๐Ÿš€ Phase 6 โ€ข Track 07 โ€” Senior Masterclass

Architecture Patterns & Decoupled Domain Modeling

Apply the Dependency Inversion Principle from Architecture Patterns with Python (Cosmic Python). Pure domain aggregates contain zero framework imports, decoupled behind Repository abstractions, Unit of Work transaction managers, and CQRS query projections.

๐Ÿ‘๏ธ Views: 3,778 Page Views
๐Ÿš€ Senior Python Interactive Systems Studio

Senior Systems Studio: Architecture Patterns & Decoupled Domain Modeling

๐Ÿ‘๏ธ 3,778 Total Page Views

Select any senior topic below to inspect pure domain aggregate invariant enforcement, test in-memory Repository and Unit of Work (UoW) transaction context managers, and execute CQRS write vs raw read path projections.

1๏ธโƒฃ Track 01: Data Flow
Generators, Bytecode & Primitives
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2๏ธโƒฃ Track 02: Metaprogramming
Descriptors, __new__, MRO super()
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3๏ธโƒฃ Track 03: Context & Robustness
Context Managers, contextvars, Decimal
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4๏ธโƒฃ Track 04: Craftsmanship
EAFP vs LBYL, Duck Typing, Guards
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5๏ธโƒฃ Track 05: Type Hints
Protocol, TypedDict & assert_never
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6๏ธโƒฃ Track 06: Testing
Rollbacks, Fakes & Hypothesis
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7๏ธโƒฃ Track 07: Architecture
Aggregates, UoW & CQRS
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8๏ธโƒฃ Track 08: DB & Concurrency
B-Tree, Locks & ORM Loading
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9๏ธโƒฃ Track 09: Backend & API
Idempotency, Backoff & Cursor
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๐Ÿ”Ÿ Track 10: Async & GIL
TaskGroup, GIL & to_thread
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1๏ธโƒฃ1๏ธโƒฃ Track 11: Performance
Profilers, __slots__ & RAM
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1๏ธโƒฃ2๏ธโƒฃ Track 12: Packaging
Wheels, Lockfiles & uv
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1๏ธโƒฃ3๏ธโƒฃ Track 13: Quality
Ruff, Pre-Commit & CI
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1๏ธโƒฃ4๏ธโƒฃ Track 14: Production
Linux, Docker & Logs
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1๏ธโƒฃ5๏ธโƒฃ Track 15: Distributed
Outbox, Brokers & Sagas
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1๏ธโƒฃ6๏ธโƒฃ Track 16: Security
SSRF, Crypto & Defense
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1๏ธโƒฃ7๏ธโƒฃ Track 17: Patterns
Pythonic GoF Idioms
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1๏ธโƒฃ8๏ธโƒฃ Track 18: Code Taste
Open-Source Forensics
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1๏ธโƒฃ9๏ธโƒฃ Track 19: Packages
Production Directory
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2๏ธโƒฃ0๏ธโƒฃ Track 20: Python vs World
Global Benchmarks & %
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๐Ÿ“Œ Select Senior Competency (4 Core Topics): Showing 5 Topics for Track 01
โšก Topic 1 of 4 โฑ๏ธ 5 min interactive
Phase 1 โ€ข Track 01

1. Data Pipelines & Sub-Generator Streaming (`yield` / `yield from`)

Decouple data production from consumption. Generators stream infinite series, paginate database records, and process multi-gigabyte logs without unbounded RAM allocation.

๐Ÿ’ก Senior Engineering Architecture Principles

  • Lazy Evaluation: Computes items on-demand, holding O(1) memory state regardless of payload length.
  • Sub-generator Delegation: `yield from` establishes a bidirectional channel forwarding iterators and exception signals.
  • Memory Safety: Prevents system crash and OutOfMemoryError (OOM) during heavy log or DB ingestion.
senior_domain_aggregate.py
Ready (Python 3.13 Active)
๐Ÿ–ฅ๏ธ Senior Sandbox Execution Output & Diagnostics stdout
=== EXECUTION RESULT ===

--- Streaming Node: node-us-east ---
Log Entry: 2026-10-07 [INFO] Server booted
Log Entry: 2026-10-07 [WARN] Memory spike
--- Streaming Node: node-eu-west ---
Log Entry: 2026-10-07 [INFO] DB connected
Log Entry: 2026-10-07 [ERROR] Timeout 500ms
โœ… Test Passed: Generator yielded 4 log entries lazily with zero heap buffering overhead!
โšก 30-Second Architecture Summary

