Java Performance Optimization Simple Tips and Tricks

Java is a versatile and widely used programming language known for its portability and robustness. However, as with any language, Java applications can face performance challenges, especially when dealing with resource-intensive tasks or large-scale systems. To ensure your Java applications run efficiently, optimizing their performance is essential. This article explores several simple Java performance optimization tips and tricks to help you build faster and more efficient applications.

For some examples, we will use Java Microbenchmark Harness (JMH) to measure method execution time.

Use StringBuilder for String Concatenation

In Java, concatenating strings using the “+” operator can be inefficient, especially within loops. Each concatenation creates a new String object, which can lead to unnecessary memory allocations and overhead. To optimize string concatenation, use StringBuilder, which is mutable and performs better for repeated string manipulations.

The String concatenation with “+”

public String createStringUsingString() {
    String str = "";
    for (int i=0; i<1000; i++) {
        str += "some_text";
    }
    return str;
}        

Execution time (in milliseconds) with Java Microbenchmark Harness (JMH)

Benchmark                                   Mode  Cnt  Score   Error  Units
MyBenchmark.createStringUsingString         avgt    5  0.444 ± 0.053  ms/op        

The String concatenation with StringBuilder

public String createStringUsingStringBuilder() {
    StringBuilder sb = new StringBuilder();
    for (int i=0; i<1000; i++) {
        sb.append("some_text");
    }
    return sb.toString();
}        

Execution time (in milliseconds) with Java Microbenchmark Harness (JMH)

Benchmark                                   Mode  Cnt  Score   Error  Units
MyBenchmark.createStringUsingStringBuilder  avgt    5  0.008 ± 0.001  ms/op        

Result

From the above two examples, we can see that concatenation with the “+” operator takes 0.444 milliseconds, and with StringBuilder, on the other hand, takes only 0.008 milliseconds. So it is evident that, in this case, StringBuilder is much faster.

Use Primitive Types

Whenever possible, prefer using primitive data types (e.g.,?int,?double,?float) instead of their wrapper classes (e.g.,?Integer,?Double,?Float). Primitive types have lower memory overhead and avoid unnecessary autoboxing and unboxing operations.

The key reasons why using?primitives?can be faster than using?wrapper classes?in Java are:

• Memory Overhead: Wrapper classes like Integer, Double, etc., are objects and therefore consume more memory than their corresponding primitive types. Objects have additional memory overhead due to the underlying object structure, metadata, and potential garbage collection overhead. This increased memory usage can lead to cache inefficiencies and higher memory requirements.
• Autoboxing and Unboxing Overhead: Autoboxing is the Java compiler’s automatic conversion between the primitive types and their corresponding wrapper classes. Unboxing is the process of extracting the primitive value from the wrapper object. These operations introduce additional overhead involving object creation and method calls, impacting performance, especially in tight loops or performance-critical code sections.
• Method Call Overhead: Invoking methods on wrapper objects requires additional method call overhead compared to direct manipulation of primitive values. This can slow down the execution of code that frequently involves operations on wrapper objects.
• Caching of Primitives: Some Java implementations can cache specific frequently used primitive values, like small integers, to avoid unnecessary object creation. This optimization does not apply to wrapper classes.
• Comparisons and Equality: Comparing primitive values using == is straightforward and involves a simple memory comparison while comparing wrapper objects using == compares object references, not their contents. This can lead to unexpected behavior when comparing wrapper objects.

It’s important to note that while using primitives can provide performance benefits, there are cases where wrapper classes are necessary or more convenient. For example, wrapper classes is often unavoidable when working with data structures requiring objects (like collections). Additionally, wrapper classes provide proper utility methods and nullability support that primitive types lack.

In modern versions of Java, the JVM performs a certain level of optimization, and the performance gap between primitives and wrapper classes has been reduced in some scenarios due to the introduction of techniques like escape analysis. However, it’s generally a good practice for performance-critical code to be aware of the potential performance implications and consider using primitives where appropriate.

