# python-error-handling — detailed worked examples ## Advanced Patterns ### Pattern 5: Custom Exceptions with Context Create domain-specific exceptions that carry structured information. ```python class ApiError(Exception): """Base exception for API errors.""" def __init__( self, message: str, status_code: int, response_body: str | None = None, ) -> None: self.status_code = status_code self.response_body = response_body super().__init__(message) class RateLimitError(ApiError): """Raised when rate limit is exceeded.""" def __init__(self, retry_after: int) -> None: self.retry_after = retry_after super().__init__( f"Rate limit exceeded. Retry after {retry_after}s", status_code=429, ) # Usage def handle_response(response: Response) -> dict: match response.status_code: case 200: return response.json() case 401: raise ApiError("Invalid credentials", 401) case 404: raise ApiError(f"Resource not found: {response.url}", 404) case 429: retry_after = int(response.headers.get("Retry-After", 60)) raise RateLimitError(retry_after) case code if 400 <= code < 500: raise ApiError(f"Client error: {response.text}", code) case code if code >= 500: raise ApiError(f"Server error: {response.text}", code) ``` ### Pattern 6: Exception Chaining Preserve the original exception when re-raising to maintain the debug trail. ```python import httpx class ServiceError(Exception): """High-level service operation failed.""" pass def upload_file(path: str) -> str: """Upload file and return URL.""" try: with open(path, "rb") as f: response = httpx.post("https://upload.example.com", files={"file": f}) response.raise_for_status() return response.json()["url"] except FileNotFoundError as e: raise ServiceError(f"Upload failed: file not found at '{path}'") from e except httpx.HTTPStatusError as e: raise ServiceError( f"Upload failed: server returned {e.response.status_code}" ) from e except httpx.RequestError as e: raise ServiceError(f"Upload failed: network error") from e ``` ### Pattern 7: Batch Processing with Partial Failures Never let one bad item abort an entire batch. Track results per item. ```python from dataclasses import dataclass @dataclass class BatchResult[T]: """Results from batch processing.""" succeeded: dict[int, T] # index -> result failed: dict[int, Exception] # index -> error @property def success_count(self) -> int: return len(self.succeeded) @property def failure_count(self) -> int: return len(self.failed) @property def all_succeeded(self) -> bool: return len(self.failed) == 0 def process_batch(items: list[Item]) -> BatchResult[ProcessedItem]: """Process items, capturing individual failures. Args: items: Items to process. Returns: BatchResult with succeeded and failed items by index. """ succeeded: dict[int, ProcessedItem] = {} failed: dict[int, Exception] = {} for idx, item in enumerate(items): try: result = process_single_item(item) succeeded[idx] = result except Exception as e: failed[idx] = e return BatchResult(succeeded=succeeded, failed=failed) # Caller handles partial results result = process_batch(items) if not result.all_succeeded: logger.warning( f"Batch completed with {result.failure_count} failures", failed_indices=list(result.failed.keys()), ) ``` ### Pattern 8: Progress Reporting for Long Operations Provide visibility into batch progress without coupling business logic to UI. ```python from collections.abc import Callable ProgressCallback = Callable[[int, int, str], None] # current, total, status def process_large_batch( items: list[Item], on_progress: ProgressCallback | None = None, ) -> BatchResult: """Process batch with optional progress reporting. Args: items: Items to process. on_progress: Optional callback receiving (current, total, status). """ total = len(items) succeeded = {} failed = {} for idx, item in enumerate(items): if on_progress: on_progress(idx, total, f"Processing {item.id}") try: succeeded[idx] = process_single_item(item) except Exception as e: failed[idx] = e if on_progress: on_progress(total, total, "Complete") return BatchResult(succeeded=succeeded, failed=failed) ```