Best Practices for Configuring Ehcache in Spring BootCaching is a powerful tool for improving application performance, reducing latency, and lowering load on databases and external services. Ehcache is a mature, widely used Java caching library that integrates smoothly with Spring Boot. This article covers best practices for configuring Ehcache in Spring Boot applications, from basic setup to advanced tuning, operational concerns, and common pitfalls.
Why use Ehcache with Spring Boot?
- Lightweight and fast: Ehcache runs in-process and is optimized for low-latency reads.
- Flexible persistence options: supports in-memory, disk overflow, and on-disk persistence.
- Integration: Spring Boot’s caching abstraction makes adopting Ehcache straightforward.
- Mature ecosystem: stable, well-documented, and used in production for years.
1. Choose the right Ehcache version and dependency
Always use a maintained Ehcache release compatible with your Spring Boot version. For modern Spring Boot apps prefer Ehcache 3 (org.ehcache:ehcache) rather than Ehcache 2.x. Add the dependency in Maven:
<dependency> <groupId>org.ehcache</groupId> <artifactId>ehcache</artifactId> <version>3.10.0</version> <!-- pick the latest stable version --> </dependency> Or Gradle:
implementation 'org.ehcache:ehcache:3.10.0' If you use Spring Cache abstraction, include Spring Boot Starter Cache (spring-boot-starter-cache) and enable caching with @EnableCaching (or rely on auto-configuration).
2. Integrate with Spring Boot caching abstraction
Spring’s CacheManager abstraction lets you switch caching implementations without changing business logic. To use Ehcache with Spring Boot:
- Add spring-boot-starter-cache.
- Provide an Ehcache configuration (XML or programmatic) and expose a CacheManager bean.
Example of a simple configuration class:
@Configuration @EnableCaching public class CacheConfig { @Bean public JCacheManagerCustomizer cacheManagerCustomizer() { return cm -> { cm.createCache("users", cacheConfiguration()); cm.createCache("books", cacheConfiguration()); }; } private javax.cache.configuration.Configuration<Object, Object> cacheConfiguration() { // create configuration using Ehcache's JSR-107 support } } You can also let Spring Boot auto-configure JCache if you place an ehcache.xml / ehcache.yaml on the classpath.
3. Prefer programmatic (Java) or declarative (YAML/XML) config appropriately
Ehcache 3 supports programmatic Java configuration, XML, and YAML. Choose based on team preference and operational needs:
- Programmatic: compile-time safety, easy to build dynamic configs.
- XML/YAML: easier to change without recompiling, friendly for ops and visibility.
Place configurations in src/main/resources/ehcache.xml or ehcache.yml so Spring Boot can detect and wire them.
4. Define cache names and sizing explicitly
Avoid using default or overly broad cache names. Define the caches your application needs and set sensible sizes:
- Give caches descriptive names (e.g., userById, productCatalog).
- Cap memory usage per cache using resource pools:
Example (XML/YAML or programmatic) should include an on-heap size:
- On-heap entries: limit number of objects stored on the JVM heap.
- Off-heap: use off-heap memory for larger caches to avoid GC pressure.
- Disk persistence: for very large datasets or restart-resilience.
A typical setting:
- small, frequently-accessed caches: on-heap entries (e.g., 500–10,000)
- large read-heavy caches: off-heap (e.g., 100MB–2GB) with eviction policy
5. Use appropriate eviction and expiry policies
Caching strategies should match data characteristics:
- Time-to-live (TTL): for data that becomes stale after a known interval.
- Time-to-idle (TTI): when items should expire after inactivity.
- Eviction strategy: LRU is default for many cases; choose based on access patterns.
Example: cache catalog data with TTL of 10 minutes; cache session-derived data with TTI of 30 minutes.
6. Minimize GC impact — prefer off-heap for large caches
Storing many objects on the JVM heap increases GC pauses. For large caches, configure off-heap resource pools:
- Off-heap reduces GC pressure and allows larger caches.
