. Then, configure the queries that the Prometheus SQL Exporter should run at the specified interval. Take these considerations into account when exporting metrics from Materialize.

    ...
    queries:
    # Prefixed with sql_ and used as the metric name.
    - name: "replica_memory_usage"
        # Required option of the Prometheus default registry. Currently NOT
        # used by the Prometheus server.
        help: "Replica memory usage"
        # Array of columns used as additional labels. All lables should
        # be of type text.
        labels:
        - "replica_name"
        - "cluster_id"
        # Array of columns used as metric values. All values should be
        # of type float.
        values:
        - "memory_percent"
        # The SQL query that is run unalterted for each job.
        query:  |
                SELECT
                   name::text AS replica_name,
                   cluster_id::text AS cluster_id,
                   memory_percent::float AS memory_percent
                FROM mz_cluster_replicas r
                JOIN mz_internal.mz_cluster_replica_utilization u ON r.id=u.replica_id;             
   

4. Once you are done with the Prometheus SQL Exporter configuration, follow the intructions in the sql_exporter repository to run the service using the configuration file from the previous step.

Step 2. Set up a metrics scraper

To scrape the metrics available in the Prometheus SQL Exporter endpoint, you must then set up a Grafana Agent for Grafana cloud, or Prometheus for the self-hosted version:

Step 3. Build a monitoring dashboard

With the Prometheus SQL Exporter running SQL queries againt your Materialize region and exporting the results as metrics, and a scraper routing these metrics to Grafana, you’re ready to build a monitoring dashboard!

Tip: use this sample to bootstrap a new dashboard with the key Materialize metrics and indicators defined in the sample config.yml.

1. Go to Grafana.

2. Navigate to Dashboards, click New and select the option Import.

3. To use the sample dashboard, copy and paste the contents of the provided sample .json file in the Import via panel json text field, click Load and then Import.

   
   

Considerations

Before adding a custom query, make sure to consider the following:

1. The label set cannot repeat across rows within the results of the same query.
2. Columns must not contain NULL values.
3. Value columns must be of type float.
4. Queries can impact cluster performance.