What is Error Code: Out of Memory

10 Ways to Fix Error Code Out of Memory

#01 Code-Level Fixes

• Avoid creating or loading a large number of arrays, objects, or image resources at one time.
• Set objects to null or use delete to release them in time after data processing.
• Limit temporary variables to function scope to avoid global pollution.
• Use try...finally to ensure that resources are released during exception handling.
• Unbind event listeners before removing DOM nodes to avoid dangling references.

#02 Memory Leak Detection Tools

• Use Chrome DevTools' Memory tab to analyze heap snapshots.
• Use heapdump to generate snapshots in Node.js applications and analyze with VisualVM.
• Introduce why-did-you-rendering Web projects to check for useless component updates.
• Mobile developers can use LeakCanary (Android) or Instruments (iOS).
• Use professional APM tools (such as New Relic, Datadog) to continuously track memory usage.

#03 Configuration-Based Fixes

• JVM increases the heap size: -Xmx2048m -Xms512m.
• Node.js startup parameters increase the memory limit: --max-old-space-size=4096.
• PHP modifies the php.ini file: memory_limit=512M.
• Docker container limit configuration: set the--memory parameter to control memory usage.
• Python can use the resource module to limit the upper limit of process memory usage.

#04 Architecture-Level Improvements

• Split large monolithic services and implement a microservice architecture.
• Introduce asynchronous message queues (such as RabbitMQ, Kafka) to reduce peaks and fill valleys.
• Handle state preservation to middleware such as Redis to reduce local storage pressure.
• High-concurrency services use thread pools to limit resource consumption.
• Use CDN and edge cache to reduce source server pressure.

#05 Cloud & Deployment-Level Solutions

• Configure container requests and limits in Kubernetes to limit resource usage.
• Use HPA (Horizontal Pod Autoscaler) to automatically scale container instances.
• Configure container restart policies, such as automatic restart when OOMKilled.
• Use cloud platform monitoring tools (such as CloudWatch, Grafana) to track memory trends.
• Enable service mesh (such as Istio) to manage traffic transfer for high-memory services.

#06 Data Processing Optimization

• Use paging interfaces (such as limit/offset) to request data in segments.
• File reading uses streaming reading (such as Node.js's fs.createReadStream).
• Avoid SELECT *when querying the database, and only get the necessary fields.
• Backend services pre-aggregate data, and the front end only gets the fields required for display.
• Use batch processing logic to avoid processing large tasks at one time.

#07 Efficient Data Structures

• Use Set instead of Array to implement deduplication.
• Use maps instead of ordinary objects to manage key-value pairs for better performance.
• If there is no need for insertion and deletion, use an Array instead of a linked list.
• Reduce the object nesting level to avoid complex reference chains.
• Avoid excessive nesting of JSON data on the front end.

#08 Garbage Collection Tuning

• Enable low-pause collectors such as G1 GC and ZGC in the JVM.
• Node.js uses the--trace-gc parameter to debug GC behavior.
• Monitor GC cycles and frequencies to prevent frequent triggering.
• Use WeakMap or WeakRef to store temporary caches to reduce persistent references.
• Turn on GC logs in Python and manually trigger garbage collection at the right time.

#09 Reduce Third-Party Dependencies

• Regularly review package.json or requirements.txt and remove useless packages.
• Replace some common tool libraries (such as lodash) with native methods.
• Enable Tree Shaking (such as Webpack) to remove unused modules.
• Avoid importing all UI libraries and only import components on demand.
• Use CDN to import public libraries to reduce local packaging and usage.

#10 Lazy Loading and Caching Strategies

• The front-end uses the lazy loading component mechanism of frameworks such as Vue and React.
• Load modules on demand (such as import(), dynamic import).
• Use the LRU (least recently used) strategy to implement the memory cache.
• Detect user behavior before loading resources (such as pictures and videos) and then decide whether to load.
• Data cache uses timestamps or version numbers to automatically clean up stale data.

How to Proactively Prevent “Out of Memory” Error Code

• The application memory needs should be estimated at the stage of the project establishment, and the limits on the usage of the memory, as well as the objectives of the optimization, are supposed to be set.
• Minimize or prevent closure abuse, overuse of global variables, non-cleaned timers, and event listeners, which typically are the cause of memory leaks.
• Not to mention memory analysis tools (Chrome DevTools, Valgrind, VisualVM) to verify the memory growth patterns regularly in the test or pre-release environment.
• The resource consumption of a third-party library should be considered before we introduce them every time to prevent too heavy dependencies or libraries with overlapping functions.
• This should be loaded as and when really required to decrease first-time memory pressure, be it a component, module, or data.
• Set up monitoring and threshold alarms for the use of memory in the production one. When the safety line is overstepped, automatic rollback, restart, or call to operation and maintenance may arise.
• The memory limit and recycling strategy are rectified according to the requirement of the memory limit of such environments as JVM, Node.js, Docker, etc.
• The straightforward logic and structure are less demanding to maintain, less memory is required, and its management is more definite, leading to avoiding hidden repetition.

Conclusion