Metrics Datadog Reporter

metrics-datadog is a simple reporting bridge between Dropwizard Metrics and the Datadog service. It includes support for:

• Datadog's tagging feature
• Metric reporting via either UDP (dogstatsd) or the Datadog HTTP API
• Tight integration with the Dropwizard framework via the dropwizard-metrics-datadog sub-project.

UDP vs HTTP

Datadog supports two main metric ingestion methods:

• POSTing metrics via their HTTP API
• Sending metrics via UDP (using a statsd-like protocol) to the local dogstatsd agent

Datadog recommends the dogstatsd UDP-based approach, but some may prefer the HTTP-based approach for various reasons e.g. a general adversity to running agents, the additional memory required by the agent and forwarder (though this is configurable), stability, security or other environment/platform-level conflicts.

Note that, in the event of a delivery failure, the HTTP-based transport does not buffer metrics in memory. It will attempt a handful of retries and then give up. Hence, when faced with an extended network partition window or a Datadog ingestion outage, some metrics will certainly be lost using this transport. That said, note that the UDP-based reporter also cannot buffer metrics forever due to memory constraints.

Usage

import org.coursera.metrics.datadog.DatadogReporter
import org.coursera.metrics.datadog.DatadogReporter.Expansion._
import org.coursera.metrics.datadog.transport.Transport
import org.coursera.metrics.datadog.transport.HttpTransport
import org.coursera.metrics.datadog.transport.UdpTransport
import scala.concurrent.duration.SECONDS

...
val expansions = EnumSet.of(COUNT, RATE_1_MINUTE, RATE_15_MINUTE, MEDIAN, P95, P99)
val httpTransport = new HttpTransport.Builder().withApiKey(apiKey).build()
val reporter = DatadogReporter.forRegistry(registry)
  .withEC2Host()
  .withTransport(httpTransport)
  .withExpansions(expansions)
  .build()

reporter.start(10, SECONDS)

Example of using UDP transport:

...
val udpTransport = new UdpTransport.Builder().build()
val reporter = 
    ...
    .withTransport(udpTransport)
    ...

Tag encoding and expansion

Datadog supports powerful tagging functionality while the Metrics API does not. Thus, metrics-datadog utilizes a special, overloaded metric naming syntax that enables tags to piggyback on metric names while passing through the Metrics library. The tags are unpacked by metrics-datadog at reporting time and are sent along to Datadog via the configured transport layer. Here's the metric name syntax:

metricName[tagName:tagValue,tagName:tagValue,...]

metrics-datadog is mainly a reporting library and doesn't currently implement a tag-aware decorator on top of the core Metrics API. It does, however, expose a TaggedName class that helps you encode/decode tags in metric names using the syntax above. You can utilize this helper class methods when registering and recording metrics. Note that in order for tag propagation to work, you'll need to use our DefaultMetricNameFormatter (or a formatter with compatible parsing logic).

We also support the notion of static, "additional tags". This feature allows you to define a set of tags that are appended to all metrics sent through the reporter. It's useful for setting static tags such as the environment, service name or version. Additional tags are configured via the DatadogReporter constructor.

Finally, we support the notion of "dynamic tags". By implementing and registering a DynamicTagsCallback with DatadogReporter, you can control the values of "additional tags" at runtime. Dynamic tags are merged with and override any additional tags set.

Performance note: Heavy use of tagging, especially tags values with high cardinality, can dramatically increase memory usage, as all tag permutations are tracked and counted in-memory by the Metrics library. Also note that some MetricRegistry APIs do defensive copies on the entire metrics set, which can be prohibitively expensive CPU and memory-wise if you have a huge, heavily tagged metric set.

Dropwizard Metrics Reporter

If you have a dropwizard project and have at least dropwizard-core 0.7.X, then you can perform the following steps to automatically report metrics to datadog.

First, add the dropwizard-metrics-datadog dependency in your POM:

    <dependency>
        <groupId>org.coursera</groupId>
        <artifactId>dropwizard-metrics-datadog</artifactId>
        <version>1.1.13</version>
    </dependency>

Then just add the following to your dropwizard YAML config file.

metrics:
  frequency: 1 minute                       # Default is 1 second.
  reporters:
    - type: datadog
      host: <host>                          # Optional with UDP Transport
      tags:                                 # Optional. Defaults to (empty)
      includes:                             # Optional. Defaults to (all).
      excludes:                             # Optional. Defaults to (none).
      prefix:                               # Optional. Defaults to (none).
      expansions:                           # Optional. Defaults to (all).
      metricNameFormatter:                  # Optional. Default is "default".
      dynamicTagsCallback:                  # Optional. Defaults to (none).
      transport:
        type: http
        apiKey: <apiKey>
        connectTimeout: <duration>          # Optional. Default is 5 seconds
        socketTimeout: <duration>           # Optional. Default is 5 seconds

Once your dropwizard application starts, your metrics should start appearing in Datadog.

