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python-resource-management
Python resource management with context managers, cleanup patterns, and streaming. Use when managing connections, file handles, implementing cleanup logic, or building streaming responses with accumulated state.
DeepseekModel
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name python-resource-management description Python resource management with context managers, cleanup patterns, and streaming. Use when managing connections, file handles, implementing cleanup logic, or building streaming responses with accumulated state. Python Resource Management Manage resources deterministically using context managers. Resources like database connections, file handles, and network sockets should be released reliably, even when exceptions occur. When to Use This Skill Managing database connections and connection pools Working with file handles and I/O Implementing custom context managers Building streaming responses with state Handling nested resource cleanup Creating async context managers Core Concepts 1. Context Managers The with statement ensures resources are released automatically, even on exceptions. 2. Protocol Methods __enter__ / __exit__ for sync, __aenter__ / __aexit__ for async resource management. 3. Unconditional Cleanup __exit__ always runs, regardless of whether an exception occurred. 4. Exception Handling Return True from __exit__ to suppress exceptions, False to propagate them. Quick Start from contextlib import contextmanager @contextmanager def managed_resource (): resource = acquire_resource() try : yield resource finally : resource.cleanup() with managed_resource() as r: r.do_work() Fundamental Patterns Pattern 1: Class-Based Context Manager Implement the context manager protocol for complex resources. class DatabaseConnection : """Database connection with automatic cleanup.""" def __init__ ( self, dsn: str ) -> None : self ._dsn = dsn self ._conn: Connection | None = None def connect ( self ) -> None : """Establish database connection.""" self ._conn = psycopg.connect( self ._dsn) def close ( self ) -> None : """Close connection if open.""" if self ._conn is not None : self ._conn.close() self ._conn = None def __enter__ ( self ) -> "DatabaseConnection" : """Enter context: connect and return self.""" self .connect() return self def __exit__ ( self, exc_type: type [BaseException] | None , exc_val: BaseException | None , exc_tb: TracebackType | None , ) -> None : """Exit context: always close connection.""" self .close() # Usage with context manager (preferred) with DatabaseConnection(dsn) as db: result = db.execute(query) # Manual management when needed db = DatabaseConnection(dsn) db.connect() try : result = db.execute(query) finally : db.close() Pattern 2: Async Context Manager For async resources, implement the async protocol. class AsyncDatabasePool : """Async database connection pool.""" def __init__ ( self, dsn: str , min_size: int = 1 , max_size: int = 10 ) -> None : self ._dsn = dsn self ._min_size = min_size self ._max_size = max_size self ._pool: asyncpg.Pool | None = None async def __aenter__ ( self ) -> "AsyncDatabasePool" : """Create connection pool.""" self ._pool = await asyncpg.create_pool( self ._dsn, min_size= self ._min_size, max_size= self ._max_size, ) return self async def __aexit__ ( self, exc_type: type [BaseException] | None , exc_val: BaseException | None , exc_tb: TracebackType | None , ) -> None : """Close all connections in pool.""" if self ._pool is not None : await self ._pool.close() async def execute ( self, query: str , *args ) -> list [ dict ]: """Execute query using pooled connection.""" async with self ._pool.acquire() as conn: return await conn.fetch(query, *args) # Usage async with AsyncDatabasePool(dsn) as pool: users = await pool.execute( "SELECT * FROM users WHERE active = $1" , True ) Pattern 3: Using @contextmanager Decorator Simplify context managers with the decorator for straightforward cases. from contextlib import contextmanager, asynccontextmanager import time import structlog logger = structlog.get_logger() @contextmanager def timed_block ( name: str ): """Time a block of code.""" start = time.perf_counter() try : yield finally : elapsed = time.perf_counter() - start logger.info( f" {name} completed" , duration_seconds= round (elapsed, 3 )) # Usage with timed_block( "data_processing" ): process_large_dataset() @asynccontextmanager async def database_transaction ( conn: AsyncConnection ): """Manage database transaction.""" await conn.execute( "BEGIN" ) try : yield conn await conn.execute( "COMMIT" ) except Exception: await conn.execute( "ROLLBACK" ) raise # Usage async with database_transaction(conn) as tx: await tx.execute( "INSERT INTO users ..." ) await tx.execute( "INSERT INTO audit_log ..." ) Pattern 4: Unconditional Resource Release Always clean up resources in __exit__ , regardless of exceptions. class FileProcessor : """Process file with guaranteed cleanup.""" def __init__ ( self, path: str ) -> None : self ._path = path self ._file: IO | None = None self ._temp_files: list [Path] = [] def __enter__ ( self ) -> "FileProcessor" : self ._file = open ( self ._path, "r" ) return self def __exit__ ( self, exc_type: type [BaseException] | None , exc_val: BaseException | None , exc_tb: TracebackType | None , ) -> None : """Clean up all resources unconditionally.""" # Close main file if self ._file is not None : self ._file.close() # Clean up any temporary files for temp_file in self ._temp_files: try : temp_file.unlink() except OSError: pass # Best effort cleanup # Return None/False to propagate any exception Detailed worked examples and patterns Detailed sections (starting with ## Advanced Patterns ) live in references/details.md . Read that file when the navigation summary above is insufficient. Best Practices Summary Always use context managers - For any resource that needs cleanup Clean up unconditionally - __exit__ runs even on exception Don't suppress unexpectedly - Return False unless suppression is intentional Use @contextmanager - For simple resource patterns Implement both protocols - Support with and manual management Use ExitStack - For dynamic numbers of resources Accumulate efficiently - List + join, not string concatenation Track metrics - Time-to-first-byte matters for streaming Document behavior - Especially exception suppression Test cleanup paths - Verify resources are released on errors
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