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Showing posts with label System Design. Show all posts
Showing posts with label System Design. Show all posts

Saturday, November 5, 2022

Understanding Spring AOP

Understanding Spring AOP
Photo by Glenn Carstens Peters

What is Spring AOP?

Spring AOP enables Aspect-Oriented Programming in spring applications. It provides the way to dynamically add the cross-cutting concerns(logging, security, transaction management, auditing, i18n etc.) before, after or around the actual logic using simple pluggable configurations. It makes easy to maintain code in the present and future as well. You can add/remove concerns without recompiling complete source code simply by changing configuration files (if you are applying aspects using XML configuration).


What is advice, joinpoint or pointcut?

  • An important term in AOP is advice. It is the action taken by an aspect at a particular join-point. 
  • Joinpoint is a point of execution of the program, such as the execution of a method or the handling of an exception. In Spring AOP, a joinpoint always represents a method execution.
  • Pointcut is a predicate or expression that matches join points.
  • Advice is associated with a pointcut expression and runs at any join point matched by the pointcut.
  • Spring uses the AspectJ pointcut expression language by default.


Pointcut

Pointcut determines the join point of interest and in the code it appears as pointcut expression. It works similarly to regular expressions, using the special syntax it matches methods with advices. Please note, that Spring AOP supports only those classes that are defined as Spring beans/component otherwise they won’t be available. Here is a pointcut expression general syntax (those parts that are in red are mandatory) with some examples:

execution(modifiers-pattern? ret-type-pattern declaring-type-pattern?name-pattern(param-pattern) throws-pattern?)

The following examples show some common pointcut expressions:

The execution of any public method, First * means that it will match any return type, and *(..) means that the expression will match any method, no matter how much arguments it contains.
    execution(public * *(..))

The execution of any method with a name that begins with set:
    execution(* set*(..))

The execution of any method defined by the OrderService interface/class:
    execution(* com.bhargav.service.AccountService.*(..))

The execution of any method defined in the service package or one of its sub-packages:
    execution(* com.bhargav.service..*.*(..))

This will be matched only for those methods in the DemoClass, which has int as a first parameter, return type as int and method should be public:
    execution(public int DemoClass.*(int, ..))

Any join point (method execution only in Spring AOP) within the service package:
    within(com.bhargav.service.*)

Advices

Spring AOP includes the following types of advice:

@Before: 
Advice that runs before a join point but that does not have the ability to prevent execution flow proceeding to the join point (unless it throws an exception).

@After: 
Advice to be run regardless of the means by which a join point exits (normal or exceptional return).

@AfterReturning: 
Advice to be run after a join point completes normally (for example, if a method returns without throwing an exception).

@AfterThrowing: 
Advice to be run if a method exits by throwing an exception.

@Around:
Advice that surrounds a join point such as a method invocation. This is the most powerful kind of advice. Around advice can perform custom behaviour before and after the method invocation. It is also responsible for choosing whether to proceed to the join point or to shortcut the advised method execution by returning its own return value or throwing an exception.


References:

Wednesday, August 4, 2021

Retry or Stability Pattern

Retry or Stability Pattern Image
Photo by Brett Jordan

One of the key characteristic of microservices architecture is inter-service communication. We can split a monolithic application into multiple smaller applications called microservices. Each microservice is responsible for a single feature or domain and can be deployed, scaled, and maintained independently.

Since microservices are distributed in nature, various things can go wrong at any point of time. The network over which we access other services or services themselves can fail. There can be intermittent network connectivity errors or firewall issues. Individual services can fail due to service unavailability, coding issue, out of memory errors, deployment failure, hardware failure and etc., to make our services resilient to these failures, we adopt the retry pattern which is also known as stability pattern.

Retry Pattern

The idea behind the retry pattern is quite simple. If service A makes a call to service B and receives an unexpected response for a request, then service A will send the same request to service B again hoping to get an expected response.

Retry Pattern Image
Retry Pattern Representation

There are several retry strategies that can be applied depending on the failure type or nature of the requirements.

Immediate Retry

This strategy is the basic one. In this approach, calling service handles the unexpected failure and immediately makes the request again. This strategy can be useful for unusual failures that occur intermittently. The chances of success are high by just retrying in these cases.

