heartbeat/docs/MESSAGE_JOURNAL.md

# Message Journal

The message journal provides persistent logging of all received heartbeat messages with automatic size-based log rotation.

## Overview

The journal logs every message received by the heartbeat daemon (hbd) in JSON format, making it easy to:
- Audit message history
- Debug connection issues
- Analyze traffic patterns
- Replay messages for testing
- Create historical reports

## Features

- **JSON Format**: Each message is logged as a single JSON line for easy parsing
- **Size-Based Rotation**: Automatically rotates logs when size threshold is reached
- **Automatic Cleanup**: Keeps only a configurable number of backup files
- **Thread-Safe**: Safe for concurrent access from multiple async tasks
- **Configurable**: All settings controllable via configuration file
- **Performance**: Non-blocking async operation with minimal overhead

## Configuration

Add these settings to your hbd configuration file (e.g., `.hb.yaml`):

```yaml
# Message journal configuration
journal_enabled: true                          # Enable/disable journaling
journal_dir: /var/log/heartbeat                # Directory for journal files
journal_file: messages.journal                 # Base filename
journal_max_size: 104857600                    # Max size in bytes (100MB default)
journal_max_backups: 10                        # Number of backup files to keep
```

### Configuration Options

| Option | Default | Description |
|--------|---------|-------------|
| `journal_enabled` | `true` | Enable or disable message journaling |
| `journal_dir` | `/var/log/heartbeat` | Directory where journal files are stored |
| `journal_file` | `messages.journal` | Base filename for the journal |
| `journal_max_size` | `104857600` (100MB) | Maximum file size before rotation |
| `journal_max_backups` | `10` | Number of rotated backup files to keep |

## File Format

Messages are logged in JSONL (JSON Lines) format - one JSON object per line:

```json
{"timestamp":1711234567.123,"datetime":"2026-03-28T12:34:56","source_ip":"192.168.1.100","source_port":50003,"message":{"ID":"HTB","name":"webserver1","interval":30,"ver":1}}
{"timestamp":1711234597.456,"datetime":"2026-03-28T12:35:37","source_ip":"192.168.1.101","source_port":50003,"message":{"ID":"PLG","plugin":"cpu_monitor","cpu_percent":45.2,"load_1min":1.5}}
```

### Entry Structure

Each journal entry contains:

| Field | Type | Description |
|-------|------|-------------|
| `timestamp` | float | Unix timestamp (seconds since epoch) |
| `datetime` | string | ISO 8601 formatted datetime |
| `source_ip` | string | Source IP address |
| `source_port` | integer | Source UDP port |
| `message` | object | Complete parsed message dictionary |

## Log Rotation

### How Rotation Works

1. Journal writes messages to the current file
2. When file size exceeds `journal_max_size`, rotation is triggered
3. Current file is renamed with timestamp: `messages.journal.YYYYMMDD-HHMMSS`
4. New empty file is created as the current journal
5. Old backup files exceeding `journal_max_backups` are deleted

### Example File Structure

```
/var/log/heartbeat/
├── messages.journal                    # Current active journal
├── messages.journal.20260328-120000   # Rotated backup
├── messages.journal.20260328-140000   # Rotated backup
└── messages.journal.20260328-160000   # Rotated backup (oldest)
```

### Rotation Behavior

- Rotation is triggered when the next message would exceed the size limit
- Rotation is automatic and requires no manual intervention
- Old backups are deleted in FIFO order (oldest first)
- Rotation is thread-safe and won't lose messages

## Usage Examples

### Reading Journal Files

#### Using Python

```python
import json

# Read all entries from current journal
with open('/var/log/heartbeat/messages.journal', 'r') as f:
    for line in f:
        entry = json.loads(line)
        print(f"{entry['datetime']} - {entry['source_ip']} - {entry['message']['ID']}")
```

#### Using jq (command line)

```bash
# View all messages
cat /var/log/heartbeat/messages.journal | jq .

# Filter by message type
cat /var/log/heartbeat/messages.journal | jq 'select(.message.ID == "HTB")'

# Filter by hostname
cat /var/log/heartbeat/messages.journal | jq 'select(.message.name == "webserver1")'

# Count messages by type
cat /var/log/heartbeat/messages.journal | jq -r '.message.ID' | sort | uniq -c

# Extract timestamps and source IPs
cat /var/log/heartbeat/messages.journal | jq -r '[.datetime, .source_ip, .message.ID] | @tsv'
```

#### Using shell tools

```bash
# Count total messages
wc -l /var/log/heartbeat/messages.journal

# View recent messages
tail -n 100 /var/log/heartbeat/messages.journal | jq .

# Search for specific host
grep -F '"name":"webserver1"' /var/log/heartbeat/messages.journal

# Check journal file size
du -h /var/log/heartbeat/messages.journal
```

### Analyzing Historical Data

```bash
# Combine all journal files (current + backups)
cat /var/log/heartbeat/messages.journal* | jq . > all_messages.json

# Count messages per host
cat /var/log/heartbeat/messages.journal* | jq -r '.message.name // "unknown"' | sort | uniq -c

# Find all plugin messages
cat /var/log/heartbeat/messages.journal* | jq 'select(.message.ID == "PLG")'

# Extract CPU metrics from plugin messages
cat /var/log/heartbeat/messages.journal* | \
    jq 'select(.message.plugin == "cpu_monitor") | {time: .datetime, host: .message.name, cpu: .message.cpu_percent}'
```

## Integration with Log Management

### Logrotate

While the journal has built-in rotation, you can also use logrotate for additional management:

```
/var/log/heartbeat/messages.journal.* {
    daily
    rotate 30
    compress
    delaycompress
    missingok
    notifempty
}
```

