Error Detection

Find and analyze errors across logs and code.

When to use

• Investigating production errors
• Analyzing log patterns
• Finding error root causes
• Correlating errors across systems

Log analysis

Find errors

# Recent errors
grep -i "error\|exception\|fatal" /var/log/app.log | tail -100

# Errors with context
grep -B 5 -A 10 "ERROR" /var/log/app.log

# Count by error type
grep -oE "Error: [^:]*" app.log | sort | uniq -c | sort -rn

# Errors in time range
awk '/2024-01-15 14:/ && /ERROR/' app.log

Pattern detection

# Find repeated errors
grep "ERROR" app.log | cut -d']' -f2 | sort | uniq -c | sort -rn | head -20

# Correlate request IDs
grep "req-12345" *.log | sort -t' ' -k1,2

# Find error spikes
grep "ERROR" app.log | cut -d' ' -f1-2 | uniq -c | sort -rn

Stack trace analysis

Parse stack traces

import re

def parse_stack_trace(log_content: str) -> list[dict]:
    pattern = r'(?P<exception>\w+Error|\w+Exception): (?P<message>.*?)\n(?P<trace>(?:\s+at .+\n)+)'

    traces = []
    for match in re.finditer(pattern, log_content):
        traces.append({
            'type': match.group('exception'),
            'message': match.group('message'),
            'trace': match.group('trace').strip().split('\n')
        })
    return traces

Common patterns

Pattern	Indicates	Action
NullPointer	Missing null check	Add validation
Timeout	Slow dependency	Add timeout, retry
Connection refused	Service down	Check health, retry
OOM	Memory leak	Profile, increase limits
Rate limit	Too many requests	Add backoff, queue

Investigation checklist

1. Capture - Get full error message and stack trace
2. Timestamp - When did it start?
3. Frequency - How often? Increasing?
4. Scope - All users or specific?
5. Changes - Recent deployments?
6. Dependencies - External services affected?

Correlation queries

-- Errors by endpoint
SELECT endpoint, count(*) as errors
FROM logs
WHERE level = 'ERROR' AND time > NOW() - INTERVAL '1 hour'
GROUP BY endpoint ORDER BY errors DESC;

-- Error rate over time
SELECT
  date_trunc('minute', time) as minute,
  count(*) filter (where level = 'ERROR') as errors,
  count(*) as total
FROM logs
WHERE time > NOW() - INTERVAL '1 hour'
GROUP BY minute ORDER BY minute;

Examples

Input: "Find why API is returning 500 errors"
Action: Search logs for 500 status, find stack traces, identify root cause

Input: "Analyze error patterns from last hour"
Action: Aggregate errors by type, find spikes, correlate with events