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:
- A Prometheus SQL Exporter.
- A Datadog Agent configured with an OpenMetrics check.
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
which has been tried and tested in production environments.
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.ymlas guidance to bootstrap your monitoring with some key Materialize metrics and indicators.
In the configuration file, define the connection to your Materialize region under
connectionsusing the credentials provided in the Materialize console.NOTE: You must escape the special
USERfor a successful connection. Example: instead of
--- 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_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' connections: - "postgres://<USER>:<PASSWORD>@<HOST>:6875/materialize?application_name=mz_datadog_integration&sslmode=require" ...
Then, configure the
queriesthat the Prometheus SQL Exporter should run at the specified
intervalto export 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;
Once you are done with the Prometheus SQL Exporter configuration, follow the intructions in the
sql_exporterrepository 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.
Follow the instructions to install and run a Datadog Agent in your host.
To configure an OpenMetrics check for the Datadog Agent installed in the previous step, edit the
openmetrics.d/conf.yamlfile at the root of the installation directory.
init_config: timeout: 50 instances: - 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
Log in to your Datadog account.
Navigate to Dashboards, and select New Dashboard.
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