Forwarding metrics to Datadog

The Apollo Datadog integration enables you to forward your enterprise graph's performance metrics to your Datadog account. Datadog supports an advanced function API, which enables you to create sophisticated visualizations and alerts for GraphQL metrics.

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query {
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  hello
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}
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query HelloQuery {
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  hello
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}

Setup

To integrate Apollo with Datadog, you provide your Datadog API key and region to GraphOS Studio. A Datadog account with administrator privileges is required to obtain an API key.

1. Go to your Datadog Integrations page and select the Apollo Integration from the list:

   

   Then go to the Configuration tab and click Install Integration at the bottom.

2. Go to your Datadog APIs page and select Create API key.

   

3. Determine your Datadog API region by looking at your browser's address bar:

   • If the domain name is app.datadoghq.com, then your API region is US.
   • If the domain name is app.datadoghq.eu, then your API region is EU.

4. In GraphOS Studio, go to your graph's Settings page, then go to This Graph > Reporting:

   

   ⓘ NOTE

   If you don't see a Datadog Forwarding section on this page, the feature isn't available as part of your organization's plan. Datadog forwarding requires an Enterprise plan.

5. In the Datadog Forwarding section, click Configure. Provide your API key and region, then click Enable.

   

   You can use the same Datadog API key for all your graphs because all forwarded metrics are tagged with the corresponding graph's ID (graph:<graph-id>).

That's it! After about five minutes, your Datadog Metrics Explorer will begin showing metrics forwarded from GraphOS Studio.

Forwarded metrics

GraphOS Studio forwards the following metrics to Datadog:

These metrics are aggregated in 60-second intervals and tagged with the GraphQL operation name as operation:<query-name>. Unique query signatures with the same operation name are merged, and queries without an operation name are ignored.

These metrics are also tagged with both the associated Apollo graph ID (as graph:<graph-id>) and the associated variant name (as variant:<variant-name>). If you haven't set a variant name, then current is used.

Exploring metrics

In the Datadog Metrics Explorer, all GraphOS Studio metrics are tagged with the graph ID (graph:<graph-id>), the variant name (variant:<variant-name>), and the operation name (operation:<query-name>). These values are normalized according to Datadog naming requirements (all letters are lowercase and illegal symbols are converted to underscores).

Tagging enables you to see data at any level of granularity, whether you want to aggregate across all operations or zoom in on a particular operation. You can control granularity by choosing a relevant set of operation tags for filtering, along with appropriate functions for time aggregation and space aggregation. Similarly, if you want to compare metrics across staging and production environments, you can filter with the appropriate variant tags.

Datadog Metrics Explorer example

Suppose you want to see the 95th percentile request latency averaged across all operations for a staging and a production graph.

In the Datadog Metrics Explorer:

1. In the Graph field, select apollo.operations.latency.95percentile.
2. In the Over field, select the name of the graph to display.
3. In the One graph per field, select variant. Choose the variants for your production and staging environments.
4. In the On each graph, aggregate with the field, select Average of reported values.

At Apollo, we use GraphOS Studio to monitor GraphOS Studio itself. For us, this graph looks something like the following:

To generate more advanced reports, open up a Datadog notebook.

Alerting with Datadog

You can configure complex alerts with Datadog monitors.

Datadog metric alert example

GraphOS Studio's Notifications feature supports alerts that trigger when the percentage of requests with an error in the last five minutes exceeds some threshold for a specific operation. Suppose that instead of alerting on a specific operation in the last five minutes, you want to alert on the error percentage over all operations in some graph in the last ten minutes, such as when the percentage exceeds 1% for the graph mygraph with variant staging.

The Datadog metric alert query needed here is:

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sum(last_10m):sum:apollo.operations.error_count{graph:mygraph,variant:staging}.as_count().rollup(sum).fill(null) / sum:apollo.operations.count{graph:mygraph,variant:staging}.as_count().rollup(sum).fill(null) > 0.01

The .rollup(sum).fill(null) is necessary because apollo.operations.count is a Datadog gauge, which means it defaults to using avg for time aggregation and defaults to linear interpolation during space aggregation and query arithmetic. The .as_count() is necessary to ensure that operation counts are summed before the division and not after.

Datadog composite monitor example

Consider the error percentage monitor from the previous example. When the number of operations is small, a few errors might cause the error percentage to exceed the threshold, resulting in a noisy monitor during low traffic periods. We want to alert only when the number of operations isn't small, for example, more than ten in the last ten minutes.

You can use Datadog composite monitors to support this kind of alert. First, create a monitor with the following metric alert query:

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sum(last_10m):sum:apollo.operations.count{graph:mygraph,variant:staging}.rollup(sum).fill(null) > 10

Then, create a composite monitor for the two monitors of the form a && b, which will have the desired behavior.