Online transaction processing

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Online transaction processing (OLTP) is a type of database system used in transaction-oriented applications, such as many operational systems. "Online" refers to that such systems are expected to respond to user requests and process them in real-time (process transactions). The term is contrasted with online analytical processing (OLAP) which instead focuses on data analysis (for example planning and management systems).

Contents

The term transaction

The term "transaction" can have two different meanings, both of which might apply: in the realm of computers or database transactions it denotes an atomic change of state, whereas in the realm of business or finance, the term typically denotes an exchange of economic entities (as used by, e.g., Transaction Processing Performance Council or commercial transactions. [1] ): 50  OLTP may use transactions of the first type to record transactions of the second.

Compared to OLAP

OLTP is typically contrasted to online analytical processing (OLAP), 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 also operates differently from batch processing and grid computing. [1] : 15 

In addition, OLTP is often contrasted to online event processing (OLEP), which is based on distributed event logs to offer strong consistency in large-scale heterogeneous systems. [2] Whereas OLTP is associated with short atomic transactions, OLEP allows for more flexible distribution patterns and higher scalability, but with increased latency and without guaranteed upper bound to the processing time.

Use

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. [3] Online transaction processing applications have high throughput and are 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 (durability). [4] 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 an online transaction processing system.

Overview

An OLTP system is an accessible data processing system in today's enterprises. Some examples of OLTP systems include order entry, retail sales, and financial transaction systems. [5] Online transaction processing systems increasingly require support for transactions that span a network and may include more than one company. For this reason, modern online transaction processing software uses 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 IBM 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.

Online transaction processing (OLTP) involves gathering input information, processing the data and updating existing data to reflect the collected and processed information. As of today, most organizations use a database management system to support OLTP. OLTP is carried in a client-server system.

Online 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 a transaction are completed successfully as a group. That is, if any steps between the transaction fail, all other steps must fail also. [6]

Systems design

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 an OLTP system, a designer must avoid excessive use of indexes and clusters.

The following elements are crucial for the performance of OLTP systems: [4]

Related Research Articles

<span class="mw-page-title-main">Data warehouse</span> Centralized storage of knowledge

In computing, a data warehouse, also known as an enterprise data warehouse (EDW), is a system used for reporting and data analysis and is considered a core component of business intelligence. Data warehouses are central repositories of integrated data from one or more disparate sources. They store current and historical data in one single place that are used for creating analytical reports for workers throughout the enterprise. This is beneficial for companies as it enables them to interrogate and draw insights from their data and make decisions.

Online analytical processing, or OLAP, is an approach to answer multi-dimensional analytical (MDA) queries swiftly in computing. OLAP is part of the broader category of business intelligence, which also encompasses relational databases, report writing and data mining. Typical applications of OLAP include business reporting for sales, marketing, management reporting, business process management (BPM), budgeting and forecasting, financial reporting and similar areas, with new applications emerging, such as agriculture.

A database transaction symbolizes a unit of work, performed within a database management system against a database, that is treated in a coherent and reliable way independent of other transactions. A transaction generally represents any change in a database. Transactions in a database environment have two main purposes:

  1. To provide reliable units of work that allow correct recovery from failures and keep a database consistent even in cases of system failure. For example: when execution prematurely and unexpectedly stops in which case many operations upon a database remain uncompleted, with unclear status.
  2. To provide isolation between programs accessing a database concurrently. If this isolation is not provided, the programs' outcomes are possibly erroneous.

In database systems, durability is the ACID property that guarantees that the effects of transactions that have been committed will survive permanently, even in case of failures, including incidents and catastrophic events. For example, if a flight booking reports that a seat has successfully been booked, then the seat will remain booked even if the system crashes.

In database systems, isolation determines how transaction integrity is visible to other users and systems.

<span class="mw-page-title-main">IBM Information Management System</span> Joint hierarchical database made by IBM

The IBM Information Management System (IMS) is a joint hierarchical database and information management system that supports transaction processing.

Oracle TimesTen In-Memory Database is an in-memory, relational database management system with persistence and high availability. Originally designed and implemented at Hewlett-Packard labs in Palo Alto, California, TimesTen spun out into a separate startup in 1996 and was acquired by Oracle Corporation in 2005.

Tuxedo is a middleware platform used to manage distributed transaction processing in distributed computing environments. Tuxedo is a transaction processing system or transaction-oriented middleware, or enterprise application server for a variety of systems and programming languages. Developed by AT&T in the 1980s, it became a software product of Oracle Corporation in 2008 when they acquired BEA Systems. Tuxedo is now part of the Oracle Fusion Middleware.

