A detailed ehcache monitoring and troubleshooting guide

Caches like Ehcache are critical for application performance. They speed up response times and help systems scale better. But like any important system component, caches can become a bottleneck if they’re not monitored properly. Misconfigurations, memory leaks, or even just the wrong eviction policy can silently degrade performance or cause outright failures.

This article discusses everything you need to know about monitoring and troubleshooting Ehcache: what metrics to monitor, how to troubleshoot security, performance, and networking related issues, and which best practices to follow to keep your infrastructure healthy and reliable.

What is ehcache?

Ehcache is a widely used, open-source Java caching library. It's lightweight, easy to integrate, and works well as a standalone cache or as part of larger frameworks like Spring and Hibernate.

Here are some of its key features:

• In-memory and disk-based storage: Ehcache can cache data in memory for speed and on disk for durability.
• Support for TTL and TTI: You can set time-to-live (TTL) and time-to-idle (TTI) policies to control how long items stay in the cache.
• Eviction policies: Supports LRU (Least Recently Used), LFU (Least Frequently Used), and FIFO strategies to manage limited cache space.
• Clustering support: With Terracotta or other backing stores, you can use Ehcache in a distributed setup.
• JMX support: Ehcache allows you to monitor cache behavior in real time using Java Management Extensions.

The most common Ehcache use cases are:

• Reducing database load by caching query results
• Storing session data in web applications
• Caching API responses to avoid repeated external calls
• Speeding up repetitive computations or lookups

Why monitor ehcache?

By proactively monitoring Ehcache, you will be able to:

• Catch memory issues that could lead to application crashes if the cache grows too large
• Spot inefficient configurations like poorly set eviction policies or overly long TTL settings
• Track hit and miss ratios to see if the cache is actually improving performance
• Identify slowdowns caused by lock contention or poor cache access patterns
• Monitor cluster health in distributed setups to detect sync issues or failed nodes
• Watch for excessive disk usage in hybrid memory-disk caches that can hurt performance

Key ehcache metrics to monitor

To keep Ehcache healthy and performing well, it's important to track the right metrics. These metrics fall into a few main categories. Each category gives insight into a different part of cache behavior.

Cache performance metrics

These metrics show how effective the cache is at serving data.

• Cache hits: Number of times requested data was found in the cache
• Cache misses: Number of times requested data was not in the cache
• Hit ratio: Percentage of total requests that resulted in a hit
• Miss ratio: Percentage of total requests that resulted in a miss
• Evictions: Number of entries removed due to space or policy limits

Memory and storage usage

These metrics help you understand how much memory or disk the cache is using.

• Heap size used: Memory used by cache on the Java heap
• Off-heap size used: Memory used outside the Java heap
• Disk usage: Amount of disk space used for overflow or persistent storage
• Max heap/off-heap size: The configured upper limits for memory use

Cache entry lifecycle metrics

These metrics deal with how long data stays in the cache and how it's managed.

• Time-to-live (TTL): How long an entry stays in the cache before expiring
• Time-to-idle (TTI): How long an entry can stay unused before it expires
• Expired entries: Number of entries removed because they reached their TTL or TTI
• Put count: Number of entries added to the cache
• Remove count: Number of entries removed explicitly or through expiration

Cluster and replication metrics

For clustered setups, these metrics help track how well nodes are working together.

• Replication success/failure counts: Number of successful or failed data syncs between nodes
• Clustered write latency: Time it takes to write to the cluster
• Cluster availability: Status of nodes in the cluster and their connectivity
• Rebalancing events: Times when data was moved between nodes for balance

Throughput and latency metrics

These metrics help measure how frequently and quickly the cache is being used.

• Get operation count: Number of times data was requested from the cache
• Put operation count: Number of times data was written to the cache
• Average get latency: Time it takes to fetch an entry from the cache
• Average put latency: Time it takes to write an entry to the cache
• Peak throughput: Highest number of operations handled in a given time frame

Persistence metrics

These show how the cache handles data saved to or read from disk storage.

• Writes to disk: Number of cache entries written to disk
• Reads from disk: Number of cache entries loaded from disk
• Disk write failures: Failed attempts to write data to disk
• Disk read failures: Failed attempts to read data from disk
• Time to persist entries: Time taken to write entries to persistent storage

Error and exception metrics

These metrics help you catch operational failures or bugs that can affect stability.

