Where's the Next 100x? 🚀

🔄 Beyond 2PL: Rethinking Concurrency

Two-Phase Locking is just one approach in a vast space of possibilities. The next 100x performance gain comes from fundamentally different architectures.

Explore non-locking systems, timestamp ordering, and micro-granularity approaches that break traditional barriers.

The Opportunity Space 📊

Most schedules in the real world aren't conflict-serializable under 2PL - but there are MANY more valid schedules we can safely execute!

Conflict serializability venn diagram showing the larger space of possible schedules

The blue and white areas represent untapped potential - valid, safe schedules that current locking systems don't generate.

Three Paths to the Next 100x 🛣️

🔬 Micro-Locking: Surgical Precision

How it works:
• Fine-grained locks on specific data elements
• Lock only exactly what you need
• Advanced deadlock detection algorithms
• Hierarchical locking for scalability

Performance: Maximum safe concurrency
Trade-off: Complex lock management overhead
Best for: High-contention concurrent systems

🌲 LSM-Tree Approach: No Locks Needed

How it works:
• Append-only writes to immutable structures
• No update-in-place operations
• Background compaction merges data
• Reads from multiple levels

Limits: Needs value to be mergeable (e.g., likes += 1)
Performance: Unlimited write throughput
Trade-off: Eventually consistent reads
Best for: Write-heavy workloads (logs, time-series, IoT)

⏰ Timestamp Ordering: Global Sequencing

How it works:
• Assign unique timestamps to all operations
• Execute operations in timestamp order
• Abort conflicts, not wait for locks
• Deterministic without coordination

Performance: No lock contention delays
Trade-off: May abort late operations
Best for: Distributed systems, global ordering needs

Choosing Your Path 🎯

📊 For Analytics Workloads

Use LSM Trees or columnar stores. Append-only writes with background compaction eliminate lock contention entirely.

Examples: ClickHouse, BigQuery, Snowflake

🌐 For Distributed Systems

Use Timestamp Ordering with vector clocks. Global ordering without distributed locking protocols.

Examples: Spanner, Calvin, FaunaDB

💰 For High-Value Transactions

Use Micro-Locking with sophisticated deadlock detection. Maximum concurrency with safety guarantees.

Examples: PostgreSQL, SQL Server, Oracle

Interview Gold: 3 Real-World Problems

One line: When you see "high write throughput," "global coordination," or "fine-grained contention," think beyond 2PL to these architectural alternatives.

10M Ops/sec

📱 TikTok's Viral Video Storm

Problem: Video goes viral during Super Bowl halftime. 10M concurrent users like, comment, share, and follow creator simultaneously. Single video entity becomes hottest contention point in the system.

Approach: Micro-Locking

Design: Separate locks for like_count, comment_list, share_count, follower_count • Field-level locking instead of entity-level • Lock-free atomic counters for simple increments • Sophisticated deadlock detection
Performance: 10M ops/sec on single entity, 99.9% concurrency vs 0.01% with entity locks
Why: Entity-level lock = 1 operation at a time. Field-level locks = 4 operations in parallel. Atomic counters eliminate locks entirely for simple increments.

100M+ Scale

🎵 Spotify's Play Counter Chaos

Problem: Ed Sheeran's new song gets 10M plays in first hour. Every play increments counters for: total plays, daily plays, monthly plays, artist stats, genre stats, playlist positions. Traditional locking creates massive contention bottlenecks.

Approach: LSM-Tree (No Locks)

Design: Append all play events to immutable SSTables • Background process aggregates counts • Real-time estimates from recent events • No write-write conflicts possible
Performance: 500K writes/sec, 50ms lag for exact counts, unlimited throughput
Why: 2PL would serialize all counter updates. 10M concurrent incrementers = 10M lock waits vs append-only writes that never conflict.

10K Agents

🤖 AI Agent Collaboration Platform

Problem: 10,000 AI agents simultaneously update shared knowledge graph for real-time research. Each agent reads facts, derives new knowledge, and writes back discoveries. Traditional 2PL creates deadlock storms between interdependent agents.

Approach: Timestamp Ordering

Design: Each agent gets unique timestamp when starting operation • All updates applied in timestamp order • Conflicts detected by comparing timestamps • Late agents abort and retry with new timestamp
Performance: 1M operations/sec, 5% abort rate, deterministic results
Why: 2PL with 10K agents creates circular wait chains. Global timestamp ordering eliminates deadlocks - conflicts resolve by "first timestamp wins" rule.