Log Analysis and Statistics Dashboard
Use the statistics panel to analyze log severity distribution, identify top error sources, measure time ranges, and detect format patterns.
Detailed Explanation
Log Analysis with Statistics
The Log Format Parser includes a statistics panel that provides instant insights into your log data without requiring any external tools or complex commands.
Severity Distribution
The severity distribution chart shows the count and percentage of each log level:
| Severity | Bar Color | Typical Meaning |
|---|---|---|
| DEBUG | Gray | Verbose debugging information |
| INFO | Blue | Normal operational messages |
| WARN | Yellow | Potential issues that may need attention |
| ERROR | Red | Failures that need investigation |
| FATAL | Dark Red | Critical failures, service crash |
A healthy application typically shows mostly INFO with occasional WARN entries. A high ERROR percentage indicates active issues.
Top Sources Analysis
The top sources section ranks log sources by frequency. This reveals:
- Which services or components generate the most log volume
- Whether errors are concentrated in a single source or distributed
- Potential candidates for log level adjustment (noisy DEBUG sources)
Time Range
The time range shows the first and last timestamp in your log data. This helps you:
- Confirm you have the right time window
- Estimate the rate of log generation
- Identify gaps where no logs were produced (potential downtime)
Format Detection
The format breakdown shows which log formats were detected. In mixed-format inputs, this reveals the composition of your log sources:
Formats: json (45), apache-combined (30), syslog (15), kubernetes (10)
Practical Analysis Workflow
- Start with Stats — get the overview before diving into individual entries
- Check ERROR ratio — if > 5% of entries are ERROR, something is likely wrong
- Identify top error source — filter by that source to understand the pattern
- Check time range — see if errors correlate with a specific time window
- Search for patterns — use keywords from error messages to find related entries
Use Case
Quick health assessment of production systems from log dumps, identifying the noisiest log sources for log level tuning, measuring error rates across services, and providing summary statistics for incident reports and post-mortems.