Perfect 👌 thanks for sharing the transcript in full — now I see exactly what the instructor covered. Let me distill this into deep structured notes + mindmap-style breakdown you can carry forward. I'll cover Database design decisions and Caching strategies separately, and highlight where decisions impact scaling, concurrency, delegation, communication (the broader NFRs).

🏛 System Design Decisions for Blogging Platform (Lecture Deep Notes)

1. Database Design Decisions

1.1 Deletion Strategy

• Hard Delete

  • Row physically removed from DB.

  • ✅ Pros: immediate space recovery.

  • ❌ Cons:

    • No recoverability for users.
    • Loss of historical/audit/legal evidence.
    • Loss of analytics/ML training data.
    • Expensive due to B+Tree rebalancing on disk every delete.

• Soft Delete

  • Mark row as deleted_at timestamp.

  • ✅ Pros:

    • Recoverability ("Trash Bin" feature).
    • Auditing / compliance (legal evidence preserved).
    • Archival possible → ML/analytics on “deleted” data.
    • DB operations faster (avoids frequent rebalancing).

  • ❌ Cons:

    • Requires additional queries (WHERE deleted_at IS NULL).
    • Storage overhead if not purged.

• Recommended Hybrid

  • Soft delete immediately.
  • Hard delete later in batched jobs (off-peak).

🔑 Takeaway: In user-generated platforms, always default to soft delete (compliance + recoverability + analytics).

1.2 Large Text Columns (Blog Body)

• Tables store rows together on disk → scanning entire row even if you select few columns.

• Problem: Large text fields (5KB–50KB) bloat row size → expensive table scans.

• DB Optimization:

  • For long text (TEXT/BLOB):

    • DB stores large content in separate location.
    • Row contains a pointer/reference.
    • ✅ Keeps rows compact → fast scans for metadata queries (title, author, views).
    • ❌ Fetching body requires an extra disk read (only when needed).

  • For short text (VARCHAR, CHAR):

    • Stored inline with row.
    • ✅ Efficient if frequently queried together.

• Design Pattern:

  • Split into two tables if necessary:

    • blogs_metadata (id, title, author, views, published_at, deleted_at)
    • blogs_content (blog_id, body, type)

  • Prevents slowing down common “list views”.

🔑 Takeaway: Always minimize row size for high-throughput queries. Store heavy fields out-of-line or in separate tables.

1.3 Timestamps / Datetime Columns

• Option 1: DATETIME / TIMESTAMP type

  • ✅ Convenient (ORM auto-converts).
  • ❌ DB must parse/compare strings → overhead.

• Option 2: Epoch Integer (Unix timestamp)

  • ✅ Efficient: integers are CPU-optimized.
  • ✅ Small fixed storage size (e.g., 4–8 bytes).
  • ✅ Indexing & range queries super fast.
  • ❌ Not human-readable.
  • ❌ Timezone handling must be external (store UTC).

• Option 3: Custom Integer Format (e.g., YYYYMMDD)

  • ✅ Efficient integer.
  • ✅ Human-readable.
  • ✅ Works well for date-range queries.
  • ❌ Loses granularity (no time part).

• Real Example (RedBus 2016):

  • Migrated huge tables suffering from datetime perf bottleneck.
  • Switched to integer YYYYMMDD.
  • ✅ Saw significant query speedup.

🔑 Takeaway:

• Use epoch int for efficiency & large-scale range queries.
• Use DATETIME only if timezone handling & readability are critical.
• For massive datasets with date-based queries, consider YYYYMMDD integer trick.

2. Caching Strategies

2.1 What Caching Really Is

• Not just Redis/in-memory.

• Definition: Any mechanism that saves an expensive operation (I/O, network, compute) by reusing precomputed results.

• Examples:

  • Browser cache.
  • CDN (Cloudflare, Akamai).
  • Disk cache on API server.
  • Redis / Memcached (classic).
  • Even DB buffer pool = cache.

🔑 Mental Model: At every layer (client → CDN → API → DB → disk), ask: Can I avoid recomputing/reading by caching here?

2.2 Standard Remote Cache Pattern

• Flow:

  • Request → Check cache → Hit → Return.
  • Miss → Query DB → Populate cache → Return.

• ✅ Saves DB load for hot content.

2.3 Advanced Techniques

(a) Debouncing / Thundering Herd Protection

• Problem:

  • Popular blog cache miss → 10K requests hit DB simultaneously.
  • DB collapses.

• Solution:

  • First request sets "populating flag" (local var or Redis key).
  • Other requests wait for cache to be filled instead of hitting DB.
  • Once filled, all get data from cache.

• Implementation:

  • Local (per server): in-memory flag.
  • Distributed: Redis "SETNX" lock.

• Analogy: One person buys 50 movie tickets for group; others wait.

🔑 Prevents redundant work and DB meltdowns under load.

(b) Load Leaking

• Problem:

  • High cache-hit (>95%) = DB underutilized.
  • Ops team scales DB down too aggressively.
  • If cache fails → DB overwhelmed instantly (cold caches).

• Solution:

  • Intentionally leak 5–10% of traffic to DB even on cache hits.

  • Keeps:

    • DB buffer pools warm.
    • DB load baseline steady.
    • System resilient to cache failure.

• Implementation:

  • On cache hit, probabilistically issue async DB query.

🔑 Cache should be an optimization, not a single point of failure.

2.4 Golden Rule

• Cache = performance enhancement.
• System should degrade gracefully if cache dies, not collapse.

3. Key Takeaways for Blogging Platform

• Deletion: Always soft delete first. Batch hard delete later.

• Large Text: Store blog body out-of-line / separate table for performance.

• Datetime: Prefer integers (epoch/yyyymmdd) for scale. Be mindful of timezone & query patterns.

• Caching:

  • Think beyond Redis.
  • Implement debouncing to prevent thundering herd.
  • Use load leaking to avoid DB “atrophy.”
  • Cache at every possible layer (client, CDN, API, DB buffer).

🧩 Mindmap Structure (Textual)

System Design (Blogging Platform)
├── Database
│   ├── Deletion
│   │   ├── Hard Delete
│   │   └── Soft Delete → recommended + batch hard delete
│   ├── Large Text
│   │   ├── Inline (short text)
│   │   └── Out-of-line / Separate table (long text)
│   ├── Timestamps
│   │   ├── DATETIME
│   │   ├── Epoch Integer
│   │   └── YYYYMMDD Integer (RedBus example)
│   └── Compliance
│       ├── Archival
│       ├── Auditing (police/legal cases)
│       └── Recoverability
├── Caching
│   ├── Concept: any shortcut to avoid expensive ops
│   ├── Layers
│   │   ├── Client cache
│   │   ├── CDN
│   │   ├── API disk cache
│   │   ├── Redis/Memcached
│   │   └── DB buffer pools
│   ├── Techniques
│   │   ├── Debouncing → avoid thundering herd
│   │   └── Load leaking → keep DB warm
│   └── Principle: Cache = enhancement, not SPOF
└── NFR Alignment
    ├── Scaling → caching layers, batch deletes
    ├── Concurrency → debouncing
    ├── Delegation → CDN, async cache fill
    ├── Communication → logs, audit trails

Would you like me to now expand this into a polished visual mindmap (XMind/Markdown-based) so you can study from it, or keep it as text + bullets?