Essbase is a multidimensional database management system (MDBMS) that provides a platform upon which to build analytic applications. Essbase began as a product from Arbor Software, which merged with Hyperion Software in 1998. Oracle Corporation acquired Hyperion Solutions Corporation in 2007. Until late 2005 IBM also marketed an OEM version of Essbase as DB2 OLAP Server.

Extensible Storage Engine (ESE), also known as JET Blue, is an ISAM data storage technology from Microsoft. ESE is the core of Microsoft Exchange Server, Active Directory, and Windows Search. It's also used by a number of Windows components including Windows Update client and Help and Support Center. Its purpose is to allow applications to store and retrieve data via indexed and sequential access.

The Access Database Engine is a database engine on which several Microsoft products have been built. The first version of Jet was developed in 1992, consisting of three modules which could be used to manipulate a database.

A transaction processing system (TPS) is a software system, or software/hardware combination, that supports transaction processing.

In database computing, Oracle Real Application Clusters (RAC) — an option for the Oracle Database software produced by Oracle Corporation and introduced in 2001 with Oracle9i — provides software for clustering and high availability in Oracle database environments. Oracle Corporation includes RAC with the Enterprise Edition, provided the nodes are clustered using Oracle Clusterware.

In computer science and engineering, transactional memory attempts to simplify concurrent programming by allowing a group of load and store instructions to execute in an atomic way. It is a concurrency control mechanism analogous to database transactions for controlling access to shared memory in concurrent computing. Transactional memory systems provide high-level abstraction as an alternative to low-level thread synchronization. This abstraction allows for coordination between concurrent reads and writes of shared data in parallel systems.

A column-oriented DBMS or columnar DBMS is a database management system (DBMS) that stores data tables by column rather than by row. Benefits include more efficient access to data when only querying a subset of columns, and more options for data compression. However, they are typically less efficient for inserting new data.

The following tables compare general and technical information for a number of online analytical processing (OLAP) servers. Please see the individual products articles for further information.

<span class="mw-page-title-main">Oracle NoSQL Database</span>

Oracle NoSQL Database is a NoSQL-type distributed key-value database from Oracle Corporation. It provides transactional semantics for data manipulation, horizontal scalability, and simple administration and monitoring.

NewSQL is a class of relational database management systems that seek to provide the scalability of NoSQL systems for online transaction processing (OLTP) workloads while maintaining the ACID guarantees of a traditional database system.

<span class="mw-page-title-main">SAP HANA</span> Database management system by SAP

SAP HANA is an in-memory, column-oriented, relational database management system developed and marketed by SAP SE. Its primary function as the software running a database server is to store and retrieve data as requested by the applications. In addition, it performs advanced analytics and includes extract, transform, load (ETL) capabilities as well as an application server.

Hybrid transaction/analytical processing (HTAP) is a term created by Gartner Inc., an information technology research and advisory company, in its early 2014 research report Hybrid Transaction/Analytical Processing Will Foster Opportunities for Dramatic Business Innovation. As defined by Gartner:

Hybrid transaction/analytical processing (HTAP) is an emerging application architecture that "breaks the wall" between transaction processing and analytics. It enables more informed and "in business real time" decision making.

References

  1. 1 2 Bog, Anja (2013). Benchmarking Transaction and Analytical Processing Systems: The Creation of a Mixed Workload Benchmark and its Application. Berlin: Springer Science & Business Media. ISBN   978-3642380709.
  2. ↑ "Online Event Processing - ACM Queue". queue.acm.org. Retrieved 2019-05-30.
  3. ↑ Heller, Martin (2022-02-18). "What is OLTP? The backbone of ecommerce". InfoWorld. Retrieved 2022-09-27.
  4. 1 2 "Application and System Performance Characteristics". Oracle.com. Retrieved 2018-05-02.
  5. ↑ "Database VLDB and Partitioning Guide". Oracle.com. Retrieved 2018-05-02.
  6. ↑ "Online Transaction Processing vs. Decision Support". Microsoft.com. Retrieved 2018-05-07.
  7. ↑ "Managing Rollback Segments". Oracle.com. Retrieved 2018-05-07.
  8. ↑ "ISelfSchooling - What is cluster table - Index Cluster and Hash Cluster". Archived from the original on 2014-05-14. Retrieved 2014-05-14.
  9. ↑ "Transaction Modes". Oracle.com. Retrieved 2018-05-07.
  10. ↑ "Data Blocks, Extents, and Segments". Oracle.com. Retrieved 2018-05-07.
  11. ↑ "Tuning the Database Buffer Cache". Oracle.com. Retrieved 2018-05-07.
  12. ↑ "Transaction processing monitor". C2.com. Retrieved 2018-05-07.
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