Cosmic Python Architecture & Dependency Inversion

  • Dependency Inversion: Domain aggregates use standard Python types (`dataclass`, `int`, `str`) with zero framework, ORM, or database imports.
  • Repository & Unit of Work: Repositories emulate in-memory collections; UoW context managers (`with uow:`) orchestrate atomic transaction boundaries.
  • CQRS Separation: Write paths route commands through aggregates to enforce invariants; Read paths query raw database views directly for sub-millisecond projections.
๐Ÿ›๏ธ Cosmic Python Architecture Diagram
       [ Outside World ] (HTTP Requests, CLI, Webhooks, Cron)
              โ”‚
              โ–ผ
  โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”
  โ”‚ 1. Entrypoints / Ports (FastAPI, CLI, Flask)          โ”‚
  โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ฌโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜
                          โ”‚ Invokes
                          โ–ผ
  โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”
  โ”‚ 2. Service Layer / Message Bus (Orchestration)         โ”‚
  โ”‚    - Coordinates Unit of Work                          โ”‚
  โ”‚    - Routes Commands & Dispatches Domain Events        โ”‚
  โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ฌโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ฌโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜
                  โ”‚ Uses                  โ”‚ Persists via
                  โ–ผ                       โ–ผ
  โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”  โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”
  โ”‚ 3. Domain Model (Aggregates) โ”‚  โ”‚ 4. Adapters          โ”‚
  โ”‚    - Pure Business Rules     โ”‚  โ”‚    - Repositories    โ”‚
  โ”‚    - Invariants & State      โ”‚  โ”‚    - DB / Filesystem โ”‚
  โ”‚    (Zero external imports)   โ”‚  โ”‚    (Implements UoW)  โ”‚
  โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜  โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜

1. Domain Model (Aggregates) & Repository Pattern

Aggregates are pure Python classes enforcing domain invariants. The Repository pattern presents persistence as an in-memory collection, hiding database engine logic:

from dataclasses import dataclass
from typing import Protocol

@dataclass
class AllocationLine:
    order_id: str
    sku: str
    qty: int

class Batch:
    """Pure Domain Aggregate with Zero External Imports."""
    def __init__(self, ref: str, sku: str, qty: int):
        self.ref = ref
        self.sku = sku
        self._available_qty = qty

    def can_allocate(self, line: AllocationLine) -> bool:
        return self.sku == line.sku and self._available_qty >= line.qty

    def allocate(self, line: AllocationLine) -> None:
        if not self.can_allocate(line):
            raise ValueError(f"Cannot allocate {line.qty} of {line.sku}")
        self._available_qty -= line.qty

class AbstractBatchRepository(Protocol):
    def add(self, batch: Batch) -> None: ...
    def get(self, ref: str) -> Batch | None: ...

2. Unit of Work (UoW) & Service Layer

The Unit of Work manages transaction lifecycles using context managers. The Service Layer orchestrates business operations without coupling entrypoints to database engines:

class AbstractUnitOfWork(Protocol):
    batches: AbstractBatchRepository

    def __enter__(self) -> Self: ...
    def __exit__(self, exc_type, exc_val, exc_tb) -> None: ...
    def commit(self) -> None: ...
    def rollback(self) -> None: ...

def allocate_order_service(line: AllocationLine, uow: AbstractUnitOfWork) -> str:
    """Service layer coordinating domain workflow via Unit of Work."""
    with uow:
        batch = uow.batches.get_by_sku(line.sku)
        if not batch:
            raise ValueError(f"No batch found for SKU {line.sku}")
        batch.allocate(line)
        uow.commit()
        return batch.ref

3. CQRS: Write Path vs Direct Read Queries

Command Query Responsibility Segregation separates complex state mutation (Write Path) from fast, raw database reads (Read Path) projecting plain `TypedDict` data directly:

from typing import TypedDict

class BatchReadView(TypedDict):
    batch_ref: str
    sku: str
    available_qty: int

def query_allocations_read_path(db_connection, sku: str) -> list[BatchReadView]:
    """CQRS Read Path: Bypasses domain aggregates; executes raw SQL."""
    cursor = db_connection.execute(
        "SELECT ref, sku, available_qty FROM batches WHERE sku = ?", (sku,)
    )
    return [
        {"batch_ref": row[0], "sku": row[1], "available_qty": row[2]}
        for row in cursor.fetchall()
    ]

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