Ehcache vs. Redis: When to Use Each Cache SolutionCaching is a foundational technique for improving application performance and scalability by keeping frequently accessed data in fast-access storage. Ehcache and Redis are two widely used caching solutions—but they serve different needs, offer different features, and integrate with systems in different ways. This article compares Ehcache and Redis across architecture, feature set, use cases, operational considerations, performance characteristics, and cost, and gives practical guidance for choosing the right tool.
What Ehcache and Redis are (short overview)
Ehcache
- In-process Java cache designed primarily for Java applications.
- Embeds directly in the JVM, offering extremely low-latency reads/writes when data is in the same process.
- Supports on-heap and off-heap storage, disk persistence, and clustering with Terracotta for distributed caching.
Redis
- In-memory data store that runs as a separate server process and is language-agnostic.
- Supports rich data structures (strings, lists, sets, hashes, sorted sets, bitmaps, streams), pub/sub, Lua scripting, transactions, and persistence options (RDB/AOF).
- Easily used across multiple languages and multiple processes/hosts.
Architecture and deployment
Ehcache
- Typically embedded directly into Java application processes (in-process cache).
- Ehcache 3 offers a flexible resource tiering model: on-heap, off-heap, and disk.
- For true distributed caching and high-availability, Ehcache integrates with Terracotta Server Array (TSA), which runs as external processes and coordinates cluster state.
Redis
- Runs as a standalone server (or cluster) accessed over TCP.
- Supports single-node, master-replica and sharded Redis Cluster deployments for scale and availability.
- Clients connect over the network; compatible with almost any language with Redis client libraries.
Data models and features
Ehcache
- Simple key-value caching (Java objects, typically) with time-to-live (TTL) and eviction policies.
- Serialization is often unnecessary when used in-process, reducing overhead.
- Persistence to disk for larger-than-memory caches; off-heap storage reduces GC impact.
- JSR-107 (JCache) compatible; integrates with Spring Cache abstraction.
Redis
- Rich data types and operations (atomic increments, list pops, sorted sets for leaderboards, hyperloglog, bit operations, streams).
- Built-in replication, persistence (RDB snapshots, AOF), and pub/sub messaging.
- Lua scripting for server-side logic, transactions, and optimistic locking (WATCH).
- Expiration, eviction policies, and eviction notifications.
Performance and latency
- Ehcache (in-process) typically gives lower latency than networked caches because it runs inside the same JVM—reads often complete in microseconds.
- Redis, though networked, is extremely fast (sub-millisecond to low-millisecond) due to being in-memory and highly optimized in C. For distributed systems with multiple processes or services, Redis often provides better overall performance and consistency when a shared cache is required.
Consistency and concurrency
- Ehcache in clustered mode (with Terracotta) supports distributed coherence but is more complex to operate.
- Redis provides strong single-node atomic operations; with Redis Cluster, data is sharded and certain multi-key operations become limited or require careful design. Replication is asynchronous by default—read-after-write consistency depends on topology and configuration.
Persistence and durability
- Ehcache offers disk persistence mainly for large caches and restart recovery; it’s not designed primarily as durable storage.
- Redis provides configurable persistence: point-in-time snapshots (RDB) and append-only logs (AOF) for near-durable storage. Redis Enterprise and some configurations offer stronger durability/replication guarantees.
Scalability
- Ehcache scales well for single-JVM or JVM-clustered apps (with Terracotta), but scaling across heterogeneous environments can be more complex.
- Redis is designed for distributed scale via sharding (Redis Cluster) and replication; it’s language-agnostic and well suited to microservices or multi-process architectures.
Operational complexity
- Ehcache embedded usage is simple—add a library, configure caches. Running Terracotta adds operational overhead for clustering.
- Redis requires running and managing separate server instances; operations include backups, persistence tuning, cluster management, and memory management, but ecosystems and managed services (e.g., managed Redis providers) simplify this.
Typical use cases
Use Ehcache when:
- Your application is Java-only and requires ultra-low in-process cache latency.
- You want simple JVM-local caching and minimal serialization overhead.
- You prefer embedding a cache in the app and avoid network calls for hot data.
- You need basic persistence/off-heap to reduce GC pressure.
Use Redis when:
- You need a language-agnostic, centralized cache accessible by many services.
- You require advanced data structures (lists, sorted sets, streams) or pub/sub messaging.
- You need cluster-level scaling, sharding, or cross-process coordination.
- You want built-in persistence and richer operational tooling.
Cost considerations
- Ehcache: lower infrastructure cost when used in-process (no separate servers). Terracotta adds infrastructure and licensing costs if clustering is needed.
- Redis: requires separate server resources and operational costs; managed Redis services increase cost but reduce operational burden.
Practical guidance and decision checklist
- Is your stack exclusively Java and most calls are single-process? Favor Ehcache.
- Do you need accessible cache across services/languages or multicontainer microservices? Favor Redis.
- Need rich data structures or pub/sub? Redis.
- Need the absolute lowest local latency and minimal serialization? Ehcache.
- Want easy horizontal scaling with sharding? Redis.
- Concerned about operational overhead for clustering? Start with in-process Ehcache; consider Redis managed service for cross-process caching.
Example scenarios
- Single JVM web app wanting to cache DB query results for microsecond reads: Ehcache.
- Microservices across Java and Node needing shared session store and pub/sub notifications: Redis.
- Leaderboards and time-series counters requiring sorted sets and atomic increments: Redis.
- Large Java app where heap contention is a problem and off-heap cache plus disk overflow is desired: Ehcache (with Terracotta for distribution if needed).
Migration and hybrid approaches
- Hybrid: use Ehcache for JVM-local hot cache tier plus Redis as a shared, larger, or fallback cache. This tiered approach combines lowest-latency reads with cross-process sharing.
- Migration tips: standardize serialization formats (JSON/MsgPack) when moving between solutions, and add cache warming and fallbacks to avoid thundering herds during cutovers.
Summary (short)
- Ehcache: best for Java in-process caching with microsecond latency, minimal serialization overhead, and simple deployment for single-process apps.
- Redis: best for cross-language, distributed caching, rich data structures, and features like persistence, pub/sub, and clustering.
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