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

Before you begin

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

NOTE: In the future, we plan to support a native Datadog integration that continually reports metrics via the Datadog API (#17601).

Step 1. Set up a Prometheus SQL Exporter

To export metrics from Materialize and expose them in a format that Datadog 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: use this sample config.yml 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, use

    Filename: config.yml

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

     # 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.
         - "replica_name"
         - "cluster_id"
         # Array of columns used as metric values. All values should be
         # of type float.
         - "memory_percent"
         # The SQL query that is run unalterted for each job.
         query:  |
                    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;             
  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 Datadog Agent

To scrape the metrics available in the Prometheus SQL Exporter endpoint, you must then set up a Datadog Agent check configured to scrape the OpenMetrics format.

  1. Follow the instructions to install and run a Datadog Agent in your host.

  2. To configure an OpenMetrics check for the Datadog Agent installed in the previous step, edit the openmetrics.d/conf.yaml file at the root of the installation directory.

    Filename: openmetrics.d/conf.yaml

        timeout: 50
      - openmetrics_endpoint: <SQL_EXPORTER_HOST>/metrics/
        # The namespace to prepend to all metrics.
        namespace: "materialize"
        metrics: [.*]

Tip: see this sample for all available configuration options.

For more details on how to configure, run and troubleshoot Datadog Agents, see the Datadog documentation.

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 the Datadog Agent routing these metrics to your Datadog account, 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. Log in to your Datadog account.

  2. Navigate to Dashboards, and select New Dashboard.

  3. To use the sample dashboard, navigate to ⚙️ in the upper right corner, and select Import dashboard JSON. Copy and paste the contents of the provided sample .json file.

    Template Datadog monitoring dashboard


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. The Datadog agent is subject to a limit of 2000 metrics.
  5. Queries can impact cluster performance.
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