SQLite is the right default database for most applications. With WAL mode enabled, it supports unlimited concurrent readers alongside a single writer that can sustain thousands of transactions per second on modern NVMe storage, handles databases up to 281 TB, and requires zero configuration, zero separate processes, and zero network latency. Unless your application specifically needs horizontal write scaling, multi-node replication, or concurrent writes from multiple processes exceeding roughly 50,000 writes per second, you should start with SQLite and migrate to PostgreSQL only when you hit a concrete, measured limitation - not a theoretical one.

You can build a fully functional feature flag system using a JSON configuration file, environment variable overrides, and a single evaluation function in roughly 100 lines of Python. This gives you gradual rollouts, kill switches, and per-environment toggles without paying for LaunchDarkly , Unleash , or any other SaaS platform. The core pattern is straightforward: define each flag with a name, a boolean or percentage-based rule, and a list of target environments, then evaluate it at runtime through a thin SDK you own and control completely.

Terminal apps used to mean raw curses calls and a lot of pain. Today, Python’s Textual and Rich libraries have flipped that experience entirely. In under 50 lines of Python you can have a full-screen app with styled layouts, interactive widgets, keyboard navigation, and live data updates - no web browser, no Electron, no JavaScript. This post walks through both libraries, shows you how they fit together, and builds up to a complete working example you can extend immediately.

You can find and fix Python memory leaks with three tools that pair well: memray for flame graphs, tracemalloc for line-level tracking, and objgraph for object reference maps. Start with memray to spot the hungry functions. Drop into tracemalloc to find the exact lines. End with objgraph to see why objects won’t get collected. Pair this with generators, __slots__, memory-mapped files, and chunked reads to cut peak memory by 50-80% in data-heavy apps.

You can self-host a private PyPI registry with pypiserver and a private npm registry with Verdaccio . Both run on a single box or inside Docker containers. You get three wins that public registries cannot match: faster installs from a LAN cache, a safe home for private packages, and cover against outages, typosquatting, and supply chain attacks. Both tools are free, open-source, and take under 30 minutes to set up.

Testcontainers spins up real databases and services as Docker containers inside your test suite. Tests run against production-grade PostgreSQL, Redis, or Kafka instead of flaky mocks. The testcontainers-python v4.14.2 library works with pytest . It automates the container life cycle. You get isolated, reproducible integration tests that catch bugs unit tests miss.

Below: setup with pytest, testing services beyond databases, performance patterns, and CI/CD configuration.

Why Mocks and In-Memory Databases Are Not Enough

Mocking db.execute() only checks if your code calls the function. It does not check if the SQL is valid. It also misses schema errors and type mismatches. You might have passing tests while your queries fail in production.