B Tree

 B-Trees are a type of self-balancing, multi-way tree data structure that are optimized for systems that have slow or limited access to disk storage. Unlike binary trees, where each node has at most two children, a B-Tree node can have multiple children. This allows B-Trees to store large amounts of data efficiently while maintaining a relatively shallow height.

In a B-Tree, each node has a specific number of keys and associated values, called "entries". The keys act as pointers to the values, and are used to determine which node to follow during a search. The entries in each node are sorted in ascending order, and the values are stored in the leaf nodes. The root node, internal nodes, and leaf nodes are all stored on disk, making B-Trees well-suited for large data sets that can't fit into memory.

When a node becomes full and needs to be split, it is divided into two new nodes, with some of its entries being moved to the new node. When a node becomes under-full, it may merge with its neighbouring node or borrow an entry from it. This helps to ensure that the tree remains balanced, which is important for maintaining efficient search times.


B-Trees are commonly used in databases, file systems, and other applications that require efficient storage and retrieval of large amounts of data. They offer advantages over other data structures, such as fast search times, low disk access cost, and good scalability. B-Trees are also very flexible and can be easily modified to meet specific requirements, such as increasing or decreasing the number of entries in each node.


In summary, B-Trees are a self-balancing multi-way tree data structure optimized for systems with slow or limited access to disk storage. They are well suited for large data sets and offer fast search times, low disk access cost, and good scalability.

Comments

Post a Comment

Popular posts from this blog

Java 8 : Find the number starts with 1 from a list of integers

  List<Integer> list = List. of ( 12 , 32 , 14 , 15 , 56 ); //Result->12,14,15 List<String> listString = list.stream().map(String::valueOf).collect(Collectors.toList()); List<String> result1 = listString.stream().filter(x->x.startsWith( "1" )) .collect(Collectors.toList()); System. out .println( "result1-->" +result1); List<String> result2 = list.stream().map(x->x+ "" ).filter(x->x.startsWith( "1" )) .collect(Collectors.toList()); System. out .println( "result2-->" +result2);
Read more

Optional Vs Null Check

Image
The purpose of Optional is not to replace every single null reference in your codebase but rather to help design better APIs in which—just by reading the signature of a method—users can tell whether to expect an optional value. In addition, Optional forces you to actively unwrap an Optional to deal with the absence of a value; as a result, you protect your code against unintended null pointer exceptions. String version = computer.getSoundcard().getUSB().getVersion(); This code can lead to NullPointerException. Handle NullPointerException - This code looks ugly. String version = "UNKNOWN"; if(computer != null){   Soundcard soundcard = computer.getSoundcard();   if(soundcard != null){     USB usb = soundcard.getUSB();     if(usb != null){       version = usb.getVersion();     }   } } String name = computer.flatMap(Computer::getSoundcard)                           .flatMap(Soundcard::getUSB)                           .map(USB::getVersion)                           .orElse("
Read more

How to prevent Singleton Class from Reflection and Serialization

Image
First create a singleton class. If we will try to clone it, it will throw Exception.  Now try to break the Singleton with Reflection How to prevent Object Creation with Reflection - Check in the private constructor if object already created then throw an exception. If we run the reflection code, we will get this exception. Prevent Singleton Class from Serialization We have to override the read resolve method
Read more