Vector Database Pinecone Agile Scrum Kanban

Agile สำหรับ Vector Database Projects

โปรเจค Vector Database เช่น Pinecone มีความซับซ้อนสูง ต้องจัดการทั้ง Data Pipeline, ML Embeddings, Search API และ Infrastructure การใช้ Agile Methodology ช่วยให้ส่งมอบงานเป็นส่วนๆได้เร็ว ได้ Feedback เร็ว และปรับเปลี่ยนได้ตาม Requirement ที่เปลี่ยน

Scrum เหมาะกับช่วง Development ที่มี Sprint Goals ชัดเจน Kanban เหมาะกับช่วง Production ที่ต้อง Handle Issues และ Feature Requests ที่มาไม่สม่ำเสมอ หลายทีมใช้ Scrumban ผสมข้อดีของทั้งสอง

Sprint Planning — Vector Search Project

# sprint_planning.py — Sprint Planning สำหรับ Vector Search Project
from dataclasses import dataclass, field
from typing import List, Optional
from enum import Enum
from datetime import datetime, timedelta

class Priority(Enum):
    CRITICAL = 1
    HIGH = 2
    MEDIUM = 3
    LOW = 4

class Status(Enum):
    BACKLOG = "backlog"
    TODO = "todo"
    IN_PROGRESS = "in_progress"
    REVIEW = "review"
    DONE = "done"

@dataclass
class UserStory:
    id: str
    title: str
    description: str
    priority: Priority
    story_points: int
    status: Status = Status.BACKLOG
    sprint: Optional[int] = None
    assignee: Optional[str] = None
    tags: List[str] = field(default_factory=list)
    acceptance_criteria: List[str] = field(default_factory=list)

class SprintPlanner:
    """Sprint Planning สำหรับ Vector Search Project"""

    def __init__(self, team_velocity=40):
        self.velocity = team_velocity
        self.backlog: List[UserStory] = []
        self.sprints = {}

    def add_story(self, story: UserStory):
        self.backlog.append(story)

    def plan_sprint(self, sprint_number, goal):
        """วางแผน Sprint"""
        available = [s for s in self.backlog
                     if s.status == Status.BACKLOG]
        available.sort(key=lambda s: s.priority.value)

        sprint_stories = []
        total_points = 0

        for story in available:
            if total_points + story.story_points <= self.velocity:
                story.sprint = sprint_number
                story.status = Status.TODO
                sprint_stories.append(story)
                total_points += story.story_points

        self.sprints[sprint_number] = {
            "goal": goal,
            "stories": sprint_stories,
            "total_points": total_points,
            "start_date": datetime.now(),
            "end_date": datetime.now() + timedelta(weeks=2),
        }

        return sprint_stories

    def print_sprint(self, sprint_number):
        """แสดง Sprint Board"""
        sprint = self.sprints.get(sprint_number)
        if not sprint:
            print(f"Sprint {sprint_number} not found")
            return

        print(f"\n{'='*60}")
        print(f"Sprint {sprint_number}: {sprint['goal']}")
        print(f"Points: {sprint['total_points']}/{self.velocity}")
        print(f"{'='*60}")

        for status in Status:
            stories = [s for s in sprint["stories"] if s.status == status]
            if stories:
                print(f"\n  [{status.value.upper()}]")
                for s in stories:
                    assignee = s.assignee or "Unassigned"
                    print(f"    [{s.id}] {s.title} ({s.story_points}pts) "
                          f"— {assignee}")

    def print_burndown(self, sprint_number):
        """แสดง Burndown Chart (text)"""
        sprint = self.sprints.get(sprint_number)
        if not sprint:
            return

        total = sprint["total_points"]
        done = sum(s.story_points for s in sprint["stories"]
                   if s.status == Status.DONE)
        remaining = total - done
        pct = done / total * 100 if total > 0 else 0

        print(f"\nBurndown: {done}/{total} points ({pct:.0f}%)")
        bar_done = "#" * int(pct / 2)
        bar_remaining = "-" * (50 - len(bar_done))
        print(f"  [{bar_done}{bar_remaining}]")

