This article is about Parallel Databases and defines a parallel database and how it is implemented into DBMS to make it easier to use databases.
Before moving, let’s learn about the Introduction to Normalization in SQL.
Table of Contents
What Database?
A database is a data warehouse where a massive amount of data is stored. Database stores data can be considered an enormous amount of data, including images, videos, text, large volumes of transaction data, etc.
What is a Database Management System?
A Database Management system is a tool or software where data is stored in good structure and allows retrieval of the data as per the request and can perform manipulation with data like insert, update, delete, modify, etc.
Goal of Database Management System
- Easy to store and retrieve the data as per the request
- Easy management of data like sorting, manipulation etc.
- Use a convenient and efficient way.
When a massive amount of data cannot be transferred from one system to another at a high transfer rate, which leads to inefficiency in sharing the data, then parallel databases come into play.
Parallel Database
Parallel Database, which improves the efficiency and transfer speed of data from one system to another by using some background resources like CPU, Disk rate, Memory speed, etc.
It also performs many parallelization operations like data loading and query processing.
Parallelism in Databases
Since Data parallelism makes it easy to access a massive amount of data at a high transfer rate, the parallelism process allows data to be partitioned into multiple disks and retrieve data quickly.
Data is partitioned into multiple disks so that data management performs well and data is manipulated according to the request.
When the retrieving request is sent to the disk, different processors connecting to each disk can read the disk and manage the efficiency and speed of data transfer at a high transfer rate.
Goal of parallel Databases
- Improves performance – The system improves by running multiple resources in parallel, like CPU, memory speed, disk rate, etc. Many small processes can also be connected to the system in parallel to improve performance.
- Improve data availability – Data availability can improve if data is copied or multiple copies of the same data are provided in various locations/systems.
- Improve Reliability – The reliability improves by providing completeness, accuracy, and data availability.
- Provide distributed access to data – Companies with many city branches can access the data with the help of a parallel database. For example Map, Bank, ATM etc.
Difference between the Parallel Database and Distributed Database
Parallel Database – Parallel Database aims to break up the user’s Database requests into parts to run them on different computers with some parallel resources, such as multiple CPUs, Disks, Memory, etc., to achieve the same result as expected from the single computer.
Features:
- Divides the large database into small parts
- Improves the performance by using parallel resources like CPUs, Disks, Memory
- Complete the database requests fast by assigning them to multiple computers.
- Increase the overall performance speed of the result.
Distributed Database – Distributed Database is a database that is distributed/stored in multiple physical locations. A database can be in different computers situated in a single physical location or spread over the network of the same interconnected computers.
Features:
- It is the collection of related data
- Data can be distributed over the network of interconnected computers
- Data can be stored in multiple physical locations
- Data can be stored on different computers situated in a single physical location
Important Points:
- More advanced approaches use several computers and many files. Sometimes at different locations.
- Parallel and distributed methods improve access speed for very large databases, access for geographically dispersed organizations and reliability for applications that depends on uptime.
- A distributed database houses data in two or more server computers simultaneously.
- The two share a link over the Internet, so Company T receives database information from Company Y on each transaction.
- A typical parallel database resides in one location with one set of files through several computers sharing the workload.
Advantages of Parallel Database
The main advantage of parallel databases is speed. The server breaks up the user database request into parts and assigns each to a different computer. They work on the request simultaneously and merge the results, passing them back to the user. This process speeds up most data requests from the user, allowing faster acceptance of database requests at a large scale.
The other main advantage is Reliability. If the parallel database is adequately set up and configured, then it can work even with the failure of any assigned computer in the row. The server detects the failed computer and reroutes the database request to the other computer.
Capacity is one of the advantages of a Parallel Database. As many users request vast amounts of database access, the server adds more computers in Parallel to increase its capacity and overall work efficiency.
When Parallel Processing is not Advantageous
The following guidelines describe situations when parallel processing is not advantageous.
- In general, parallel processing is less advantageous when the cost of synchronization becomes too high and, therefore, throughput decreases.
- If many users on a large number of nodes modify a small data set, then synchronization is likely to be very high. However, if they just read data, then no synchronization is required.
- Parallel processing is not advantageous when a dispute occurs between instances on a single block or row.
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