### Elasticsearch/OpenSearch

Import journal data into Elasticsearch for advanced analysis:

```python
from elasticsearch import Elasticsearch
import json

es = Elasticsearch(['localhost:9200'])

with open('/var/log/heartbeat/messages.journal', 'r') as f:
    for line in f:
        entry = json.loads(line)
        es.index(index='heartbeat-messages', body=entry)
```

### Splunk

Create a Splunk input for the journal:

```ini
[monitor:///var/log/heartbeat/messages.journal*]
sourcetype = heartbeat_json
index = heartbeat
```

## Performance Considerations

### Overhead

- Journal writing is async and non-blocking
- Typical overhead: < 1ms per message
- Minimal impact on heartbeat processing

### Disk Usage

Calculate expected disk usage:

```
Messages per day = (86400 seconds / interval) * number_of_hosts
Average message size ≈ 200-500 bytes
Daily disk usage = Messages per day * Average message size

Example:
- 100 hosts
- 30 second interval  
- 2880 messages/day per host
- 288,000 messages/day total
- ~60-140 MB/day
```

### Recommendations

- **Small deployments** (< 50 hosts): Default settings work well
- **Medium deployments** (50-500 hosts): Increase `journal_max_size` to 500MB, `journal_max_backups` to 20
- **Large deployments** (> 500 hosts): Consider 1GB+ journal files, 30+ backups, or external log aggregation

## Monitoring

### Check Journal Status

The journal exposes statistics that can be queried:

```python
from hbd.journal import get_journal

journal = get_journal()
stats = journal.get_stats()
print(f"Current size: {stats['current_size']:,} bytes")
print(f"Rotation threshold: {stats['rotation_threshold']}")
```

### Log Messages

Journal operations are logged at appropriate levels:

- `INFO`: Initialization, rotation events, cleanup
- `DEBUG`: Individual message logging
- `WARNING`: Non-critical issues
- `ERROR`: Critical failures

Check hbd logs for journal-related messages:

```bash
grep journal /var/log/heartbeat.log
```

## Troubleshooting

### Journal Files Not Created

**Problem**: No journal files appear in the configured directory.

**Solutions**:
- Check `journal_enabled: true` in configuration
- Verify directory exists and hbd has write permissions
- Check hbd logs for initialization errors
- Verify disk space is available

### Rotation Not Working

**Problem**: Journal file grows beyond `journal_max_size`.

**Solutions**:
- Check that `journal_max_size` is properly configured
- Verify hbd has permission to rename/create files
- Check for filesystem issues
- Review hbd logs for rotation errors

### Missing Messages

**Problem**: Some messages don't appear in journal.

**Solutions**:
- Verify `journal_enabled: true`
- Check for write errors in hbd logs
- Verify sufficient disk space
- Check if filesystem is read-only

### Performance Issues

**Problem**: Journal causing slow message processing.

**Solutions**:
- Use faster storage (SSD) for journal directory
- Increase `journal_max_size` to reduce rotation frequency
- Disable journal if not needed: `journal_enabled: false`
- Consider async syslog forwarding instead

## Security Considerations

### File Permissions

Ensure proper permissions on journal files:

```bash
# Journal directory
chmod 750 /var/log/heartbeat
chown hbd:hbd /var/log/heartbeat

# Journal files
chmod 640 /var/log/heartbeat/messages.journal*
```

### Sensitive Data

Journal files may contain:
- Hostnames and IP addresses
- System metrics
- Custom message content

**Recommendations**:
- Restrict read access to authorized users only
- Consider encryption for archived journals
- Implement log retention policies
- Sanitize data if sharing for debugging

## API Reference

### MessageJournal Class

```python
class MessageJournal:
    def __init__(self, config: Dict[str, Any])
    async def initialize(self) -> bool
    async def log_message(self, msg: Dict, addr: tuple, timestamp: float)
    async def close(self)
    def get_stats(self) -> Dict[str, Any]
```

### Module Functions

```python
def get_journal(config: Dict = None) -> MessageJournal
async def log_message(msg: Dict, addr: tuple, timestamp: float = None)
```

## Example: Custom Message Processing

Process journal messages in real-time:

```python
import asyncio
import json
from pathlib import Path

async def tail_journal(journal_path):
    """Follow journal file and process new messages."""
    path = Path(journal_path)
    
    with open(path, 'r') as f:
        # Jump to end
        f.seek(0, 2)
        
        while True:
            line = f.readline()
            if line:
                entry = json.loads(line)
                await process_message(entry)
            else:
                await asyncio.sleep(0.1)

async def process_message(entry):
    """Process a journal entry."""
    msg = entry['message']
    
    # Alert on boot messages
    if msg.get('boot'):
        print(f"ALERT: {msg['name']} rebooted at {entry['datetime']}")
    
    # Track CPU usage
    if msg.get('ID') == 'PLG' and msg.get('plugin') == 'cpu_monitor':
        cpu = msg.get('cpu_percent', 0)
        if cpu > 90:
            print(f"WARNING: {entry['source_ip']} CPU usage: {cpu}%")
```

## Future Enhancements

Potential improvements for future versions:

- Compression of rotated logs (gzip)
- Time-based rotation in addition to size-based
- Filtering to exclude certain message types
- Structured logging output formats (CEF, GELF)
- Remote syslog forwarding
- Message deduplication
- Journal file encryption
- Signed journal entries

## See Also

- [Configuration Guide](../hbd/config.py) - Full configuration options
- [UDP Protocol](../hbd/udp.py) - Message handling
- [Server Architecture](../hbd/server.py) - Server initialization