- Server sets request_update=1 in ACK when host.plugin_data is empty - hbc: AsyncConnection.request_info_event; handle_ack sets it on request_update - hbc: _info_plugin_refresh_loop clears InfoPlugin caches and resends on demand - hbc_mini: same via _request_info event and _info_refresh_loop - docs/USERS.md: document client-declared owner config key - docs/PLUGIN_DEVELOPMENT.md: document server-initiated InfoPlugin refresh Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
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Plugin Development Guide
This guide explains how to create custom plugins for the Heartbeat monitoring system.
Table of Contents
- Plugin Architecture
- Plugin Types
- Creating a Plugin
- Plugin Lifecycle
- Server-initiated InfoPlugin refresh
- Configuration
- Best Practices
- Examples
- Testing
Plugin Architecture
Heartbeat's plugin system is designed to be simple yet powerful. Plugins are Python classes that inherit from one of the base plugin types and implement a few key methods.
Key Concepts
- Plugin Registry: Central registry that manages all loaded plugins
- Plugin Loader: Automatically discovers and loads plugins from the
hbd/plugins/directory - Plugin Types: InfoPlugin (static data) and MonitorPlugin (periodic metrics)
- Async/Await: All plugin methods are async for non-blocking operation
Plugin Types
InfoPlugin
InfoPlugins collect static information that doesn't change frequently (OS version, hardware specs, etc.).
- Runs once at startup (interval = 0)
- Cached - data is collected once and reused
- Lightweight - no periodic overhead
Use InfoPlugin for:
- Operating system details
- Hardware information
- Software versions
- Configuration data
- Static inventory
MonitorPlugin
MonitorPlugins collect metrics that change over time (CPU usage, memory, network traffic).
- Runs periodically based on configured interval
- Scheduled - collected at regular intervals
- Dynamic - captures changing system state
Use MonitorPlugin for:
- Resource usage (CPU, memory, disk, network)
- Performance metrics
- Counters and gauges
- Time-series data
Creating a Plugin
Step 1: Choose Plugin Type
Decide whether your plugin collects static information (InfoPlugin) or dynamic metrics (MonitorPlugin).
Step 2: Create Plugin File
Create a new Python file in hbd/plugins/ directory:
"""
My awesome plugin for Heartbeat.
Brief description of what this plugin does.
"""
import logging
from typing import Dict, Any, Optional
# Import psutil or other dependencies if needed
try:
import psutil
except ImportError:
psutil = None
from hbd.plugin import MonitorPlugin # or InfoPlugin
logger = logging.getLogger(__name__)
class MyAwesomePlugin(MonitorPlugin): # or InfoPlugin
"""
One-line description of the plugin.
Collects:
- List of metrics/data collected
- Another metric
Configuration:
interval: Collection interval in seconds (default: 60)
option1: Description of option1 (default: value)
option2: Description of option2 (default: value)
"""
name = "my_awesome_plugin" # Unique plugin name
interval = 60 # For MonitorPlugin, use 0 for InfoPlugin
def __init__(self, config: Optional[Dict[str, Any]] = None):
"""Initialize the plugin with optional configuration."""
super().__init__(config)
# Extract configuration options
self.option1 = self.config.get('option1', 'default_value')
self.option2 = self.config.get('option2', True)
# Check dependencies
if psutil is None:
raise ImportError("psutil is required for my_awesome_plugin")
async def initialize(self):
"""
Initialize the plugin.
This is called once when the plugin is loaded.
Use this to verify dependencies, establish connections, etc.
Returns:
True if initialization successful, False otherwise
"""
logger.info(f"My awesome plugin initialized (option1: {self.option1})")
return True
async def collect(self) -> Dict[str, Any]:
"""
Collect data.
This is called periodically (MonitorPlugin) or once (InfoPlugin).
Returns:
Dictionary of collected data (will be sent to server)
"""
try:
data = await self._collect_metrics()
logger.debug(f"Collected {len(data)} metrics")
return data
except Exception as e:
logger.error(f"Error collecting data: {e}")
return {"error": str(e)}
async def _collect_metrics(self) -> Dict[str, Any]:
"""Internal method to collect actual metrics."""
metrics = {}
# Collect your data here
metrics['metric1'] = self._get_metric1()
metrics['metric2'] = self._get_metric2()
return metrics
def _get_metric1(self):
"""Helper method for metric collection."""