# === สร้าง Backlog สำหรับ Vector Search Project ===
planner = SprintPlanner(team_velocity=40)

stories = [
    UserStory("VS-001", "Setup Pinecone Index",
              "สร้าง Index บน Pinecone สำหรับ Product Embeddings",
              Priority.CRITICAL, 5, tags=["infrastructure"],
              acceptance_criteria=["Index created", "SDK connected"]),
    UserStory("VS-002", "Embedding Pipeline",
              "สร้าง Pipeline แปลง Product Data เป็น Embeddings",
              Priority.CRITICAL, 8, tags=["data-pipeline"],
              acceptance_criteria=["Batch processing works", "Error handling"]),
    UserStory("VS-003", "Search API Endpoint",
              "สร้าง REST API สำหรับ Semantic Search",
              Priority.HIGH, 8, tags=["api"],
              acceptance_criteria=["GET /search works", "Pagination"]),
    UserStory("VS-004", "Search Results UI",
              "สร้างหน้า Search Results แสดงผลลัพธ์",
              Priority.HIGH, 5, tags=["frontend"],
              acceptance_criteria=["Results displayed", "Loading state"]),
    UserStory("VS-005", "Metadata Filtering",
              "เพิ่ม Filter ตาม Category Price Brand",
              Priority.MEDIUM, 5, tags=["api", "frontend"]),
    UserStory("VS-006", "Performance Monitoring",
              "ติดตั้ง Monitoring สำหรับ Search Latency",
              Priority.MEDIUM, 3, tags=["monitoring"]),
    UserStory("VS-007", "A/B Testing Framework",
              "สร้าง Framework สำหรับ A/B Test Search Algorithms",
              Priority.LOW, 8, tags=["testing"]),
    UserStory("VS-008", "Image Search",
              "เพิ่มค้นหาด้วยรูปภาพ (Visual Search)",
              Priority.LOW, 13, tags=["ml", "api"]),
]

for s in stories:
    planner.add_story(s)

# Plan Sprint 1
sprint1 = planner.plan_sprint(1, "Search API พร้อมใช้งาน")
planner.print_sprint(1)

# จำลองว่าบาง Story เสร็จแล้ว
stories[0].status = Status.DONE
stories[1].status = Status.IN_PROGRESS
stories[1].assignee = "Dev A"
stories[2].status = Status.TODO
stories[2].assignee = "Dev B"

planner.print_sprint(1)
planner.print_burndown(1)

Kanban Board สำหรับ Production

# kanban_board.py — Kanban Board สำหรับ Vector Search Production
from collections import defaultdict
from datetime import datetime

class KanbanBoard:
    """Kanban Board พร้อม WIP Limits"""

    def __init__(self, wip_limits=None):
        self.columns = ["Backlog", "To Do", "In Progress",
                        "Review", "Done"]
        self.wip_limits = wip_limits or {
            "To Do": 5, "In Progress": 3, "Review": 2,
        }
        self.cards = []

    def add_card(self, title, card_type="task", priority="medium",
                 column="Backlog"):
        card = {
            "id": len(self.cards) + 1,
            "title": title,
            "type": card_type,
            "priority": priority,
            "column": column,
            "created": datetime.now(),
            "moved": datetime.now(),
        }
        self.cards.append(card)
        return card

    def move_card(self, card_id, to_column):
        """ย้าย Card ตรวจสอบ WIP Limit"""
        card = next((c for c in self.cards if c["id"] == card_id), None)
        if not card:
            print(f"Card {card_id} not found")
            return False

        # ตรวจสอบ WIP Limit
        current_count = sum(1 for c in self.cards if c["column"] == to_column)
        limit = self.wip_limits.get(to_column, float("inf"))

        if current_count >= limit:
            print(f"WIP Limit reached for '{to_column}' "
                  f"({current_count}/{limit})")
            return False

        card["column"] = to_column
        card["moved"] = datetime.now()
        return True

    def display(self):
        """แสดง Board"""
        print(f"\n{'='*70}")
        print(f"Kanban Board — Vector Search Production")
        print(f"{'='*70}")

        for col in self.columns:
            cards_in_col = [c for c in self.cards if c["column"] == col]
            limit = self.wip_limits.get(col, "-")
            print(f"\n  [{col}] ({len(cards_in_col)}/{limit})")

            for card in cards_in_col:
                type_icon = {"bug": "BUG", "feature": "FEA",
                            "task": "TSK"}.get(card["type"], "TSK")
                print(f"    [{type_icon}] #{card['id']} {card['title']} "
                      f"({card['priority']})")

    def metrics(self):
        """คำนวณ Metrics"""
        done = [c for c in self.cards if c["column"] == "Done"]

        if done:
            cycle_times = []
            for c in done:
                ct = (c["moved"] - c["created"]).total_seconds() / 3600
                cycle_times.append(ct)

            avg_ct = sum(cycle_times) / len(cycle_times)
            print(f"\nMetrics:")
            print(f"  Total Cards:     {len(self.cards)}")
            print(f"  Done:            {len(done)}")
            print(f"  Throughput:      {len(done)} cards")
            print(f"  Avg Cycle Time:  {avg_ct:.1f} hours")

