Online transaction processing
Online transaction processing (OLTP) is a class of information systems that facilitate and manage transaction-oriented applications, typically for data entry and retrieval transaction processing on a database management system.
The term is somewhat ambiguous; some understand a "transaction" in the context of computer or database transactions, while others (such as the Transaction Processing Performance Council) define it in terms of business or commercial transactions. OLTP has also been used to refer to processing in which the system responds immediately to user requests. An automated teller machine (ATM) for a bank is an example of a commercial transaction processing application. Online transaction processing applications are high throughput and insert or update-intensive in database management. These applications are used concurrently by hundreds of users. The key goals of OLTP applications are availability, speed, concurrency and recoverability. Reduced paper trails and the faster, more accurate forecast for revenues and expenses are both examples of how OLTP makes things simpler for businesses. However, like many modern online information technology solutions, some systems require offline maintenance, which further affects the cost–benefit analysis of on line transaction processing system.
OLTP is typically contrasted to OLAP (online analytical processing), which is generally characterized by much more complex queries, in a smaller volume, for the purpose of business intelligence or reporting rather than to process transactions. Whereas OLTP systems process all kinds of queries (read, insert, update and delete), OLAP is generally optimized for read only and might not even support other kinds of queries.
OLTP system is a popular data processing system in today's enterprises. Some examples of OLTP systems include order entry, retail sales, and financial transaction systems. On line transaction processing system increasingly requires support for transactions that span a network and may include more than one company. For this reason, modern on line transaction processing software use client or server processing and brokering software that allows transactions to run on different computer platforms in a network.
In large applications, efficient OLTP may depend on sophisticated transaction management software (such as CICS) and/or database optimization tactics to facilitate the processing of large numbers of concurrent updates to an OLTP-oriented database.
For even more demanding decentralized database systems, OLTP brokering programs can distribute transaction processing among multiple computers on a network. OLTP is often integrated into service-oriented architecture (SOA) and Web services.
On line transaction processing (OLTP) involves gathering input information, processing the information and updating existing information to reflect the gathered and processed information. As of today, most organizations use a database management system to support OLTP. OLTP is carried in a client server system.
On line transaction process concerns about concurrency and atomicity. Concurrency controls guarantee that two users accessing the same data in the database system will not be able to change that data or the user has to wait until the other user has finished processing, before changing that piece of data. Atomicity controls guarantee that all the steps in transaction are completed successfully as a group. That is, if any steps between the transaction fail, all other steps must fail also.
To build an OLTP system, a designer must know that the large number of concurrent users does not interfere with the system's performance. To increase the performance of OLTP system, designer must avoid the excessive use of indexes and clusters.
The following elements are crucial for the performance of OLTP systems:
- Rollback segments
- Rollback segments are the portions of database that record the actions of transactions in the event that a transaction is rolled back. Rollback segments provide read consistency, roll back transactions, and recover the database.
- A cluster is a schema that contains one or more tables that have one or more columns in common. Clustering tables in database improves the performance of join operation.
- Discrete transactions
- All changes to the data are deferred until the transaction commits during a discrete transaction. It can improve the performance of short, non-distributed transaction.
- Block (data storage) size
- The data block size should be a multiple of the operating system's block size within the maximum limit to avoid unnecessary I/O.
- Buffer cache size
- To avoid unnecessary resource consumption, tune SQL statements to use the database buffer cache.
- Dynamic allocation of space to tables and rollback segments
- Transaction processing monitors and the multi-threaded server
- A transaction processing monitor is used for coordination of services. It is like an operating system and does the coordination at a high level of granularity and can span multiple computing devices.
- Partition increases performance for sites that have regular transactions while still maintain availability and security.
- With database tuning, OLTP system can maximize its performance as efficiently and rapidly as possible.
- Transaction Processing Performance Council website
- Application and System Performance Characteristics
- What is an OLTP System
- On line Transaction Processing vs. Decision Support
- Application and System Performance Characteristics
- cluster table
- Discrete Transactions
- Data Block
- Database buffer Cache
- Transaction processing monitor
|Look up OLTP in Wiktionary, the free dictionary.|
- H-Store Project (architectural and application shifts affecting OLTP performance)
- IBM CICS official website
- Transaction Processing Performance Council
- OLTP Schema
- Transaction Processing: Concepts & Techniques Management