Ehcache vs Redis: When to Choose Each for Your ApplicationCaching can dramatically improve application performance, reduce latency, and lower load on backend systems. Two popular choices are Ehcache and Redis — but they serve different needs and operate under different assumptions. This article compares Ehcache and Redis across architecture, performance, data models, durability, scalability, operational complexity, cost, and typical use cases to help you decide which one fits your application.
Executive summary
- Ehcache is a Java-native, in-process cache designed for JVM applications; best when you need extremely low-latency local caching, simple integration in Java apps, and optionally some clustering via Terracotta.
- Redis is a standalone, networked, in-memory data store supporting rich data structures, persistence, pub/sub, and advanced features; best when you need cross-process/shared cache, data structures beyond simple key-value, persistence, or features like streaming and leaderboards.
1. Architecture and deployment
Ehcache
- Embedded in the JVM as a library. Cache access is local (in-process), offering nanosecond–microsecond latency because no network hop is required.
- Ehcache 3 supports tiered storage: on-heap, off-heap, and disk. For distributed caching and coherent clustering it integrates with Terracotta Server (commercial/open-source combo depending on features) which runs as a separate process.
- Simpler deployment for single-app or microservice where cache is local to each instance.
Redis
- Runs as a separate server process accessed over TCP. Clients connect via network (or Unix socket).
- Single-node or clustered mode (Redis Cluster) provides sharding and high availability via replicas and failover.
- Operates as a central cache/database shared across multiple services and languages.
When to prefer:
- Choose Ehcache when you want ultra-low latency local caching tightly integrated in a Java process.
- Choose Redis when you need a shared cache across services or language ecosystems.
2. Data model and features
Ehcache
- Primarily key-value with Java object storage (serializable or via serializers). Simple and predictable.
- Supports expiry/TTL, eviction policies (LRU, LFU, etc.), and read-through/write-through caching patterns.
- Integrates well with JSR-107 (JCache) API for standardized caching in Java.
Redis
- Rich data structures: strings, hashes, lists, sets, sorted sets, bitmaps, hyperloglogs, streams.
- Advanced operations: atomic counters, Lua scripting, transactions (MULTI/EXEC), pub/sub, geospatial indexes, streams.
- Offers expiration, eviction policies, persistence options (RDB snapshots, AOF), and modules (RedisJSON, RediSearch, RedisGraph).
When to prefer:
- Use Ehcache for straightforward object caching inside Java when data structures beyond key-value aren’t needed.
- Use Redis when you need advanced structures (e.g., counters, sorted sets for leaderboards), messaging (pub/sub), or server-side processing.
3. Performance and latency
Ehcache
- Because it’s in-process, Ehcache provides the lowest possible latency for cache hits — no serialization/network overhead if you store objects on-heap.
- Off-heap storage and disk tiers add overhead but improve capacity.
- Local caches mean each JVM has its own copy, which may increase memory usage across many instances.
Redis
- Network hop adds latency compared to in-process caches, but Redis is highly optimized and often sub-millisecond for nearby clients.
- Serialization cost depends on client and data format; using native strings/bytes minimizes overhead.
- Redis’ single-threaded event loop design gives excellent single-key operation throughput; clustering spreads load across nodes.
When to prefer:
- Choose Ehcache for microsecond-level local cache needs.
- Choose Redis when slightly higher latency is acceptable in exchange for centralization and rich features.
4. Consistency, durability, and persistence
Ehcache
- Local caches are eventually consistent across instances unless you use Terracotta for coherence.
- Persistence options: disk-tiering allows data to survive restarts (depending on configuration), but common use is ephemeral caching.
- With clustered setups (Terracotta), you can have coherent distributed caches and stronger consistency guarantees.
Redis
- Provides configurable durability: RDB snapshots (periodic) and AOF (append-only log) for replayable writes. AOF can be configured for fsync behavior to balance durability vs throughput.
- Replication and Redis Sentinel/Cluster enable failover; strong consistency guarantees vary by setup (e.g., async replication may lose recent writes on failover).
- Redis Cluster provides sharding; cross-shard transactions are limited.
When to prefer:
- Choose Redis if you need optional persistence, replication, and stronger centralized durability semantics.
- Choose Ehcache for ephemeral local caching or when JVM-local persistence suffices.
5. Scalability and high availability
Ehcache
- Scales by replicating local caches on each JVM; capacity scales with number of instances but increases memory duplication.
- Terracotta Server provides centralized storage and coordination for coherent, clustered caching and scalability, but adds operational complexity and potential cost.
Redis
- Horizontal scaling using Redis Cluster with sharding. Read scaling via replicas; writes go to primary nodes for each shard.
- Mature HA options: Sentinel for failover, enterprise offerings with stronger SLAs, and clustering for partitioning.
- Easier to share a single cache across many services and languages.
When to prefer:
- Choose Redis for large-scale, multi-service shared caching with robust HA and sharding.
- Choose Ehcache for per-instance caching or when combined with Terracotta for centralized needs and you’re comfortable with that ecosystem.
