Data Streams Monitoring (DSM) propagates context through message headers. Use manual instrumentation to set up DSM if you are using:

• a message queue technology that is not supported by DSM
• a message queue technology without headers, such as Kinesis, or
• Lambdas

Manual instrumentation installation

1. Ensure you’re using the Datadog Agent v7.34.0 or later.

2. On services sending or consuming messages, declare the supported types. For example:

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   kinesis, kafka, rabbitmq, sqs, sns, servicebus

3. Call the Data Streams Monitoring checkpoints when messages are produced and when they are consumed, as shown in the example code below:

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   import datadog.trace.api.experimental.*;
   
   ​Carrier headersAdapter = new Carrier(headers);
   ​
   // before calling produce
   DataStreamsCheckpointer.get().setProduceCheckpoint("<datastream-type>", "<queue-or-topic-name>", headersAdapter);
   ​
   // after calling consume
   DataStreamsCheckpointer.get().setConsumeCheckpoint("<datastream-type>", "<queue-or-topic-name>", headersAdapter);
   ​
   // example: logging a kafka consume checkpoint on the 'customer-checkout' topic would look like
   DataStreamsCheckpointer.get().setConsumeCheckpoint("kafka", "customer-checkout", headersAdapter);
   ​
   // replace headers with whatever you're using to pass the context
   private class Carrier implements DataStreamsContextCarrier {
   	private Headers headers;
   	
   	public Carrier(Headers headers) {
   		this.headers = headers;
   	}
   ​
   	public Set<Entry<String, Object>> entries() {
   		return this.headers.entrySet();
   	}
   ​
   	public void set(String key, String value){
   		this.headers.put(key, value);
   	}
   }

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   const tracer = require('dd-trace').init({})
   
   // before calling produce
   const headers = {}
   tracer.dataStreamsCheckpointer.setProduceCheckpoint(
   "<datastream-type>", "<queue-name>", headers
   )
   
   // after calling consume
   tracer.dataStreamsCheckpointer.setConsumeCheckpoint(
   "<datastream-type>", "<queue-name>", headers
   )

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   from ddtrace.data_streams import set_consume_checkpoint
   from ddtrace.data_streams import set_produce_checkpoint
   
   # before calling produce
   headers = {}
   set_produce_checkpoint(
   "<datastream-type>", "<datastream-name>", headers.setdefault
   )
   
   # after calling consume
   set_consume_checkpoint(
   "<datastream-type>", "<datastream-name>", headers.get
   )

Further Reading

Documentation, liens et articles supplémentaires utiles:

Kafka IntegrationDOCUMENTATION Software CatalogDOCUMENTATION