ALLPCB
Introduction
Many operations engineers use Python scripts to automate routine tasks. Python is a popular programming language with a rich ecosystem of third-party libraries and strong automation capabilities. In operations, Python scripts are often used for tasks such as:
- Connecting to remote servers and executing commands
- Parsing log files and extracting useful information
- Monitoring system status and sending alerts
- Bulk deploying software or updating systems
- Performing backup and restore tasks
Using Python can improve operational efficiency and reduce manual errors. Specific responsibilities vary by company, but learning Python is generally beneficial for operations engineers. Other scripting languages such as Bash, Perl, and Ruby are also used for automation and can be chosen based on preference and requirements.
1. Connect to Remote Servers and Execute Commands
Connecting to remote servers and executing commands is a common operation task. Common protocols include SSH and Telnet. In Python, the paramiko library can be used for SSH connections. Example:
import paramiko # Create SSH client ssh = paramiko.SSHClient() # Set to automatically accept server host keys ssh.set_missing_host_key_policy(paramiko.AutoAddPolicy()) # Connect to remote server ssh.connect(hostname='remote.server.com', username='user', password='password') # Execute command stdin, stdout, stderr = ssh.exec_command('ls -l /tmp')
2. Parse Log Files and Extract Useful Information
Parsing log files and extracting relevant information is another common task. The regex library provides powerful regular expression tools for parsing logs. Example:
import regex # Read log file with open('log.txt', 'r') as f: log = f.read() # Use regular expression to match error messages errors = regex.findall(r'ERROR:\s+(.*)', log) # Print all matched error messages for error in errors: print(error)
In practice, more complex regular expressions and additional regex features can be used. Other libraries such as loguru or python-logstash may also help with log parsing and processing.
3. Monitor System Status and Send Alerts
Python can be used to monitor system resources and send alerts. The psutil library provides information on CPU, memory, disk, and network usage. Example:
import psutil import smtplib # Get CPU usage percentage cpu_percent = psutil.cpu_percent() # Check if CPU usage exceeds threshold if cpu_percent > 80: # Establish SMTP connection server = smtplib.SMTP('smtp.example.com') server.login('user', 'password') # Construct email content message = 'CPU usage exceeded 80%: current usage is {}%'.format(cpu_percent) subject = 'Alert: High CPU Usage' # Send email server.sendmail('alert@example.com', 'admin@example.com', message) server.quit()
Thresholds and notification channels can be adjusted as needed. Other monitoring tools and clients, such as nagios-api and sensu-client, can also be integrated.
4. Bulk Deploy Software or Update Systems
Bulk deployment and system updates can be automated with Python. The fabric library provides remote execution tools to run commands on multiple servers. Example:
from fabric import task @task def update_system(c): c.run('apt-get update')
Fabric tasks can be extended to perform package installation, configuration changes, and more. Other automation tools like Ansible and Puppet are also commonly used for large-scale deployments.
5. Perform Backup and Restore Tasks
Python's shutil module can be used for file and directory backups. Example: backing up a single file:
import shutil # Backup file shutil.copy('/path/to/file', '/path/to/backup/file')
Backing up a directory:
import shutil # Backup directory shutil.copytree('/path/to/dir', '/path/to/backup')
Other Uses
Python can also be used for automated testing (for example, pytest for unit tests and selenium for browser automation), data analysis and visualization with numpy, pandas, matplotlib, and seaborn, and for machine learning and natural language processing with libraries such as scikit-learn, TensorFlow, and nltk. Overall, Python has broad applicability in operations and can help engineers complete tasks more efficiently.
