Monitoring Runner

Part of the Apify Monitoring Suite (apify/monitoring)

Overview

The Monitoring Runner is a core orchestration actor within the Apify Monitoring Suite. It doesn't perform monitoring checks itself. Instead, its job is to execute other actors (checkers and reporters) according to a defined schedule and configuration, centralizing the management of monitoring workflows.

Key Features

• Orchestration: Automates the sequential running of checker actors (which perform the actual monitoring, like website uptime or API health checks) and reporter actors (which process and send the results).
• Centralized Configuration: Manage your entire monitoring suite—which checks to run, their order, and where to send alerts—from a single actor's input.
• Integration Ready: Designed to work seamlessly with other actors in the apify/monitoring ecosystem.

How to Use

1. Configure the Input: Define the list of checker and reporter actors to run in the JSON input (see Input section below).
2. Set a Schedule (Optional): Configure the actor to run on a recurring schedule (e.g., every 5 minutes) via the Apify platform's scheduler.
3. Run: Start the actor manually or let the scheduler trigger it. It will execute your defined checkers, pass their results to the specified reporters, and store the run details in the dataset.

Input

The actor expects a JSON input configuration. Here's the structure:

{
  "checkers": [
    {
      "actorId": "apify/website-monitoring-checker",
      "input": { /* Checker-specific configuration */ }
    }
    // ... more checkers
  ],
  "reporters": [
    {
      "actorId": "apify/monitoring-slack-reporter",
      "input": { /* Reporter-specific configuration */ }
    }
    // ... more reporters
  ]
}

• checkers: An array of objects, each defining a monitoring checker actor to run and its specific input.
• reporters: An array of objects, each defining a reporter actor that will receive and process the results from the checkers.

Output

The actor's primary output is stored in the associated dataset. Each dataset item represents the execution details of one checker-reporter cycle, typically containing:
* Timestamp of the run.
* Configuration used.
* Results from the executed checkers.
* Status and outputs from the reporters.

The specific format of the results within each item depends on the checker and reporter actors used in your configuration.