Why distributed database system

A machine configured as a web server will differ from a machine configured as a data warehouse database server. If performance problems arise in a distributed architecture, it is much easier not only to identify problems but also to solve them without the risk of compromising other components. Since applications and databases do not reside on the same machines, there is no particular reason why they even need to reside on the same type of machine.

Whether you perform a major platform change such as moving from VMS to Unix or a minor upgrade such as from Solaris 2. The failure of a single component in a distributed architecture is much less drastic than in an environment in which databases and applications are housed on the same machine.

For example, database machines might implement parallel server or synchronous replication to protect against failure of a database machine, whereas application servers may have backup hardware available so that the application can run on a new machine if an application server fails.

Protecting against failure of machines that house data is generally much more complicated than protecting against failure of machines that simply run applications.

Other applications may or not scale this way, but if the applications have their own host, system administrators can understand their requirements and allocate hardware resources appropriately. L ocation transparency means that neither applications nor users need to be concerned with the logistics of where data actually resides or how it is distributed.

Needless to say, being shielded from these specifics enhances the usability of a database because developers and users do not need to consider such details as connect strings. Moreover, data can be relocated from one database instance to another with minimal impact on users and applications. Generally, the network of computers used for distributed systems could be located in the same physical location or they may be located globally in various parts of the world connected through a single management system.

These distributed databases could be hosted on either a network server or on corporate intranets and Extranets or more recently and most popularly on decentralized independent computers hosted on the internet. One of the main reasons for the need for distributed systems is its Tunability.

With the complex nature of organizations and various transactions that happen every second, it becomes important that the servers and the application are flexible enough to accommodate their ever-changing needs without hampering the overall functions of the database management system.

For example, a machine that houses a web server would service a high volume of small transactions whereas a database server with a data warehouse would serve the low volume of large or complex transactions. In distributed database systems these functions are mutually separated and therefore can be configured by end-users and developers as per their requirements. Imagine a system where the applications and the server are physically located on the same computer or system and one of the components fails and disrupts the entire system!

As an organization grows, so does its data and need for a better database management system. With distributed systems, organizations can scale their existing network of the system easily and maneuver the system as per their needs. It costs less to create a network of smaller computers spread across geographies than a single large computer. As the data on a distributed system is spread across locations, it becomes imperative to have a Cataloguing of what is stored at what location along with details to its autonomy and confidentiality.

A significant challenge in designing and managing a distributed database is the inherent lack of centralized knowledge of the entire database. This also applies to things like data governance and security in a distributed database.

Lack of a consistent approach to both of these aspects introduces risks, and any data breach can quickly tarnish the image of an enterprise and be expensive. Distributed databases are complex, needing a fully dedicated operational team to manage your data infrastructure. The costs associated with running a distributed database, such as hardware procurement, maintenance, and hiring costs across different geographies, adds up pretty fast to make it costlier than a typical DBMS. Fauna is a flexible, developer-friendly, transactional cloud database delivered to you as a secure data API built for modern web applications embracing the cloud.

Fauna rethinks traditional DBMS as a "data API", providing you with all the capabilities of an old-guard database without sacrificing flexibility, scale, and performance.

With Fauna, developers can quickly wrap business logic into programmable functions and run them near the data in a serverless fashion.

This accelerates application performance and offers more developer agility. Data model Relational and document-oriented based Relational Non-relational schema-less data items in a table Non-relational document-based data model If the data model fits perfectly for your use-case there are several benefits for your application. A query model defines how apps interact. To simplify app development, you need a modern and simple query model. APIs fit this bill. Transactional Model Uses mechanics of Calvin to support strictly serializable, externally consistent transactions in a distributed environment ACID based transactional model.

Serializable multi-item read and write transactions. Only ACID compliant within the region they occur in. Multi-document ACID transactions Depending on the use-case, a certain transaction model might be more desired than another. Consistency Model All Fauna queries are consistent across all deployment models and offer strictly serializable isolation levels.

It is impossible to perform write transactions or consistent read-only queries in non-primary processes and regions. Strong consistency is available to some degree but only within the context of a single region. To ensure consistency, developers must prevent queries from including data that could be rolled back, and ensure that no writes will occur during the read Database with a strong consistency model is preferred to ensure that use-case functionality is not negatively affected Indexing Provides field-based secondary indexing.

Aurora also supports spatial indexes. Supports primary and secondary indexes local and global. Provides primary and secondary indexing, and specialized indexes such as hashed indexes, wildcard indexes, and geo indexes. With multiple indexing options, there is more room to tune your database performance as your data grows. The result is better user experience for your application users. The transaction protocol replicates data in at least three locations ensuring that Fauna never loses your data.

This functionality means that your DB cluster can tolerate a failure of an Availability Zone without any loss of data and only a brief interruption of service. The leader nodes are potential bottlenecks as only they are capable of accepting writes and strongly consistent reads Node failures in MongoDB are handled by the replica set.

If the primary fails, a new primary is elected by the remaining nodes with a prioritized version of Raft , and the system continues to operate normally.

A database system with a high degree of fault tolerance is preferred by users building mission-critical applications. Security Fauna offers a web-native security model. This means it provides authN with keys and tokens. Fauna clusters require authentication and cannot be left accidentally unprotected.

You are also responsible for other factors including the sensitivity of your data, your organization's requirements, and applicable laws and regulations. If application data is breached, the organization faces huge risks and penalties. Lack of sufficient features also means that the database might not be suitable for regulated industry use-cases.

Scalability Fauna delivers unlimited scale with zero input from customers. Fauna achieves this by maintaining several consistent, full replicas of customer data and scaling up its infrastructure behind the scenes.

Aurora storage automatically scales with the data in your cluster volume. DynamoDB aims to absorb the responsibility of scaling to customer needs. Unlike Fauna, it still leaves significant operational work and overhead for customers making it less favourable MongoDB provides sharded clusters as a way to horizontally scale the largest of workloads across many replica sets.

If the application faces an influx of new users, the ability to have easy scalability is a must. Operations Fauna does not require any operational work from users to manage the scalability and availability of the system. It all happens automatically. Fauna does offer a CLI for users to help script schema administration tasks. Amazon Aurora releases updates regularly. Updates are applied to Aurora DB clusters during system maintenance windows. Does not require any operational work from users to manage the scalability and availability of the system.

With reduced operational overheads to run a database, developers can focus more time building their applications. The data API for modern applications is here. Sign-up for free without a credit card and get started instantly. Sign-up now. Try our quick start guide to get up and running with your first Fauna database, in only 5 minutes!

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