# Implementation here
return 42
def _get_metric2(self):
"""Helper method for metric collection."""
# Implementation here
return "hello"
async def cleanup(self):
"""
Cleanup resources.
This is called when the plugin is unloaded or the client shuts down.
Use this to close connections, release resources, etc.
"""
logger.info("My awesome plugin cleanup")
# Plugin instance for automatic discovery
plugin = MyAwesomePlugin
Step 3: Test Your Plugin
Create a test script to verify your plugin works:
#!/usr/bin/env python3
import asyncio
import sys
from pathlib import Path
# Add parent directory to path
sys.path.insert(0, str(Path(__file__).parent))
from hbd.plugins.my_awesome_plugin import MyAwesomePlugin
async def test():
# Create plugin instance
plugin = MyAwesomePlugin({'option1': 'test_value'})
# Initialize
if not await plugin.initialize():
print("Failed to initialize")
return False
# Collect data
data = await plugin.collect()
print(f"Collected data: {data}")
# Cleanup
await plugin.cleanup()
return True
if __name__ == '__main__':
success = asyncio.run(test())
sys.exit(0 if success else 1)
Plugin Lifecycle
Understanding the plugin lifecycle helps you implement plugins correctly:
1. Plugin Discovery
└─> Loader scans hbd/plugins/ directory
└─> Finds Python files (except those starting with _)
└─> Imports modules
2. Plugin Instantiation
└─> Creates instance with configuration
└─> __init__() is called
3. Plugin Initialization
└─> initialize() is called
└─> Plugin verifies dependencies, establishes connections
└─> Returns True/False for success/failure
4. Plugin Registration
└─> If initialization succeeds, plugin is registered
└─> Plugin becomes active
5. Data Collection
└─> For InfoPlugin: collect() called once after initialization
└─> For MonitorPlugin: collect() called periodically based on interval
└─> Data is sent to server via PLG message
6. Plugin Shutdown
└─> cleanup() is called
└─> Plugin releases resources, closes connections
Server-initiated InfoPlugin refresh
When a heartbeat packet arrives from a host the server has no plugin data for (e.g. after a server restart), the server sets request_update = 1 in the ACK reply. The client detects this flag and immediately re-runs all InfoPlugins — clearing their cached results first — then resends the data as PLG messages.
This means InfoPlugin data will always reach the server as soon as possible without requiring a client restart. No action is needed from plugin authors: the framework handles cache invalidation and re-collection automatically.
The lifecycle for this case looks like:
Server restarts, host reconnects
└─> hbd receives HTB with no existing plugin_data for host
└─> hbd sets request_update=1 in ACK
Client receives ACK
└─> Detects request_update flag
└─> Clears _cache on every registered InfoPlugin
└─> Calls collect() on each InfoPlugin
└─> Sends fresh PLG messages to server
If you write an InfoPlugin with side effects in _collect_info() (opening connections, writing files, etc.), be aware it may be called more than once per client session when this mechanism triggers.
Configuration
Plugin-Specific Configuration
Plugins receive configuration through the config parameter in __init__:
def __init__(self, config: Optional[Dict[str, Any]] = None):
super().__init__(config)
# Access configuration with defaults
self.interval = self.config.get('interval', 60)
self.threshold = self.config.get('threshold', 80)
self.enabled_features = self.config.get('features', ['feature1', 'feature2'])
Client Configuration File
Users configure plugins in the client configuration YAML:
plugins:
my_awesome_plugin:
enabled: true
interval: 120
option1: custom_value
option2: false
Best Practices
1. Error Handling
Always handle errors gracefully:
async def collect(self) -> Dict[str, Any]:
try:
return await self._collect_metrics()
except Exception as e:
logger.error(f"Error collecting metrics: {e}")
return {"error": str(e)}
2. Logging
Use appropriate log levels:
logger.debug("Detailed information for debugging")
logger.info("Normal operation messages")
logger.warning("Warning messages for unusual but handled situations")
logger.error("Error messages for failures")
3. Dependencies
Check for optional dependencies:
try:
import some_optional_library
except ImportError:
some_optional_library = None
# Later in __init__:
if some_optional_library is None:
raise ImportError("some_optional_library is required")
4. Performance
- Keep collection methods fast (< 1 second)
- Use async/await for I/O operations
- Cache expensive computations
- Don't block the event loop
5. Data Structure
Return clean, structured data:
{
'metric_name': value,
'nested_data': {
'sub_metric': value
},
'list_data': [item1, item2],
'timestamp': time.time() # Optional timestamp
}
6. Documentation
Document your plugin thoroughly:
- Class docstring with description and configuration
- Method docstrings explaining purpose and return values
- Inline comments for complex logic
Examples
Example 1: Simple InfoPlugin
from hbd.plugin import InfoPlugin
import platform
class SimpleInfoPlugin(InfoPlugin):
"""Collect basic system information."""
