What is the use of Destructor in Java?

What is a Destructor?
A destructor is a special method that gets called automatically as soon as the life-cycle of an object is finished. A destructor is called to de-allocate and free memory. The following tasks get executed when a destructor is called.

- Releasing the release locks
- Closing all the database connections or files
- Releasing all the network resources
- Other Housekeeping tasks
- Recovering the heap space allocated during the lifetime of an object

Destructors in Java also known as finalizers are non-deterministic. The allocation and release of memory are implicitly handled by the garbage collector in Java.

Finalizers in Java have to be implicitly invoked since their invocation is not guaranteed, unlike C# finalizers which get invoked during the .NET run-time.

Let’s take a look at key properties of a destructor:

- Overloading or inheritance is not allowed
- No specification of access modifiers or parameters
- Automatic invocation and no explicit call from the user
- Used in classes but not in structures
- The order of the class varies from the most derived class to the least derived class
- Also called when the object instance is no longer eligible for access
- Used for releasing un-managed resources instead of managed resources held by the object

Garbage Collector
A garbage collector is a program that runs on the Java virtual machine to recover the memory by deleting the objects which are no longer in use or have finished their life-cycle. An object is said to be eligible for garbage collection if and only if the object is unreachable.

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Important Sorting Algorithms-

Bubble Sort: Bubble Sort is the most basic sorting algorithm, and it works by repeatedly swapping adjacent elements if they are out of order.

Merge Sort: Merge sort is a sorting technique that uses the divide and conquer strategy.

Quicksort: Quicksort is a popular sorting algorithm that performs n log n comparisons on average when sorting an array of n elements. It is a more efficient and faster sorting algorithm.

Heap Sort: Heap sort works by visualizing the array elements as a special type of complete binary tree known as a heap.

Important Searching Algorithms-

Binary Search: Binary search employs the divide and conquer strategy, in which a sorted list is divided into two halves and the item is compared to the list’s middle element. If a match is found, the middle element’s location is returned.

Breadth-First Search(BFS): Breadth-first search is a graph traversal algorithm that begins at the root node and explores all neighboring nodes.

Depth-First Search(DFS): The depth-first search (DFS) algorithm begins with the first node of the graph and proceeds to go deeper and deeper until we find the goal node or node with no children.

Dynamic Programming (DP) is an algorithmic technique for solving an optimization problem by breaking it down into simpler sub-problems and taking advantage of the fact that the optimal solution to the overall problem is dependent on the optimal solution to its sub-problems.

Recursion is a problem-solving technique in which the solution is dependent on solutions to smaller instances of the same problem. Computing factorials is a classic example of recursive programming.

Every recursive program follows the same basic sequence of steps:

Set up the algorithm. Recursive programs frequently require a seed value, to begin with. This is accomplished by either using a parameter passed to the function or by providing a non-recursive gateway function that sets up the seed values for the recursive calculation.

Check to see if the current value(s) being processed correspond to the base case. If so, process the value and return it.

Rephrase the solution in terms of a smaller or simpler sub-problem or sub-problems.

Apply the algorithm to the sub-problem.

In order to formulate an answer, combine the results.

Return the results.

Hashing is a technique or process that uses a hash function to map keys and values into a hash table. It is done to allow for quicker access to elements. The efficiency of mapping is determined by the hash function’s efficiency.

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