---
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
```
## 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
```
## 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