- Monitor native memory usage; off-heap still consumes host memory.
- Ensure your environment has enough RAM and configure appropriate limits.
Example programmatic snippet (Ehcache 3):
CacheConfigurationBuilder.newCacheConfigurationBuilder( Long.class, String.class, ResourcePoolsBuilder.newResourcePoolsBuilder() .heap(1000, EntryUnit.ENTRIES) .offheap(256, MemoryUnit.MB) ) 7. Persistence and recovery: enable carefully
Ehcache offers disk persistence to maintain cache state across restarts. Use it when re-populating cache is expensive, but weigh trade-offs:
- Disk persistence improves warm-up time after restart.
- It introduces IO and complexity; ensure disk throughput and reliability.
- Configure persistence path and retention policies; avoid using ephemeral container storage unless intentionally acceptable.
8. Monitoring and metrics
Observability is essential. Expose metrics to track hit rate, eviction rate, sizes, and latencies:
- Use Micrometer to bridge Ehcache metrics into Prometheus, Datadog, etc.
- Monitor JVM heap, off-heap usage, GC pauses, and disk IO.
- Track per-cache hit/miss ratios to identify inefficient cache keys or TTLs.
Example: register Ehcache metrics with Micrometer or use JMX beans provided by Ehcache.
9. Cache key design and serialization
Keys should be simple, stable, and small:
- Prefer primitive or short String keys rather than large objects.
- If using composite keys, implement efficient equals/hashCode.
- When caches cross process boundaries (e.g., distributed setups), use explicit serializers. For local Ehcache, Java serialization may be acceptable but consider performance.
10. Concurrency and cache stampede protection
Protect expensive data loads from concurrent miss storms:
- Use lock-aside logic or a singleflight pattern: ensure only one thread loads and populates the cache while others wait.
- Implement Request coalescing or use a LoadingCache-like pattern where the cache loader is atomic.
- Use small TTLs with background refresh if data becomes stale frequently.
11. Testing and local development
- Use smaller cache sizes and TTLs in tests to exercise eviction behavior.
- For integration tests, consider disabling persistence or pointing it to a temp directory.
- Use mocks or in-memory-only configs for unit tests.
12. Security and multi-tenant considerations
- Avoid caching sensitive data unencrypted on disk.
- For multi-tenant apps, include tenant identifiers in cache keys or use separate caches per tenant.
- Secure cache persistence directories and restrict access.
13. Operational tips
- Start with conservative cache sizes and tune with metrics.
- Use rolling restarts when changing disk persistence or resource settings that may corrupt on-disk state.
- Document cache purpose, TTLs, and expected size for future maintainers.
14. Common pitfalls
- Over-caching: caching everything can lead to stale data and wasted memory.
- Using unbounded caches that exhaust memory.
- Storing heavy objects on-heap causing long GC pauses.
- Relying on disk persistence without accounting for disk I/O and container ephemeral storage.
Example: Minimal Ehcache XML for Spring Boot
<config xmlns:xsi='http://www.w3.org/2001/XMLSchema-instance' xmlns='http://www.ehcache.org/v3' xsi:schemaLocation="http://www.ehcache.org/v3 http://www.ehcache.org/schema/ehcache-core.xsd"> <cache alias="userById"> <heap unit="entries">1000</heap> <offheap unit="MB">256</offheap> <expiry> <ttl unit="minutes">10</ttl> </expiry> </cache> <cache alias="productCatalog"> <heap unit="entries">500</heap> <expiry> <ttl unit="minutes">60</ttl> </expiry> </cache> </config> Summary
- Pick Ehcache 3 for modern Spring Boot apps.
- Define caches explicitly, set sizes, and choose on-heap vs off-heap deliberately.
- Use TTL/TTI and eviction policies that match data patterns.
- Monitor metrics and tune iteratively.
- Protect against cache stampedes and avoid caching sensitive data on disk.
Applying these practices will help you get reliable, high-performance caching with Ehcache in Spring Boot.