gabepublic 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()
# Broad except here: rollback on any failure, then re-raise. See
# [python-error-handling](../python-error-handling/SKILL.md) (transaction scope).
@asynccontextmanager
async def database_transaction(conn: AsyncConnection):
"""BEGIN / COMMIT, or ROLLBACK on any exception, then re-raise."""
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
Advanced Patterns
Pattern 5: Selective Exception Suppression
Only suppress specific, documented exceptions.
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.
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.
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.
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.
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
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