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The most commonly used thing for Python to connect to PostgreSQL is the psycopg2 library. You need to install it through pipinstallpsycopg2 first; 1. Use the psycopg2.connect() method to pass in the host, database name, user name, password and port information to establish a connection; 2. Create a cursor object to execute SQL query and get the results; 3. Be sure to close the cursor and connection after the operation is completed; Common problems include connection failure, SQL statement errors, uncommitted transactions, and resource leakage, etc. It is recommended to use try-except for exception handling and cooperate with conn.commit() to submit transactions; if performance is required, you can use SQLAlchemy to multiplex database connections with connection pools.

The event-driven architecture triggers and transmits information through events, which can be effectively implemented by Python and Celery. 1. Use celery.send_task to send tasks and trigger events; 2. Define unified event formats such as user_registered and order_paid; 3. Decouple the corresponding queues of each service listening; 4. Turn on message confirmation and persistence to ensure reliability; 5. Pay attention to task retry, event order, idempotence and log tracking issues. Celery supports asynchronous processing based on message middleware, which is suitable for background tasks and event response scenarios, improving system scalability and real-timeness.

TosecuredatainPython,usethecryptographylibraryforencryption,understandsymmetricvsasymmetricencryption,andapplysigningfordataintegrity.1)InstallandusethecryptographylibrarywithFernetforsymmetricencryption,ensuringkeysafety.2)Choosesymmetricencryptionf

Python is widely used in data governance, mainly reflected in the following aspects: 1. Data cleaning and standardization, using pandas for field replacement, missing value filling, format uniformity, etc.; 2. Automation of data quality inspection, realizing null value detection, range verification, enumeration matching and regular scanning tasks through scripts; 3. Metadata management and directory generation, extracting database structures with SQLAlchemy and pandas and exporting them into structured documents; 4. Cooperating with other tools to improve efficiency, such as Airflow to implement timing task scheduling, Flask/Django build management backend, and GreatExpectations to perform data verification. Python has its strong ecological support and spirit

Lock is a basic tool used in Python multithreading to synchronize threads to access shared resources, solving the race conditions and data inconsistency caused by multiple threads to modify shared data at the same time. 1.Lock ensures that only one thread executes protected code segments at the same time; 2. Common lock types include Lock, RLock, Condition, Semaphore and Event; 3. Use the with statement to automatically acquire and release the lock to protect the shared resource access path; 4. When using Lock, you should narrow the lock range, avoid deadlocks, and select Lock or RLock according to whether you call recursively.

The more complex reason for debugging asynchronous Python code is the uncertainty caused by event loops, coroutines, and callback mechanisms. 1. The asynchronous function is not executed because it is not put into the event loop and needs to be called using asyncio.run or await; 2. Log confusion is caused by the alternating execution of concurrent tasks, which can improve readability by adding task names or unique identifiers; 3. The debugger cannot enter the coroutine, so async/await support needs to be enabled, such as VSCode's launch.json configuration or using pdb.set_trace(). Mastering these basic methods can help quickly locate problems.

The strategy model is suitable for multiple similar algorithms that require dynamic switching scenarios, such as payment methods, promotional strategies, etc. When using it, first define a unified interface or protocol, then implement specific policy classes or functions separately, and call them through context. The structure includes context, policy interface, and specific policies. In Python, it can be implemented through classes, functions or protocols, and the functional writing method is more concise. The policy should be stateless as possible, and multiple policies can be managed by factories or dictionaries. Whether the runtime switching is supported is determined by the requirements, and the set_strategy method is omitted without it.