Phase 8Intermediate9 of 17 in Redis

Redis in Your Application

Client libraries, connection pools and timeouts, Spring Boot and Spring Cache, serialization, distributed sessions, WebSocket fan-out, and how Redis fits next to PostgreSQL and Kafka.

Most Redis incidents start in the application, not in Redis: a pool that's too small, no timeouts, a Java-serialized object nobody can read after a deploy, @Cacheable silently bypassed by self-invocation, or a session store that can't log users out everywhere.

This phase is about integrating Redis properly. Language-specific notes appear only where the client really behaves differently.

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6 topics ~55 min 14 code blocks & diagrams

Start with the first topic

8.1

Client Architecture: Pools, Multiplexing, Timeouts, Retries

Redis clients either pool many connections (Jedis, redis-py) or multiplex many requests over one connection (Lettuce, ioredis, node-redis). Either way, you must set timeouts, bound retries, size pools deliberately, and handle topology changes, or a slow Redis becomes a full application outage.

10 min 2 code practice

8.2

Spring Boot, RedisTemplate, and Spring Cache

Spring Data Redis gives you StringRedisTemplate/RedisTemplate for direct access and the Spring Cache abstraction (@Cacheable, @CachePut, @CacheEvict) backed by RedisCacheManager. Configure serializers and TTLs explicitly, and know the proxy pitfalls that make annotations silently do nothing.

9 min 3 code practice

8.3

Serialization: Size, Speed, Compatibility, Security

How you encode values decides memory, CPU, cross-language readability, safety of rolling deploys, and security. Prefer explicit, schema-aware formats (JSON for readability, MessagePack or Protobuf for size), version your keys, compress large values, and never deserialize untrusted native object formats.

9 min 2 code practice

8.4

Distributed Session Management

Storing sessions in Redis makes app servers stateless: any instance can serve any user, deployments don't log people out, and you can revoke sessions centrally. Design for TTL, logout everywhere, session fixation, concurrent device limits, and what happens when Redis fails.

9 min 1 diagram 2 code practice

8.5

Distributed WebSockets with Redis

When users are connected to different WebSocket servers, a message for user B arriving at server 1 must reach server 2. Redis provides the routing layer: Pub/Sub for fast fan-out, a presence map for connection ownership, and Streams or a database for messages that must survive disconnects.

9 min 1 diagram 1 code practice

8.6

Redis Next to PostgreSQL and Kafka

In most architectures PostgreSQL is the source of truth, Kafka is the durable event backbone, and Redis holds fast derived state. The hard part is keeping them consistent: write to the database first, publish changes through an outbox or CDC, and let consumers update Redis idempotently.