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.
Install
npx skills add https://github.com/wshobson/agents/tree/main/plugins/python-development/skills/python-resource-management
claude plugin marketplace add https://llmmart.ai/marketplace.json && claude plugin install wshobson-agents@llmmart
git clone https://github.com/wshobson/agents.git
The skills CLI installs just this skill, for any of its supported agents. Claude Code installs the whole wshobson/agents collection as a plugin from our marketplace. Git is the plain clone.
Skill manifest
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
Falseunless suppression is intentional - Use @contextmanager - For simple resource patterns
- Implement both protocols - Support
withand 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
Files (agents)
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references
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details.md 4.6 KB
# python-resource-management — detailed worked examples ## Advanced Patterns ### Pattern 5: Selective Exception Suppression Only suppress specific, documented exceptions. ```python class StreamWriter: """Writer that handles broken pipe gracefully.""" def __init__(self, stream) -> None: self._stream = stream def __enter__(self) -> "StreamWriter": return self def __exit__( self, exc_type: type[BaseException] | None, exc_val: BaseException | None, exc_tb: TracebackType | None, ) -> bool: """Clean up, suppressing BrokenPipeError on shutdown.""" self._stream.close() # Suppress BrokenPipeError (client disconnected) # This is expected behavior, not an error if exc_type is BrokenPipeError: return True # Exception suppressed return False # Propagate all other exceptions ``` ### Pattern 6: Streaming with Accumulated State Maintain both incremental chunks and accumulated state during streaming. ```python from collections.abc import Generator from dataclasses import dataclass, field @dataclass class StreamingResult: """Accumulated streaming result.""" chunks: list[str] = field(default_factory=list) _finalized: bool = False @property def content(self) -> str: """Get accumulated content.""" return "".join(self.chunks) def add_chunk(self, chunk: str) -> None: """Add chunk to accumulator.""" if self._finalized: raise RuntimeError("Cannot add to finalized result") self.chunks.append(chunk) def finalize(self) -> str: """Mark stream complete and return content.""" self._finalized = True return self.content def stream_with_accumulation( response: StreamingResponse, ) -> Generator[tuple[str, str], None, str]: """Stream response while accumulating content. Yields: Tuple of (accumulated_content, new_chunk) for each chunk. Returns: Final accumulated content. """ result = StreamingResult() for chunk in response.iter_content(): result.add_chunk(chunk) yield result.content, chunk return result.finalize() ``` ### Pattern 7: Efficient String Accumulation Avoid O(n²) string concatenation when accumulating. ```python def accumulate_stream(stream) -> str: """Efficiently accumulate stream content.""" # BAD: O(n²) due to string immutability # content = "" # for chunk in stream: # content += chunk # Creates new string each time # GOOD: O(n) with list and join chunks: list[str] = [] for chunk in stream: chunks.append(chunk) return "".join(chunks) # Single allocation ``` ### Pattern 8: Tracking Stream Metrics Measure time-to-first-byte and total streaming time. ```python import time from collections.abc import Generator def stream_with_metrics( response: StreamingResponse, ) -> Generator[str, None, dict]: """Stream response while collecting metrics. Yields: Content chunks. Returns: Metrics dictionary. """ start = time.perf_counter() first_chunk_time: float | None = None chunk_count = 0 total_bytes = 0 for chunk in response.iter_content(): if first_chunk_time is None: first_chunk_time = time.perf_counter() - start chunk_count += 1 total_bytes += len(chunk.encode()) yield chunk total_time = time.perf_counter() - start return { "time_to_first_byte_ms": round((first_chunk_time or 0) * 1000, 2), "total_time_ms": round(total_time * 1000, 2), "chunk_count": chunk_count, "total_bytes": total_bytes, } ``` ### Pattern 9: Managing Multiple Resources with ExitStack Handle a dynamic number of resources cleanly. ```python from contextlib import ExitStack, AsyncExitStack from pathlib import Path def process_files(paths: list[Path]) -> list[str]: """Process multiple files with automatic cleanup.""" results = [] with ExitStack() as stack: # Open all files - they'll all be closed when block exits files = [stack.enter_context(open(p)) for p in paths] for f in files: results.append(f.read()) return results async def process_connections(hosts: list[str]) -> list[dict]: """Process multiple async connections.""" results = [] async with AsyncExitStack() as stack: connections = [ await stack.enter_async_context(connect_to_host(host)) for host in hosts ] for conn in connections: results.append(await conn.fetch_data()) return results ```
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SKILL.md 6.8 KB
--- 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 ```python 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. ```python 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. ```python 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. ```python 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. ```python 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 1. **Always use context managers** - For any resource that needs cleanup 2. **Clean up unconditionally** - `__exit__` runs even on exception 3. **Don't suppress unexpectedly** - Return `False` unless suppression is intentional 4. **Use @contextmanager** - For simple resource patterns 5. **Implement both protocols** - Support `with` and manual management 6. **Use ExitStack** - For dynamic numbers of resources 7. **Accumulate efficiently** - List + join, not string concatenation 8. **Track metrics** - Time-to-first-byte matters for streaming 9. **Document behavior** - Especially exception suppression 10. **Test cleanup paths** - Verify resources are released on errors
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