Wrapper Class usage

public Integer sumWithWrapperClass(Integer a, Integer b) {
    Integer sum = a + b;
    return sum;
}        

Execution time (in milliseconds) with Java Microbenchmark Harness (JMH)

Benchmark                        (a)  (b)  Mode  Cnt   Score    Error  Units
MyBenchmark.sumWithWrapperClass    5    0  avgt    3  ≈ 10??           ms/op
MyBenchmark.sumWithWrapperClass    5   -5  avgt    3  ≈ 10??           ms/op
MyBenchmark.sumWithWrapperClass    5    5  avgt    3  ≈ 10??           ms/op
MyBenchmark.sumWithWrapperClass   -5    0  avgt    3  ≈ 10??           ms/op
MyBenchmark.sumWithWrapperClass   -5   -5  avgt    3  ≈ 10??           ms/op
MyBenchmark.sumWithWrapperClass   -5    5  avgt    3  ≈ 10??           ms/op
MyBenchmark.sumWithWrapperClass    0    0  avgt    3  ≈ 10??           ms/op
MyBenchmark.sumWithWrapperClass    0   -5  avgt    3  ≈ 10??           ms/op
MyBenchmark.sumWithWrapperClass    0    5  avgt    3  ≈ 10??           ms/op        

Primitives usage

public int sumWithPrimitives(int a, int b) {
    int sum = a + b;
    return sum;
}        

Execution time (in milliseconds) with Java Microbenchmark Harness (JMH)

Benchmark                        (a)  (b)  Mode  Cnt   Score    Error  Units
MyBenchmark.sumWithPrimitives      5    0  avgt    3  ≈ 10??           ms/op
MyBenchmark.sumWithPrimitives      5   -5  avgt    3  ≈ 10??           ms/op
MyBenchmark.sumWithPrimitives      5    5  avgt    3  ≈ 10??           ms/op
MyBenchmark.sumWithPrimitives     -5    0  avgt    3  ≈ 10??           ms/op
MyBenchmark.sumWithPrimitives     -5   -5  avgt    3  ≈ 10??           ms/op
MyBenchmark.sumWithPrimitives     -5    5  avgt    3  ≈ 10??           ms/op
MyBenchmark.sumWithPrimitives      0    0  avgt    3  ≈ 10??           ms/op
MyBenchmark.sumWithPrimitives      0   -5  avgt    3  ≈ 10??           ms/op
MyBenchmark.sumWithPrimitives      0    5  avgt    3  ≈ 10??           ms/op        

Result

From the above two examples, we can see that summing 2 Integers takes ≈ 10?? (≈ 0.00001) milliseconds, and the same operation with 2 primitives, on the other hand, takes ≈ 10?? (≈ 0.000001) milliseconds. So it is evident that, in this case, primitives are much faster.

Use Efficient Data Structures

Data structures are containers for storing and organizing data in memory. They provide various methods for accessing, inserting, and deleting data elements. The choice of data structure directly influences the efficiency of these operations. Different data structures have different strengths and weaknesses, making them suitable for specific use cases. You can optimize your application’s performance by understanding the characteristics of other data structures. Selecting data structures best suited for the specific operations you need to perform is crucial.

Example 1: Insert elements in the middle of the List (ArrayList vs LinkedList)

ArrayList

The inserting element in the middle of the ArrayList is a pretty slow procedure.

When inserting an element in the middle of an ArrayList, you must shift the existing features to accommodate the new element. Here’s a step-by-step process to insert an element in the middle of an ArrayList:

• Create the ArrayList
• Calculate the Insertion Index: Determine the index at which you want to insert the new element. This index will be the middle index plus one if the ArrayList has an odd number of elements or the exact middle index if it has an even number of elements.
• Shift Elements: To insert an element in the middle, you must shift the elements to the right to make space for the new element. Start from the last element and move backward to the insertion index, shifting each element’s position to the right.
• Insert the New Element: After shifting the elements, you can insert the new element at the calculated insertion index.
• Element Inserted: The new element has been successfully inserted in the middle of the ArrayList, and the ArrayList size has increased by one.

public void insertInTheMiddleArrayList() {
    List<Integer> arrayList = new ArrayList<>();
    for (int i=0; i<1000000; i++) {
        arrayList.add(i);
    }
    arrayList.add(500000, -1);
}        

Execution time (in milliseconds) with Java Microbenchmark Harness (JMH)

Benchmark                                Mode  Cnt   Score   Error  Units
MyBenchmark.insertInTheMiddleArrayList   avgt    3  42.363 ± 9.906  ms/op        