• Cache operation failures: General failures during cache usage
• Serialization errors: Failures when converting objects for storage or retrieval
• Deserialization errors: Failures during object reconstruction from stored data
• Replication errors: Problems syncing data between clustered nodes
• OutOfMemoryErrors: Errors when the cache exceeds memory limits
• Disk I/O errors: Problems reading from or writing to disk

The easiest way to start monitoring all these key metrics is to use the dedicated Ehcache monitoring tool by Site24x7. It’s easy to set up, covers several important metrics out of the box, and also lets you track custom attributes based on your application needs. You can set up alerts to get notified when any metric crosses a threshold, and visualize real-time and historical data from a centralized dashboard.

Common ehcache issues and how to troubleshoot

Let's shift our attention to troubleshooting. This section looks at the most common Ehcache issues, grouped by category. Each issue includes symptoms to watch for and step-by-step troubleshooting tips to help you fix the problem quickly and correctly.

Caching inefficiency issues

Even if Ehcache is running without errors, it might not be delivering the performance boost you expect. Let’s look at some issues related to caching inefficiency and how you can debug them.

High cache miss ratio

A high miss ratio means the cache isn’t storing or serving the data it should.

Symptoms

• Frequent fallback to database or external service
• Slow response times despite cache usage

Troubleshooting

• Check if the keys being used to store and fetch data are consistent and properly constructed
• Review what data is being cached and make sure it's frequently reused and worth caching
• Use JMX or the Site24x7 Ehcache monitoring tool to track hit/miss patterns across different caches
• Validate TTL and TTI settings to make sure data isn't expiring too quickly
• Investigate whether evictions are happening too aggressively due to size constraints
• Profile your application logic to confirm that it’s actually reading from and writing to the cache

Stale data being served

Stale data usually means that expired or outdated entries are staying in the cache too long.

Symptoms

• Users see outdated values or inconsistent results
• Data changes in the backend aren't reflected in real time

Troubleshooting

• Double-check TTL and TTI settings to confirm they reflect your freshness requirements
• If using manual cache updates, make sure cache invalidation or refresh is being called correctly after data changes
• In clustered setups, ensure that changes are replicated across nodes properly
• Consider enabling write-through or refresh-ahead strategies if your use case needs up-to-date data
• Look for situations where exceptions during cache population might be skipping proper updates
• Monitor the expired entries metric to see if expiration is happening as expected

Cache growing too large without improving performance

A large cache isn’t always better. If it's not helping with performance, it's just using up resources.

Symptoms

• High memory usage with low hit ratio
• Little to no improvement in response times

Troubleshooting

• Review the cache population logic to make sure that only useful and reusable data is being stored
• Set realistic size limits to prevent storing unnecessary or rarely accessed data
• Analyze usage patterns to see which entries are actually being hit often
• Use eviction policies like LRU or LFU instead of FIFO or no policy
• Clear the cache and monitor how it grows over time to identify what’s filling it up
• Check for unbounded or poorly scoped cache keys that lead to too many unique entries

Security issues

Security is often ignored while monitoring caches, but it shouldn’t be. Ehcache can store sensitive data like session tokens, user profiles, or authorization results. If not secured properly, this data can be exposed or misused.

Unauthorized access to cached data

Improper access controls can allow users or systems to retrieve sensitive cached information.

Symptoms

• Unauthenticated users accessing cache-backed content
• Sensitive data appearing in logs, monitoring dashboards, or unintended parts of the app

Troubleshooting

• Audit your cache access patterns to confirm that only authorized parts of the app can read or write to the cache
• Review application-level access controls to ensure that cached data isn’t exposed through unsecured endpoints
• If you are using disk persistence, enable encryption at rest for all your sensitive data
• Avoid caching sensitive user-specific data unless absolutely necessary, and if cached, apply strict TTL
• Use role-based access restrictions for JMX if you're exposing Ehcache metrics or controls
• Consider masking or hashing sensitive values before writing them to the cache

Insecure JMX exposure

Leaving JMX exposed without proper controls can allow attackers to inspect or manipulate cache behaviour.

Symptoms

• JMX endpoints are accessible over unsecured ports
• Cache stats or contents are visible to unauthorized users

Troubleshooting

• Secure JMX endpoints with authentication and TLS
• Configure JMX to bind only to internal interfaces if remote access isn’t required
• Set proper access control roles for JMX operations like clear, remove, or reset
• Review firewall settings to block external access to JMX ports
• Disable JMX entirely if it’s not being used for monitoring or management
• Rotate any exposed credentials tied to JMX authentication

Resource utilization issues

Next, let’s look at some common resource utilization problems.

Disk usage growing rapidly

If disk persistence is enabled and not configured properly, the cache can fill up disk space quickly and impact performance.

