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Redis In-Memory & Distributed Systems – Complete Masterclass Notes

⚡ Redis In-Memory Data Structures & Distributed Systems

High-Performance In-Memory Architecture, Advanced Data Structures (ZSETs, Hashes, Bitmaps), Caching Patterns, Eviction Algorithms (LRU/LFU), Persistence (RDB vs AOF) aur Distributed Locks (Redlock) ka complete practical guide.

Module 01

1️⃣ Why Redis? RAM-First Engine & Single-Threaded Event Loop

Definition: Redis (Remote Dictionary Server) ek open-source, ultra-fast in-memory key-value data store hai jiska primary storage RAM (Random Access Memory) me hota hai. Iska core execution model single-threaded non-blocking I/O multiplexing (epoll/kqueue) par operate karta hai, jo race-condition-free atomic execution deliver karta hai.

Criteria Traditional Disk DB (Postgres/MySQL) Redis In-Memory Engine
Primary Storage SSD / NVMe Disk with Buffer Pool DRAM (Physical RAM)
Read/Write Latency 5 - 25 milliseconds ($10^{-3}$ s) Sub-millisecond (100 - 500 microseconds, $10^{-6}$ s)
Concurrency Control Complex table/row locks & MVCC Atomic Single-Threaded Event Loop (Zero locking overhead)
Module 02

2️⃣ Advanced Core Data Structures & Complexity

Data Structure Key Commands Time Complexity Ideal Production Use Case
Strings SET, GET, INCRBY, SETNX $O(1)$ Session tokens, rate-limiting counters, serialized JSON caches.
Hashes HSET, HGET, HGETALL $O(1)$ per field User profiles, cart metadata (Memory-efficient ziplist/hashtable).
Lists LPUSH, RPOP, BLPOP $O(1)$ ends, $O(N)$ lookup Producer-Consumer message queues, activity feeds.
Sets SADD, SISMEMBER, SINTER $O(1)$ add/check, $O(N)$ union Unique visitor IP sets, tag intersections, friend graphs.
Sorted Sets (ZSET) ZADD, ZRANGEBYSCORE, ZRANK $O(\log N)$ (Skip List) Real-time gaming leaderboards, sliding window rate-limiters. 
# Sliding Window Rate Limiter using ZSET (Allow max 100 requests per 60 seconds)
$now = CURRENT_EPOCH_MS
$window_start = $now - 60000

MULTI
ZREMRANGEBYSCORE "rate_limit:user_102" 0 $window_start
ZADD "rate_limit:user_102" $now $now
ZCARD "rate_limit:user_102"
EXPIRE "rate_limit:user_102" 60
EXEC
# If ZCARD count > 100 → Reject request with HTTP 429 Too Many Requests!
Module 03

3️⃣ Enterprise Caching Strategies & Stampede Prevention

Pattern Application Flow Consistency & Tradeoff
Cache-Aside (Lazy Loading) Read from Cache → If miss, read DB → Write to Cache. Data can get stale until TTL expires. Simplest to maintain.
Write-Through App writes to Cache → Cache synchronously writes to DB. Strong consistency; higher write latency overhead.
Write-Behind (Write-Back) App writes to Cache immediately → Background async batch worker syncs DB. Ultra-fast writes; risk of data loss if Redis crashes before DB commit.
Cache Stampede (Thundering Herd) Defense: Jab koi high-traffic key expire hoti hai aur lakho concurrent requests ek sath database par hammer karte hain:
• Mutex Locking: Sirf pehla thread lock lekar DB hit kare, baaki threads 50ms wait karein.
• Probabilistic Early Expiration (XFetch Algorithm): Key ke actual TTL khatam hone se thoda pehle background thread asynchronously cache refresh kar de ($-\beta \cdot \log(\text{rand}()) \cdot \Delta > \text{TTL}$).
Module 04

4️⃣ Persistence Deep Dive: RDB vs AOF

Persistence Mechanism How It Works Data Loss Risk Recovery Speed
RDB (Redis Database Snapshot) Periodic point-in-time binary dump file (dump.rdb) via bgsave (COW - Copy On Write). High (Lost writes between snapshot intervals, e.g. 5-15 mins). Ultra-Fast: Direct memory restoration from compact file.
AOF (Append Only File) Har write query ko append log file me log karta hai (fsync everysec). Low (Maximum 1 second of transactions lost). Slower (Must sequentially replay commands; uses AOF rewrite).
Module 05

5️⃣ Memory Management & Eviction Policies

Jab Redis ka allocated RAM limit (maxmemory) full ho jata hai, tab naye keys accept karne ke liye configured policy execute hoti hai:

Eviction Policy Target Key Pool Algorithm Mechanism
noeviction None Naye write queries par OOM (Out Of Memory) error throw karta hai.
allkeys-lru All keys Approximated Least Recently Used keys ko evict karta hai. (General Caching default).
volatile-lru Keys with TTL set Sirf un keys ko evict karta hai jinka expiry time configured ho.
allkeys-lfu All keys Least Frequently Used keys ko drop karta hai frequency access counters ke basis par.
Live Redis In-Memory Engine & Cache Simulator
[redis-cli 127.0.0.1:6379]: Connected. Memory: 1.8MB / 2.0MB Max | Eviction: allkeys-lru. Select a scenario above...
Module 06

6️⃣ Distributed Locking: Redlock & Lua Scripts

Multiple microservice nodes ke beech shared resources ko mutate hone se bachane ke liye distributed locks:

# 1. Atomic Lock Acquisition with Random Token (UUID) and 10s auto-release
SET resource_lock "uuid_payload_8829" NX PX 10000

# 2. Safe Atomic Release via Lua Script (Prevents releasing another worker's expired lock)
if redis.call("get", KEYS[1]) == ARGV[1] then
    return redis.call("del", KEYS[1])
else
    return 0
end
Module 07

7️⃣ Redis Streams vs Pub/Sub & Cluster Sharding

  • Pub/Sub (Fire & Forget): Real-time notifications ke liye jahan message retention ki zaroorat nahi hoti (Agar subscriber offline hai toh message permanently drop ho jata hai).
  • Redis Streams (XADD, XREADGROUP): Kafka-like consumer groups, persistence log aur consumer acknowledgment (ACK) capabilities provide karta hai.
  • Redis Cluster Sharding: Data ko 16,384 Hash Slots me divide karke horizontally scale karta hai (Key hash slot = CRC16(key) % 16384).

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