LinkedList

Inserting an element in the middle of a LinkedList in Java is generally more efficient than an ArrayList because LinkedList is implemented as a doubly linked list, making it easier to insert elements in the middle without shifting the entire list. Here’s a step-by-step process to insert an element in the middle of a LinkedList:

• Create the LinkedList
• Create a New Node
• Traverse to the Desired Position: Traverse through the linked list until you reach the position where you want to insert the new node. Keep track of the previous node to perform the insertion.
• Update Pointers:?Once you’ve reached the desired position, update the pointers. Set the next pointer of the new node to the node currently at the desired position. Set the previous node’s next pointer to point to the new node.

public void insertInTheMiddleLinkedList() {
    List<Integer> linkedList = new LinkedList<>();
    for (int i=0; i<1000000; i++) {
        linkedList.add(i);
    }
    linkedList.add(500000, -1);
}        

Execution time (in milliseconds) with Java Microbenchmark Harness (JMH)

Benchmark                                Mode  Cnt   Score   Error  Units
MyBenchmark.insertInTheMiddleLinkedList  avgt    3  13.547 ± 1.300  ms/op        

Result

From the above two examples, we can see that adding an element in the middle of an ArrayList takes 42.363 milliseconds, and adding an element in the middle of a LinkedList takes 13.547 milliseconds. So it is evident that, in this case, adding an element in the middle of a LinkedList is much faster. Remember that this process involves shifting elements, which can be inefficient for large ArrayLists. In scenarios where you need frequent middle insertions, and performance is critical, you might consider using a different data structure like a linked list.

Example 2: Remove the element from the middle of the List (ArrayList vs LinkedList)

ArrayList

Deleting an element from the middle of the ArrayList is also a pretty slow procedure.

The steps are similar to the previous example when it determines the index at which you want to remove an element, removes the element at the specified index, and shifts the remaining elements to fill the gap.

public void removeFromTheMiddleOfArrayList() {
    List<Integer> arrayList = new ArrayList<>();
    for (int i=0; i<1000000; i++) {
        arrayList.add(i);
    }
    arrayList.remove(500000);
}        

Execution time (in milliseconds) with Java Microbenchmark Harness (JMH)

Benchmark                                    Mode  Cnt   Score    Error  Units
MyBenchmark.removeFromTheMiddleOfArrayList   avgt    3  42.117 ± 86.527  ms/op        

LinkedList

The steps are similar to the previous example when it determines the index at which you want to remove an element and removes it at the specified index by adjusting pointers.

public void removeFromTheMiddleOfLinkedList() {
    List<Integer> linkedList = new LinkedList<>();
    for (int i=0; i<1000000; i++) {
        linkedList.add(i);
    }
    linkedList.remove(500000);
}        

Execution time (in milliseconds) with Java Microbenchmark Harness (JMH)

Benchmark                                    Mode  Cnt   Score    Error  Units
MyBenchmark.removeFromTheMiddleOfLinkedList  avgt    3   7.272 ±  6.501  ms/op        

Result

From the above two examples, we can see that removing an element from the middle of an ArrayList takes 42.117 milliseconds, and removing an element from the middle of a LinkedList takes 7.272 milliseconds. So it is evident that, in this case, removing an element from the middle of a LinkedList is much faster.

Example 3: Access by index (ArrayList vs LinkedList)

LinkedList

A LinkedList is implemented using a doubly-linked list. Each element in the list (node) references the previous and next elements. This data structure allows for efficient insertion and deletion operations at any position, but when accessing elements by index, LinkedList operations can be slower than ArrayList. Accessing elements by index in a LinkedList has a linear time complexity on average, O(n), where n is the index you’re trying to access. This is because the list needs to traverse from the beginning or end of the list, depending on which index is closer, until it reaches the desired position.

public int accessByIndexLinkedList() {
    List<Integer> linkedList = new LinkedList<>();
    for (int i = 0; i < 1500000; i++) {
        linkedList.add(i);
    }
    int number = linkedList.get(1400000);
    return number;
}        

Execution time (in milliseconds) with Java Microbenchmark Harness (JMH)