6. Operational complexity and ecosystem
Ehcache
- Simpler for single-JVM usage — add dependency and configure caches.
- Terracotta adds an operational component for clustering; maintenance, monitoring, and capacity planning are required.
- Strong Java ecosystem integration (Spring Cache, Hibernate second-level cache via integrations).
Redis
- Requires running and operating one or more Redis servers, managing persistence, failover, and clustering.
- Large ecosystem of client libraries across languages, managed cloud offerings (e.g., AWS Elasticache, Azure Cache for Redis), and a rich tooling ecosystem for monitoring and backup.
- Many third-party modules extend capabilities for search, graph, JSON, time-series.
When to prefer:
- Choose Ehcache for lower ops overhead in JVM-only contexts.
- Choose Redis if you need multi-language support, rich tooling, or cloud-managed convenience.
7. Cost considerations
Ehcache
- Minimal direct infrastructure cost if used as local cache (heap/off-heap within existing app hosts).
- Terracotta (for advanced clustering/capacity) may introduce licensing or additional server costs.
Redis
- Requires dedicated servers or managed service nodes; cost increases with memory footprint and HA/replication needs.
- Managed Redis services reduce ops burden but add recurring costs.
When to prefer:
- Choose Ehcache to avoid extra infra costs when a local cache suffices.
- Choose Redis when the business value justifies dedicated, shared cache infrastructure.
8. Security and access control
Ehcache
- Security is mostly inherited from the host JVM and network environment; local caches are not exposed over the network unless using Terracotta.
- Terracotta and enterprise layers may provide access control and encryption in transit between servers.
Redis
- Exposes network endpoints; secure deployment requires authentication (ACLs), TLS, and network controls.
- Managed services often provide built-in security features (VPC, encryption, IAM integrations).
When to prefer:
- Use Ehcache if you want local-only caches with fewer network-exposure concerns.
- Use Redis when you’re prepared to secure networked services and need centralized access.
9. Typical use cases and decision matrix
Common scenarios where Ehcache fits best:
- JVM applications needing ultra-low-latency local caching (e.g., caching computed values, local lookup tables).
- Hibernate second-level cache or JCache-compliant caching within a Java app.
- When minimizing infrastructure footprint is important and duplication across instances is acceptable.
Common scenarios where Redis fits best:
- Cross-service shared caching across heterogeneous services and languages.
- Use cases needing advanced data structures: counters, leaderboards, queues, pub/sub messaging, streams.
- When persistence, replication, and centralized operational control are required.
Comparison table
| Aspect | Ehcache | Redis |
|---|---|---|
| Deployment model | In-process (JVM) | Standalone server(s) |
| Latency | Lowest (micro–nanoseconds) | Low (sub-ms typical) |
| Data model | Java objects, key-value | Rich data types (strings, hashes, lists, sets, streams) |
| Persistence | Disk tier optional; commonly ephemeral | RDB/AOF persistence configurable |
| Clustering | Terracotta for coherence | Redis Cluster, replicas, Sentinel |
| Multi-language support | Java-centric | Multi-language clients |
| Use cases | Local caching, Hibernate L2 | Shared cache, advanced data structures, messaging |
| Operational cost | Low (local) / higher with Terracotta | Higher (servers/managed) |
10. Practical guidance & checklist
If most answers are “yes” to the following, pick Ehcache:
- Are your apps Java-only and performance-critical for in-process calls?
- Is extremely low latency for cache hits a must?
- Can you tolerate per-instance cache duplication across JVMs?
If most answers are “yes” to these, pick Redis:
- Do multiple services or languages need shared access to cached data?
- Do you need advanced data structures, pub/sub, or persistence?
- Do you require centralized caching with HA and sharding?
Hybrid patterns
- Many architectures use both: Ehcache for ultra-fast local read-through caches and Redis as a centralized cache/coordination store. For example, use Ehcache as a near-cache and Redis as a backing/coherent layer for cross-instance consistency.
Example patterns
- Hibernate L2 cache: Ehcache as local L2 cache for entity caching.
- Rate limiting: Redis with INCR and TTL or Lua scripts for atomic checks.
- Leaderboards: Redis sorted sets for efficient range queries and scores.
- Near-cache: Application uses Ehcache in-process and falls back to Redis when a miss occurs.
11. Migration and testing tips
- Benchmark realistic workloads: measure hit/miss latency, serialization overhead, and network impact.
- Profile memory usage per JVM for Ehcache; plan JVM heap and off-heap accordingly.
- For Redis, size memory for data plus overhead; test persistence and failover behavior.
- Implement metrics and tracing to observe cache hit rate, eviction rate, latency, and operational errors.
Conclusion
Choose Ehcache when you need the fastest possible in-process caching for Java apps with minimal extra infrastructure. Choose Redis when you need a centralized, language-agnostic cache with rich data structures, persistence options, and robust scaling/HA features. Many systems benefit from a hybrid approach that leverages both: Ehcache for near-cache performance and Redis for shared, durable functionality.
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