EhCache 3.10.x Memory Allocation Explained

EhCache 3.10.x allocates heap memory dynamically as entries are added to the cache rather than pre-allocating the configured heap size upon initialization. The 50MB heap configuration serves as a maximum limit that the cache will not exceed, rather than a reserved amount of memory that’s allocated upfront. This dynamic allocation approach allows for more efficient memory usage within the JVM environment, where pre-allocation would be redundant given the JVM’s own memory management through garbage collection.

Contents

• Memory Allocation Fundamentals in EhCache 3.10.x
• Heap vs. Off-Heap Memory Allocation Behavior
• Configuration Syntax and Purpose
• Performance Implications of Dynamic Allocation
• Best Practices for Cache Configuration
• Sources
• Conclusion

Memory Allocation Fundamentals in EhCache 3.10.x

Understanding how EhCache 3.10.x handles memory allocation is crucial for optimal cache performance. Unlike some caching solutions that pre-allocate memory upon initialization, EhCache takes a more sophisticated approach to memory management that leverages the JVM’s built-in capabilities.

The key principle here is that EhCache does not pre-allocate heap memory when a cache is initialized. Instead, it allocates memory dynamically as entries are added to the cache. This behavior makes perfect sense when you consider that the JVM already manages memory through its garbage collection mechanisms. Pre-allocating a fixed amount of heap space would be redundant and potentially wasteful.

As noted in the Stack Overflow discussion on EhCache memory allocation, “There is indeed not point in pre-allocating in the context of the JVM. It would force ehcache to ‘free’ sections of the pre-allocation every time there is an entry added to the cache.”

Dynamic Allocation Process

When you configure your cache with a heap size limit like <heap unit="MB">50</heap>, you’re setting a maximum threshold rather than requesting immediate allocation. Here’s how the process typically works:

1. Initialization: The cache is created with knowledge of its configured heap limit but without any memory allocated
2. First entry: When the first item is added, EhCache allocates just enough memory to store it
3. Subsequent entries: As more items are added, EhCache continues to allocate memory incrementally
4. Limit enforcement: Once the cache approaches its configured limit (e.g., 50MB), EhCache begins applying eviction policies to make room for new entries

This approach provides several advantages:

• Efficient memory utilization (only using what you need)
• Reduced memory overhead during application startup
• Better integration with the JVM’s garbage collection
• More responsive to actual cache usage patterns

Heap vs. Off-Heap Memory Allocation Behavior

EhCache 3.10.x treats heap and off-heap memory allocation differently, which is an important distinction for understanding overall memory behavior.

Heap Memory Allocation

For heap memory (the most common configuration), EhCache employs dynamic allocation as described above. The heap configuration in your XML serves as a maximum limit rather than an allocation request. This means:

• No memory is reserved when the cache starts
• Memory grows as entries are added
• The JVM’s garbage collector manages the actual memory
• The 50MB limit is a soft boundary that triggers eviction when approached

Off-Heap Memory Allocation

Off-heap memory behaves differently. According to the EhCache Tiering documentation, off-heap memory is allocated upfront in chunks. This is visible in the GitHub issue where we see logs like “Allocating 2.4GB in chunks” and attempts to allocate “1073741824 byte offheap buffer.”

This difference in behavior between heap and off-heap allocation makes sense because:

• Heap memory: Managed by the JVM, so pre-allocation would be redundant
• Off-heap memory: Not managed by the JVM, so EhCache must allocate and manage it directly

The official documentation emphasizes that off-heap storage comes with performance tradeoffs: “data stored off-heap will have to be serialized and deserialized - and is thus slower than heap. You should thus favor off-heap for large amounts of data where on-heap would have too severe an impact on garbage collection.”

Memory Tiering Considerations

EhCache 3.10.x supports multiple tiers of storage (heap, off-heap, disk), and the allocation behavior can vary across these tiers. When configuring your cache, it’s important to understand:

1. Heap tier: Dynamic allocation, size acts as limit
2. Off-heap tier: Upfront allocation, size acts as actual allocation
3. Disk tier: Uses file system allocation, behavior depends on OS

This tiered approach allows you to balance performance, memory usage, and persistence requirements according to your specific use case.

Configuration Syntax and Purpose

The XML configuration you provided is excellent for understanding how EhCache interprets memory settings. Let’s break down what each part means in terms of memory allocation behavior:

<cache alias="orderDetailsCache">
 <key-type> java.lang.String </key-type>
 <value-type> java.lang.String </value-type>

 <expiry> 
 <ttl units="seconds">120</ttl>
 </expiry>
 <resources>
 <heap unit="MB">50</heap>
 </resources>
</cache>

Resource Configuration Elements

The <resources> section defines how EhCache manages memory for this cache. Within this section:

• <heap>: Specifies the maximum amount of heap memory this cache should use
• <unit>: Defines the measurement unit (MB, GB, etc.)
• The value (50): Sets the upper limit, not an allocation request

According to the EhCache documentation on cache sizing, “caches must use bytes-based attributes to claim a portion of a pool; entries-based attributes such as maxEntriesLocal cannot be used with a pool.”

Pool-Based Allocation

In EhCache 3.x, memory allocation often works through a pool-based system where:

1. Cache Manager Level: A global pool of memory is established
2. Cache Level: Individual caches claim portions of this pool
3. Dynamic Sharing: Caches that don’t specify sizes share the remaining pool

This pooling mechanism allows for more flexible memory management across multiple caches within the same CacheManager instance.