Retry After Delay

In this strategy, we introduce a delay before retrying service call again, hoping that the cause of the fault would have been rectified. Retry after delay is an appropriate strategy when a request timeout occurs due to busy or failures or network-related issue.

Sliding Retry

In this strategy, the service will continue to retry the service call by adding an incremental time delays on each subsequent attempts. For example, the first retry may wait 500 MS, the second will wait 1000 MS, the third will wait 1500 MS until the retry count has not been exceeded. By adding an increasing delay, we reduce the number of retries to the service and avoid adding any additional load to a service which is already overloaded.

Retry with Exponential Backoff

In this strategy, we take the Sliding Retry strategy and ramp up the retry delay exponentially. If we started with a 500 MS delay, we would retry again after 1500 MS, then 3000 MS. Here we are trying to give the service more time to recover before we try to invoke it again.

Abort Retry

As we understand, we can't have a retry process happening forever. We need to have a threshold on the maximum number of retry attempts, we try for a failed service call. We need to maintain the counter and when it reaches the threshold value, our best strategy is to abort the retry process and let the error propagate to the calling service.

Conclusion

The retry pattern allows the calling service to retry failed attempts with a hope that the service will respond within an acceptable time.

With the varying interval between retries we provide the dependent service more time to recover and respond for our request.

It is recommend that, we need to keep a track of failed operations as it will be very useful information to find recurring errors and also the required infrastructure like thread pool, thread strategy etc.

At some point, we just need to abort the retry and we must acknowledge that the service is not responding and notify the calling service with an error.

References

Monday, July 19, 2021

Understanding Load Balancer

Load Balancer Image
Photo by Jon Flobrant

A load balancer is an important component of any distributed system. It helps to distribute the client requests within a cluster of servers to improve the responsiveness and availability of applications or websites.

It distributes workloads uniformly across servers or other compute resources to optimize the network efficiency, reliability and capacity. Load balancing is performed by an appliance either physical or virtual that identifies in real time which server [pod incase of kubernetes] in a pool can best meet a given client request, while ensuring heavy network traffic doesn't overwhelm any single server [ or pod]. Another important task of load balancer is to carry out continuous health checks on servers [or pods] to ensure they can handle requests. It ensures better use of system resources by balancing user requests and guarantees 100% availability of service.

Load Balancer Image
Reverse Proxy/Load Balancer Communication Flow

During the system design, horizontal scaling is a very common strategy or solution to scale any system when the user base is huge in number. It also ensures better overall throughput of the application or website. Latencies should occur less often as requests are not blocked, and users need not to wait for their requests to be processed/served.

Availability is a key characteristic of any distributed system. In case of a full server failure, there won’t be any impact on the user experience as the load balancer will simply send the client request to a healthy server. Instead of a single resource performing or taking heavy load, load balancer ensures that several resources perform a bearable amount of work.


Categories of Load Balancer

Layer 4 Category Load Balancer

Load balancers distribute traffic based on transport data, such as IP addresses and Transmission Control Protocol (TCP) port numbers. Examples - Network Load balances in AWS and Internal Load balancer in GCP

Layer 7 Category Load Balancer

Load balancers make routing decisions based on application characteristics that include HTTP header information or the actual contents of the message such as URLs, Cookies etc. Examples - Applications Load balancer in AWS and Gloabl Load balancer in GCP


Types of Load Balancing

Hardware Load Balancing Type

Vendors of hardware‑based solutions load proprietary software onto the machine they provide, which often uses specialized components or resources. To handle the increasing traffic to the application or website, one has to buy specific h/w from the vendors. Example - F5 Load balancer from F5 networks 

Software Load Balancing Type

Software solutions generally run on regular hardware, making them economical and more flexible. You can install the software on the hardware of your choice or in cloud environments like AWS, GCP, Azure etc.


Load Balancing Techniques

There are various types of load balancing methods and every type uses different algorithms for distributing the requests. Here is a list of load balancing techniques:

Random Selection

As the name itself says, the servers are selected randomly. There are no other factors considered in selection of the server. This method might cause a problem, where some of the servers gets overloaded with requests and other might be sitting idle.

Round Robin

One of the most commonly used load balancing methods. It’s a method where the load balancer redirects incoming traffic between a set of servers in a certain order. As per the above diagram, we have have 3 application servers; the first request goes to App Server 1, the second one goes to App Server 2, and so on. When load balancer reaches the end of the server list, it starts over again from the beginning which is from App Server 1. It almost evenly balances the traffic between the servers. All servers need to be of same specification for this method to work successfully. Otherwise, a low specification server may have the same load as a high processing capacity server.

Weighted Round Robin

It's a bit more complex than the Round Robin, as this method is designed to handle servers with different characteristics. A weight is assigned to each server in the configuration. This weight can be an integer value that varies according to the specifications of the server. Higher specification servers get more weightage, which is the key parameter for traffic redirection.

Least Response Time

This algorithm sends the client requests to the server with the least active connections and the lowest average response time. The backend server that responds the fastest receives the next request.

Least Connections

In this method, the traffic redirection happens based on the server with the least number of active connections.

IP Hash

In this method, a hash of the source/client's IP address is generated which is used to select a server for redirection. Once the server is allocated, same server will be used for the client’s  consecutive requests. It becomes more like a sticky where requests of a client will be sent to same server irrespective of how busy the server with requests. In some use cases, this method will come very handy and even improve the performance.


Conclusion

Availability is a key characteristic of a distributed system. In case of a one server failure scenario, it won’t affect the end user experience as the load balancer will simply send the client request to another healthy server.

While designing a distributed system, one of the important task is to choose the load balancing strategy according to the application or website requirements.

HAProxy (High Availability Proxy) is open source proxy and load balancing server software. It provides high availability at the network (TCP) and application (HTTP/S) layers, improving speed and performance by distributing workload across multiple servers.

Nginx is a very efficient HTTP load balancer to distribute traffic to several application servers and to improve performance, scalability and reliability of web applications.


References

Thursday, June 24, 2021

Template Method Pattern - Skeleton is Defined in Base Class

Template Method Design Pattern
Photo Courtesy Unsplash

What is the Template Method Design Pattern?

The Template Method Design Pattern defines a sequence of steps of an algorithm and allows the subclasses to override the steps but do not allow to change the sequence. The Key to the Template Design Pattern is that we put the general logic in the abstract parent class and let the child classes define the specifics.

Template Method pattern falls under the behavioural design pattern, is one of the easiest to understand and implement. This design pattern is used popularly in framework development and also helps to avoid code duplication.

An abstract class contains the templateMethod which should be made final so that it cannot be overridden. This template method makes use of other operations available in order to run the algorithm but is decoupled for the actual implementation of these methods. Concrete class implements all the methods required by the templateMethod that were defined as abstract in the parent class.

Pattern Implementation

As am a coffee person, I will take an example of preparing the coffee using a template method pattern which should allow to understand the pattern easily instead of taking some framework implementations such as AbstractController, RequestProcessor, HttpServlet, InputStream, OutputStream etc.

public abstract class CoffeeMaker {
   protected boolean sugarFree = false;

   public void prepareCoffee() {
      boilWater();
      addMilk();
      if (!isSugarFree()) {
         addSugar();
      } else {
 System.out.println("- No Sugar")
      }
      addCoffeePowder();
      System.out.println("- Coffee is Ready!!!");
   }

   public final void boilWater() {
      System.out.println("- Boiling Water");
   }

   public boolean isSugarFree() {
      return sugarFree;
   }

   public void setSugarFree(boolean sugarFree) {
      this.sugarFree = sugarFree;
   }

   abstract void addMilk();
   abstract void addSugar();
   abstract void addCoffeePowder();
}

Now we will create 2 concrete classes which will provide different implementations.

public class CothasCoffeeMaker extends CoffeeMaker {
   @Override
   public void addMilk() {
      System.out.println("- Adding Milk");
   }

   @Override
   public void addSugar() {
      System.out.println("- Adding Sugar");
   }

   @Override
   public void addCoffeePowder() {
      System.out.println("- Adding Cothas Coffee Powder");
   }
}

public class BruCoffeeMaker extends CoffeeMaker {
   @Override
   public void addMilk() {
      System.out.println("- Adding Milk");
   }

   @Override
   public void addSugar() {
      System.out.println("- Adding Sugar");
   }

   @Override
   public void addCoffeePowder() {
      System.out.println("- Adding Bru Coffee Powder");
   }
}

Now it's time to write the main method to execute the above code and see the output of the two variations of the coffee.

public class CoffeeMakerExample {
   public static void main(String[] args) {
      System.out.println("Cothas Coffee Preparation")
      CoffeeMaker coffeeMaker = new CothasCoffeeMaker();
      coffeeMaker.prepareCoffee();

      System.out.println("Bru Coffee Preparation");
      coffeeMaker = new BruCoffeeMaker();
      coffeeMaker.setSugarFree(true);
      coffeeMaker.prepareCoffee();
   }

}

The CoffeeMaker class is an abstract class containing the algorithm skeleton. The prepareCoffee() is the method that contains the process steps. The boilWater() method is common step for the process of any coffee preparation and no customization is required, hence it has been made as final so the subclasses will not override and provide a different implementation. 

We have two subclasses CothasCoffeeMaker and BruCoffeeMaker which follows the same preparation process but implementation are given at each individual subclass.

If you execute the above program, you should see the following output.

Cothas Coffee Preparation
- Boiling Water
- Adding Milk
- Adding Sugar
- Adding Cothas Coffee Powder
- Coffee is Ready!!!

Bru Coffee Preparation
- Boiling Water
- Adding Milk
- No Sugar
- Adding Bru Coffee Powder
- Coffee is Ready!!!

Conclusion

  • The pattern promotes the code reusability and decoupling, but at the expense of using inheritance.
  • The pattern adhers to the Single Responsibility and Open/Closed principles of S.O.L.I.D - Design Principles.

Reference

Tuesday, May 25, 2021

Singleton Design Pattern - Build It, Break It and Fix It!

Singleton Design Pattern
Photo Courtesy Unsplash


Singleton pattern is one of the simplest design patterns and falls under creational pattern as this pattern provides one of the best ways to create an object. This pattern involves a single class which is responsible for creating an object while making sure that the instantiation of a class to only one object. This class provides a way to access its only object which can be accessed directly without instantiating the object of the class.

Let’s see various design options for implementing the pattern. If you have a good hold on static class variables and access modifiers this is not a difficult task.

Lazy Instantiation

public class Singleton implements Serializable {

  private static Singleton singletonObject;

  //Adding a private constructor so that no one creates object
  private Singleton() {
  }

  public static Singleton getInstance() {
    if (singletonObject == null) {
      singletonObject = new Singleton();
    }
    return singletonObject;
  }

}

Here we have declared getInstance() as static so that we can call it without instantiating the class. First time, when getInstance() method is called, it creates a new Singleton object and later it just returns the same object reference. Note that singletonObject is not created until we call the getInstance() method, as we are using the lazy instantiation of the object.

The main problem with the above method is that it is not thread safe. If we have two threads T1 and T2 which invokes the getInstance() method at the same time, the execution sequence creates two objects for Singleton.

The other option is to make getInstance() method as synchronized. Here, using synchronized keyword makes sure that only one thread is allowed at a time to execute getInstance() method. The main disadvantage of this is method is, that using synchronized every time while creating the Singleton object is expensive and may decrease the performance of your program. However if performance of getInstance() is not critical for the application this method provides a clean and simple solution.

Eager Instantiation

public class Singleton implements Serializable {

  private static Singleton singletonObject = new Singleton();

  //Adding a private constructor so that no one creates object
  private Singleton() {
  }

  public static Singleton getInstance() {
    return singletonObject;
  }
  
}


Here we have created instance of Singleton with the help of static initializer. JVM executes static initializer when the class is loaded and hence this is guaranteed to be thread safe. Use this method only when the Singleton class is light and is used throughout the execution of the program.

Double Check and Locking Instantiation

public class Singleton implements Serializable {

  private static volatile Singleton singletonObject;

  //Adding a private constructor so that no one creates object
  private Singleton() {
  }

  public static Singleton getInstance() {
    if (singletonObject == null) {
      // Making thread safe
      synchronized(Singleton.class) {
        // Check again as object is still null
        if (singletonObject == null) {
          singletonObject = new Singleton();
        }
      }
    }
    return singletonObject;
  }
    
}

In the above implementation, we have declared the singletonObject as volatile. Using volatile is yet another way (like synchronized, atomic wrapper) of making class thread safe. Thread safe means that a method or class instance can be used by multiple threads at the same time without any problem. This method drastically reduces the overhead of calling the synchronized method every time.

Now that we know how to implement a Singleton pattern in 3 different ways. Let’s see whether we can break the Singleton pattern ?

Reflection

Reflection is a feature in the Java programming language. It allows an executing Java program to examine or introspect upon itself, and manipulate internal properties of the program. For example, it’s possible for a Java class to obtain the names of all its members and display them.

We will use Reflection and see whether we can break the Singleton or not ?

class Singleton implements Serializable {

  private static Singleton singletonObject = new Singleton();

  //Adding a private constructor so that no one creates object
  private Singleton() {
  }

  public static Singleton getInstance() {
    return singletonObject;
  }
  
}

public class SingletonExample {
  
  public static void main(String[] args) {
    try {
        Singleton obj1 = Singleton.getInstance();
        System.out.println("Singleton.getInstance().hashCode: " + obj1.hashCode());

        Constructor constructor = Singleton.class.getDeclaredConstructor();
        constructor.setAccessible(true);
        Singleton obj2 = (Singleton) constructor.newInstance();
        System.out.println("Reflection.newInstance().hashCode: " + obj2.hashCode());
    }
    catch (Exception e) {
        e.printStackTrace();
    }
  }
  
}

If you execute the above program, you should see in the output that the hashCode of both objects are different and it clearly violates Singleton pattern.

Singleton.getInstance().hashCode: 1418481495
Reflection.newInstance().hashCode: 303563356

Let’s fix this loop hole by making some simple code change in the Singleton constructor.

private Singleton() {
    if (singletonObject != null) {
        throw new IllegalStateException("Already Initialised!");
    }
}

Now if you execute the program with the above changes, the line 24 will call the constructor and it will throw an IllegalStateException. Thus prevents the creation of the second instance.

Caused by: java.lang.IllegalStateException: Already initialised!

What if I change the access level of the singleton field from private to public using the Reflection? Oops… it will allow me to set the singleton object to null and it will create a new object. Let’s modify our main method to hack the Singleton class.

public static void main(String[] args) {
    try {
        Singleton obj1 = Singleton.getInstance();
        System.out.println("Singleton.getInstance().hashCode: " + obj1.hashCode());

        Field field = Singleton.class.getDeclaredField("singletonObject");
        field.setAccessible(true);
        field.set(field, null);
        
        Constructor constructor = Singleton.class.getDeclaredConstructor();
        constructor.setAccessible(true);
        Singleton obj2 = (Singleton) constructor.newInstance();
        System.out.println("Reflection.newInstance().hashCode: " + obj2.hashCode());
    }
    catch (Exception e) {
        e.printStackTrace();
    }
}

Using Reflection, we are changing the value of the instance field to NULL after the object gets created. When we invoke the constructor, the condition fails as singleton instance is null hence it will allow you to create another instance.

We can prevent it by declaring the singletonObject instance as FINAL so that it’s value can’t be changed once assigned.

private final static Singleton singletonObject = new Singleton();

Now if you execute the program, it will throw an IllegalAccessException. Reflection can’t convert final field to non-final field. Please note, this approach only works with Eager Instantiation method.

Serialization

Java provides a mechanism, called object serialization where an object can be represented as a sequence of bytes that includes the object’s data as well as information about the object’s type and the types of data stored in the object.

After a serialized object has been written into a file, it can be read from the file and deserialized, i.e., the type information and bytes that represent the object and its data can be used to recreate the object in memory.

Most impressive is that the entire process is JVM independent, meaning an object can be serialized on one platform and deserialized on an entirely different platform.

We will use Serialization and see whether we can break the Singleton or not ?

class Singleton implements Serializable {

  private final static Singleton singletonObject = new Singleton();

  //Adding a private constructor so that no one creates object
  private Singleton() {
    if (singletonObject != null) {
      throw new IllegalStateException("Already Initialised!");
    }
  }

  public static Singleton getInstance() {
    return singletonObject;
  }
  
}

public class SingletonExample {
  
  public static void main(String[] args) {
    try {
        Singleton obj1 = Singleton.getInstance();
        System.out.println("Singleton.getInstance().hashCode: " + obj1.hashCode());
      
        // Serialize the object to file
        ObjectOutput objectOutput = new ObjectOutputStream(new FileOutputStream("file.txt"));
        objectOutput.writeObject(obj);
        objectOutput.close();

        // DeSerailize the object from file
        ObjectInput objectInput = new ObjectInputStream(new FileInputStream("file.txt"));
        Singleton obj2 = (Singleton) objectInput.readObject();
        in.close();

        System.out.println("Serialization.getInstance().hashCode: " + obj2.hashCode());
    }
    catch (Exception e) {
        e.printStackTrace();
    }
  }
  
}

If you execute the above program, you should see in the output that the hashCode of both objects are different and it clearly violates Singleton pattern.

Singleton.getInstance().hashCode: 1418481495
Serialization.getInstance().hashCode: 565760380

Let’s fix this loop hole by making some simple code change in the Singleton class. We have to implement readResolve() method and return the singletonObject reference.

protected Object readResolve() {
    return singletonObject;
}

Conclusion

  • We saw how to implement the Singleton Pattern in different ways like Lazy Instantiation, Eager Instantiation and Double Check and Locking Instantiation.
  • We saw how we can break the Singleton Pattern using the Reflection and Serialization methods.
  • Also, we saw how it can be prevented by making few code changes so the Singleton Pattern won’t allow you to create more than one instance.

References:




Friday, May 7, 2021

Working With ThreadPoolTaskExecutor of Spring


Working With ThreadPoolTaskExecutor of Spring
Photo Courtesy Unsplash

ThreadPoolTaskExecutor is a java bean that allows for configuring a ThreadPoolExecutor in a bean style by setting up the values for the instance variables like corePoolSize, maxPoolSize, keepAliveSeconds, queueCapacity and exposing it as a Spring TaskExecutor.

One of the added Advantage of using ThreadPoolTaskExecutor of Spring is that it is well suited for management and monitoring via JMX.

The default configuration of core pool size is 1, max pool size and queue capacity as 2147483647.

This is roughly equivalent to Executors.newSingleThreadExecutor(), sharing a single thread for all tasks. And setting queueCapacity to 0 mimics Executors.newCachedThreadPool(), with immediate scaling of threads in the pool to a very high number.

If you are using XML file for configuring the bean, you can setup the ThreadPoolTaskExecutor like below:

<bean id="taskExecutor" class="org.springframework.scheduling.concurrent.ThreadPoolTaskExecutor">
   <property name="corePoolSize" value="5" />
   <property name="maxPoolSize" value="10" />
   <property name="WaitForTasksToCompleteOnShutdown" value="true" />
</bean>

If you are using Java Annotation to define the bean, you can setup like below:

@Bean
public TaskExecutor threadPoolTaskExecutor() {
   ThreadPoolTaskExecutor executor = new ThreadPoolTaskExecutor();
   executor.setCorePoolSize(5);
   executor.setMaxPoolSize(10);
   executor.initialize();
   return executor;
}

CorePoolSize

Is the minimum number of threads that remain active at any given point of time. If you don’t provide a value explicitly then it will have default value as 1. The TaskExecutor delegates the value to the underlying class ThreadPoolExecutor.

MaxPoolSize

Is the maximum number of threads that can be created. The TaskExecutor delegates the value to the underlying ThreadPoolExecutor. The maxPoolSize relies on queueCapacity because ThreadPoolTaskExecutor creates a new thread only if the number of items in the queue exceeds queue capacity.

Let’s test out the theory with some code so that it will be more clear for all of us. Here is a code snippet that will create new thread from TaskExecutor.

