Performance Tuning In Finacial Applications With Large Volumn Of Data

Financial Applications is the fundamental of a modern financial company if he wants successes. A few of the facts made finical application complex: Financial application need to handle persistent data. The data is persistent for several yeas. The volume of data is usually very large. The structure of data is complex since the business logic underlie the data is complex. Financial application rarely lives on an island. Usually they need to integrate with other applications in the same enterprise. It's a great effort to migrate old data. Many users may access data currently.It need to access data a few months or a few years ago to calculate a financial report. It needs to apply complex calculation on such large volume of data that we may encounter performance in this stage. Lots of concurrent user needs to modify data and lock will cause serious performance issue if we don't handle it properly. When we develop new program to fulfill user requirements, we need to migrate lots of old data. Since the amount of data we need to migrate is large and the mapping between old data and new data is complex, the data sync module's performance is not good. Application code is not written properly and the performance is very bad when multiple user using the system concurrently. All of these issues made financial application's performance can't fulfill business requirements. It brings large pressure to software developers and system supporters.We need an efficient and reliable method to tuning performance. In this paper, I focus on performance issues caused by the process of large volume of data. By using efficient tools and efficient method, we can find the root cause of bad performance. Thus we can solve the problem efficiently.