How to Implement Distributed Caching: Redis vs Memcached vs Hazelcast

Distributed caching reduces database load and latency by storing frequently accessed data in RAM. Selecting the right engine depends on data complexity, persistence needs, and scaling strategy.

Architectural Comparison

Memcached

Multithreaded architecture utilizing all CPU cores for high throughput. Uses Slab Allocation to prevent memory fragmentation. Treats values as opaque byte strings, requiring client-side serialization. No built-in persistence; data is volatile.

Redis

Primary command execution remains single-threaded for atomicity, but Redis 6.0+ introduces I/O threading for network read/write operations, and Redis 7.0+ enhances I/O multiplexing for improved concurrency. Supports complex data structures like Lists, Sets, Sorted Sets, and Hashes. Offers persistence via RDB snapshots and AOF logs. Supports clustering for horizontal scaling and high availability.

Hazelcast

Java-based In-Memory Data Grid (IMDG). Distributes data across a cluster using partitioning. Supports Embedded Mode (local memory access) and Client Mode. Offers distributed computing capabilities (EntryProcessors) to move logic to data.

Implementation Examples

Redis Implementation

import redis

r = redis.Redis(host='localhost', port=6379, db=0)

# Set and Get
r.set('user:1001', '{"name": "Alice", "role": "admin"}', ex=3600)
user_data = r.get('user:1001')

Connects to Redis instance, sets a JSON string with a 1-hour TTL, and retrieves it.

Memcached Implementation

from pymemcache.client.base import Client

client = Client(('localhost', 11211))

# Set and Get
client.set('user:1001', '{"name": "Bob", "role": "user"}', expire=3600)
user_data = client.get('user:1001')

Connects to Memcached, stores a serialized string, and retrieves it. Note the lack of native data types.

Hazelcast Implementation

Config config = new Config();
config.getNetworkConfig().getJoin().getMulticastConfig().setEnabled(false);
config.getNetworkConfig().getJoin().getTcpIpConfig().addMember("127.0.0.1").setEnabled(true);

HazelcastInstance hz = Hazelcast.newHazelcastInstance(config);
IMap<String, String> map = hz.getMap("users");

map.put("user:1001", "{\"name\": \"Charlie\", \"role\": \"editor\"}");
String user_data = map.get("user:1001");

Configures a cluster node, retrieves a distributed map, and performs a put/get operation.

Critical Concepts

Eviction Policies

Engines remove data when memory limits are reached. LRU (Least Recently Used) evicts items not accessed recently. LFU (Least Frequently Used) evicts items with low access frequency. TTL (Time To Live) expires keys after a set duration. Redis supports all three; Memcached uses LRU with slab classes; Hazelcast offers configurable eviction.

Cache Warming

Pre-populating cache with frequently accessed data before serving traffic. Reduces cold-start latency and prevents cache storms during deployment or scaling events.

Cache Stampede Mitigation

Also known as thundering herd. Occurs when multiple clients request expired data simultaneously, overwhelming the backend. Mitigation strategies include request coalescing (single request populates cache for all waiters), probabilistic early expiration, and using locks or leases.

Hot Key Handling

Keys with disproportionate access rates can create bottlenecks. Mitigation involves sharding hot keys across multiple cache nodes, adding a local cache layer, or using read replicas for specific keys.

Consistency Models

Strong consistency ensures all reads return the most recent write. Eventual consistency allows temporary divergence before convergence. Caches typically offer eventual consistency; strong consistency requires coordination patterns like write-through with invalidation.

Cache Invalidation Strategies

Write-Through: Application writes to cache and database synchronously. Ensures consistency but increases write latency. Write-Back (Write-Behind): Application writes to cache immediately; database updated asynchronously. Improves write performance but risks data loss on failure. Write-Around: Application writes directly to database; cache populated on read. Reduces cache pollution for write-heavy workloads.

Security Considerations

Redis: Supports TLS for encryption in transit, ACLs (Access Control Lists) for fine-grained user permissions, and password authentication. Redis 6.0+ includes enhanced security features. Memcached: Supports SASL authentication and TLS (version 1.5.13+). Hazelcast: Offers TLS, SSL, and Kerberos authentication with role-based access control.

Selection Criteria

• Memcached: Best for simple read-heavy workloads requiring raw multithreaded throughput. Ideal for caching database query results or rendered HTML fragments. Can outperform Redis for pure string caching due to lower overhead and simpler architecture.
• Redis: Best for complex data structures, pub/sub messaging, rate limiting, and use cases requiring durability. I/O threading in v6.0+ significantly improves network throughput. General-purpose choice for most applications, but Memcached may be preferable for high-throughput, simple-key scenarios.
• Hazelcast: Best for Java-centric applications requiring distributed data structures, shared state across a cluster, and in-memory computation.

Getting Started

1. Define requirements for data types, persistence, and throughput.
2. Select the engine based on the criteria above.
3. Deploy locally using Docker for testing.
4. Integrate the appropriate client library into your application.
5. Implement caching patterns:
   • Cache-Aside: Application checks cache; on miss, loads from database and populates cache. Most common pattern.
   • Read-Through: Cache handles misses by loading from database automatically. Simplifies application code.
   • Write-Through: Application writes to cache; cache synchronously writes to database. Ensures consistency.
6. Monitor hit ratios, memory usage, and eviction rates in production.