git-workflow-best-practices

pytest async-first testing best practices — test layer structure, fixture design, async testing with pytest-asyncio, test data with factory_boy + faker, HTTP mocking with respx, time mocking with freezegun, property-based testing with hypothesis, DB transaction-rollback isolation, flush() pattern for chained fixtures, pytest-xdist parallelism

SQLAlchemy 2.0 async-first best practices — engine and pool config, Mapped[T] models, relationship loading strategies (selectin/select/write_only), query optimization, async sessions and transactions, FastAPI dependency injection

FastAPI production best practices — domain-organized project structure, async correctness, DI patterns with per-request caching and chaining, Pydantic v2 split create/update/response models, Pydantic Settings v2, async testing with httpx.AsyncClient

Python gRPC async-first Kubernetes-native best practices using grpc.aio — server bootstrap with graceful shutdown, shared per-process channel factory, canonical decorator pattern for cross-cutting concerns, max_connection_age_* for HPA rebalancing, client-side LB via dns:/// + round_robin over a headless Service (no service mesh), ERROR_MAP + rich google.rpc.Status error model, client-side translation hierarchy

Python observability best practices using OpenTelemetry — SDK bootstrap with off-switch contract and idempotency guard, auto-instrumentation (gRPC / SQLAlchemy / FastAPI / Logging), the LoggingInstrumentor + structlog correlation pattern with custom log hook and CleanLoggingHandler, two-tier log field taxonomy, span attribute conventions with <service>.<key> prefix for cross-service Tempo search, sensitive-field redaction at the serialization boundary, status taxonomy, metrics cardinality control, tail-based sampling, and Sentry-with-OTel integration