Monitoring

This page is a work in progress and will have more detail in the coming months. If you have specific questions, feel free to file a GitHub issue.

Materialize supports integration with monitoring tools using both the Prometheus format and via SQL interface that provide more information about internal state.

This page provides an overview of the tools available to diagnose a running materialized instance. You can use the same features that are used by these tools to integrate Materialize with other observability infrastructure.

Quick monitoring dashboard

WARNING!

The monitoring dashboard is provided on a best-effort basis. It relies on Materialize’s unstable Prometheus metrics and occasionally lags behind changes to these metrics.

For best results, use only the latest version of the dashboard with the latest version of Materialize.

Materialize provides a recommended Grafana dashboard and an all-inclusive Docker image preconfigured to run it as materialize/dashboard.

The only configuration required to get started with the Docker image is the MATERIALIZED_URL=<host>:<port> environment variable.

As an example, if you are running Materialize in a cloud instance at the IP address 172.16.0.0, you can get a dashboard by running this command and opening http://localhost:3000 in your web browser:

$ docker run -d -p 3000:3000  -e MATERIALIZED_URL=172.16.0.0:6875 materialize/dashboard
#               expose ports  ______point it at materialize______

To instead run the dashboard on the machine on which you are running Materialize, see the Observing local Materialize section below.

The dashboard Docker image bundles Prometheus and Grafana together to make getting insight into Materialize’s performance easy. It is not particularly configurable, and in particular is not designed to handle large metric volumes or long uptimes. It will start truncating metrics history after about 1GB of storage, which corresponds to about 3 days of data with the very fine-grained metrics collected inside the container.

So, while the dashboard is provided as a convenience and should not be relied on for production monitoring, if you would like to persist metrics across restarts of the container you can mount a Docker volume onto /prometheus:

$ docker run -d \
    -v /tmp/prom-data:/prometheus -u "$(id -u):$(id -g)" \
    -p 3000:3000 -e MATERIALIZED_URL=172.16.0.0:6875 \
    materialize/dashboard

Observing local Materialize

Using the dashboard to observe a Materialize instance running on the same machine as the dashboard is complicated by Docker. The solution depends upon your host platform.

Inside Docker Compose or Kubernetes

Local schedulers like Docker Compose (which we use for our demos) or Kubernetes will typically expose running containers to each other using their service name as a public DNS hostname, but only within the network that they are running in.

The easiest way to use the dashboard inside a scheduler is to tell the scheduler to run it. Check out the example configuration for Docker Compose.

On macOS, with Materialize running outside of Docker

The problem with this is that localhost inside of Docker does not, on Docker for Mac, refer to the macOS network. So instead you must use host.docker.internal:

docker run -p 3000:3000 -e MATERIALIZED_URL=host.docker.internal:6875 materialize/dashboard

On Linux, with Materialize running outside of Docker

Docker containers use a different network than their host by default, but that is easy to override by using the --network host option. Using the host network means that ports will be allocated from the host, so the -p flag is no longer necessary:

docker run --network host -e MATERIALIZED_URL=localhost:6875 materialize/dashboard

Health check

WARNING! The health check is not part of Materialize’s stable interface. Backwards-incompatible changes to the health check may be made at any time.

Materialize supports a basic HTTP health check at http://<materialized_hostname>:6875/status.

The health check returns HTTP status code 200 as long as Materialize has enough resources to respond to HTTP requests. It does not otherwise assess the state of the system.

Use this endpoint to integrate Materialize with monitoring and orchestration tools that support HTTP health checks, like Kubernetes liveness probes or AWS load balancer health checks.

To perform health checks that assess other metrics, consider using the Prometheus metrics endpoint.

Memory usage visualization

WARNING! The memory usage visualization is not part of Materialize’s stable interface. Backwards-incompatible changes to the visualization may be made at any time.

Materialize exposes an interactive, web-based memory usage visualization at http://<materialized host>:6875/memory to aid in diagnosing unexpected memory consumption. The visualization can display a diagram of the operators in each running dataflow overlaid with the number of rows stored by each operator.

