DISTRIBUTED DATABASE TUTORIAL EBOOK
Distributed DBMS About this Tutorial Distributed Database Management This tutorial discusses the important theories of distributed database systems. All the content and graphics published in this e-book are the property of Tutorials Point. Distributed Database Systems discusses the recent and emerging technologies in the field of distributed database technology. The mainstream areas of. Concurrency Control in Distributed Databases. ▫ Availability. ▫ Distributed Query Processing. ▫ Heterogeneous Distributed Databases. ▫ Directory Systems. 2.
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Distributed Database Management Systems: A Practical Approach Similar Free eBooks Database Management System [DBMS] Tutorial - Tutorials Point. Distributed DBMS Tutorial for Beginners - Learn Distributed DBMS in simple and examples including DBMS Concepts, Distributed Databases, Environments. This tutorial discusses the important theories of distributed database systems. and graphics published in this e-book are the property of Tutorials Point (I).
Follows fixed schema. Meaning, the columns are defined and locked before data entry. In addition, each row contains data for each column. Supports vertical scaling. Follows dynamic schemas. Meaning, you can add columns anytime. Supports horizontal scaling.
You can scale across multiple servers. Multiple servers are cheap commodity hardware or cloud instances, which make scaling cost-effective compared to vertical scaling.
In the next section, we will discuss the various benefits of NoSQL. Primary and Analytic Data Source Capability The first criterion of an enterprise-class NoSQL solution is it must serve as a primary or active data source that receives data from different business applications.
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It also must act as a secondary data source or analytic database that enhances business intelligence applications. From business perspective, the NoSQL database must be capable of quickly integrating all types of data viz structured, semi-structured, or unstructured. In addition, it must be able to execute high-performance queries.
Once all the required data is collected in the database, data administrators may want to perform an analysis in real time and in map-reduce form. An enterprise-class NoSQL database can easily handle such requests using the same database. It does not require loading the data into a separate analytic database for analysis. However, an enterprise-class NoSQL solution can scale to manage large volumes of data from terabytes to petabytes.
In addition to storing large volumes of data, it delivers high performance for data velocity, variety, and complexity. Moreover, rather than providing the continuous availability feature outside the software, the NoSQL solution delivers inherent continuous availability.
The NoSQL databases must include the following key features: All nodes in a cluster must be able to serve read request even if some machines are down. Must be capable of easily replicating and segregating data between different physical shelves in a data center. This helps avoid hardware outages. Must be able to support data distribution designs that are multi-data centers, on-premises or in the cloud. Multi-Data Center Capability Typically, business enterprises own highly distributed databases that are spread across multiple data centers and geographic locales.
However, none can offer a simple model of data distribution between various data centers without any performance issue.
A simple method includes the ability to handle multiple data centers without concerning the occurrences of the read and write operations.
A good NoSQL enterprise solution must support multi data-center deployment and must provide configurable option to maintain a balance between performance and consistency. These include a read-anywhere and write-anywhere capability with full location-independence support. This means you can write data to any node in a cluster, have it replicated on other nodes, and make it available to all users irrespective of their location.
In addition, the write capability on any node must ensure that the data is safe in the event of a power failure or any other incident.
Thus, it does not require a specific caching layer to store data. NoSQL database eliminates the problem of synchronizing cache data with the persistent database.
Thus, it supports simple scalability with fewer management issues. Cloud-Ready As an adaptation of cloud infrastructure is increasing day by day, an enterprise-class NoSQL solution must be cloud-ready.
A NoSQL database cluster must be able to function in a cloud setting, such as site EC2, and also must be able to expand and contract a cluster when necessary. It also must support a hybrid solution where part of the database is hosted within the enterprise premise and another part is hosted in a cloud setting.
Typically, the performance of database systems may go down when additional nodes are added to a cluster.
However, a good NoSQL solution increase performance for both read and writes operations when additional nodes are added. These performance gains are linear in nature.
Flexible Schema Support An enterprise-class NoSQL database offers a flexible or dynamic schema design to manage all types of data—structured, semi-structured, and non-structured.
Therefore, the need to have different vendors to support the different data types does not arise. Therefore, choosing an appropriate database based on application requirement is a key design decision. The flexible or dynamic schema support ensures that you can make schema changes to a structure without making the structure offline.
This support is critical considering the near-zero downtime and round-the-clock availability for business applications. In addition, it must run on a product hardware that does not require any tweaks or other proprietary add-ons.
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The NoSQL database must provide client interfaces and drivers for all common developer languages. In other words, it must be easy to implement and use and must offer sturdy functionality to handle various enterprise applications. In addition, the NoSQL vendor must supply good management tools that assist data professionals to perform various administrative tasks, such as adding capacity to a cluster, running utility tasks, and so on.
The NoSQL database must allow easy growth without making any change to the front-end of the business application. Thriving Open Source Community For an open source NoSQL database, having a vibrant community is essential to make a regular contribution to enhance the core software.
Moreover, open source communities generally provide excellent quality assurance or QA testing. Distributed database is a system in which storage devices are not connected to a common processing unit.
Database is controlled by Distributed Database Management System and data may be stored at the same location or spread over the interconnected network. It is a loosely coupled system.
Shared nothing architecture is used in distributed databases. The above diagram is a typical example of distributed database system, in which communication channel is used to communicate with the different locations and every system has its own memory and database.
Goals of Distributed Database system. The concept of distributed database was built with a goal to improve: In distributed database system, if one system fails down or stops working for some time another system can complete the task. In distributed database system reliability can be achieved even if sever fails down. Another system is available to serve the client request. Performance can be achieved by distributing database over different locations. So the databases are available to every location which is easy to maintain.
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Types of distributed databases. The two types of distributed systems are as follows:This takes into consideration the fact that the frequency of accessing the tables vary considerably from site to site.
A DBMS is a software package to work upon a database. Why is Apache Cassandra so widely used? The basic idea is to just test the DB alone even before the integration with the front end UI is made.
For example, dynamic programming technique. Checkpointing Checkpoint is a point of time at which a record is written onto the database from the buffers.
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