more-itertools

From a security perspective, more-itertools is remarkably low-risk. It's pure Python with zero dependencies beyond the standard library, which dramatically reduces supply chain attack surface. The code is straightforward iteration utilities—chunking, windowing, grouping—with no network calls, file I/O, or cryptographic operations to misconfigure.

The library follows secure-by-default principles through simplicity: functions handle edge cases predictably without exposing internals through exceptions. Memory exhaustion is the main concern with infinite iterators, but this is inherent to the problem domain and well-documented. Error messages are clean and don't leak sensitive data—you'll get standard Python TypeErrors or ValueErrors that expose only what you'd expect.

In production use, the deterministic behavior makes it easy to reason about. Functions like `chunked()` and `windowed()` handle empty iterables gracefully without surprising exceptions. The lack of configuration means no security settings to get wrong—it just works as documented.

more-itertools is a reliable workhorse that extends Python's itertools with practical utilities you'll reach for constantly. Functions like `chunked()`, `flatten()`, and `first()` feel obvious in retrospect—things that should have been in the stdlib. The API design is intuitive and Pythonic, with sensible defaults and clear naming that makes discovery easy through IDE autocomplete.

The documentation is genuinely good, with clear examples for each function and a helpful categorization system (grouping, filtering, combining, etc.) that helps you find what you need. Error messages are straightforward when you pass wrong types or invalid arguments.

The main frustration is incomplete type hints—many functions have basic annotations but lack full generic type support, so mypy can't always infer return types correctly. You'll occasionally need explicit type annotations or `cast()` calls. Also, with 100+ functions, there's some overlap and you might implement something yourself before discovering it exists. Overall though, it's a productivity booster that reduces boilerplate in data processing pipelines.

In production environments, more-itertools shines because it maintains the lazy evaluation principles of itertools while adding genuinely useful utilities. Functions like `chunked()`, `batched()`, and `peekable()` handle common patterns without materializing entire sequences in memory. I've used it extensively in data pipelines processing millions of records where memory constraints matter, and it consistently performs well.

The library is essentially zero-dependency pure Python, which means no surprises with native extensions or version conflicts. Error handling is predictable—functions raise standard Python exceptions (StopIteration, ValueError) without custom exception hierarchies to learn. The API is stable across versions with clear deprecation notices when changes occur. One gotcha: some functions like `consume()` and `take()` will silently exhaust iterators, which can cause head-scratching bugs if you're not careful about iterator state.

From an operations perspective, there's no connection pooling, threading, or async complexity to configure. It's deterministic, testable, and adds negligible overhead. The documentation includes time/space complexity notes for many functions, which is invaluable for performance-sensitive code. No observability hooks, but given its purely synchronous nature, standard profiling tools work fine.