Coding Challenge #1: Log Level Counter

Your application is spitting out thousands of log entries daily, and your manager just asked: "How many errors did we have yesterday?"

You could manually grep through the logs, but there's got to be a better way. Plus, you suspect there might be patterns in when errors occur that could help prevent future issues.

Your Mission

Build a log analyzer that counts different log levels and gives you insights into your application's health.

Requirements

Your tool must:

• Read log files and count occurrences of each log level (ERROR, WARN, INFO, DEBUG)

• Display results sorted by frequency (highest first)

• Handle case-insensitive matching

• Support custom log formats via command-line arguments

• Calculate percentage distribution of each log level

• Show timestamps of first and last occurrence for each level

Sample Input (app.log)

2025-07-18 09:15:23 INFO Application starting up
2025-07-18 09:15:45 INFO Database connection established  
2025-07-18 09:16:12 WARN Low disk space detected
2025-07-18 09:17:33 ERROR Failed to connect to payment gateway
2025-07-18 09:18:01 INFO Processing user request
2025-07-18 09:19:15 ERROR Database timeout occurred
2025-07-18 09:20:47 INFO User session created
2025-07-18 09:21:22 DEBUG Cache hit for user profile
2025-07-18 09:22:18 WARN Rate limit approaching
2025-07-18 09:23:44 ERROR Invalid API key provided

Expected Output

Log Analysis Report
===================
Total log entries: 10

Log Level Distribution:
-----------------------
ERROR: 3 (30.0%) | First: 09:17:33 | Last: 09:23:44
INFO:  3 (30.0%) | First: 09:15:23 | Last: 09:20:47
WARN:  2 (20.0%) | First: 09:16:12 | Last: 09:22:18
DEBUG: 1 (10.0%) | First: 09:21:22 | Last: 09:21:22

Summary: 🔴 3 errors detected - investigate payment gateway and API issues

Starter Code Templates

Python Approach

import re
import sys
from collections import defaultdict
from datetime import datetime

def parse_log_line(line):
    """Extract timestamp and log level from a log line"""
    # Basic pattern: YYYY-MM-DD HH:MM:SS LEVEL message
    pattern = r'(\d{4}-\d{2}-\d{2} \d{2}:\d{2}:\d{2})\s+(ERROR|WARN|INFO|DEBUG)'
    match = re.search(pattern, line, re.IGNORECASE)
    
    if match:
        return {
            'timestamp': match.group(1),
            'level': match.group(2).upper()
        }
    return None

def analyze_logs(filename):
    """Main analysis function"""
    # Your implementation here
    pass

if __name__ == "__main__":
    if len(sys.argv) != 2:
        print("Usage: python log_analyzer.py <logfile>")
        sys.exit(1)
    
    analyze_logs(sys.argv[1])

Go Approach

package main

import (
    "bufio"
    "fmt"
    "os"
    "regexp"
    "strings"
)

type LogEntry struct {
    Timestamp string
    Level     string
}

func parseLogLine(line string) *LogEntry {
    // Pattern: YYYY-MM-DD HH:MM:SS LEVEL message
    pattern := regexp.MustCompile(`(\d{4}-\d{2}-\d{2} \d{2}:\d{2}:\d{2})\s+(ERROR|WARN|INFO|DEBUG)`)
    matches := pattern.FindStringSubmatch(line)
    
    if len(matches) == 3 {
        return &LogEntry{
            Timestamp: matches[1],
            Level:     strings.ToUpper(matches[2]),
        }
    }
    return nil
}

func analyzeLogs(filename string) error {
    // Your implementation here
    return nil
}

func main() {
    if len(os.Args) != 2 {
        fmt.Println("Usage: go run log_analyzer.go <logfile>")
        os.Exit(1)
    }
    
    if err := analyzeLogs(os.Args[1]); err != nil {
        fmt.Printf("Error: %v\n", err)
        os.Exit(1)
    }
}

Test Your Solution

1. Save the sample input as app.log

2. Run your solution: python log_analyzer.py app.log or go run log_analyzer.go app.log

3. Verify your output matches the expected format

Bonus Challenges

• Add support for different timestamp formats

• Include hourly distribution analysis

• Generate alerts when error percentage exceeds threshold

• Export results to JSON format

• Add color-coded output for terminal display

Why This Challenge Matters

Log analysis is a daily task for developers and DevOps engineers. This challenge teaches:

• Regular expressions for pattern matching

• File I/O and text processing

• Data aggregation and statistics

• Command-line tool development

• Real-world debugging skills

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Complete Python Solution