RabbitMQ and OpenObserve Integration

Input and output integration overview

This plugin reads metrics from RabbitMQ servers, providing essential insights into the performance and state of the messaging system.

This configuration pairs Telegraf’s HTTP output with OpenObserve’s native JSON ingestion API, turning any Telegraf agent into a first-class OpenObserve collector.

Integration details

RabbitMQ

The RabbitMQ plugin for Telegraf allows users to gather metrics from RabbitMQ servers via the RabbitMQ Management Plugin. This capability is crucial for monitoring the performance and health of RabbitMQ instances, which are widely utilized for message queuing and processing in various applications. The plugin provides comprehensive insights into key RabbitMQ metrics, including message rates, queue depths, and node health statistics, thereby enabling operators to maintain optimal performance and robustness of their messaging infrastructure. Additionally, it supports secret-stores for managing sensitive credentials securely, making integration with existing systems smoother. Configuration options allow for flexibility in specifying the nodes, queues, and exchanges to monitor, providing valuable adaptability for diverse deployment scenarios.

OpenObserve

OpenObserve is an open source observability platform written in Rust that stores data cost-effectively on object storage or local disk. It exposes REST endpoints such as /api/{org}/ingest/metrics/_json that accept batched metric documents conforming to a concise JSON schema, making it an attractive drop-in replacement for Loki or Elasticsearch stacks. The Telegraf HTTP output plugin streams metrics to arbitrary HTTP targets; when the "data_format = "json"" serializer is selected, Telegraf batches its metric objects into a payload that matches OpenObserve’s ingestion contract. The plugin supports configurable batch size, custom headers, TLS, and compression, allowing operators to authenticate with Basic or Bearer tokens and to enforce back-pressure without additional collectors. By reusing existing Telegraf agents already collecting system, application, or SNMP data, organizations can funnel rich telemetry into OpenObserve dashboards and SQL-like analytics with minimal overhead, enabling unified observability, long-term retention, and real-time alerting without vendor lock-in.

Configuration

RabbitMQ

[[inputs.rabbitmq]]
  ## Management Plugin url. (default: http://localhost:15672)
  # url = "http://localhost:15672"
  ## Tag added to rabbitmq_overview series; deprecated: use tags
  # name = "rmq-server-1"
  ## Credentials
  # username = "guest"
  # password = "guest"

  ## 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

  ## Optional request timeouts
  ## ResponseHeaderTimeout, if non-zero, specifies the amount of time to wait
  ## for a server's response headers after fully writing the request.
  # header_timeout = "3s"
  ##
  ## client_timeout specifies a time limit for requests made by this client.
  ## Includes connection time, any redirects, and reading the response body.
  # client_timeout = "4s"

  ## A list of nodes to gather as the rabbitmq_node measurement. If not
  ## specified, metrics for all nodes are gathered.
  # nodes = ["rabbit@node1", "rabbit@node2"]

  ## A list of queues to gather as the rabbitmq_queue measurement. If not
  ## specified, metrics for all queues are gathered.
  ## Deprecated in 1.6: Use queue_name_include instead.
  # queues = ["telegraf"]

  ## A list of exchanges to gather as the rabbitmq_exchange measurement. If not
  ## specified, metrics for all exchanges are gathered.
  # exchanges = ["telegraf"]

  ## Metrics to include and exclude. Globs accepted.
  ## Note that an empty array for both will include all metrics
  ## Currently the following metrics are supported: "exchange", "federation", "node", "overview", "queue"
  # metric_include = []
  # metric_exclude = []

  ## Queues to include and exclude. Globs accepted.
  ## Note that an empty array for both will include all queues
  # queue_name_include = []
  # queue_name_exclude = []

  ## Federation upstreams to include and exclude specified as an array of glob
  ## pattern strings.  Federation links can also be limited by the queue and
  ## exchange filters.
  # federation_upstream_include = []
  # federation_upstream_exclude = []

OpenObserve

[[outputs.http]]
  ## OpenObserve JSON metrics ingestion endpoint
  url = "https://api.openobserve.ai/api/default/ingest/metrics/_json"

  ## Use POST to push batches
  method = "POST"

  ## Basic auth header (base64 encoded "username:password")
  headers = { Authorization = "Basic dXNlcjpwYXNzd29yZA==" }

  ## Timeout for HTTP requests
  timeout = "10s"

  ## Override Content-Type to match OpenObserve expectation
  content_type = "application/json"

  ## Force Telegraf to batch and serialize metrics as JSON
  data_format = "json"

  ## JSON serializer specific options
  json_timestamp_units = "1ms"

  ## Uncomment to restrict batch size
  # batch_size = 5000

Input and output integration examples

RabbitMQ

1. Monitoring Queue Performance Metrics: Use the RabbitMQ plugin to keep track of queue performance over time. This involves setting up monitoring dashboards that visualize crucial queue metrics such as message rates, the number of consumers, and message delivery rates. With this information, teams can proactively address any bottlenecks or performance issues by analyzing trends and making data-informed decisions about scaling or optimizing their RabbitMQ configuration.

2. Alerting on System Health: Integrate the RabbitMQ plugin with an alerting system to notify operational teams of potential issues within RabbitMQ instances. For example, if the number of unacknowledged messages reaches a critical threshold or if queues become overwhelmed, alerts can trigger, allowing for immediate investigation and swift remedial action to maintain the health of message flows.

3. Analyzing Message Processing Metrics: Employ the plugin to gather detailed metrics on message processing performance, such as the rates of messages published, acknowledged, and redelivered. By analyzing these metrics, teams can evaluate the efficiency of their message consumer applications and make adjustments to configuration or code where necessary, thereby enhancing overall system throughput and resilience.

4. Cross-System Data Integration: Leverage the metrics collected by the RabbitMQ plugin to integrate data flows between RabbitMQ and other systems or services. For example, use the gathered metrics to drive automated workflows or analytics pipelines that utilize messages processed in RabbitMQ, enabling organizations to optimize workflows and enhance data agility across their ecosystems.

OpenObserve

1. Edge Device Health Mirror: Deploy Telegraf on thousands of industrial IoT devices to capture temperature, vibration, and power metrics, then use this output to push JSON batches to OpenObserve. Plant operators gain a real-time overview of machine health and can trigger maintenance based on anomalies without relying on heavyweight collectors.

2. Blue-Green Deployment Canary: Attach a lightweight Telegraf sidecar to each Kubernetes release-candidate pod that scrapes /metrics and forwards container stats to a dedicated “canary” stream in OpenObserve. Continuous comparison of error rates between blue and green versions empowers the CI pipeline to auto-roll back poor performers within seconds.

3. Multi-Tenant SaaS Billing Pipeline: Emit per-customer usage counters via Telegraf and tag them with tenant_id; the HTTP plugin posts them to OpenObserve where SQL reports aggregate usage into invoices, eliminating separate metering services and simplifying compliance audits.

4. Security Threat Scoring: Fuse Suricata events and host resource metrics in Telegraf, deliver them to OpenObserve’s analytics engine, and run stream-processing rules that correlate spikes in suspicious traffic with CPU saturation to produce an actionable threat score and automatically open tickets in a SOAR platform.

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