Online Transaction Processing System

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What is an OLTP System?
Online Transaction Processing (OLTP) systems are one of the most common data processing systems in today's enterprises. Classical examples of OLTP systems are order entry, retail sales, and financial transaction systems. OLTP systems are primarily characterized through a specific data usage that is different from data warehousing environments, yet some of the characteristics, such as having large volumes of data and lifecycle-related data usage and importance, are identical. The main characteristics of an OLTP environment are:

* Short response time
The nature of OLTP environments is predominantly any kind of interactive ad hoc usage, such as telemarketeers entering telephone survey results. OLTP systems require short response times in order for users to remain productive. * Small transactions

OLTP systems normally read and manipulate highly selective, small amounts of data; the data processing is mostly simple and complex joins are relatively rare. There is always a mix of queries and DML workload. For example, one of many call center employees retrieves customer details for every call and enters customer complaints while reviewing past communication with the customer. * Data maintenance operations

It is not uncommon to have reporting programs and data updating programs that need to run either periodically or on an ad hoc basis. These programs, which run in the background while users continue to work on other tasks, may require a large number of data-intensive computations. For example, a University may start batch jobs assigning students to classes while students can still sign up online for classes themselves. * Large user populations

OLTP systems can have immeasurably large user populations where many users are trying to access the same data at once. For example, an online auction Web site can have hundreds of thousands (if not millions) of users accessing data on its Web site at the same time. * High concurrency

Due to the large user population, the short response times, and small transactions, the concurrency in OLTP environments is very high. A requirement for thousands of concurrent users is not uncommon. * Large data volumes

Depending on the application type, the user population, and the data retention time, OLTP systems can become very large. For example, every customer of a bank could have access to the online banking system which shows all their transactions for the last 12 months. * High availability

The availability requirements for OLTP systems are often extremely high. An unavailable OLTP system can impact a very large user population, and organizations can suffer major losses if OLTP systems are unavailable. For example, a stock exchange system has extremely high availability requirements during trading hours. * Lifecycle related data usage

Similar to data warehousing environments, OLTP systems often experience different data access patterns over time. For example, at the end of the month, monthly interest is calculated for every active account. The following are benefits of partitioning for OLTP environments: * Support for bigger databases

Backup and recovery, as part of a high availability strategy, can be performed on a low level of granularity to cope with the size of the database. OLTP systems usually remain online during backups and users may continue to access the system while the backup is running. The backup process should not introduce major performance degradation for the online users. Partitioning helps to reduce the space requirements for the OLTP system because part of a database object can be stored compressed while other parts can remain uncompressed. Update transactions against uncompressed rows are more efficient than updates on compressed data. Partitioning can be used to store data transparently on different storage tiers to lower the cost of storing vast amounts of data. * Partition maintenance...
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