Singleton Pattern - The Correct Usage

The singleton pattern is perhaps the most widely (mis)used pattern. Developers who program in object-oriented languages use the singleton pattern whenever they think of a service or manager class. I have seen a lot of projects use singletons even when the singleton class never encapsulates any private data that needs to be handled for multiple instantiations. Usage of the singleton pattern - with a private constructor and a getInstance() method - ensures that only a single instance of a class exists and all threads use the single instance. A database manager class that maintains a local data cache is an ideal candidate for a singleton, as we wouldn't want to maintain multiple local caches; the singleton cache should be loaded once and preserved in memory for faster access. Operations on this cache should also be thread-safe. If a class does not hold and control access to private data, then simple object instantiation makes perfect sense.

Another common mistake with the singleton pattern implementation is doing the object instantiation in the getInstance() method by making it thread-safe and having a check to see if the object is already instantiated. Given below is the implementation logic in Java. The method getInstance() is made thread-safe by using the synchronized keyword. This approach degrades the application performance as a lock is obtained on the object every time a thread calls the getInstance() method.


MySingleton mySingleton = null;


public synchronized MySingleton getInstance() {
  if (mySingleton == null) {
    mySingleton = new MySingleton();
  }
  return mySingleton
}


A better alternative is to instantiate the singleton object at the time of declaration. In this case, the ClassLoader instantiates the singleton when it comes across its reference, well before any thread is active in the application. Since the getInstance() is no longer synchronized, this offers better performance.



MySingleton mySingleton = new MySingleton();  // Object instantiated during declaration


// No need to synchronize method
public MySingleton getInstance() {
  return mySingleton
}


The concurrency-optimized implementation of the Singleton pattern is explained in many Java Concurrency books, but hopefully now you won't have to read the whole book to learn this neat trick.

Using Database Constraints

For many developers, a relational database is just a simple data store. They really don't believe is having any constraints in the database, and prefer to implement all checks-and-bounds in the application logic. This is really a recipe for disaster, and in this post, I will look at some key database constraints that should always be set for an application.

1. Foreign Key: A relational database is called "relational" for a reason - the term signifies the ability to relate tables (and data) with foreign key constraints. A foreign key constraint avoids data integrity problems and prevents programming errors. Having foreign keys is essential for parent-child and other association relationships. A commonly quoted example is the "Purchase Order" and "Line Item" data, where the purchase order id is the foreign key in the line item table. Not having the foreign key relationship between these tables allows for the possibility of having an incorrect purchase order id in the line item table. This value may be inserted mistakenly by the application code or perhaps manually from the SQL prompt, which in turn results in unpredictable errors in the application and perhaps defensive code to check if the purchase order id is correct before performing further processing. It is much easier to avoid these problems by having proper foreign key constraints in the database.
2. Not Null Constraint: This is another commonly overlooked problem. Even with limited domain knowledge, it is not too difficult to ascertain which columns can never be null and enforce this in the database by adding a "not null" constraint on the column. Again, the avoids the need to have defensive code all over the place that checks if the variable - that stores the data from this column - is not null before using it for further processing.
3. Unique Constraint: Unique constraints can be placed on a single column, or on a group of columns. To create an unique constraint on a set of columns, it is required to create an index on them and set the index as unique. Without the unique index, it is common for application code to query the database to check if certain key data is already present before inserting a new record with the data. As an example, if the user id in the user table needs to be unique, then it is better to enforce this with a constraint, instead of querying the database to see if the user id is already exists. Attempting to insert with the duplicate user id results in an exception, which can be caught and the user can be requested to select a different user id. There is a significant performance benefit of using the constraint approach, instead of the using the query to check for duplicates.
4. Using ENUM: Most programming languages support the enum datatype, and now most databases also support enum. Before enums, if a column could have a fixed set of values, then the solution was generally to have integer (or char) constants denote the various values. As an example, if the user account could be 'active' or 'disable', then the 'state' column would generally have integers (0 and 1) to denote the different states, or perhaps characters 'A' or 'D' for the same purpose. With enums, it is possible to enumerate all the values that a column might store, and use the same names in the code so that data comparison becomes easier.

While there may be other useful constraints, I generally find the above-mentioned ones most useful and find it difficult to comprehend how an application can do without them.

Detecting Concurrency Problems using TestNG

With the emphasis on product quality, unit and integration testing is gaining widespread momemtum, and TestNG seems to have become the defacto standard for writing these tests. A great feature in TestNG - that is often missed in all the JUnit vs. TestNG comparisons on the web - is the ability to execute a test in parallel using multiple threads. This feature is pretty useful in detecting concurrency problems in the code. A developer could write a test at the Controller level that executes the Service and DAO (Data Access Object) code, and if this code contains any concurrency constructs for thread-safe access, then the TestNG threads will detect concurrency problems such as race conditions and deadlocks.

Using the TestNG framework, it is relatively easy to specify that a given test be executed in parallel. This can be done using additional parameters to the @Test annotation.

As an example, consider the following test:

@Test (threadPoolSize = 3, invocationCount = 9, timeOut = 1000)

public void myTest() {

// write your test here

}

The threadPoolSize determines the number of threads that are used to execute the tests, and the invocationCount determines the total number of times that the test is executed. The timeOut parameter - 1 second in the above example - guarantees that none of the threads will block on the others, in effect avoiding a deadlock. However, this is not something that a developer should try to avoid, because if there is a deadlock, it is better to detect it sooner that later. Therefore, in general, it is better of omit the timeOut parameter.

With the advent of these super-easy testing frameworks and the value-proposition that they bring, most developers are jumping on the unit/integration testing bandwagon. This is great for the software engineering field in general, as eventually some day bugs will not be considered as the norm in a software product.

Ant build under Eclipse - Error running javac.exe compiler

I ran into this problem twice, where the ant build script executes perfectly under the command prompt, but fails under Eclipse with the message - "Error running javac.exe compiler". It took me a while to figure out the problem, and it turns out that the problem arises from the javac task in ant. If the task contains an attribute fork="yes", then ant tries to spawn a new instance of javac to compile the source. Now if the referenced Java library in the project is a JRE, Ant can't find javac and therefore gives the above-mentioned error. If the parameter is changed to fork="no", then the build completes successfully, but a better solution is to include the JDK - and not the default JRE - as the referenced library for the project.

Concurrency - Optimistic vs. Pessimistic Approach

Whenever developers think of concurrency, the first thing that comes to their mind is semaphores and mutex that provide serial access to a critical section of code. Most languages provide an extensive API for thread synchronization and very often folks just start using the synchronization primitive without much thought into the what they are trying to accomplish. As an example, the most abused concurrency primitive is the "synchronized" keyword provided by Java, which is often put anywhere and everywhere that a developer feels that there is a possibility of concurrent access. "Synchronized" is a monitor, and as such, it doesn't require explicit lock and release statements, as a semaphore or mutex would. This is why perhaps people generally add the synchronized keyword to methods, whenever they feel that the method does something that needs protection from concurrent access. It is not uncommon to come across instances of deeply nested method calls, with each method having a synchronized keyword in the declaration. Synchronized implicitly obtains and releases a lock on the object every time the method with the modifier is called. This is a computationally intensive operation that makes the application slower than it needs to be. Now Java 5 provides some powerful concurrency primitives, but before jumping the bandwagon and starting to use those primitives all over the code, it is better to evaluate the concurrency needs of the application that is being built.

There are generally two approaches to handle concurrency in a software program, each with its pros and cons. An engineering team should consider and evaluate both approaches and decide to use either one, or both, based on the needs of the product they are building. The two approaches are:

Optimistic approach: In this approach, there are no semaphores or mutex to protect a critical section of code that handles the shared data. There is a master copy of the shared data, with each thread getting a local copy to work on. When a thread wishes to update its local copy of the data, the local copy is compared with the master copy to ascertain if the data has been modified since it was last read by the thread. If not, then the update is successful; however, if the data has indeed been modified, then a concurrent modification exception is thrown and the user is expected to re-apply the modifications on the new copy of the data. This approach is common in databases, and it is also used by Java for collections that are not thread-safe by default (HashMap, HashSet, ArrayList).

Pros:

1. Due to the absence of semaphores and mutex, the application exhibits better performance and scalability.
2. The modifications of the first thread that performs the update are persisted, whereas the other threads are informed of the change in data and requested to repeat the update on the modified data.

Cons:

1. User may need to perform the modifications again, if another thread updates the data after it was read by the user thread. This may cause frustration in a multi-user heavy-transaction environment.

Pessimistic approach: This approach requires the use of a semaphore, mutex or monitor to ensure serial access to a critical section in code. In this approach, a single copy of the data is maintained and serial access is provided to threads requesting access to this data. When a given thread enters the critical section, no other thread is allowed to access this data until the thread exits the critical section.

Pros:

1. Suitable for situations where there is no shared data, however, serial access need to be provided to a shared resource, such as a socket.

Cons:

1. If the semaphore or mutex is not released properly, it leads to a memory leak. This degrades the application performance over time.
2. Another problem with semaphores and mutex is the possibility of a deadlock, which occurs when a circular dependency is introduced between two threads, each requesting a lock on a resource that is currently held by the other.
3. Since serial access is provided to concurrently executing threads that wish to update shared data, the changes made by the last thread are persisted, whereas the other threads are unaware of what happened to their modifications.

A given application may use either one, or both, of the above-mentioned approaches. For shared data access among multiple threads, it is preferable to use the optimistic approach, whereas, for shared resource access (socket etc.), it is generally better to use the pessimistic approach.

Software Engineering - Art or Science?

I have been pondering over this question for a while now. In my opinion, Software Engineering is both an Art and a Science, as aspects from both the fields are relevant in designing a software product. I guess the association to science is easier to understand, as there is direct relevance to the scientific method, which in simple form consists of the following steps:

• Formulate the hypothesis.
• Conduct the experiment, collect the results and verify if the hypothesis is correct.
• If desirable results are not obtained, make changes to certain parameters, and repeat.

This is how we test our software too:

• Formulate what a program is intended to do.
• Run the program, collect the results and check if it matches the expectations.
• If not, tweak the program code or input, and repeat.

While the association of software engineering to the scientific side may be a bit obvious, it is the artistic elements that are difficult to relate to. An obvious question is - what is artistic about lines of program code or instructions that are always difficult to read and comprehend? In my opinion, the artistic elements of software engineering are more in the design of the program than in the actual code itself, however, that is not to say that good code doesn't have any artistic elements.

A program that considers the following design elements is generally considered more artistic than one that doesn't.

• Consider a component-based design that follows the high-cohesion-low-coupling paradigm, with a well defined API that specifies the component contract.
• While designing classes, consider the responsibility of each class and ensure that a class doesn't do too much or too little.
• Follow a general naming convention for components and classes. A good guideline for the MVC (Model-View-Controller) architectural style is to have classes with the names such as - xxxxView, xxxController, xxxManager. Classes that are a part of a component that offers a service could be named as xxxService; as an example - DatabaseService, LoggingService etc.
• Consider using design patterns when possible, as they offer a consistent - and often familiar - solution to a known problem.
• Have a long-term view while designing the components and classes. Remember, a good artist paints what she sees, whereas a great one paints from her imagination, what no one else sees.

There certainly are artistic elements to be considered while implementing the code too, some of which are:

• Follow the same structure while laying out the source code. Always have a comment block for every class and method.
• Avoid methods that are too long. A general guideline is to have methods that generally fit within a screen length for the new high-resolution monitors.
• While doing defensive null checks, consider having return statements when the object is null, instead of having deeply nested "if" statement that contain logic when the object is not null.
• Consider using a static analysis tool, so that consistent coding guidelines and good coding practices are enforced throughout the code.

While doing all the above may not make your program work any better than it currently does, it would certainly improve the readability and maintainability of the code, enabling others to understand it and extend it. And, if others can understand and relate to your work, then there is definitely something artistic about it.

REST vs. SOAP

There is a lot of information on the web pertaining to REST; however, there is nothing relevant that compares REST to SOAP. This post contains a brief introduction to REST, and provides a REST vs. SOAP comparison. The reader is expected to have some familiarity with SOAP.

REST(Representational State Transfer) is an architectural style for networked applications, which is based on the Ph.D. dissertation of Roy Fieldings. REST introduces a different paradigm for web services, which are traditionally thought of as a RPC-based services, using a SOAP+WSDL combination. Web services written using the REST style adhere to the Resource Oriented Architecture (ROA) paradigm, a term given to a set of rules for designing such services. Typically, a user of a web application progresses through a series of pages or URLs, resulting in the state being transferred from one traversed resource to the next. REST attempts to formalize this model using four important concepts - resources, their names, their representations and the links between the resources. All RESTful services are judged by four important properties – addressability, statelessness, connectedness and the uniform interface.

REST architectural rules are also called “constraints”. Unconstrained architecture allows method calls, RPC and other messages that are understood by a specific component or module (client or server) involved in the interaction. REST eliminates ad-hoc messages and radically shifts the focus of API development towards defining pieces of information that can be retrieved and manipulated. The motivation for REST was to create an architectural model for how the web should work, such that it would serve as the guiding framework for the web protocol standards. REST prescribes the use of standards such as HTTP, URI and XML.

REST objects are called “resources”, with the information in resources being called “state”. This information has to be encoded to include it in a message, this encoding are called “representation”. Method invocations transfer state in representations. The following is a list of the HTTP methods and their implied meaning in REST:

· GET to an identifier means, give me your information.

· PUT to an identifier means, replace your information with the new one provided.

· POST adds new information.

· DELETE removes the information.

Resources are identified by URIs and manipulated through their representation. HTTP is a compliant RESTful protocol; however, it is possible to apply REST concepts to other protocols and systems. The statelessness property of REST ensures that any resource can be served by any server, thereby making REST solutions highly scalable. REST services may be described using WSDL or WRDL (Web Resource Description Language). The following are the characteristics of a REST-based system:

· Client-Server: A pull-based interaction style.

· Stateless: Request from client to server must contain all the information necessary.

· Cache: To improve network efficiency, responses must be capable of being labeled as cacheable or non-cacheable.

· Uniform interface: All resources are accessed via the generic HTTP methods.

· Named Resources: Every resource in a RESTful service is appropriately named.

· Interconnected resource representation: Enables a client to progress from one state to another.

A logical question is: how is REST different from SOAP? SOAP offers a RPC-oriented paradigm, where the participating components are interacting in a closed environment, using a proprietary API. REST offers a solution based on commonly used web standards and offers a more open solution, where even unknown clients can connect to a server component and use its capabilities using standard HTTP requests / responses. In addition to this basic difference in the two approaches, the following are some additional differences between these two paradigms.

· Security: A proxy server can look at the REST request and determine the resource being requested, based on which the request may be allowed or denied. Whereas for the SOAP message, the resource is identified inside the envelope, which is not accessible, unless the SOAP message is written using RDF (Resource Description Framework) or DAML (DARPA Agent Markup Language). Therefore, for a SOAP-based web service, security is generally built into the proprietary API.

· State Transitions: Each resource representation received by the client causes it to transition to the next state. The decision about which link to navigate is either hard-coded in the client or determined dynamically using XLINK (xlink:role). In a SOAP network, state transitions are always hard-coded in the client.

· Caching: Network communication has always been a bottleneck, and therefore the HTTP headers can contain a request to cache data. SOAP is always a HTTP POST and since the SOAP URI is directed to the server and not the resource, no caching is possible with SOAP. However, since REST uses the generic HTTP interface, it is possible for intermediate proxies to cache the results from a RESTful service call, in an effort to achieve a better performance.

· Evolving the Web (Semantic Web): It is envisioned that eventually the web will be accessed by people and computers alike, each being capable of intelligently processing the data returned by services on the web. In this vision of the Semantic Web, every resource has a unique URI and is accessible using standard HTTP methods. SOAP is not consistent with the Semantic Web vision, whereas REST is completely aligned with it.

· Generic Interface: Using REST, access to every resource is made using HTTP GET, POST, PUT and DELETE. With SOAP, the application needs to define its own proprietary methods.

· Interoperability: With interoperability, the key is standardization. Web has standardized on certain things, such as URI for address and naming, HTTP for generic resource interface and HTML/XML/GIF/JPEG for resource representation. REST uses these standards, whereas SOAP depends on customizations. SOAP clumping of resources behind a single URI is contrary to the vision for the web. SOAP is best utilized for closed systems, where all participants are known beforehand.