Apprendre Parsing Log Files | Monitoring and Log Analysis

Log files are essential tools in DevOps for monitoring systems and troubleshooting issues. They record events, errors, and informational messages generated by applications and services. By reviewing log files, you can detect failures, track system behavior over time, and quickly identify the root causes of incidents. Effective log analysis helps you maintain system reliability and respond to problems before they escalate.


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log_lines = [
    "2024-06-25 09:15:32,123 INFO Starting backup process",
    "2024-06-25 09:15:35,456 ERROR Failed to connect to database",
    "2024-06-25 09:15:36,789 WARNING Low disk space",
    "2024-06-25 09:15:37,101 INFO Backup process completed",
    "2024-06-25 09:15:38,202 ERROR Could not send notification email"
]

error_messages = []

for line in log_lines:
    if "ERROR" in line:
        # Extract the part after the log level
        parts = line.split("ERROR", 1)
        if len(parts) > 1:
            message = parts[1].strip()
            error_messages.append(message)

print("Extracted error messages:")
for msg in error_messages:
    print(msg)

To analyze log files in Python, you often rely on string methods and basic pattern matching. Methods like split(), startswith(), endswith(), and in help you isolate log levels, timestamps, or specific keywords. These tools allow you to filter relevant lines, extract important details, and summarize trends. For more complex patterns, regular expressions can be used, but for many DevOps tasks, simple string operations are both efficient and readable.


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log_lines = [
    "2024-06-25 09:15:32,123 INFO Starting backup process",
    "2024-06-25 09:15:35,456 ERROR Failed to connect to database",
    "2024-06-25 09:15:36,789 WARNING Low disk space",
    "2024-06-25 09:15:37,101 INFO Backup process completed",
    "2024-06-25 09:15:38,202 ERROR Could not send notification email"
]

log_counts = {"INFO": 0, "ERROR": 0, "WARNING": 0}

for line in log_lines:
    for level in log_counts:
        if f" {level} " in line:
            log_counts[level] += 1

print("Log level counts:")
for level, count in log_counts.items():
    print(f"{level}: {count}")

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Section 2. Chapitre 2

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Suggested prompts:

Can you explain how the code extracts error messages from the log lines?

What are some other ways to analyze log files in Python?

How can I modify the code to extract warning messages instead of errors?

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log_lines = [
    "2024-06-25 09:15:32,123 INFO Starting backup process",
    "2024-06-25 09:15:35,456 ERROR Failed to connect to database",
    "2024-06-25 09:15:36,789 WARNING Low disk space",
    "2024-06-25 09:15:37,101 INFO Backup process completed",
    "2024-06-25 09:15:38,202 ERROR Could not send notification email"
]

error_messages = []

for line in log_lines:
    if "ERROR" in line:
        # Extract the part after the log level
        parts = line.split("ERROR", 1)
        if len(parts) > 1:
            message = parts[1].strip()
            error_messages.append(message)

print("Extracted error messages:")
for msg in error_messages:
    print(msg)


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log_lines = [
    "2024-06-25 09:15:32,123 INFO Starting backup process",
    "2024-06-25 09:15:35,456 ERROR Failed to connect to database",
    "2024-06-25 09:15:36,789 WARNING Low disk space",
    "2024-06-25 09:15:37,101 INFO Backup process completed",
    "2024-06-25 09:15:38,202 ERROR Could not send notification email"
]

log_counts = {"INFO": 0, "ERROR": 0, "WARNING": 0}

for line in log_lines:
    for level in log_counts:
        if f" {level} " in line:
            log_counts[level] += 1

print("Log level counts:")
for level, count in log_counts.items():
    print(f"{level}: {count}")

Tout était clair ?

Merci pour vos commentaires !

Section 2. Chapitre 2

Parsing Log Files

1. What is the purpose of parsing log files?

2. Which Python string methods are useful for log analysis?

3. How can log parsing help in incident response?

Parsing Log Files

1. What is the purpose of parsing log files?

2. Which Python string methods are useful for log analysis?

3. How can log parsing help in incident response?