Benchmark                            Mode  Cnt    Score    Error  Units
MyBenchmark.accessByIndexLinkedList  avgt    5  178.962 ± 75.787  ms/op        

ArrayList

An ArrayList is implemented using a dynamic array. It’s essentially an array that automatically resizes itself when necessary as elements are added or removed. Retrieving an element by index in an ArrayList is relatively fast because you directly access an element in a contiguous memory location. Accessing elements by index in an ArrayList has a constant time complexity on average, O(1), assuming the underlying dynamic array resizing doesn’t happen frequently.

public int accessByIndexArrayList() {
    List<Integer> arrayList = new ArrayList<>();
    for (int i = 0; i < 1500000; i++) {
        arrayList.add(i);
    }
    int number = arrayList.get(1400000);
    return number;
}        

Execution time (in milliseconds) with Java Microbenchmark Harness (JMH)

Benchmark                            Mode  Cnt    Score    Error  Units
MyBenchmark.accessByIndexArrayList   avgt    5   63.011 ±  3.797  ms/op        

Result

From the above two examples, we can see that getting an element by index from the LinkedList takes 178.962 milliseconds and only 63.011 milliseconds in the ArrayList case. Obviously that getting an element by an index is much faster for ArrayList.

Example 4: Array vs ArrayList

One typical decision developers face is choosing between arrays and the ArrayList class. Arrays are a fundamental data structure in Java, while ArrayList is a part of the Java Collections Framework. Arrays have a fixed size, while ArrayLists can dynamically resize as needed. Default ArrayList capacity equals 10. The formula for increasing the size of ArrayList is?(OLD_CAPACITY * 3) / 2 + 1.

ArrayList

public List createArrayList() {
    List<Integer> arrayList = new ArrayList<>();
    for (int i = 0; i < 1500000; i++) {
        arrayList.add(i);
    }
    return arrayList;
}        

Execution time (in milliseconds) with Java Microbenchmark Harness (JMH)

Benchmark                    Mode  Cnt   Score   Error  Units
MyBenchmark.createArrayList  avgt    5  71.240 ± 6.042  ms/op        

Array

public int[] createArray() {
    int array[] = new int[1500000];
    for (int i = 0; i < 1500000; i++) {
        array[i] = i;
    }
    return array;
}        

Execution time (in milliseconds) with Java Microbenchmark Harness (JMH)

Benchmark                    Mode  Cnt   Score   Error  Units
MyBenchmark.createArray      avgt    5   0.788 ± 0.028  ms/op        

Result

The above two examples show that creating ArrayList takes 71.240 milliseconds and only 0.788 milliseconds to create Array. If the number of elements is known ahead of time, it is much faster to create an Array than ArrayList.

Example 5: Insert into HashMap vs TreeMap

Another critical choice is between the HashMap and TreeMap classes for storing key-value pairs. HashMap uses hash-based indexing for quick access, while TreeMap maintains elements in sorted order based on their keys. If you prioritize fast retrieval times and don’t need a specific order, HashMap is generally more efficient due to its constant-time lookups. However, if you require elements to be sorted by keys, TreeMap can provide better performance for tasks like range queries.

HashMap performs better for insertion and lookup due to its average O(1) time complexity. On the other hand, TreeMap has an average O(log n) time complexity for insertion and lookup, making it slower as the number of elements increases.

TreeMap

public void insertTreeMap() {
    Map<Integer, String> treeMap = new TreeMap<>();
    for (int i = 0; i < 1000000; i++) {
        treeMap.put(i, "Value " + i);
    }
}        

Execution time (in milliseconds) with Java Microbenchmark Harness (JMH)

Benchmark                  Mode  Cnt    Score    Error  Units
MyBenchmark.insertTreeMap  avgt    5  205.077 ± 94.451  ms/op        

HashMap

public void insertHashMap() {
    Map<Integer, String> hashMap = new HashMap<>();
    for (int i = 0; i < 1000000; i++) {
        hashMap.put(i, "Value " + i);
    }
}        

Execution time (in milliseconds) with Java Microbenchmark Harness (JMH)

Benchmark                  Mode  Cnt    Score    Error  Units
MyBenchmark.insertHashMap  avgt    5  103.690 ±  9.183  ms/op        

Result

The above two examples show that inserting into TreeMap takes 205.077 milliseconds and 103.690 milliseconds in the HashMap case. If you don’t need a specific order, the HashMap is much faster than the TreeMap.

Example 6: Sort and iterate HashMap vs TreeMap

In this example, TreeMap outperforms HashMap when iterating through the entries in sorted order. The TreeMap maintains the keys in sorted order, simplifying sorted iteration. However, remember that this advantage comes at the cost of slower insertion and lookup times compared to the HashMap.

HashMap

public void sortAndPrintResultsHashMap() {
    Map<Integer, String> hashMap = new HashMap<>();
    for (int i = 1000000 - 1; i >= 0; i--) {
        hashMap.put(i, "Value " + i);
    }

    List<Integer> sortedKeys = new ArrayList<>(hashMap.keySet());
    Collections.sort(sortedKeys);

    for (Integer key : sortedKeys) {
        System.out.println(key + " : " + hashMap.get(key));
    }
}        

Execution time (in milliseconds) with Java Microbenchmark Harness (JMH)

Benchmark                               Mode  Cnt      Score       Error  Units
MyBenchmark.sortAndPrintResultsHashMap  avgt    5  13698.239 ± 21647.247  ms/op        

TreeMap

public void sortAndPrintResultsTreeMap() {
    Map<Integer, String> treeMap = new TreeMap<>();
    for (int i = 1000000 - 1; i >= 0; i--) {
        treeMap.put(i, "Value " + i);
    }

    for (Map.Entry<Integer, String> entry : treeMap.entrySet()) {
        System.out.println(entry.getKey() + " : " + entry.getValue());
    }
}        

Execution time (in milliseconds) with Java Microbenchmark Harness (JMH)

Benchmark                               Mode  Cnt      Score       Error  Units
MyBenchmark.sortAndPrintResultsTreeMap  avgt    5   7849.875 ±  1979.701  ms/op        

Result

The above two examples show that sorting elements and iterating through them in TreeMap takes 205.077 milliseconds and 103.690 milliseconds in the HashMap case. In this case, TreeMap is much faster than HashMap when a specific order is needed.

Optimize Loops

Loops are an integral part of Java programs; optimizing them can substantially impact performance. Here are some key points to consider when optimizing loops:

• Reduce Loop Iterations: Analyze if the loop can be executed fewer times without affecting the desired outcome.
• Use break or continue statements when possible to exit or skip unnecessary iterations.
• Minimize Method Calls: Move method calls outside the loop if they don’t depend on loop variables. Method calls have overhead, and minimizing them can speed up the loop.
• Use Local Variables: Access frequently used object properties through local variables to reduce memory access overhead.
• Avoid Recalculation: If a loop involves calculations that don’t change within the loop, calculate them outside the loop and reuse the result.
• Use Enhanced for Loop:?If you’re iterating over collections like lists, arrays, or sets, use the enhanced for loop (for-each loop) as it’s optimized for these scenarios.
• Loop Unrolling:?Manually unroll loops for small iteration counts. This reduces loop overhead by processing multiple iterations in a single loop cycle.
• Prefer Primitive Types: Use primitive data types instead of their wrapper classes, as they are more memory-efficient and can improve loop performance.
• Avoid Object Instantiation: Minimize object creation within loops, as object creation and garbage collection overhead can impact performance.
• Use System.arraycopy: When copying array elements, consider using System.arraycopy instead of manual loops for better performance. The primary advantage of System.arraycopy is that it’s highly optimized and can be faster in many cases compared to manual loops. It’s implemented at a lower level, often in native code, which can lead to better memory management and cache utilization. Manual loops, on the other hand, can give you more control over the copying process and allow for customization. However, they may be slower in specific scenarios due to the additional overhead of the loop and indexing operations. In terms of performance, System.arraycopy is generally faster for large arrays because it can take advantage of low-level memory copying mechanisms. However, manual loops might be better for small arrays or when you need more control over the copying process. Next, there is an example of comparisons of manual looping and System.arraycopy.

