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Hybrid OLTP and OLAP

Chapter · January 2018

DOI: 10.1007/978-3-319-63962-8_179-1

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Hybrid OLTP and OLAP

Jana Giceva

and Mohammad Sadoghi

Department of Computing, Imperial College

London, London, UK

University of California, Davis, CA, USA

Synonyms

HTAP; Hybrid transactional and analytical pro-

cessing; Operational analytics; Transactional an-

alytics

Deﬁnitions

Hybrid transactional and analytical processing

(HTAP) refers to system architectures and tech-

niques that enable modern database management

systems (DBMS) to perform real-time analytics

on data that is ingested and modiﬁed in the

transactional database engine. It is a term that was

originally coined by Gartner where Pezzini et al.

(2014) highlight the need of enterprises to close

the gap between analytics and action for better

business agility and trend awareness.

Overview

The goal of running transactions and analytics on

the same data has been around for decades, but

has not fully been realized due to technology lim-

itations. Today, businesses can no longer afford

to miss the real-time insights from data that is in

their transactional system as they may lose com-

petitive edge unless business decisions are made

on latest data (Analytics on latest data implies

allowing the query to run on any desired level of

isolations including dirty read, committed read,

snapshot read, repeatable read, or serializable.)

or fresh data (Analytics on fresh data implies

running queries on a recent snapshot of data

that may not necessarily be the latest possible

snapshot when the query execution began or a

consistent snapshot.). As a result, in recent years

in both academia and industry, there has been

an effort to address this problem by designing

techniques that combine the transactional and

analytical capabilities and integrate them in a sin-

gle hybrid transactional and analytical processing

(HTAP) system.

Online transaction processing (OLTP) systems

are optimized for write-intensive workloads.

OLTP systems employ data structures that are

designed for high volume of point access queries

with a goal to maximize throughput and minimize

latency. Transactional DBMSs typically store

data in a row format, relying on indexes and

efﬁcient mechanisms for concurrency control.

Online analytical processing (OLAP) systems

are optimized for heavy read-only queries that

touch large amounts of data. The data structures

used are optimized for storing and accessing large

volumes of data to be transferred between the

storage layer (disk or memory) and the process-

S. Sakr, A. Zomaya (eds.), Encyclopedia of Big Data Technologies,

https://doi.org/10.1007/978-3-319-63962-8_179-1

Hybrid OLTP and OLAP

Jana Giceva

and Mohammad Sadoghi

Department of Computing, Imperial College

London, London, UK

University of California, Davis, CA, USA

Synonyms

HTAP; Hybrid transactional and analytical pro-

cessing; Operational analytics; Transactional an-

alytics

Deﬁnitions

Hybrid transactional and analytical processing

(HTAP) refers to system architectures and tech-

niques that enable modern database management

systems (DBMS) to perform real-time analytics

on data that is ingested and modiﬁed in the

transactional database engine. It is a term that was

originally coined by Gartner where Pezzini et al.

(2014) highlight the need of enterprises to close

the gap between analytics and action for better

business agility and trend awareness.

Overview

The goal of running transactions and analytics on

the same data has been around for decades, but

has not fully been realized due to technology lim-

itations. Today, businesses can no longer afford

to miss the real-time insights from data that is in

their transactional system as they may lose com-

petitive edge unless business decisions are made

on latest data (Analytics on latest data implies

allowing the query to run on any desired level of

isolations including dirty read, committed read,

snapshot read, repeatable read, or serializable.)

or fresh data (Analytics on fresh data implies

running queries on a recent snapshot of data

that may not necessarily be the latest possible

snapshot when the query execution began or a

consistent snapshot.). As a result, in recent years

in both academia and industry, there has been

an effort to address this problem by designing

techniques that combine the transactional and

analytical capabilities and integrate them in a sin-

gle hybrid transactional and analytical processing

(HTAP) system.

Online transaction processing (OLTP) systems

are optimized for write-intensive workloads.

OLTP systems employ data structures that are

designed for high volume of point access queries

with a goal to maximize throughput and minimize

latency. Transactional DBMSs typically store

data in a row format, relying on indexes and

efﬁcient mechanisms for concurrency control.

Online analytical processing (OLAP) systems

are optimized for heavy read-only queries that

touch large amounts of data. The data structures

used are optimized for storing and accessing large

volumes of data to be transferred between the

storage layer (disk or memory) and the process-

S. Sakr, A. Zomaya (eds.), Encyclopedia of Big Data Technologies,

https://doi.org/10.1007/978-3-319-63962-8_179-1

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