# === Production Kanban Board ===
board = KanbanBoard(wip_limits={
    "To Do": 5, "In Progress": 3, "Review": 2,
})

# เพิ่ม Cards
board.add_card("Fix: Search timeout on large queries", "bug", "high", "In Progress")
board.add_card("Add rate limiting to Search API", "task", "high", "In Progress")
board.add_card("Investigate slow embedding generation", "bug", "medium", "To Do")
board.add_card("Add caching for frequent queries", "feature", "medium", "To Do")
board.add_card("Update Pinecone SDK to v3", "task", "low", "Backlog")
board.add_card("Add search analytics dashboard", "feature", "medium", "Backlog")
board.add_card("Optimize Docker image size", "task", "low", "Backlog")
board.add_card("Fixed CORS issue on /search endpoint", "bug", "high", "Done")

board.display()
board.metrics()

CI/CD Pipeline สำหรับ Vector Search

# === GitHub Actions CI/CD สำหรับ Vector Search Service ===
# .github/workflows/vector-search-ci.yml

name: Vector Search CI/CD
on:
  push:
    branches: [main, develop]
  pull_request:
    branches: [main]

env:
  PINECONE_API_KEY: }
  PINECONE_INDEX: search-staging

jobs:
  test:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - uses: actions/setup-python@v5
        with:
          python-version: "3.11"
          cache: pip

      - name: Install Dependencies
        run: pip install -r requirements.txt -r requirements-dev.txt

      - name: Lint
        run: |
          ruff check src/
          mypy src/ --ignore-missing-imports

      - name: Unit Tests
        run: pytest tests/unit/ -v --cov=src --cov-report=xml

      - name: Integration Tests
        run: pytest tests/integration/ -v -m "not slow"
        env:
          PINECONE_INDEX: search-test

      - name: Upload Coverage
        uses: codecov/codecov-action@v4

  deploy-staging:
    needs: test
    if: github.ref == 'refs/heads/develop'
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4

      - name: Build Docker Image
        run: docker build -t vector-search:staging .

      - name: Deploy to Staging
        run: |
          kubectl set image deployment/vector-search \
            vector-search=vector-search:staging \
            -n staging

  deploy-production:
    needs: test
    if: github.ref == 'refs/heads/main'
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4

      - name: Build Docker Image
        run: docker build -t vector-search:} .

      - name: Deploy to Production (Canary)
        run: |
          kubectl set image deployment/vector-search-canary \
            vector-search=vector-search:} \
            -n production

      - name: Smoke Tests
        run: pytest tests/smoke/ -v

      - name: Promote to Full Production
        run: |
          kubectl set image deployment/vector-search \
            vector-search=vector-search:} \
            -n production

Best Practices

• Sprint Goal ชัดเจน: ทุก Sprint ต้องมี Goal ที่วัดผลได้ เช่น "Search API รองรับ 100 QPS"
• WIP Limits: จำกัด Work In Progress ป้องกัน Context Switching ที่ลดประสิทธิภาพ
• Definition of Done: กำหนดชัดว่า "เสร็จ" คือ Code + Tests + Review + Deploy to Staging
• Retrospective: ทำทุก Sprint เรียนรู้จากปัญหาและปรับปรุง Process
• Continuous Deployment: ใช้ CI/CD Deploy อัตโนมัติ ลด Manual Steps
• Metrics-driven: ติดตาม Velocity, Cycle Time, Lead Time ใช้ข้อมูลตัดสินใจ

Agile Scrum คืออะไร

Agile Framework ทำงานเป็น Sprint 2-4 สัปดาห์ มี Product Owner, Scrum Master, Development Team มี Sprint Planning, Daily Standup, Sprint Review, Retrospective ใช้ Backlog จัดลำดับงาน

สรุป

การใช้ Agile (Scrum/Kanban) กับ Vector Database Project ช่วยจัดการความซับซ้อนได้ดี ใช้ Scrum ช่วง Development มี Sprint Goals ชัดเจน ใช้ Kanban ช่วง Production จำกัด WIP ลด Bottleneck CI/CD Pipeline Deploy อัตโนมัติ ติดตาม Metrics เช่น Velocity Cycle Time สิ่งสำคัญคือ Retrospective ทุก Sprint เพื่อปรับปรุง Process อย่างต่อเนื่อง