When encountering insufficient memory error (OOM), you should first check the resource usage, optimize the code structure, and then configure the operating environment reasonably. Specific methods include: 1. Avoid creating a large number of temporary variables in the loop, using a generator instead of list comprehension, and closing the file handle and database connection in time; 2. For Python programs, you can manually call gc.collect() to assist garbage collection; 3. Appropriately adjust the runtime memory limit under the premise that there is no problem with the code, such as setting JVM parameters in Java, setting a reasonable memory limit in Python with 64-bit interpreter, and Docker; 4. Use tracemalloc, memory_profiler, VisualVM and other tools to locate memory bottlenecks to avoid blindly increasing memory to cover up fundamental problems.
It is really a headache to encounter an out of memory error (OOM), especially when the program suddenly crashes when it runs to a critical step. In fact, most of these problems can be alleviated or even solved by checking resource usage, optimizing code structure and reasonably configuring the operating environment.
Check and optimize memory usage in your code
Many OOM problems are actually because the code itself has waste of resources or is unreasonable use. For example, large amounts of cached data are not released, large objects are repeatedly loaded, temporary objects are frequently created in loops, etc.
- Avoid creating a large number of temporary variables in a loop
- Use generators instead of list comprehension to process large data sets
- Timely close file handles, database connections and other resources that are no longer used
- If using Python, please note that some libraries (such as Pandas) will not release memory by default. You can manually call
gc.collect()
to trigger garbage collection
For example, if you load all the content into memory at once while reading multiple large files, it is easy to explode. It will be safer to change to reading by row or processing by block.
Appropriately increase the available memory configuration
If you confirm that the code logic is fine, but the amount of data itself is large, you need to consider adjusting the runtime memory limit.
for example:
- In Java, you can set the maximum heap memory through JVM parameters:
-Xmx4g
- If Python scripts run on the system, they may need to adjust the memory limit of the operating system or use a 64-bit interpreter
- Docker containers must be set to ensure that they have set reasonable memory limits to avoid being killed by the system OOM Killer.
It should be noted that blindly adding memory is only a treatment for symptoms but not the root cause. If there is a memory leak on the program itself, no matter how much you add it, there will be problems sooner or later.
Use tools to assist diagnosis and monitoring
Many times you don’t know where the memory is consumed, so you need to use some analysis tools.
- Python can use
tracemalloc
ormemory_profiler
to track memory allocation - Java can use VisualVM, MAT and other tools to view heap memory snapshots
- At the system level, you can look at the output of commands such as
top
,htop
, andfree
to see the overall memory trend.
These tools can help you locate which part of the memory is consuming too many instances of a certain class? Or is a cache not cleaned? Only by finding the root cause can you prescribe the right medicine.
Basically these methods. OOM looks scary, but as long as you check it step by step, you can find the reason in most cases. The key is not to think about increasing the memory as soon as you come up, as it will easily cover up the real problem.
The above is the detailed content of How to handle out of memory errors?. For more information, please follow other related articles on the PHP Chinese website!

Hot AI Tools

Undress AI Tool
Undress images for free

Undresser.AI Undress
AI-powered app for creating realistic nude photos

AI Clothes Remover
Online AI tool for removing clothes from photos.

Clothoff.io
AI clothes remover

Video Face Swap
Swap faces in any video effortlessly with our completely free AI face swap tool!

Hot Article

Hot Tools

Notepad++7.3.1
Easy-to-use and free code editor

SublimeText3 Chinese version
Chinese version, very easy to use

Zend Studio 13.0.1
Powerful PHP integrated development environment

Dreamweaver CS6
Visual web development tools

SublimeText3 Mac version
God-level code editing software (SublimeText3)

Hot Topics

The difference between HashMap and Hashtable is mainly reflected in thread safety, null value support and performance. 1. In terms of thread safety, Hashtable is thread-safe, and its methods are mostly synchronous methods, while HashMap does not perform synchronization processing, which is not thread-safe; 2. In terms of null value support, HashMap allows one null key and multiple null values, while Hashtable does not allow null keys or values, otherwise a NullPointerException will be thrown; 3. In terms of performance, HashMap is more efficient because there is no synchronization mechanism, and Hashtable has a low locking performance for each operation. It is recommended to use ConcurrentHashMap instead.

StaticmethodsininterfaceswereintroducedinJava8toallowutilityfunctionswithintheinterfaceitself.BeforeJava8,suchfunctionsrequiredseparatehelperclasses,leadingtodisorganizedcode.Now,staticmethodsprovidethreekeybenefits:1)theyenableutilitymethodsdirectly

The JIT compiler optimizes code through four methods: method inline, hot spot detection and compilation, type speculation and devirtualization, and redundant operation elimination. 1. Method inline reduces call overhead and inserts frequently called small methods directly into the call; 2. Hot spot detection and high-frequency code execution and centrally optimize it to save resources; 3. Type speculation collects runtime type information to achieve devirtualization calls, improving efficiency; 4. Redundant operations eliminate useless calculations and inspections based on operational data deletion, enhancing performance.

Instance initialization blocks are used in Java to run initialization logic when creating objects, which are executed before the constructor. It is suitable for scenarios where multiple constructors share initialization code, complex field initialization, or anonymous class initialization scenarios. Unlike static initialization blocks, it is executed every time it is instantiated, while static initialization blocks only run once when the class is loaded.

Factory mode is used to encapsulate object creation logic, making the code more flexible, easy to maintain, and loosely coupled. The core answer is: by centrally managing object creation logic, hiding implementation details, and supporting the creation of multiple related objects. The specific description is as follows: the factory mode handes object creation to a special factory class or method for processing, avoiding the use of newClass() directly; it is suitable for scenarios where multiple types of related objects are created, creation logic may change, and implementation details need to be hidden; for example, in the payment processor, Stripe, PayPal and other instances are created through factories; its implementation includes the object returned by the factory class based on input parameters, and all objects realize a common interface; common variants include simple factories, factory methods and abstract factories, which are suitable for different complexities.

InJava,thefinalkeywordpreventsavariable’svaluefrombeingchangedafterassignment,butitsbehaviordiffersforprimitivesandobjectreferences.Forprimitivevariables,finalmakesthevalueconstant,asinfinalintMAX_SPEED=100;wherereassignmentcausesanerror.Forobjectref

Synchronizationistheprocessofcoordinatingtwoormorethingstostayaligned,whetherdigitalorphysical.Intechnology,itensuresdataconsistencyacrossdevicesthroughcloudserviceslikeGoogleDriveandiCloud,keepingcontacts,calendarevents,andbookmarksupdated.Outsidete

There are two types of conversion: implicit and explicit. 1. Implicit conversion occurs automatically, such as converting int to double; 2. Explicit conversion requires manual operation, such as using (int)myDouble. A case where type conversion is required includes processing user input, mathematical operations, or passing different types of values ??between functions. Issues that need to be noted are: turning floating-point numbers into integers will truncate the fractional part, turning large types into small types may lead to data loss, and some languages ??do not allow direct conversion of specific types. A proper understanding of language conversion rules helps avoid errors.
