AMQP and M3DB Integration

Input and output integration overview

The AMQP Consumer Input Plugin allows you to ingest data from an AMQP 0-9-1 compliant message broker, such as RabbitMQ, enabling seamless data collection for monitoring and analytics purposes.

This plugin allows Telegraf to stream metrics to M3DB using the Prometheus Remote Write protocol, enabling scalable ingestion through the M3 Coordinator.

Integration details

AMQP

This plugin provides a consumer for use with AMQP 0-9-1, a prominent implementation of which is RabbitMQ. AMQP, or Advanced Message Queuing Protocol, was originally developed to enable reliable, interoperable messaging between diverse systems in a network. The plugin reads metrics from a topic exchange using a configured queue and binding key, delivering a flexible and efficient means of collecting data from AMQP-compliant messaging systems. This enables users to leverage existing RabbitMQ implementations to monitor their applications effectively by capturing detailed metrics for analysis and alerting.

M3DB

This configuration uses Telegraf’s HTTP output plugin with prometheusremotewrite format to send metrics directly to M3DB through the M3 Coordinator. M3DB is a distributed time series database designed for scalable, high-throughput metric storage. It supports ingestion of Prometheus remote write data via its Coordinator component, which manages translation and routing into the M3DB cluster. This approach enables organizations to collect metrics from systems that aren’t natively instrumented for Prometheus (e.g., Windows, SNMP, legacy systems) and ingest them efficiently into M3’s long-term, high-performance storage engine. The setup is ideal for high-scale observability stacks with Prometheus compatibility requirements.

Configuration

AMQP

[[inputs.amqp_consumer]]
  ## Brokers to consume from.  If multiple brokers are specified a random broker
  ## will be selected anytime a connection is established.  This can be
  ## helpful for load balancing when not using a dedicated load balancer.
  brokers = ["amqp://localhost:5672/influxdb"]

  ## Authentication credentials for the PLAIN auth_method.
  # username = ""
  # password = ""

  ## Name of the exchange to declare.  If unset, no exchange will be declared.
  exchange = "telegraf"

  ## Exchange type; common types are "direct", "fanout", "topic", "header", "x-consistent-hash".
  # exchange_type = "topic"

  ## If true, exchange will be passively declared.
  # exchange_passive = false

  ## Exchange durability can be either "transient" or "durable".
  # exchange_durability = "durable"

  ## Additional exchange arguments.
  # exchange_arguments = { }
  # exchange_arguments = {"hash_property" = "timestamp"}

  ## AMQP queue name.
  queue = "telegraf"

  ## AMQP queue durability can be "transient" or "durable".
  queue_durability = "durable"

  ## If true, queue will be passively declared.
  # queue_passive = false

  ## Additional arguments when consuming from Queue
  # queue_consume_arguments = { }
  # queue_consume_arguments = {"x-stream-offset" = "first"}

  ## A binding between the exchange and queue using this binding key is
  ## created.  If unset, no binding is created.
  binding_key = "#"

  ## Maximum number of messages server should give to the worker.
  # prefetch_count = 50

  ## Max undelivered messages
  ## This plugin uses tracking metrics, which ensure messages are read to
  ## outputs before acknowledging them to the original broker to ensure data
  ## is not lost. This option sets the maximum messages to read from the
  ## broker that have not been written by an output.
  ##
  ## This value needs to be picked with awareness of the agent's
  ## metric_batch_size value as well. Setting max undelivered messages too high
  ## can result in a constant stream of data batches to the output. While
  ## setting it too low may never flush the broker's messages.
  # max_undelivered_messages = 1000

  ## Timeout for establishing the connection to a broker
  # timeout = "30s"

  ## Auth method. PLAIN and EXTERNAL are supported
  ## Using EXTERNAL requires enabling the rabbitmq_auth_mechanism_ssl plugin as
  ## described here: https://www.rabbitmq.com/plugins.html
  # auth_method = "PLAIN"

