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What Is Segmentation in Operating System?

The idea of segmentation In OS is similar in function to paging, which is used to manage memory. Segmentation is a way to divide the user program and data into segments. Segmentation and paging are two different things. Each page in paging has a fixed size, while segments have a variable size.

Segmentation maps the user's view onto actual physical memory. We will briefly discuss segmentation and its workflow in the next section. We will also discuss segmentation using an example and conclude the discussion with some advantages and disadvantages.

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Native Memory May Cause Unknown Memory Leaks

Recently I came across a strange case: the memory usage of my program exceeded the maximum value intended for the heap. Even after running GC, part of the memory was not free. I already knew that a part of  JVM memory would be allocated to native memory and part of native memory allocated to C code, but I did not have even one line of native code in my program. After reviewing and profiling my code several times, I found an interesting issue. Before diving into the problem, let's look at Java memory concepts.

Memory Management in Java

 JVM divides memory into two major spaces, heap and native memory. Heap spaces are used for allocating Java objects whereas native memory is the memory available to the OS. There is a key difference between Java 7 and 8 in the memory management model. Java 7 has PermGen; PermGen is the memory area in the heap that is used by the JVM to store class metadata, static content, primitive variables. Java 8 has eliminated PermGen and added Metaspace; actually, Metaspace and PermGen do the same thing. The main difference is that PermGen is part of the Java heap while Metaspace is NOT part of the heap. Rather Metaspace is part of native memory, which is only limited by the Host Operating System.

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How to do Memory Management for DataWeave

About

This article goes over how to perform memory management in order to effectively run larger MuleSoft DataWeave scripts. I tried to create helpful images to explain how DataWeave memory management works. 

Introduction

DataWeave is a powerful and MuleSoft proprietary data transformation expression language, tightly integrated with powerful Mule Runtime Engine.

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Deep Dive Into .NET Garbage Collection

Garbage collection, and memory management in general, will be the first and last things to work on. It is the main source of the most obvious performance problems, those that are the quickest to fix but need to be constantly monitored. Many problems are actually caused by an incorrect understanding of the garbage collector’s behavior and expectations. 

You have to think about memory performance just as much as about CPU performance. This is also true for unmanaged code performance, but in .NET it is easier to deal with. Once you understand how GC works, it becomes clear how to optimize your program for its operation. It can give you better overall heap performance in many cases because it deals with allocation and fragmentation better.

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Automatic Reference Counting (ARC) and Memory Management in Swift

Get ARC to work for you

Memory management is a key factor when we develop apps. If a program is using a lot of memory, it can make apps run slowly or even cause crashes. To handle this in Swift, you can work with Automatic Reference Counting (ARC) to keep your apps memory usage minimal. This doesn’t mean you can completely forget about the memory in your app, but it does take care of most things for you.

It’s important to note that ARC only works when working with classes. Structs and enums are value types and therefore are stored differently in memory than classes, which are stored and passed by reference.

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Why Your Spark Applications Are Slow or Failing, Part 1: Memory Management

Spark applications are easy to write and easy to understand when everything goes according to plan. However, it becomes very difficult when Spark applications start to slow down or fail. Sometimes a well-tuned application might fail due to a data change, or a data layout change. Sometimes an application which was running well starts behaving badly due to resource starvation. The list goes on and on.

It's not only important to understand a Spark application, but also its underlying runtime components like disk usage, network usage, contention, etc., so that we can make an informed decision when things go bad.

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Duplicate Objects in Java: Not Just Strings

When a Java application consumes a lot of memory, it can be a problem on its own and can lead to increased GC pressure and long GC pauses. In one of my previous articles, I discussed one common source of memory waste in Java: duplicate strings. Two java.lang.String objects a and b are duplicates when a != b && a.equals(b). In other words, there are two (or more) separate strings with the same contents in the JVM memory. This problem occurs very frequently, especially in business applications. In such apps, strings represent a lot of real-world data, and yet, the respective data domains (e.g. customer names, country names, product names) are finite and often small. From my experience, in an unoptimized Java application, duplicate strings typically waste between 5 and 30 percent of the heap. However, did you ever think that instances of other classes, including arrays, can sometimes be duplicate as well, and waste a considerable amount of memory? If not, read on.

Object Duplication Scenarios

Object duplication in memory occurs whenever the number of distinct objects of a certain type is limited but the app keeps creating such objects without trying to cache/reuse the existing ones. Here are just a few concrete examples of object duplication that I've seen: