- Excessive Layering – Most of the underlying performance starts with the excessive layering antipattern. The application design has grown over the usage of controllers, commands and facades. In order to decouple each layer, the designers are adding facades at each of the tiers. Now, for every request at the web tier, the request call goes through multiple layers just to fetch the results. Imagine doing this for thousands of requests coming in and the load the JVM need to handle to process these requests. The number of objects that get created and destroyed when making these calls add to the memory overhead. This further limits the amount of requests that can be handled by each server node.
Based on the size of the application, deployment model, the number of user’s, appropriate decision need to be taken to reduce the number of layers. E.g. if the entire application gets deployed in the same container, there is no need to create multiple layers of process beans, service beans(business beans), data access objects etc. Similarly, when developing an internet scale application, large number of layers start adding overheads to the request processing.
Remember, large number of layers means large number of classes which effectively start impacting the overall application maintainability.
- Round Tripping– With the advent of ORM mappings, Session/DAO objects, the programmer starts making calls to beans for every data. This leading to excessive calls between the layers. Another side issue is the number of method calls each layer start having to support this model. Worse case is, when the beans are web service based. Client tier making multiple web service calls within a single user request have a direct impact on the application performance.
- Overstuffed Session– Session object is a feature provided by the JEE container to track user session during the web site visit. The application start with the promise of putting very minimal information in the session but over a period of time, the session object keeps on growing. Too much of data or wrong kind of data is stuffed into the session object. Large data objects will mean that the objects placed in the session will linger on till the session object is destroyed. This impacts the number of user’s that can be served by the application server node. Further, I have seen, application using session clustering to support availability requirements but adding significant overheads to the network traffic and ability of application to handle higher number of users.
To unstuff the session object, take an inventory of what all goes there, see what is necessary, what objects can be defaulted to request scope. For others, remove the objects from session when their usage is over.
- Golden Hammer (Everything is a Service) – With the advent of SOA, there is tendency to expose the business services, which can be orchestrated into process services. In the older applications, one can observe similar pattern being implemented with EJBs. This pattern coupled with the bottom up design approach at times, means exposing each and every data entity as a business service. This kind of design might be working correctly functionally, but from the performance and maintenance point of view, it soon becomes a night mare. Every web service call adds overhead in terms of data serialization and deserialization. At times, the data(XML) being passed with web service calls is also huge leading to performance issues.
The usage of services or ejb’s should to be evaluated from application usage perspective. Attention needs to be paid on the contract design.
- Chatty Services – Another pattern observed is the way the service is implemented via multiple web service calls each of which is communicating a small piece of data. This results in explosion of web services and which leads to degradation of performance and unmaintainable code. Also, from the deployment perspective, the application starts running into problems. I have come across projects which have hundred plus services all getting crammed into a single deployment unit. When the application comes up, the base heap requirement is already in 2Gb range leaving not much space for application to run.
If the application is having too many fine grained services, then it an indication towards the application of this antipattern.
The above mentioned antipatterns are frequent causes of application performance issues. The teams usually start with the right intentions but over a period of time, things will start slipping. Some of the common reasons
- Lack of design standards and reviews processes – even if these exists, the delivery pressure is leading to skipping these processes
- Team members inexperience or narrow view leads to every programmer only looking at their module and nobody is looking at the overall application performance
- Continuous Integration(CI) tools not integrated with compliance check tools like PMD, Checkstyle, FindBugs etc
- No focus on profiling of the application on regular basis during the code construction phase
- Not evaluating the results from the Load tests to decipher and fix the underlying issue (blaming the poor infrastructure setup)
What are the other antipatterns you have observed that have contributed to the degradation in the application performance. Do share!