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OLTP

Online transactional processing, more commonly referred to as OLTP, is an approach to database processing that is focused on reading and writing records (rows) made up of multiple fields in order to execute day-to-day transactions such as personal banking or retail checkout. Some people think of it as a “traditional” database. OLTP typically involves inserting, updating, and/or deleting small amounts of data in a database efficiently, frequently, and quickly. An OLTP environment experiences very high concurrency due to the large user population, small transactions, and very short response times.

 

OLTP is often contrasted with OLAP because most databases are designed for one or the other. OLTP is associated with row-oriented databases and is designed for user transactions such as online shopping cart experiences, tellers, customer account managers, etc., whereas OLAP is associated with column-oriented databases and is better suited for analytical workloads that process complex queries on large amounts of data for periodic reports used by executives for decision-making and future planning.

 

Intended to ensure integrity in OLTP systems—in the case that errors or unforeseen circumstances that may affect the data—there are four properties that should be met. If a database meets these four properties, it is known to be ACID compliant. The four properties are atomicity, consistency, isolation, and durability. ACID compliance is meant to prevent things such as double bookings in a ticketing sales app or recovering compromised data, should the system get hacked.

OLTP processing in a row-oriented database example

OLTP processing manages data on a row-by-row basis and is well-suited to manage transactional data workloads.

OLTP at Molecula

Molecula has developed a database platform that is neither row-oriented nor column-oriented. Molecula’s feature-oriented database, FeatureBase, represents data independently of rows and columns, enabling OLAP workloads to perform faster with less hardware than the traditional columnar database. FeatureBase is particularly useful for ML and AI applications due to the nature of its size, speed, volume, and real-time data capabilities. Because of this and the fact that FeatureBase is built on a machine-native bitmap architecture, FeatureBase processing is referred to as OLMP (online machine processing).

row-wise vs. columnar vs. feature-oriented data format

To learn more, see FeatureBase.

 

OLTP Related Terms

Row-oriented database

Feature-oriented database

OLAP

OLMP

 

Learn More About OLTP

Video: Column vs Row Oriented Databases Explained by Hussein Nasser

Other Occurrences

Related Terms

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