SlideShare una empresa de Scribd logo
1 de 7
Column Oriented Database
 A column-oriented DBMS is a database
management system (DBMS) that stores data
tables as sections of columns of data rather than
as rows of data.
 The goal of a columnar database is to efficiently
write and read data to and from hard disk storage
in order to speed up the time it takes to return a
query.
 Column Oriented Database has advantages
for data warehouses, customer relationship
management (CRM) systems, and library card
catalogs, and other adhoc inquiry systems where
aggregates are computed over large numbers of
similar data items.
Advantages of Column Database
 One of the main benefits of a columnar database
is that data can be highly compressed. The
compression permits columnar operations — like
MIN, MAX, SUM, COUNT and AVG— to be
performed very rapidly.
 Another benefit is that because a column-based
DBMSs is self-indexing, it uses less disk space
than a relational database management system
(RDBMS) containing the same data.
 Column architecture doesn’t read unnecessary
columns.
 Avoids decompression costs and perform
operations faster.
 Use compression schemes allow us to lower our
Disadvantages of Column
Database
 Increased Disk Seek Time
 Increased cost of Inserts
 Load time: Converting the data source into
columnar format can be unbearably slow where
tens or hundreds of gigabytes of data are
involved.
 Incremental loads: Incremental loads can be
performance problematic.
 Data compression: Some columnar systems
greatly compress the source data. However,
uncompressing the data to read it can slow
performance.
Row Oriented Database
 In the context of a relational database, a row—
also called a record or tuple—represents a single,
implicitly structured data item in a table.
 In simple terms, a database table can be thought
of as consisting of rows and columns or fields.
Each row in a table represents a set of related
data, and every row in the table has the same
structure.
Advantages and Disadvantages
 Advantages:
 Row-oriented organizations are more efficient when many
columns of a single row are required at the same time, and
when row-size is relatively small, as the entire row can be
retrieved with a single disk seek.
 Row-oriented organizations are more efficient when writing
a new row if all of the column data is supplied at the same
time as the entire row can be written with a single disk
seek.
 Disadvantages:
 In a RDBMS, data values are collected and managed as
individual rows and events containing related rows.
 A row-oriented database must read the entire record or
“row” in order to access the needed attributes or column
data.
 Queries most often end up reading significantly more data
than is needed to satisfy the request and it creates very
large I/O burdens.
Comparison of Columnar
Database and Row Database
 Column-oriented organizations are more efficient when an
aggregate needs to be computed over many rows but only
for a notably smaller subset of all columns of data,
because reading that smaller subset of data can be faster
than reading all data.
 Column-oriented organizations are more efficient when
new values of a column are supplied for all rows at once,
because that column data can be written efficiently and
replace old column data without touching any other
columns for the rows.
 Row-oriented organizations are more efficient when many
columns of a single row are required at the same time, and
when row-size is relatively small, as the entire row can be
retrieved with a single disk seek.
 Row-oriented organizations are more efficient when writing
a new row if all of the column data is supplied at the same
time, as the entire row can be written with a single disk
Example
 Here is an example of a simple database table with 4
columns and 3 rows [2].
 ID Last First Bonus
 1 Doe John 8000
 2 Smith Jane 4000
 3 Beck Sam 1000
 In a column-oriented database management system,
the data would be stored like
this: 1,2,3;Doe,Smith,Beck;John,Jane,Sam;8000,400
0,1000;
 In a row-oriented database management system, the
data would be stored like
this: 1,Doe,John,8000;2,Smith,Jane,4000;3,Beck,Sa
m,1000;

Más contenido relacionado

La actualidad más candente

Columnar Databases (1).pptx
Columnar Databases (1).pptxColumnar Databases (1).pptx
Columnar Databases (1).pptx
ssuser55cbdb
 
HBase Tutorial For Beginners | HBase Architecture | HBase Tutorial | Hadoop T...
HBase Tutorial For Beginners | HBase Architecture | HBase Tutorial | Hadoop T...HBase Tutorial For Beginners | HBase Architecture | HBase Tutorial | Hadoop T...
HBase Tutorial For Beginners | HBase Architecture | HBase Tutorial | Hadoop T...
Simplilearn
 

La actualidad más candente (20)

Columnar Databases (1).pptx
Columnar Databases (1).pptxColumnar Databases (1).pptx
Columnar Databases (1).pptx
 
Key-Value NoSQL Database
Key-Value NoSQL DatabaseKey-Value NoSQL Database
Key-Value NoSQL Database
 
