Three Common Application Performance Challenges for Developers
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One of the great benefits of Java is its ability to take care of the memory model for you. When objects aren’t in use, Java helps you out by doing the clean up. Older languages need you to do your memory management manually, but you would rather spend time focusing on core application logic than worrying about memory allocation.
Having said that, it’s not to say that Java memory management guarantees zero memory problems. By managing the memory model for you, or rather the creation/destroying of objects that are unused, Java puts them in a heap. Memory leaks typically happen as a result of improper programming – usually when the developer didn’t relieve all references to an object. Thus, your heap builds up and your app comes to a grinding halt.
Most people use heap dumps and/or profilers to diagnose memory leaks. A heap dump allows you to see which object is holding a reference to the collections. It gives you an idea of where the collection is, but doesn’t tell you who is accessing the collection or their characteristics to let you drill down to root cause. Heap dumps are also usually quite large, in gigabytes, and it takes significant resources to analyze and open a heap dump, then read it and identify the issue.
The second method, a combination of a heap dump and a profiler, gets you a little bit closer, but not much. Memory profilers try to help you analyze your heap dump. They have live data and now you know who is creating the objects, but you still don’t know what’s actually causing the leak.
Both heap dumps and profilers can be helpful in development and pre-production, but once your apps are out in the wild, profilers just aren’t useable. One of the most effective ways to isolate and address memory leaks is through transaction and code path analysis. By taking a snapshot of the transaction, you can get a better idea of where the issue is and who is causing it, which usually leads to less downtime and better MTTR.
Almost every application uses a JDBC database. A very common problem with applications is badly performing SQL. This can be due to fields not being indexed, too much data being fetched, and other various problems. This affects application performance adversely because most applications use multiple SQL invocations per user request.
There could be many causes slow SQL.. But one in particular stands out: the Object Relational Mapper (ORM).
The ORM has become a method of choice for bringing together the two foundational technologies that we base business applications on today – object-oriented applications (Java, .NET) and relational databases (Oracle, mySQL, PostgreSQL, etc.). Most applications today use a relational database. For many developers, this technology can eliminate the need to drill-down into the intricacies of how these two technologies interact. However, ORMs can place an additional burden on applications, significantly impacting performance while everything looks fine on the surface.
In the majority of cases, the time and resources taken to retrieve data are orders of magnitude greater than what’s required to process it. It is no surprise that performance considerations should always include the means and ways of accessing and storing data.
While intuitive for an application developer to use (they do hide the translation complexities), an ORM can also be a significant weight on an application’s performance. Make sure you understand what’s going on under the hood.
Issues arising from synchronization are often hard to recognize and their impact on performance can become significant.
The fundamental need to synchronize lies with Java’s support for concurrency. This is implemented by allowing the execution of code by separate threads within the same process. Separate threads can share the same resources, objects in memory. While being a very efficient way to get more work done (while one thread waits for an IO operation to complete, another thread gets the CPU to run a computation), the application is also exposed to interference and consistency problems.
To prevent such a scenario programmers use the “synchronized” keyword in his/her program to force order on concurrent thread execution. Using “synchronized” prevents threads from obtaining the same object at the same time and prevents data inconsistencies.
In practice, however, this simple mechanism comes with substantial side effects. Modern business applications are typically highly multi-threaded. Many threads execute concurrently, and consequently “contend” heavily for shared objects. Synchronization effectively forces concurrent processing back into sequential execution.
There isn’t a silver bullet for addressing thread and synchronization issues today. Some developers rely on ‘defensive’ coding practices like locking, while others may rely on Software Transactional Memory Systems (STM) to help mitigate the issue. The best development organizations are the ones that can walk the fine line of balancing code review/rewrite burdens and concessions to performance.
These are just a few application performance issues Java developers face on a daily basis. There are a variety of helpful application performance tools and vendors out there that can help reduce these issues dramatically.
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