    for (int i=0; i<1000000; i++) {
        sourceArray[i] = "Value " + i;
    }
    String[] destinationArray = new String[1000000];
    System.arraycopy(sourceArray, 0, destinationArray, 0, sourceArray.length);
}

public void copyWithManualLoop() {
    String[] sourceArray = new String[1000000];
    for (int i=0; i<1000000; i++) {
        sourceArray[i] = "Value " + i;
    }
    String[] destinationArray = new String[1000000];
    for (int i = 0; i < sourceArray.length; i++) {
        destinationArray[i] = sourceArray[i];
    }
}        

Execution time (in milliseconds) with Java Microbenchmark Harness (JMH)

Benchmark                            Mode  Cnt   Score   Error  Units
MyBenchmark.copyWithManualLoop       avgt    5  62.734 ± 2.847  ms/op
MyBenchmark.copyWithSystemArrayCopy  avgt    5  52.476 ± 0.998  ms/op        

• Parallelism: Consider using Java’s parallel streams or threads to process loop iterations concurrently for large data sets. Next is an example of comparisons of for loop, stream API parallel stream, and threads.

Loop

public void processWithForLoop() {
  String[] data = new String[1000000];
    for (int i = 0; i < data.length; i++) {
        data[i] = "Value " + i;
    }
    for (String value : data) {
        System.out.println("New value: " + value.toUpperCase());
    }
}        

Execution time (in milliseconds) with Java Microbenchmark Harness (JMH)

Benchmark                       Mode  Cnt     Score     Error  Units
MyBenchmark.processWithForLoop  avgt    5  14946.115 ± 812.357  ms/op        

Parallel Stream

public void processWithParallelStream() {
  String[] data = new String[1000000];
    for (int i = 0; i < data.length; i++) {
        data[i] = "Value " + i;
    }
    Arrays.stream(data)
                .parallel()
                .forEach(this::processValue);
}        

Execution time (in milliseconds) with Java Microbenchmark Harness (JMH)

Benchmark                              Mode  Cnt      Score      Error  Units
MyBenchmark.processWithParallelStream  avgt    5  7588.523 ± 2797.955  ms/op        

Threads

public void processWithThreads() throws InterruptedException {
  String[] data = new String[1000000];
    for (int i = 0; i < data.length; i++) {
        data[i] = "Value " + i;
    }

  int numThreads = Runtime.getRuntime().availableProcessors();

    Thread[] threads = new Thread[numThreads];
    for (int i = 0; i < numThreads; i++) {
        int startIndex = i * (data.length / numThreads);
        int endIndex = (i == numThreads - 1) ? data.length : (i + 1) * (data.length / numThreads);
        threads[i] = new Thread(new DataProcessor(data, startIndex, endIndex));
        threads[i].start();
    }

    for (Thread thread : threads) {
        thread.join();
    }
}

class DataProcessor implements Runnable {
    private String[] data;
    private int startIndex;
    private int endIndex;

    public DataProcessor(String[] data, int startIndex, int endIndex) {
        this.data = data;
        this.startIndex = startIndex;
        this.endIndex = endIndex;
    }

    @Override
    public void run() {
        for (int i = startIndex; i < endIndex; i++) {
            processValue(data[i]);
        }
    }

    private void processValue(String value) {
        System.out.println("New value: " + value.toUpperCase());
    }
}        

Execution time (in milliseconds) with Java Microbenchmark Harness (JMH)

Benchmark                       Mode  Cnt      Score      Error  Units
MyBenchmark.processWithThreads  avgt    5  6946.115 ± 2467.378  ms/op        

• Cache Awareness:?Arrange loop operations to use CPU caches best, avoiding cache thrashing.
• Avoid I/O Inside Loops: Minimize I/O operations like file or network access within loops to prevent unnecessary performance hits.

Employ the ‘final’ Keyword

Using the final keyword can indeed impact optimization and performance in certain cases, although it might not always result in significant improvements. Here are a few scenarios where using the final keyword can potentially optimize and increase performance in a Java application:

• Constants and Inlining: When you declare a variable as final, the Java compiler can treat it as a compile-time constant. This can allow the compiler to perform constant folding and replace the variable with its actual value during compilation. This primarily benefits numeric constants, reducing runtime computations and improving performance.
• Method Invocation and Overhead:?When you mark a method as final, the compiler knows that subclasses cannot override the method. This knowledge allows the compiler to perform direct method invocation instead of dynamically dispatching the method call. This can reduce the overhead of dynamic method dispatch and lead to faster method calls.
• Thread Safety:?When a field is marked as final in a class, it ensures that its value is assigned only once, typically during object construction. This can help in creating thread-safe objects, as there is no need for synchronization mechanisms like locks to ensure proper initialization of fields.
• Optimizations by the JIT Compiler:?The Java Just-In-Time (JIT) compiler can use the final keyword for specific optimizations. For example, the JIT compiler might inline final methods, optimize away unnecessary null checks for final fields, or eliminate bounds checks for final array lengths.
• Escape Analysis: When a variable is marked as final, and its scope is limited, the Java Virtual Machine (JVM) might perform an escape analysis to determine that the variable’s reference doesn’t escape the current method or thread. This can enable additional optimizations like stack allocation instead of heap allocation.

It’s important to note that while using the final keyword can offer potential performance benefits, the impact might not always be substantial. Modern JVMs are equipped with powerful optimization techniques, and the JVM can often optimize code without explicitly using the final. It’s generally recommended to focus on writing clean, maintainable, and readable code first and then consider using final for performance optimization when appropriate.

Optimize Exception Handling

Exception handling is a critical aspect of Java programming, as it ensures the robustness and reliability of your applications by gracefully managing unexpected errors and failures. However, how you handle exceptions can significantly impact the performance of your Java applications.

• Use Specific Exceptions: Java provides a hierarchy of exception classes covering various errors. When catching exceptions, it’s essential to be as specific as possible. Catching a general Exception class can lead to inefficiencies, as it may catch more exceptions than necessary. By catching more specific exception classes, you can pinpoint the exact issues that need to be handled, allowing your program to bypass unnecessary catch blocks and proceed with execution more efficiently.
• Limit Exception Throwing:?Java introduced the concept of suppressed exceptions to handle exceptions during the cleanup phase of try-with-resources statements. While this feature is valid, it can affect performance. Only use suppressed exceptions when necessary to optimize exception handling and avoid unnecessary suppression that might slow down your code.
• Minimize Catch Block Work: The code within a catch block should focus on error handling and recovery rather than performing extensive computations. Complex operations within catch blocks can lead to performance bottlenecks, as they execute during exception handling, impacting the overall performance of your application.
• Use Checked Exceptions Wisely:?Checked exceptions require the caller to handle or declare them. While they enforce better error-handling practices, they can lead to cluttered code and performance issues if not used judiciously. Consider whether a particular exception should be checked or unchecked based on the likelihood of occurrence and the level of control required.
• Asynchronous Exception Handling: Asynchronous exception handling can be considered for scenarios where performance is crucial and exceptions are expected to be rare. This involves handling exceptions in a separate thread or using reactive programming techniques. However, this approach adds complexity to the codebase and should be used cautiously.

Leverage Java Concurrency Utilities

Java, a widely-used programming language, offers a comprehensive set of Concurrency Utilities that allow developers to optimize the performance of their applications by efficiently utilizing available hardware resources.

Concurrency in Java refers to the ability of a program to execute multiple tasks simultaneously, enabling more efficient resource utilization and improved responsiveness. However, managing concurrency manually can be complex and error-prone, leading to issues like deadlocks, race conditions, and inconsistent behavior. Java’s Concurrency Utilities provide a higher-level, safer, and more manageable approach to handling concurrent tasks.

Key Concurrency Utilities in Java

• Executor Framework:?The Executor framework simplifies managing thread creation and execution. It offers different implementations like ThreadPoolExecutor and ScheduledThreadPoolExecutor, allowing you to manage thread pools, control thread reuse, and schedule tasks for future execution. By using thread pools, you can avoid the overhead of creating and destroying threads frequently, improving overall performance.
• Fork/Join Framework:?The ForkJoinPool class is designed for parallelizing tasks that can be divided into smaller subtasks. It is beneficial for recursive algorithms where the problem can be broken down into smaller pieces that can be executed concurrently. The framework provides features like work-stealing, where idle threads can steal work from busy threads, ensuring efficient workload distribution.
• Concurrent Collections:?Java offers thread-safe implementations of commonly used collections like ConcurrentHashMap, ConcurrentLinkedQueue, and CopyOnWriteArrayList. These collections allow multiple threads to access and modify data concurrently without explicit synchronization, reducing contention and improving performance in multi-threaded scenarios.
• Synchronization Utilities: Classes like CountDownLatch, CyclicBarrier, and Semaphore provide synchronization mechanisms to coordinate the execution of multiple threads. They are valuable for scenarios where threads must wait for specific conditions before proceeding, facilitating efficient inter-thread communication and synchronization.
• CompletableFuture: CompletableFuture simplifies asynchronous programming by enabling the composition of multiple asynchronous operations. It allows you to chain together operations and define exception-handling strategies, leading to more readable and maintainable asynchronous code. This can significantly improve the responsiveness of applications that rely on I/O operations.