Non relational databases-no sql
Non relational databases-no sqlNon relational databases-no sql
Non relational databases-no sql
 
Apache HBase™
Apache HBase™Apache HBase™
Apache HBase™
 
NOSQL Databases types and Uses
NOSQL Databases types and UsesNOSQL Databases types and Uses
NOSQL Databases types and Uses
 
RDBMS vs NoSQL
RDBMS vs NoSQLRDBMS vs NoSQL
RDBMS vs NoSQL
 
NoSql
NoSqlNoSql
NoSql
 
Building an Effective Data Warehouse Architecture
Building an Effective Data Warehouse ArchitectureBuilding an Effective Data Warehouse Architecture
Building an Effective Data Warehouse Architecture
 
Modernizing to a Cloud Data Architecture
Modernizing to a Cloud Data ArchitectureModernizing to a Cloud Data Architecture
Modernizing to a Cloud Data Architecture
 
Free Training: How to Build a Lakehouse
Free Training: How to Build a LakehouseFree Training: How to Build a Lakehouse
Free Training: How to Build a Lakehouse
 
Relational and non relational database 7
Relational and non relational database 7Relational and non relational database 7
Relational and non relational database 7
 
Building Lakehouses on Delta Lake with SQL Analytics Primer
Building Lakehouses on Delta Lake with SQL Analytics PrimerBuilding Lakehouses on Delta Lake with SQL Analytics Primer
Building Lakehouses on Delta Lake with SQL Analytics Primer
 
Graph based data models
Graph based data modelsGraph based data models
Graph based data models
 
Data Warehousing Trends, Best Practices, and Future Outlook
Data Warehousing Trends, Best Practices, and Future OutlookData Warehousing Trends, Best Practices, and Future Outlook
Data Warehousing Trends, Best Practices, and Future Outlook
 
HBase Tutorial For Beginners | HBase Architecture | HBase Tutorial | Hadoop T...
HBase Tutorial For Beginners | HBase Architecture | HBase Tutorial | Hadoop T...HBase Tutorial For Beginners | HBase Architecture | HBase Tutorial | Hadoop T...
HBase Tutorial For Beginners | HBase Architecture | HBase Tutorial | Hadoop T...
 
Nosql databases
Nosql databasesNosql databases
Nosql databases
 
Introduction to Redis
Introduction to RedisIntroduction to Redis
Introduction to Redis
 
NoSql
NoSqlNoSql
NoSql
 
Architecting a datalake
Architecting a datalakeArchitecting a datalake
Architecting a datalake
 
Data Lakehouse, Data Mesh, and Data Fabric (r2)
Data Lakehouse, Data Mesh, and Data Fabric (r2)Data Lakehouse, Data Mesh, and Data Fabric (r2)
Data Lakehouse, Data Mesh, and Data Fabric (r2)
 

Similar a Column oriented database

Introduction to databases
Introduction to databasesIntroduction to databases
Introduction to databases
akanksha007
 

Similar a Column oriented database (20)

Rise of Column Oriented Database
Rise of Column Oriented DatabaseRise of Column Oriented Database
Rise of Column Oriented Database
 
nosql.PPT.pptx
nosql.PPT.pptxnosql.PPT.pptx
nosql.PPT.pptx
 
Big data hbase
Big data hbase Big data hbase
Big data hbase
 
Sql Server Basics
Sql Server BasicsSql Server Basics
Sql Server Basics
 
Column db dol
Column db dolColumn db dol
Column db dol
 
Row or Columnar Database
Row or Columnar DatabaseRow or Columnar Database
Row or Columnar Database
 
Open Source Datawarehouse
Open Source DatawarehouseOpen Source Datawarehouse
Open Source Datawarehouse
 
Uint-5 Big data Frameworks.pdf
Uint-5 Big data Frameworks.pdfUint-5 Big data Frameworks.pdf
Uint-5 Big data Frameworks.pdf
 
Uint-5 Big data Frameworks.pdf
Uint-5 Big data Frameworks.pdfUint-5 Big data Frameworks.pdf
Uint-5 Big data Frameworks.pdf
 
Databases and its representation
Databases and its representationDatabases and its representation
Databases and its representation
 
Introduction to database
Introduction to databaseIntroduction to database
Introduction to database
 
Presentation DBMS (1)
Presentation DBMS (1)Presentation DBMS (1)
Presentation DBMS (1)
 
database concepts.pptx
database concepts.pptxdatabase concepts.pptx
database concepts.pptx
 
