Prerequisites

• Datadog Agent v7.34.0 or later

Supported libraries

Installation

To enable Data Streams Monitoring, set the following environment variables to true on services that are sending or consuming messages:

• DD_DATA_STREAMS_ENABLED
• DD_TRACE_REMOVE_INTEGRATION_SERVICE_NAMES_ENABLED

environment:
  - DD_DATA_STREAMS_ENABLED: "true"
  - DD_TRACE_REMOVE_INTEGRATION_SERVICE_NAMES_ENABLED: "true"

java -javaagent:/path/to/dd-java-agent.jar -Ddd.data.streams.enabled=true -Ddd.trace.remove.integration-service-names.enabled=true -jar path/to/your/app.jar

One-Click Installation

To set up Data Streams Monitoring from the Datadog UI without needing to restart your service, use Configuration at Runtime. Navigate to the APM Service Page and Enable DSM.

Setup

Use Datadog’s Java tracer, dd-trace-java, to collect information from your Kafka Connect workers.

1. Add the dd-java-agent.jar file to your Kafka Connect workers. Ensure that you are using dd-trace-java v1.44+.
2. Modify your Java options to include the Datadog Java tracer on your worker nodes. For example, on Strimzi, modify STRIMZI_JAVA_OPTS to add -javaagent:/path/to/dd-java-agent.jar.

Monitoring SQS pipelines

Data Streams Monitoring uses one message attribute to track a message’s path through an SQS queue. As Amazon SQS has a maximum limit of 10 message attributes allowed per message, all messages streamed through the data pipelines must have 9 or fewer message attributes set, allowing the remaining attribute for Data Streams Monitoring.

Monitoring RabbitMQ pipelines

The RabbitMQ integration can provide detailed monitoring and metrics of your RabbitMQ deployments. For full compatibility with Data Streams Monitoring, Datadog recommends configuring the integration as follows:

instances:
  - prometheus_plugin:
      url: http://<HOST>:15692
      unaggregated_endpoint: detailed?family=queue_coarse_metrics&family=queue_consumer_count&family=channel_exchange_metrics&family=channel_queue_exchange_metrics&family=node_coarse_metrics

This ensures that all RabbitMQ graphs populate, and that you see detailed metrics for individual exchanges as well as queues.

Monitoring SNS-to-SQS pipelines

To monitor a data pipeline where Amazon SNS talks directly to Amazon SQS, you must perform the following additional configuration steps:

Monitoring Kinesis pipelines

There are no message attributes in Kinesis to propagate context and track a message’s full path through a Kinesis stream. As a result, Data Streams Monitoring’s end-to-end latency metrics are approximated based on summing latency on segments of a message’s path, from the producing service through a Kinesis Stream, to a consumer service. Throughput metrics are based on segments from the producing service through a Kinesis Stream, to the consumer service. The full topology of data streams can still be visualized through instrumenting services.

Manual instrumentation

Data Streams Monitoring propagates context through message headers. If you are using a message queue technology that is not supported by DSM, a technology without headers (such as Kinesis), or Lambdas, use manual instrumentation to set up DSM.

Monitoring connectors

Confluent Cloud connectors

Data Streams Monitoring can automatically discover your Confluent Cloud connectors and visualize them within the context of your end-to-end streaming data pipeline.

Setup

1. Install and configure the Datadog-Confluent Cloud integration.

2. In Datadog, open the Confluent Cloud integration tile.

   

   Under Actions, a list of resources populates with detected clusters and connectors. Datadog attempts to discover new connectors every time you view this integration tile.

3. Select the resources you want to add.

4. Click Add Resources.

5. Navigate to Data Streams Monitoring to visualize the connectors and track connector status and throughput.

Self-hosted Kafka connectors

Data Streams Monitoring can collect information from your self-hosted Kafka connectors. In Datadog, these connectors are shown as services connected to Kafka topics. Datadog collects throughput to and from all Kafka topics. Datadog does not collect connector status or sinks and sources from self-hosted Kafka connectors.

Further reading

추가 유용한 문서, 링크 및 기사:

Kafka IntegrationDOCUMENTATION Software CatalogDOCUMENTATION Autodiscover Confluent Cloud connectors and easily monitor performance in Data Streams MonitoringBLOG