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Python message queues

The Python agent supports the Pika RabbitMQ client library, Kafka Python client library, and the Confluent Kafka client library giving you visibility into the performance of your message processing, for both incoming and outgoing messages.

The New Relic UI will show transactions initiated via message receipt (subscribe/consume messages) as Message background tasks. Message creation also appears in transaction traces.

Requirements

Message Queue

Python agent version required

Pika RabbitMQ

2.88.0.72

Kafka Python

8.2.0.181

Confluent Kafka

8.2.0.181

For more information, see the release notes.

Caution

RabbitMQ message tasks are not started for consumers using TornadoConnection.

Caution

When consuming messages via Kafka, transactions are reported for each message as it is consumed by the consumer. Transactions will not be reported for bulk consumption operations where multiple messages are consumed at once.

Performance improvements with background tasks

One way to increase responsiveness of web applications is to delegate work to background processes. Message queues are commonly used for this inter-process communication.

In the context of message queuing systems, applications typically interact with message brokers to send and receive messages. In the case of RabbitMQ Pika, this client library allows Python applications to interface with message brokers that implement the Advanced Message Queueing Protocol (AMQP) 0.9 or higher.

The Python agent shows messages sent and received using the client libraries. With this visibility, you can see details including:

  • Number of messages produced by your app
  • Time your app spends publishing/producing messages
  • Time your app spends processing/consuming messages

APM conveniently groups and reports operations that interact with queues. By analyzing this information, you can more easily identify bottlenecks and areas for performance improvement in your message passing architecture.

Supported Metrics

Message Queue

Pika RabbitMQ

Kafka Python

Confluent Kafka

Publish a Message

Yes--Referred to as Put

Yes--Referred to as Produce

Yes--Referred to as Produce

Receive a Message

Yes--Referred to as Take

Yes--Referred to as Consume

Yes--Referred to as Consume

Serialization

No

Yes

Yes

Deserialization

No

No

Yes

Heartbeat

No

Yes

No

View in New Relic UI

Queue operations appear on APM's Transactions page for the selected app.

The Put (publish a message) and Take (receive a message) metrics appear in the Breakdown table and are categorized as MessageBroker metrics for RabbitMQ. Here is an example:

one.newrelic.com > All capabilities > APM & services > (select an app) > Monitor > Transactions > (select a transaction): The transaction’s Breakdown table categorizes queue operations as MessageBroker metrics for RabbitMQ and labels them as Put (publish a message) or Take (receive a message).

Transaction traces also provide additional details for messages.

Tip

You can select transaction traces from the app's Summary or Transactions pages in APM.

The Transaction trace summary page may show Put and Take operations for RabbitMQ in the Slowest components section. For example:

one.newrelic.com > All capabilities > APM & services > (select an app) > (select a transaction trace): In this example, the selected transaction trace's Summary shows RabbitMQ operations in the colored bar chart. The Slowest components section also lists the RabbitMQ Put and Take results.

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