In Python DevOps, storing results in a database is essential for data persistence and retrieval. You can use libraries like `sqlite3`, `SQLAlchemy`, or ORM frameworks to interact with your database. Below is a basic example of how to store results in a SQLite database using Python.
import sqlite3
# Connect to the SQLite database (or create it if it doesn't exist)
conn = sqlite3.connect('results.db')
c = conn.cursor()
# Create a table for storing results
c.execute('''CREATE TABLE IF NOT EXISTS results (id INTEGER PRIMARY KEY, result TEXT)''')
# Function to store a result
def store_result(result):
c.execute('INSERT INTO results (result) VALUES (?)', (result,))
conn.commit()
# Example of storing a result
store_result("Completed task successfully.")
# Close the connection
conn.close()
How do I avoid rehashing overhead with std::set in multithreaded code?
How do I find elements with custom comparators with std::set for embedded targets?
How do I erase elements while iterating with std::set for embedded targets?
How do I provide stable iteration order with std::unordered_map for large datasets?
How do I reserve capacity ahead of time with std::unordered_map for large datasets?
How do I erase elements while iterating with std::unordered_map in multithreaded code?
How do I provide stable iteration order with std::map for embedded targets?
How do I provide stable iteration order with std::map in multithreaded code?
How do I avoid rehashing overhead with std::map in performance-sensitive code?
How do I merge two containers efficiently with std::map for embedded targets?