name = "simple_info"
interval = 0 # InfoPlugin
async def initialize(self):
return True
async def collect(self) -> Dict[str, Any]:
return {
'hostname': platform.node(),
'system': platform.system(),
'python_version': platform.python_version()
}
async def cleanup(self):
pass
plugin = SimpleInfoPlugin
Example 2: MonitorPlugin with State
from hbd.plugin import MonitorPlugin
import time
class CounterPlugin(MonitorPlugin):
"""Track a counter over time."""
name = "counter"
interval = 30
def __init__(self, config=None):
super().__init__(config)
self._counter = 0
self._start_time = time.time()
async def initialize(self):
return True
async def collect(self) -> Dict[str, Any]:
self._counter += 1
uptime = time.time() - self._start_time
return {
'count': self._counter,
'uptime': uptime,
'rate': self._counter / uptime
}
async def cleanup(self):
pass
plugin = CounterPlugin
Example 3: Plugin with External Command
from hbd.plugin import MonitorPlugin
import asyncio
class CommandPlugin(MonitorPlugin):
"""Execute external command and capture output."""
name = "command_executor"
interval = 60
def __init__(self, config=None):
super().__init__(config)
self.command = self.config.get('command', 'echo "no command"')
async def initialize(self):
return True
async def collect(self) -> Dict[str, Any]:
try:
process = await asyncio.create_subprocess_shell(
self.command,
stdout=asyncio.subprocess.PIPE,
stderr=asyncio.subprocess.PIPE
)
stdout, stderr = await asyncio.wait_for(
process.communicate(),
timeout=30
)
return {
'exit_code': process.returncode,
'stdout': stdout.decode('utf-8'),
'stderr': stderr.decode('utf-8')
}
except Exception as e:
return {'error': str(e)}
async def cleanup(self):
pass
plugin = CommandPlugin
Testing
Unit Testing
Create unit tests for your plugins:
import unittest
import asyncio
class TestMyPlugin(unittest.TestCase):
def setUp(self):
self.plugin = MyAwesomePlugin({'option1': 'test'})
def test_initialization(self):
result = asyncio.run(self.plugin.initialize())
self.assertTrue(result)
def test_collection(self):
asyncio.run(self.plugin.initialize())
data = asyncio.run(self.plugin.collect())
self.assertIsInstance(data, dict)
self.assertIn('metric1', data)
self.assertGreater(data['metric1'], 0)
def tearDown(self):
asyncio.run(self.plugin.cleanup())
if __name__ == '__main__':
unittest.main()
Integration Testing
Test your plugin with the actual client:
# Create test configuration
cat > test_config.yaml <<EOF
server: localhost
plugins:
my_awesome_plugin:
enabled: true
interval: 10
option1: test_value
EOF
# Run client in test mode
python -m hbd.hbc -c test_config.yaml --verbose
Troubleshooting
My plugin isn't loading
- Check filename doesn't start with underscore
- Verify plugin class inherits from InfoPlugin or MonitorPlugin
- Check
initialize()returns True - Look for import errors in logs
Plugin loads but doesn't collect data
- Check
intervalis set correctly (0 for InfoPlugin, > 0 for MonitorPlugin) - Verify
collect()returns a dictionary - Check for exceptions in
collect()method - Enable DEBUG logging to see detailed errors
Data isn't appearing on server
- Verify client is connected to server
- Check server logs for PLG message handling
- Verify returned data is JSON-serializable
- Check for large data sizes (may exceed UDP packet size)
Further Reading
- Plugin Framework Source - Core plugin implementation
- Built-in Plugins - Examples of working plugins
- Nagios Integration - Running external plugins
- Configuration Guide - Full configuration reference