List of RDBMSs that support parallel operations

List the RDBMSs that are supporting parallel operations / Parallel processing enabled RDBMSs / List of RDBMSs that are providing parallelism

List of RDBMSs that support parallel operations

RDBMS	Developer	Description
Adaptive Server Enterprise (ASE)	Sybase	·        It is a relational model database server product. ·        Commonly known as Sybase DB. ·        Available for Unix and Windows. ·        Since version 11.5, ASE has supported intra-query parallelism.
Clustrix	Clustrix, Inc.	·        They are the developer of NewSQL database. ·        Clustrix offers a scale-out SQL database that lets you simply add more nodes to your cluster as demand grows. ·        ClustrixDB employs massively parallel processing (MPP) across its distributed cluster to parallelize and distribute SQL queries, and uses all available resources of the cluster to accelerate the queries.
DB2	IBM	·        IBM employed MPP to parallelize queries. ·        Shared Nothing Architecture is used. ·        DB2 can use two different methods for achieving query parallelism: I/O or CPU.
EXASolution	EXASOL	·        EXASolution is an in-memory, column-oriented RDBMS. ·        It is a parallelized RDBMS runs on cluster of standard hardware servers. ·        Shared nothing architecture is used. ·        Massively Parallel Processing (MPP) ·        Highly scalable.
HSQLDB	Available under BSD license.	·        Written in Java ·        Fully mutithreaded
Informix Dynamic Server	IBM	·        Uses Parallel Database Query (PDQ) feature to run queries in parallel. ·        PDQ along with table fragmentation feature works well. ·        Uses combination of shared memory and shared nothing architecture
Microsoft SQL Server	Microsoft	·        By default, SQL Server will use all available CPUs during query execution ·        Massively Parallel Processing ·        Shared nothing architecture
MonetDB	MonetDB Developer team	·        Open source ·        Column-oriented DBMS ·        Designed for multi-core parallel execution on desktops
Oracle	Oracle Corporation	·        Relies on Shared everything architecture ·        Oracle can flexibly parallelize almost all operations in various ways and degrees, independent of the underlying data layout without overloading the system. ·        Massively Parallel Processing
PostgreSQL	PostgreSQL Global Development Group	·        Postgres currently supports full parallelism in client-side code ·        Implements intraquery parallelism with threads. ·        Still in process to a complete parallelism support like Oracle or DB2.
SQL Anywhere	SAP (Systems, Applications & Products in Data Processing) *world’s 3rd largest software company	·        It supports inter-query and intra-query parallelism ·        It can assign multiple threads to a single request, thus achieving intra-query parallelism. ·        Each request can run on a single thread and execute on a single processor, thus achieving inter-query parallelism
Teradata	Public type company	·        Teradata RDBMS is designed for parallelism ·        The virtual processor (VPROC) is the basic unit of parallelism ·        Teradata PT (Teradata Parallel Transporter) provides parallelism support for data warehouse. ·        Teradata PT supports Pipeline, Data, and Multiple parallelism

Different options for distributing a database in a distributed database system

Various options for distributing databases / What are the different options for distributing databases over a distributed database system? / List the various possible options for distributing a database in a DDBS. /  List and discuss the techniques for distributing a database

 

Various options for distributing databases

A database is distributed over network and stored on various sites in geographically different locations for ease of access. This we know earlier. In an actual case, a database may be stored in multiple sites as it is, or some tables of a database might be stored at one site, the others at some other site and so on. So, what are the various options available to us to distribute database over different locations? They are,

1. Data replication – it is about keeping the same copies at different sites.

·        The whole database may be reproduced and maintained at all or few of the sites, or

·        A particular table may be reproduced and maintained at all or few of the sites

2. Horizontal partitioning – it is about partitioning a table by records without disturbing the structure of the table. For example, if you have a table EMP which stores data according to a schema EMP(Eno, Ename, Dept, Dept_location), then horizontal partitioning of EMP on Dept_location is about breaking employee records according to the department location values and store different set of employee details at different locations. The data at different locations will be different, but the schema will be the same, ie., EMP(Eno, Ename, Dept, Dept_location).

3. Vertical partitioning – it is about partitioning a table vertically, ie., decomposition. Hence, the partition of tables at different locations will of different structure.

For example, assume the schema EMP(Eno, Ename, Dept, Dept_location). If you would like to break the above schema like one to store employee details and the other to store the department details, it can be done as follows;

EMP(Eno, Ename, Dept), and DEPT(Dept, Dept_location)

These two tables might be stored at different locations for ease of access according to the defined organization policies for example.

4. Hybrid approach – it is a combination of few or all of the above said techniques. That is, it may be a combination like the few listed below;

·        Horizontal partitioning and replication of few or all horizontal partitions.

·        Vertical partitioning and replication of few or all vertical partitions.

·        Vertical partitioning, followed by horizontal partitioning of some vertical partitions, followed by replication of few horizontal partitions, etc.

Reasons for using ORDBMS over RDBMS

Why do we need ORDBMS / Reasons for using ORDBMS / Advantages of ORDBMS

 

Reasons for having ORDBMSs over conventional RDBMSs

• Storage requirements of newer applications - Conventional RDBMSs cannot handle the data like engineering designs, scientific experiments, GIS, and multimedia data.

• Handling of data stored for newer applications – retrieve and manipulate data stored for newer applications like other data with RDBMS functionalities.

• Ability to define user defined data types – to handle new type of data as and when required.

• Reusability nature of objects – One of the main features of using objects, and methods.

• Database extensibility – easy addition of data types and operations

And finally, the inability of RDBMS to handle the above said things.