AMQP and SigNoz 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 configuration turns any Telegraf agent into a Remote Write publisher for SigNoz, streaming rich metrics straight into the SigNoz backend with a single URL change.

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.

SigNoz

SigNoz is an open source observability platform that stores metrics, traces, and logs. When you deploy SigNoz, its signoz-otel-collector-metrics service exposes a Prometheus Remote Write receiver (default :13133/api/v1/write). By configuring Telegraf’s Prometheus plugin to point at this endpoint, you can push any Telegraf collected metrics, SNMP counters, cloud services, or business KPIs—directly into SigNoz. The plugin natively serializes metrics in the Remote Write protobuf format, supports external labels, metadata export, retries, and TLS or bearer-token auth, so it fits zero-trust and multi-tenant SigNoz clusters. Inside SigNoz, the data lands in ClickHouse tables that back Metrics Explorer, alert rules, and unified dashboards. This approach lets organizations unify Prometheus and OTLP pipelines, enables long-term retention powered by ClickHouse compression, and avoids vendor lock-in while retaining PromQL-style queries.

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"

SigNoz

[[outputs.prometheusremotewrite]]
  ## SigNoz OTEL-Collector metrics endpoint (Prometheus Remote Write receiver)
  ## Default port is 13133 when you install SigNoz with the Helm chart
  url = "http://signoz-otel-collector-metrics.monitoring.svc.cluster.local:13133/api/v1/write"

  ## Add identifying labels so you can slice & dice the data later
  external_labels = { host = "${HOSTNAME}", agent = "telegraf" }

  ## Forward host metadata for richer dashboards (SigNoz maps these to ClickHouse columns)
  send_metadata = true

  ## ----- Authentication (comment out what you don’t need) -----
  # bearer_token   = "$SIGNOZ_TOKEN"          # SaaS tenant token
  # basic_username = "signoz"                 # Basic auth (self-hosted)
  # basic_password = "secret"

  ## ----- TLS options (for SaaS or HTTPS self-hosted) -----
  # tls_ca                  = "/etc/ssl/certs/ca.crt"
  # tls_cert                = "/etc/telegraf/certs/telegraf.crt"
  # tls_key                 = "/etc/telegraf/certs/telegraf.key"
  # insecure_skip_verify    = false

  ## ----- Performance tuning -----
  max_batch_size = 10000      # samples per POST
  timeout        = "10s"
  retry_max      = 3

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.

SigNoz

1. Multi-Cluster Federated Monitoring: Drop a Telegraf DaemonSet into each Kubernetes cluster, tag metrics with cluster=<name>, and Remote Write them to a central SigNoz instance. Ops teams get a single PromQL window across prod, staging, and edge clusters without running Thanos sidecars.

2. Factory-Floor Edge Gateway: A rugged Intel NUC on the shop floor runs Telegraf to scrape Modbus PLCs and environmental sensors. It batches readings every 5 seconds and pushes them over an intermittent 4G link to SigNoz SaaS. ClickHouse compression keeps costs low while AI-based outlier detection in SigNoz flags overheating motors before failure.

3. SaaS Usage Metering: Telegraf runs alongside each micro-service, exporting per-tenant counters (api_calls, gigabytes_processed). Remote Write streams the data to SigNoz where a scheduled ClickHouse materialized view aggregates usage for monthly billing—no separate metering stack required.

4. Autoscaling Feedback Loop: Combine Telegraf’s Kubernetes input with the Remote Write output to publish granular pod CPU and queue-length metrics into SigNoz. A custom SigNoz alert fires when P95 latency breaches 200 ms and a GitOps controller reads that alert to trigger a HorizontalPodAutoscaler tweak—closing the loop between observability and automation.

Feedback

Thank you for being part of our community! If you have any general feedback or found any bugs on these pages, we welcome and encourage your input. Please submit your feedback in the InfluxDB community Slack.