Database.pptx
Database.pptxDatabase.pptx
Database.pptx
 
Dbms relational model
Dbms relational modelDbms relational model
Dbms relational model
 
Introduction to databases
Introduction to databasesIntroduction to databases
Introduction to databases
 
Bt0066 database management system1
Bt0066 database management system1Bt0066 database management system1
Bt0066 database management system1
 
Presentation1
Presentation1Presentation1
Presentation1
 
Cassandra data modelling best practices
Cassandra data modelling best practicesCassandra data modelling best practices
Cassandra data modelling best practices
 
Introduction of Data Structure
Introduction of Data StructureIntroduction of Data Structure
Introduction of Data Structure
 

Column oriented database

  • 1. Column Oriented Database  A column-oriented DBMS is a database management system (DBMS) that stores data tables as sections of columns of data rather than as rows of data.  The goal of a columnar database is to efficiently write and read data to and from hard disk storage in order to speed up the time it takes to return a query.  Column Oriented Database has advantages for data warehouses, customer relationship management (CRM) systems, and library card catalogs, and other adhoc inquiry systems where aggregates are computed over large numbers of similar data items.
  • 2. Advantages of Column Database  One of the main benefits of a columnar database is that data can be highly compressed. The compression permits columnar operations — like MIN, MAX, SUM, COUNT and AVG— to be performed very rapidly.  Another benefit is that because a column-based DBMSs is self-indexing, it uses less disk space than a relational database management system (RDBMS) containing the same data.  Column architecture doesn’t read unnecessary columns.  Avoids decompression costs and perform operations faster.  Use compression schemes allow us to lower our
  • 3. Disadvantages of Column Database  Increased Disk Seek Time  Increased cost of Inserts  Load time: Converting the data source into columnar format can be unbearably slow where tens or hundreds of gigabytes of data are involved.  Incremental loads: Incremental loads can be performance problematic.  Data compression: Some columnar systems greatly compress the source data. However, uncompressing the data to read it can slow performance.
  • 4. Row Oriented Database  In the context of a relational database, a row— also called a record or tuple—represents a single, implicitly structured data item in a table.  In simple terms, a database table can be thought of as consisting of rows and columns or fields. Each row in a table represents a set of related data, and every row in the table has the same structure.
  • 5. Advantages and Disadvantages  Advantages:  Row-oriented organizations are more efficient when many columns of a single row are required at the same time, and when row-size is relatively small, as the entire row can be retrieved with a single disk seek.  Row-oriented organizations are more efficient when writing a new row if all of the column data is supplied at the same time as the entire row can be written with a single disk seek.  Disadvantages:  In a RDBMS, data values are collected and managed as individual rows and events containing related rows.  A row-oriented database must read the entire record or “row” in order to access the needed attributes or column data.  Queries most often end up reading significantly more data than is needed to satisfy the request and it creates very large I/O burdens.
  • 6. Comparison of Columnar Database and Row Database  Column-oriented organizations are more efficient when an aggregate needs to be computed over many rows but only for a notably smaller subset of all columns of data, because reading that smaller subset of data can be faster than reading all data.  Column-oriented organizations are more efficient when new values of a column are supplied for all rows at once, because that column data can be written efficiently and replace old column data without touching any other columns for the rows.  Row-oriented organizations are more efficient when many columns of a single row are required at the same time, and when row-size is relatively small, as the entire row can be retrieved with a single disk seek.  Row-oriented organizations are more efficient when writing a new row if all of the column data is supplied at the same time, as the entire row can be written with a single disk
  • 7. Example  Here is an example of a simple database table with 4 columns and 3 rows [2].  ID Last First Bonus  1 Doe John 8000  2 Smith Jane 4000  3 Beck Sam 1000  In a column-oriented database management system, the data would be stored like this: 1,2,3;Doe,Smith,Beck;John,Jane,Sam;8000,400 0,1000;  In a row-oriented database management system, the data would be stored like this: 1,Doe,John,8000;2,Smith,Jane,4000;3,Beck,Sa m,1000;

Notas del editor

  1. In a columnar database, all the column 1 values are physically together followed by all the column 2 values, etc. The data is stored in record order, so the 100th entry for column 1 and the 100th entry for column 2 belong to the same input record. This allows individual data elements, such as customer name for instance, to be accessed in columns as a group, rather than individually row-by-row.