Configuration Validation

EhCache performs validation at startup to ensure that cache-level configurations don’t exceed available resources. The documentation notes: “On startup, the sizes specified by caches are checked to ensure that any CacheManager-level pools are not over-allocated.”

This validation helps prevent configuration errors that could lead to memory issues or OutOfMemoryError exceptions during application runtime.

Performance Implications of Dynamic Allocation

The dynamic memory allocation behavior in EhCache 3.10.x has several important performance implications that cache administrators should consider:

Memory Efficiency Benefits

Dynamic allocation provides significant memory efficiency advantages:

• Reduced initial memory footprint: Your application starts with minimal memory commitment to the cache
• Adaptive memory usage: Memory usage scales with actual cache usage patterns
• Better JVM integration: Leverages the JVM’s sophisticated garbage collection rather than duplicating its functionality
• No artificial memory pressure: Avoids creating unnecessary memory pressure during startup

Garbage Collection Impact

Since heap memory is managed by the JVM, the dynamic allocation approach directly impacts garbage collection behavior:

• Lower GC pressure during startup: No large initial allocations to trigger GC cycles
• More natural GC patterns: Memory usage grows organically with cache population
• Potential for better GC tuning: The JVM can optimize its collection strategies based on actual memory usage patterns

However, it’s worth noting that very large caches can still impact GC performance, even with dynamic allocation. This is where off-heap storage might be beneficial for reducing GC overhead.

Memory Fragmentation Considerations

Dynamic allocation can lead to memory fragmentation over time, as the heap is used and freed by the cache alongside other application objects. EhCache manages this through:

• Eviction policies: When approaching memory limits, less frequently used items are removed
• Memory compaction: The JVM’s garbage collector handles memory compaction
• Tiered storage: Moving less active items to off-heap or disk storage

Performance Monitoring Recommendations

To ensure optimal cache performance with dynamic allocation, consider monitoring:

1. Cache hit/miss ratios: Indicate whether your cache size is appropriate for your workload
2. Memory usage patterns: Track how much memory is actually being used vs. configured limits
3. Garbage collection metrics: Monitor GC frequency and duration
4. Eviction rates: High eviction rates may indicate insufficient memory allocation

These metrics will help you fine-tune your cache configuration for optimal performance given your specific application requirements.

Best Practices for Cache Configuration

Based on the dynamic allocation behavior of EhCache 3.10.x, here are some best practices for configuring your cache memory settings:

Setting Appropriate Heap Limits

When configuring heap limits like your 50MB example:

1. Start conservative: Begin with smaller limits and increase based on usage
2. Monitor actual usage: Use cache metrics to determine if you’re under- or over-provisioned
3. Consider data size: Account for both key and value sizes in your calculations
4. Leave headroom: Don’t configure heap limits that approach your JVM’s maximum heap

Memory Tier Optimization

For optimal performance, consider EhCache’s tiered storage approach:

• Hot data: Keep frequently accessed items in heap memory
• Warm data: Store moderately accessed items in off-heap memory
• Cold data: Persist rarely accessed items to disk

The EhCache Tiering documentation provides excellent guidance on configuring these tiers effectively.

Avoiding Common Configuration Mistakes

Be aware of these common pitfalls when configuring EhCache memory settings:

1. Assuming pre-allocation: Don’t expect memory to be reserved at startup
2. Setting limits too tightly: Leave some buffer for unexpected spikes
3. Ignoring eviction policies: Configure appropriate eviction strategies for your use case
4. Over-reliance on default settings: Customize configurations based on your specific requirements

Testing and Validation

Before deploying to production:

1. Load testing: Simulate production traffic to validate memory behavior
2. Memory profiling: Use tools like VisualVM or YourKit to monitor memory usage
3. Performance benchmarking: Measure cache performance under various load conditions
4. Failure scenario testing: Validate behavior when approaching memory limits

These testing practices will help ensure your cache configuration performs optimally in your production environment.

Sources

1. Stack Overflow Discussion — Does ehcache reserve heap memory set with maxBytesLocalHeap: https://stackoverflow.com/questions/28990597/does-ehcache-reserve-allocate-heap-memory-set-with-maxbyteslocalheap
2. EhCache Tiering Documentation — Official guide to memory tiering options in EhCache 3.10: https://www.ehcache.org/documentation/3.10/tiering.html
3. EhCache Cache Size Configuration — Documentation on cache sizing and memory pool configuration: https://www.ehcache.org/documentation/2.7/configuration/cache-size.html
4. EhCache GitHub Issue — Memory allocation behavior in off-heap tier: https://github.com/ehcache/ehcache3/issues/1477

Conclusion

EhCache 3.10.x employs dynamic memory allocation for heap storage, meaning your configured 50MB heap limit serves as a maximum threshold rather than an upfront allocation. This design choice leverages the JVM’s built-in memory management capabilities, providing efficient memory usage while avoiding redundant pre-allocation. The cache only allocates memory as entries are added, growing organically with actual usage patterns and applying eviction policies when approaching configured limits.

Understanding this allocation behavior is crucial for proper cache configuration. Unlike off-heap memory which is allocated upfront, heap memory allocation is reactive and adaptive, making EhCache more flexible and memory-efficient for most use cases. By setting appropriate heap limits based on actual monitoring of cache usage patterns, you can optimize performance while avoiding unnecessary memory overhead in your Java applications.