  ## Optional TLS Config
  # tls_ca = "/etc/telegraf/ca.pem"
  # tls_cert = "/etc/telegraf/cert.pem"
  # tls_key = "/etc/telegraf/key.pem"
  ## Use TLS but skip chain & host verification
  # insecure_skip_verify = false

  ## Content encoding for message payloads, can be set to
  ## "gzip", "identity" or "auto"
  ## - Use "gzip" to decode gzip
  ## - Use "identity" to apply no encoding
  ## - Use "auto" determine the encoding using the ContentEncoding header
  # content_encoding = "identity"

  ## Maximum size of decoded message.
  ## Acceptable units are B, KiB, KB, MiB, MB...
  ## Without quotes and units, interpreted as size in bytes.
  # max_decompression_size = "500MB"

  ## Data format to consume.
  ## Each data format has its own unique set of configuration options, read
  ## more about them here:
  ## https://github.com/influxdata/telegraf/blob/master/docs/DATA_FORMATS_INPUT.md
  data_format = "influx"

M3DB

# Configuration for sending metrics to M3
[outputs.http]
  ## URL is the address to send metrics to
  url = "https://M3_HOST:M3_PORT/api/v1/prom/remote/write"

  ## HTTP Basic Auth credentials
  username = "admin"
  password = "password"

  ## Data format to output.
  data_format = "prometheusremotewrite"

  ## Outgoing HTTP headers
  [outputs.http.headers]
    Content-Type = "application/x-protobuf"
    Content-Encoding = "snappy"
    X-Prometheus-Remote-Write-Version = "0.1.0"

Input and output integration examples

AMQP

1. Integrating Application Metrics with AMQP: Use the AMQP Consumer plugin to gather application metrics that are published to a RabbitMQ exchange. By configuring the plugin to listen to specific queues, teams can gain insights into application performance, track request rates, error counts, and latency metrics, all in real-time. This setup not only aids in anomaly detection but also provides valuable data for capacity planning and system optimization.

2. Event-Driven Monitoring: Configure the AMQP Consumer to trigger specific monitoring events whenever certain conditions are met within an application. For instance, if a message indicating a high error rate is received, the plugin can feed this data into monitoring tools, generating alerts or scaling events. This integration can improve responsiveness to issues and automate parts of the operations workflow.

3. Cross-Platform Data Aggregation: Leverage the AMQP Consumer plugin to consolidate metrics from various applications distributed across different platforms. By utilizing RabbitMQ as a centralized message broker, organizations can unify their monitoring data, allowing for comprehensive analysis and dashboarding through Telegraf, thus maintaining visibility across heterogeneous environments.

4. Real-Time Log Processing: Extend the use of the AMQP Consumer to capture log data sent to a RabbitMQ exchange, processing logs in real time for monitoring and alerting purposes. This application ensures that operational issues are detected and addressed swiftly by analyzing log patterns, trends, and anomalies as they occur.

M3DB

1. Large-Scale Cloud Infrastructure Monitoring: Deploy Telegraf agents across thousands of virtual machines and containers to collect metrics and stream them into M3DB through the M3 Coordinator. This provides reliable, long-term visibility with minimal storage overhead and high availability.

2. Legacy System Metrics Ingestion: Use Telegraf to gather metrics from older systems that lack native Prometheus exporters (e.g., Windows servers, SNMP devices) and forward them to M3DB via remote write. This bridges modern observability workflows with legacy infrastructure.

3. Centralized App Telemetry Aggregation: Collect application-specific telemetry using Telegraf’s plugin ecosystem (e.g., exec, http, jolokia) and push it into M3DB for centralized storage and query via PromQL. This enables unified analytics across diverse data sources.

4. Hybrid Cloud Observability: Install Telegraf agents on-prem and in the cloud to collect and remote-write metrics into a centralized M3DB cluster. This ensures consistent visibility across environments while avoiding the complexity of running Prometheus federation layers.

Feedback

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