Symptoms

• Disk space warnings or failures
• Increased latency during read/write operations

Troubleshooting

• Confirm that you are setting disk store size limits in the Ehcache configuration
• Regularly monitor disk usage metrics and clear old or expired entries
• Make sure that expired entries are being properly evicted from disk
• Avoid persisting short-lived data that doesn’t need to survive a restart
• Investigate whether high put rates are overwhelming the disk I/O
• Check logs for disk write or serialization errors that may slow down cleanup

High CPU usage caused by cache operations

Cache operations should be lightweight, but bad usage patterns or inefficient object handling can drive up CPU usage.

Symptoms

• CPU usage spikes during heavy cache activity
• Threads stuck in serialization or locking operations

Troubleshooting

• Use a profiler to inspect which cache operations are consuming CPU
• Avoid frequent cache clears, which force reloading and increase computation
• If custom serialization is in use, test its performance and look for bottlenecks
• Reduce the complexity of the objects being cached, especially if they include nested structures
• Avoid synchronized blocks around cache access unless they can’t be avoided
• Check for lock contention if multiple threads are accessing or updating the same keys

Networking issues

Finally, here are some networking issues you can face while working with Ehcache:

Cluster nodes failing to sync

When nodes in an Ehcache cluster don’t stay in sync, it can result in inconsistent data or unexpected behaviour.

Symptoms

• Some nodes return outdated or missing data
• Errors or warnings in logs about cluster communication

Troubleshooting

• Check network connectivity between nodes, including firewalls, ports, and DNS resolution
• Review the Terracotta server or other clustering layer configuration for node list and address settings
• Confirm that all nodes are running compatible versions and configurations
• Monitor replication metrics for dropped or failed events
• Inspect logs for timeouts, dropped packets, or serialization failures during replication
• Validate that cluster rejoin and failover settings are properly configured to handle disconnects

Latency spikes during cache replication

In a distributed cache, slow replication can cause delays in updates or reads.

Symptoms

• Higher-than-usual response times after writes
• Delayed visibility of updated data across nodes

Troubleshooting

• Use Site24x7 to measure replication latency and pinpoint slow links
• Review network bandwidth and packet loss between cluster members
• Avoid excessive writes to replicated caches — batch or debounce frequent updates where possible
• Tune replication settings (e.g., asynchronous vs. synchronous replication) based on use case
• Evaluate cache design to limit replication scope to data that truly needs to be shared
• Inspect logs for signs of backpressure or throttling in the replication channel

Network partitions causing inconsistent data

If a network partition splits the cluster, some nodes may operate with stale or incomplete data.

Symptoms

• Data differences across nodes after a temporary network issue
• Partial cache updates or silent data loss

Troubleshooting

• Configure split-brain protection mechanisms for your cluster
• Review cluster quorum rules and define the expected behaviour during network partitions
• Enable detailed logging for cluster health and failover events
• Ensure consistent recovery behaviour by validating how nodes rejoin and resync after partition heals
• Regularly test how your cache behaves during controlled failure scenarios
• Document expected behaviour and communication flows between nodes to use as a reference during incidents

Ehcache best practices

Finally, here are some best practices to help you keep Ehcache stable and efficient over time:

• Define clear size limits for each cache region to avoid uncontrolled memory growth.
• Choose the right eviction policy based on your access patterns (e.g., LRU for most recent, LFU for most used).
• Avoid caching large objects unless necessary, and use compression or lightweight formats when possible.
• Set appropriate TTL and TTI values to balance freshness and resource usage.
• Use separate cache regions for different types of data instead of mixing unrelated entries.
• Monitor key metrics regularly and set up alerts for anomalies like high miss rates or memory pressure.
• Use meaningful cache keys to avoid collisions and make debugging easier.
• Review your cache configuration during load testing to catch issues before they appear in production.
• Enable JMX or a monitoring tool like Site24x7 to get visibility into cache behaviour over time.
• Handle cache failures gracefully so your application can fall back if needed.
• Avoid using unbounded caches in production, even if memory appears sufficient during development.
• Pre-warm critical caches at application startup if cold starts would cause performance problems.
• Use async loading or background refresh strategies for heavy or slow-to-fetch data.
• Rotate persistent cache stores during deployments if stale data could interfere with new logic.
• Log cache puts and evictions during development to catch unnecessary or redundant writes.

Conclusion

Ehcache is a stable caching solution that’s been optimizing Java-based systems for decades. However, despite its fault-tolerance and reliability, it can still run into issues if not monitored and managed properly.

To stay on top of Ehcache performance and catch issues before they cause trouble, don’t forget to try out the Ehcache monitoring tool by Site24x7.