Prometheus

WARNING! The Prometheus metrics are not part of Materialize’s stable interface. Backwards-incompatible changes to the exposed metrics may be made at any time.

Materialize exposes Prometheus metrics at the default path, <materialized host>/metrics.

Materialize broadly publishes the following types of data there:

Materialize-specific data with a mz_* prefix. For example, rate(mz_responses_sent_total[10s]) will show you the number of responses averaged over 10 second windows.
Standard process metrics with a process_* prefix. For exmple, process_cpu.

System catalog SQL interface

The mz_catalog SQL interface provides a variety of ways to introspect Materialize. An introduction to this catalog is available as part of our SQL documentation.

A user guide for debugging a running materialized using the system catalog is available in the form of a walkthrough of useful diagnostic queries.

Grafana

Materialize provides a recommended dashboard that you can import into Grafana. It relies on you having configured Prometheus to scrape Materialize.

Datadog

Materialize metrics can be sent to Datadog via the OpenMetrics agent check, which is bundled with recent versions of the Datadog agent.

Simply add the following configuration parameters to openmetrics.d/conf.yaml:

Configuration parameter	Value
`prometheus_url`	`http://<materialized host>/metrics`
`namespace`	Your choice
`metrics`	`[mz*]` to select all metrics, or a list of specific metrics

Logging

Materialize periodically emits messages to its log file. These log messages serve several purposes:

To alert operators to critical issues
To record system status changes
To provide developers with visibility into the system’s execution when troubleshooting issues

We recommend that you monitor for messages at the WARN or ERROR levels. Every message at either of these levels indicates an issue that must be investigated and resolved.

Message format

Each log message is a single line with the following format:

<timestamp> <level> <module>: [<tag>]... <body>

For example, Materialize emits the following log message when a table named t is created:

2021-04-08T04:12:25.927738Z  INFO coord::catalog: create table materialize.public.t (u1)

The timestamp is always in UTC and formatted according to ISO 8601.

The log level is one of the five levels described in the next section, formatted with all uppercase letters.

The module path reflects the log message’s location in Materialize’s source code. Module paths change frequently from release to release and are not part of Materialize’s stable interface.

The tags, if included, further categorize the message. Tags are surrounded by square brackets. The currently used tags are:

[customer-data]: the message includes clear-text contents of data in the system
[deprecation]: a feature in use will be removed or changed in a future release

The body is unstructured text intended for human consumption. It may include embedded newlines.

Levels

Every log message is associated with a level that indicates the severity of the message. The levels are, in decreasing order of severity, ERROR, WARN, INFO, DEBUG, and TRACE.

Log levels are used in the --log-filter command-line option to determine which log messages to emit.

Messages at each level must meet the indicated standard:

ERROR: Reports an error that has caused data corruption, data loss, or unavailability. You should page on-call staff immediately about these errors.

Examples:
- Authentication with an external system (e.g., Amazon S3) has failed.
- A source ingested the deletion of a record that does not exist, causing a “negative multiplicity.”
WARN: Reports an issue that may lead to data corruption, data loss, or unavailability. It is reasonable to check for WARN-level messages once per day during normal business hours.

Examples:
- A network request (e.g., downloading an object from Amazon S3) has failed several times, but a retry is in progress.
INFO: Reports normal system status changes. Messages at this level may be of interest to operators, but do not typically require attention.

Examples:
- A view was created.
- A view was dropped.
DEBUG: Provides information that may help when troubleshooting issues. Messages at this level are primarily of interest to Materialize engineers.

Examples:
- An HTTP request was routed through a proxy specified by the http_proxy environment variable.
- An S3 object downloaded by an S3 source had an invalid Content-Encoding header that was ignored, but the object was nonetheless decoded successfully.
TRACE: Like DEBUG, but the information meets a lower standard of relevance or importance.

Enabling TRACE logs can generate multiple gigabytes of log messages per hour. We recommend that you only enable this level in development or at the direction of a Materialize engineer.

Examples:
- A Kafka source consumed a message.
- A SQL client issued a command.

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