While Concurrency Utilities offer powerful tools to optimize performance, using them incorrectly can lead to bugs and degraded performance. Here are some best practices to keep in mind:

• Start Simple:?Identify bottlenecks in your application and focus on the areas where concurrency can benefit most. Start with simple tasks and gradually introduce more complex concurrency mechanisms.
• Minimize Shared State:?Reduce shared mutable state among threads as much as possible. This minimizes the chances of race conditions and simplifies synchronization requirements.
• Choose the Right Concurrency Utility: Select the appropriate Concurrency Utility for your specific use case. For example, use ForkJoinPool for recursive divide-and-conquer algorithms and Executor frameworks for managing thread pools.
• Limit Thread Creation: Creating excessive threads can lead to resource exhaustion. Use thread pools to control the number of threads, and consider using cached thread pools when tasks are short-lived.
• Error Handling: Implement proper error-handling strategies for concurrent tasks. Uncaught exceptions in individual threads could lead to thread pool instability and unexpected behavior.

Reduce object creation wherever possible

One common area that often contributes to performance bottlenecks is excessive object creation. Creating and disposing of objects can result in increased memory usage, garbage collection overhead, and slower execution times.

Object Pooling

Object Pooling is a technique that involves creating a pool of reusable objects upfront and reusing them whenever needed rather than creating new objects each time. This approach can be particularly practical for frequently used objects like database connections, thread instances, or network sockets.

import java.sql.Connection;
import java.sql.DriverManager;
import java.sql.SQLException;
import java.util.ArrayList;
import java.util.List;

public class ConnectionPool {
    private static final int MAX_POOL_SIZE = 10;
    private List<Connection> connections = new ArrayList<>(MAX_POOL_SIZE);

    public ConnectionPool() throws SQLException {
        for (int i = 0; i < MAX_POOL_SIZE; i++) {
            Connection connection = DriverManager.getConnection("jdbc:mysql://localhost:3306/db", "username", "password");
            connections.add(connection);
        }
    }

    public Connection getConnection() {
        if (connections.isEmpty()) {
            throw new IllegalStateException("Connection pool exhausted");
        }
        return connections.remove(0);
    }

    public void releaseConnection(Connection connection) {
        if (connections.size() < MAX_POOL_SIZE) {
            connections.add(connection);
        } else {
            try {
                connection.close();
            } catch (SQLException e) {
                // Handle exception
            }
        }
    }
}        

Immutable Objects

Immutable objects are instances whose state cannot be changed after creation. Since their state remains constant, these objects can be safely shared across threads without synchronization, leading to better performance.

public final class Point {
    private final int x;
    private final int y;

    public Point(int x, int y) {
        this.x = x;
        this.y = y;
    }

    public int getX() {
        return x;
    }

    public int getY() {
        return y;
    }
}        

Reusing Objects

In scenarios where objects can be reused without compromising code readability or maintainability, consider modifying objects in place instead of creating new ones.

public class ReuseExample {
    public static void main(String[] args) {
        StringBuilder reusableBuilder = new StringBuilder();
        for (int i = 0; i < 10000; i++) {
            reusableBuilder.setLength(0); // Clear the builder
            reusableBuilder.append(i).append(", ");
            String result = reusableBuilder.toString();
            // Use the result
        }
    }
}        

Conclusion

Optimizing your Java performance requires a deep understanding of your application and its architecture. By following these tips and tricks, you should be well on the way to improving your Java application’s performance. Remember that performance optimization is an ongoing process, and you should always be looking for ways to improve your code. With a little effort and discipline, you can create Java applications that are fast, efficient, and capable of handling even the most demanding workloads.