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Memory overflow scenarios usually occur when there is a memory leak in the application, the life cycle of an object is too long, and objects are created frequently but not recovered in time. Below are a few real-world business scenarios that incorporate the memory overflow problem and propose optimizations from multiple perspectives to improve memory usage efficiency.

Scenario 1: Heap Memory Overflow Due to Large Business Data Cache

Scene Description:

An enterprise-level web application uses a large amount of in-memory cache to store business data, such as user information, order data, and so on. Due to improper caching strategy, a large amount of invalid data is stored in heap memory for a long time, resulting in theOutOfMemoryError(heap memory overflow).

Solution Idea:

1. Optimizing the caching strategy：
   • utilizationLRU (Least Recently Used) algorithm to replace the current caching policy to ensure that frequently used data is retained and data that has not been accessed for a long time is purged in a timely manner.
   • utilizationSoftReference to store cached objects and automatically reclaim soft references when the system runs out of memory.
   • Reduce caching time for data of low business importance or frequent updates, or use theweak reference（WeakReference), making it easier for the garbage collector to reclaim data in the cache.
2. Distributed Cache Replaces Local Cache：
   • Use a distributed cache (such as Redis or Memcached) to reduce JVM memory pressure, move the cache from heap memory to an external cache service, and improve the overall memory management efficiency of the system.
3. Cache Granularity Control：
   • Control the granularity of cached objects, don't cache objects that are too large. If there are complex objects, split them into multiple parts for caching.
4. Load on Demand：
   • Implement Lazy Loading to load and cache data only when needed, avoiding preloading unnecessarily large amounts of data.

Optimize the effect:

By adjusting the cache policy and reference types, using distributed caching, and optimizing the granularity of cached data, you can reduce the pressure on the JVM heap memory and avoid memory overflow. At the same time, through a reasonable caching strategy, you can allow the system to increase the efficiency of memory usage without increasing physical resources.5-10 times。

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Scenario 2: Heap Memory Overflow due to Loop Generation of Large Number of Objects

Scene Description:

The system timer task processes a large amount of order data every once in a while, and each time it does so, it creates a large number of objects in a loop. Because these objects are created too often and not released in time, heap memory is gradually exhausted, resulting in theOutOfMemoryError。

Solution Idea:

1. object pooling：
   • pull intoobject pool(Object Pooling), reuse objects to avoid creating a large number of new objects each time you process data. Object Pooling can be used to reuse some fixed logic objects to reduce GC pressure.
2. batch：
   • Break tasks into small batches to avoid loading and processing too much data at once. For example, process 1,000 orders at a time instead of loading 100,000 orders at once.
3. Reduce the creation of temporary objects：
   • Optimize the creation of objects in your code to avoid creating unnecessary temporary objects, especially those created in loops. For example, using theStringBuilder interchangeabilityString of frequent splicing operations.
4. Garbage Collection Tuning：
   • Adjust the GC strategy to addSurvivor The size of the zone ensures that objects with short life cycles are promptly removed from theEden zone reclamation to avoid excessive memory pressure in older generations.
   • riseMaxTenuringThreshold, giving younger generation subjects more opportunity to be recycled rather than prematurely promoted to older generations.

Optimize the effect:

Through the object pool reuse objects, batch processing tasks, reduce the creation of temporary objects and garbage collection tuning, can significantly reduce the system in the case of high concurrency memory occupation, improve the efficiency of task processing5-10 times, and reduce the risk of memory overflows.

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Scenario 3: Heap memory overflow due to a long-running web service

Scene Description:

A Web application is a long-running service that generates a large number of objects on the server side when handling highly concurrent requests. After a long period of time, some objects in memory cannot be reclaimed in time, resulting in heap memory overflow.

Solution Idea:

1. Memory Leak Detection：
   • Use tools such asVisualVM maybeMAT (Memory Analyzer Tool) Analyze heap memory to find possible memory leaks.
   • Check if a long lifecycle object references a short lifecycle object, causing the short lifecycle object to not be recovered by GC.
2. Optimize thread usage：
   • Using a thread pool (e.g.ThreadPoolExecutor) Optimize thread creation and destruction to avoid frequent creation of threads with short life cycles.
   • Avoid holding large object references in the thread to ensure that the GC can recover the relevant objects in time after the thread's task ends.
3. utilizationWeakHashMap Handling objects with short life cycles：
   • For some objects with short lifecycles, such as some data in the request context, you can use theWeakHashMap storage, avoiding objects that persist throughout the application lifecycle.
4. Timed Memory Cleanup：
   • If the system must be maintained for a long period of time, periodically trigger Full GC and combine it with log monitoring to proactively clean up useless objects and ensure that heap memory is used within a reasonable range.
5. Tuning Heap Memory and GC Policies：
   • Increasing the size of the young generation ensures that objects with short lifecycles can be quickly recycled by GC.
   • utilizationCMS maybeG1 collector to optimize Full GC times and reduce stalls due to GC during long runs.

Optimize the effect:

By identifying memory leaks, optimizing thread management, managing weakly referenced objects, and tuning GC strategies, heap memory usage can be significantly reduced while maintaining high concurrency, and memory efficiency can be improved.5-10 timesand avoid memory overflow.

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Scenario 4: Old age overflow during high volume data processing

Scene Description:

In enterprise-level systems, data batch processing tasks often load large amounts of historical data into memory for processing, resulting in old-age heap memory overflow due to excessive data volume.

Solution Idea:

1. Chunking data：
   • utilizationpaging search maybestreaming way to avoid loading too much data into memory at once. For example, using theResultSet for JDBC become man and wifecursor (computing) (*) Get data in chunks.
2. Using external storage：
   • The results of a large number of intermediate computations can be temporarily stored in an external storage system (e.g., Redis, a file system, or a database) rather than all in memory.
3. Improving GC Efficiency in Older Ages：
   • utilizationG1 GC to manage the reclamation of older generations, allowing objects in older generations to be reclaimed more efficiently through regionalized memory management.
4. Enlargement of old age memory：
   • If the system has enough physical memory, appropriately increase the old-age memory size by the parameter-Xmx cap (a poem)-XX:NewRatio to regulate the ratio of younger to older generations.

Optimize the effect:

By processing data in chunks, using external storage, and improving the efficiency of GC recovery, you can greatly reduce memory pressure, especially the overflow problem of the old era, improve the execution efficiency of data processing tasks, and improve memory utilization5-10 times。

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