Transport options

HTTP Transport:

metrics:
  frequency: 1 minute                       # Default is 1 second.
  reporters:
    - type: datadog
      host: <host>
      transport:
        type: http
        apiKey: <apiKey>
        connectTimeout: <duration>          # Optional. Default is 5 seconds
        socketTimeout: <duration>           # Optional. Default is 5 seconds

UDP Transport:

metrics:
  frequency: 1 minute                       # Default is 1 second.
  reporters:
    - type: datadog
      transport:
        type: udp
        prefix:                             # Optional. Default is (empty)
        statsdHost: "localhost"             # Optional. Default is "localhost"
        port: 8125                          # Optional. Default is 8125

Filtering

If you want to filter only a few metrics, you can use the includes or excludes key to create a set of metrics to include or exclude respectively.

metrics:
  frequency: 1 minute                       # Default is 1 second.
  reporters:
    - type: datadog
      host: <host>
      includes:
        - jvm.
        - ch.

The check is very simplistic so be as specific as possible. For example, if you have "jvm.", the filter will check if the includes has that value in any part of the metric name (not just the beginning).

Expansions

If you want to limit the set of expansions applied to each metric, you can specify a custom set.

The full set of expansions can be found in the Expansion enum.

metrics:
  reporters:
    - type: datadog
      expansions:
        - COUNT
        - RATE_1_MINUTE
        - MAX
        - P95

Prefix

By default, the metric names are sent as-is (e.g. io.dropwizard.jetty.MutableServletContextHandler.2xx-responses) The prefix option adds a custom prefix to each metric name:

metrics:
  reporters:
    - type: datadog
      prefix: custom.prefix

would produce: custom.prefix.io.dropwizard.jetty.MutableServletContextHandler.2xx-responses

Metric Name Formatter

The metricNameFormatter option can be used to add custom logic when processing each metric's name. By default it will use the DefaultMetricNameFormatter which handles Datadog tags but does not modify the metric name.

metrics:
  reporters:
    - type: datadog
      metricNameFormatter:
        type: custom

Adding a custom formatter requires a few things:

1. Create a MetricNameFormatter

public class CustomMetricNameFormatter extends DefaultMetricNameFormatter {
  @Override
  public String format(String name, String... path) {
    // Make response metrics names less verbose
    String newName = name.replace("io.dropwizard.jetty.MutableServletContextHandler", "");
    
    // Call DefaultMetricNameFormatter to handle tags
    return super.format(newName, path);
  }
}

2. Create a MetricNameFormatterFactory with @JsonTypeName annotation

@JsonTypeName("custom") // This must match the name specified in the configuration
public class CustomMetricNameFormatterFactory implements MetricNameFormatterFactory {
  @Override
  public MetricNameFormatter build() {
    return new CustomMetricNameFormatter();
  }
}

3. Add the Factory to org.coursera.metrics.datadog.MetricNameFormatterFactory file

We need to make sure our CustomMetricNameFormatterFactory is added to the list of subTypes for MetricNameFormatterFactory, otherwise the "custom" in our config won't be recognized.

Add a file called org.coursera.metrics.datadog.MetricNameFormatterFactory to src/main/resources/META-INF/services and add the full path to your class to the file (e.g. com.company.CustomMetricNameFormatterFactory)

See: http://www.dropwizard.io/1.0.0/docs/manual/configuration.html#polymorphic-configuration for details

Dynamic Tags Callback

Similar to the MetricNameFormatter steps, we need to:

1. Create a DynamicTagsCallback
2. Create a DynamicTagsCallbackFactory with @JsonTypeName annotation
3. Add the Factory to org.coursera.metrics.datadog.DynamicTagsCallbackFactory file

See above instructions for details.

Maven Info

Metrics datadog reporter is available as an artifact on Maven Central

• Group: org.coursera
• Artifact: metrics-datadog
• Version: 1.1.13

Dropwizard datadog reporter is available as an artifact on Maven Central

• Group: org.coursera
• Artifact: dropwizard-metrics-datadog
• Version: 1.1.13

Contributing

We follow Google's Java Code Style