ActiveMQ and GroundWork Integration

Input and output integration overview

The ActiveMQ Input Plugin collects metrics from the ActiveMQ message broker through its Console API, providing insights into the performance and status of message queues, topics, and subscribers.

This plugin writes to a GroundWork Monitor instance, allowing for effective metrics management and monitoring in a centralized manner.

Integration details

ActiveMQ

The ActiveMQ Input Plugin interfaces with the ActiveMQ Console API to gather metrics related to queues, topics, and subscribers. ActiveMQ, a widely-used open-source message broker, supports various messaging protocols and provides a robust Web Console for management and monitoring. This plugin allows users to track essential metrics including queue sizes, consumer counts, and message counts across different ActiveMQ entities, thereby enhancing observability within messaging systems. Users can configure various parameters such as the WebConsole URL and basic authentication credentials to tailor the plugin to their environment. The metrics collected can be used for monitoring the health and performance of messaging applications, facilitating proactive management and troubleshooting.

GroundWork

The GroundWork plugin enables Telegraf to send metrics to a GroundWork Monitor instance, specifically supporting GW8 and newer versions. This integration allows users to leverage the robust monitoring capabilities of GroundWork, enabling comprehensive oversight of metrics collected from diverse sources. Users can specify various parameters such as the GroundWork instance URL, agent IDs, and authentication credentials, allowing for a tailored fit within their existing monitoring setups. It also supports secret-store secrets to enhance security for sensitive fields like username and password. Tags used within the plugin provide fine-grained control over how metrics are categorized and displayed within the GroundWork interface, allowing for custom configurations that adapt to different monitoring needs. However, users should be aware that string metrics are currently not supported by GroundWork, impacting how they manage their data.

Configuration

ActiveMQ

[[inputs.activemq]]
  ## ActiveMQ WebConsole URL
  url = "http://127.0.0.1:8161"

  ## Required ActiveMQ Endpoint
  ##   deprecated in 1.11; use the url option
  # server = "192.168.50.10"
  # port = 8161

  ## Credentials for basic HTTP authentication
  # username = "admin"
  # password = "admin"

  ## Required ActiveMQ webadmin root path
  # webadmin = "admin"

  ## Maximum time to receive response.
  # response_timeout = "5s"

  ## 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

GroundWork

[[outputs.groundwork]]
  ## URL of your groundwork instance.
  url = "https://groundwork.example.com"

  ## Agent uuid for GroundWork API Server.
  agent_id = ""

  ## Username and password to access GroundWork API.
  username = ""
  password = ""

  ## Default application type to use in GroundWork client
  # default_app_type = "TELEGRAF"

  ## Default display name for the host with services(metrics).
  # default_host = "telegraf"

  ## Default service state.
  # default_service_state = "SERVICE_OK"

  ## The name of the tag that contains the hostname.
  # resource_tag = "host"

  ## The name of the tag that contains the host group name.
  # group_tag = "group"

Input and output integration examples

ActiveMQ

1. Proactive Queue Monitoring: Use the ActiveMQ plugin to monitor queue sizes in real-time for a high-volume trading application. This implementation allows teams to receive alerts when queue sizes exceed a certain threshold, enabling rapid response to potential downtime caused by backlogs, thereby ensuring continuous availability of trading operations.

2. Performance Baselines and Anomaly Detection: Integrate this plugin with machine learning frameworks to establish performance baselines for message throughput. By analyzing historical data collected through this plugin, teams can flag anomalies in processing rates, leading to quicker identification of issues impacting service reliability and performance.

3. Cross-Messaging System Analytics: Combine metrics from ActiveMQ with those from other messaging systems in a centralized dashboard. Users can visualize and compare performance data, such as enqueue and dequeue rates, providing valuable insights into the overall messaging architecture and assisting in optimizing the message flow between different brokers.

4. Subscriber Performance Insights: Leverage the subscriber metrics collected by this plugin to analyze behavior patterns and optimize configuration for consumer applications. Understanding metrics such as dispatched queue size and counter values can guide adjustments to improve processing efficiency and resource allocation.

GroundWork

1. Centralized Monitoring Dashboard: Use the GroundWork plugin to aggregate metrics from multiple Telegraf instances into a single GroundWork Monitor dashboard. This configuration offers complete visibility into system health across various components, enabling swift identification of performance bottlenecks and improved incident response times.

2. Service Health Monitoring with Alerts: Configure this plugin to send critical service metrics to GroundWork, establishing a robust alerting system. Metrics such as CPU usage and service statuses can trigger alerts based on threshold values, informing administrators of potential issues before they escalate, thereby enhancing system reliability.

3. Historical Data Analysis: Leverage the historical metric capabilities of GroundWork using this plugin to conduct trend analysis over time. This application allows organizations to make data-driven decisions based on comprehensive historical performance metrics, which can assist in capacity planning and optimize resource allocation.

4. Custom Service Tags for Enhanced Monitoring: Extend the functionality of this plugin by utilizing custom tags for different services and hosts. By customizing these tags, users can filter and categorize metrics more effectively within their monitoring framework, leading to tailored monitoring experiences that align specifically with business objectives.

Feedback

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