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In-Memory Caching & Event Loop Architecture

Eliminate DB latency bottlenecks using single-threaded event loops, Epoll I/O multiplexing, and LRU/LFU memory eviction.

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intermediate

💡 Why It Matters

Databases stall under concurrent read spikes. Placing an in-memory cache layer in front of PostgreSQL or Cassandra reduces p99 read latencies from 50ms to under 1ms, protecting disk I/O.

🌐 Real-World Production Usages & Examples

API Gateway Rate-Limiting

Scenario: Scaling API gateway checking 50,000 requests/sec against user quotas.

Outcome: Redis INCR + EXPIRE executes atomically in <1ms without DB locks.

Stripe Session Caching

Scenario: High-volume user authentication token lookups.

Outcome: Cache-Aside pattern delivers 99.4% cache hit ratio, shielding auth DB.

✓ When to Use

  • •Read-heavy workloads (>80% reads, <20% writes).
  • •Session storage, rate-limiting counters, and leaderboards.
  • •Expensive compute queries or third-party API response caching.

✕ When NOT to Use

  • •Strict ACID transactional write-heavy workloads.
  • •Datasets larger than available RAM without tiering.
  • •Data with near-zero read reuse frequency.

🔑 Core Architecture & Rules

  • 1Redis Single-Threaded Event Loop handles commands sequentially in CPU L1/L2 cache without thread context switching.
  • 2I/O Multiplexing (epoll/kqueue) allows one OS thread to monitor 10,000+ client TCP socket connections asynchronously.
  • 3Eviction policies (allkeys-lru, volatile-lru, volatile-ttl) automatically reclaim memory when maxmemory bounds are reached.

⚖️ Architectural Trade-Off Analysis

Concurrency Model

PROSingle thread avoids deadlocks, lock contention, and OS context switching overhead.
CONLong-running O(N) operations (e.g. KEYS *) block all concurrent clients.

Write Strategy (Cache-Aside vs Write-Through)

PROCache-Aside handles cache failures gracefully by falling back to DB.
CONCache stale risk during DB updates requires strict invalidation or TTL.

⚠️ Failure Modes & Mitigations

Cache Stampede (Thundering Herd)

Hot cache key expires simultaneously under high traffic, causing 10,000 concurrent DB queries.

Mitigation: Use probabilistic early expiration (XFetch algorithm) or distributed lock mutex (SETNX).

Single-Thread Blockage

Executing KEYS * or large HGETALL halts the Redis event loop for seconds.

Mitigation: Use cursor-based SCAN / HSCAN instead of blocking full keyspace queries.

🛠️ Example Usages

Reviewed practical implementations and concrete code walkthroughs for In-Memory Caching & Event Loop Architecture.

2 Reviewed Examples
code exampleexecutable tested

Single-Threaded Epoll Event Loop in C

Demonstrates non-blocking socket I/O multiplexing with Linux epoll_wait in a single-threaded server loop.

Lang: cPractice in scenario →
system design problemexecutable tested

High-Concurrency Flash Sale Seat Reservation System

Design a system that handles 100,000 concurrent seat reservations without double booking or crashing the database.

⬡Explore Related Concepts & Guides

Curated technical reading and direct concept relationships.

Part of:Distributed Systems & Storage Learning Journey →Track progress
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