@Bean
public TaskExecutor threadPoolTaskExecutor() {
   ThreadPoolTaskExecutor executor = new ThreadPoolTaskExecutor();
   executor.setCorePoolSize(5);public void createTasks(ThreadPoolTaskExecutor taskExecutor, int numTasks) {

    for (int i=0; i<numTasks; i++) {
       taskExecutor.execute(() -> {
       try {
          long sleepTime = ThreadLocalRandom.current().nextLong(1, 10) * 100;
          Thread.sleep(sleepTime);
       } 
       catch (InterruptedException e) {
          Thread.currentThread().interrupt();
       }
       });
   }
}

Now let’s create a main method where we can try out various use cases by setting values to the ThreadPoolTaskExecutor.

public static void main(String[] args) {
    // Creating ThreadPoolTaskExecutor
    ThreadPoolTaskExecutor taskExecutor = new ThreadPoolTaskExecutor();
    taskExecutor.afterPropertiesSet();

    // Creating Tasks within ThreadPoolTaskExecutor
    createTasks(taskExecutor, 6);

    // Getting PoolSize
    System.out.println("Properties " +
        "- corePoolSize: " + taskExecutor.getCorePoolSize() +
        ", maxPoolSize: " + taskExecutor.getMaxPoolSize() +
        ", poolSize: " + taskExecutor.getPoolSize() +
        ", activeCount: " + taskExecutor.getActiveCount());

    // Shutting Down ThreadPoolTaskExecutor
    taskExecutor.setWaitForTasksToCompleteOnShutdown(true);
    taskExecutor.shutdown();
}

If execute the above code snippet, you will the below output on the console. You can see that corePoolSize is 1 by default and maxPoolSize is 2147483647. Since the QueueCapacity is also 2147483647, you will see that it will make use of the one thread to process all 6 tasks.

17:37:21.696 [main] INFO org.springframework.scheduling.concurrent.ThreadPoolTaskExecutor - Initializing ExecutorService

Properties - corePoolSize: 1, maxPoolSize: 2147483647, poolSize: 1, activeCount: 1

17:37:21.747 [main] INFO org.springframework.scheduling.concurrent.ThreadPoolTaskExecutor - Shutting down ExecutorService

Let’s modify our code to setup some of the parameters so that we can see whether it will have any behavioural change in the TaskExecutor.

ThreadPoolTaskExecutor taskExecutor = new ThreadPoolTaskExecutor();
taskExecutor.setCorePoolSize(1);
taskExecutor.setMaxPoolSize(4);
taskExecutor.afterPropertiesSet();

You will still observe that the TaskExecutor will still make use of one thread only though the max pool size is set to 4.

17:42:43.396 [main] INFO org.springframework.scheduling.concurrent.ThreadPoolTaskExecutor - Initializing ExecutorService

Properties - corePoolSize: 1, maxPoolSize: 4, poolSize: 1, activeCount: 0

17:42:43.447 [main] INFO org.springframework.scheduling.concurrent.ThreadPoolTaskExecutor - Shutting down ExecutorService

Let’s modify our code to setup queue capacity parameter so we see the behavioural change in the TaskExecutor.

ThreadPoolTaskExecutor taskExecutor = new ThreadPoolTaskExecutor();
taskExecutor.setCorePoolSize(1);
taskExecutor.setMaxPoolSize(4);
taskExecutor.setQueueCapacity(2);
taskExecutor.afterPropertiesSet();

Now when you run the same piece of code, you will see that the pool size increases but it will not exceed max pool size.

17:47:29.986 [main] INFO org.springframework.scheduling.concurrent.ThreadPoolTaskExecutor - Initializing ExecutorService

Properties - corePoolSize: 1, maxPoolSize: 4, poolSize: 4, activeCount: 4

17:47:30.036 [main] INFO org.springframework.scheduling.concurrent.ThreadPoolTaskExecutor - Shutting down ExecutorService

TaskExecutor creates a new thread only if the number of items in the queue exceeds queue capacity. As a result, you will observe that pool size increases.

To conclude, it’s always a good practice to define the core pool size, max pool size and queue capacity for the TaskExecutor explicitly from our end instead of leaving it to use the default values.

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