Nosql why

As more and more customer engagements take place online via web and mobile apps, availability becomes a major, if not primary, concern. These mission-critical applications have to be available 24 hours a day, 7 days a week — no exceptions. Delivering 24x7 availability is a challenge for relational databases that are deployed to a single physical server or that rely on clustering with shared storage.

If deployed as a single server and it fails, or as a cluster and the shared storage fails, the database becomes unavailable. In contrast to relational technology, a distributed, NoSQL database partitions and distributes data to multiple database instances with no shared resources.

In addition, the data can be replicated to one or more instances for high availability intercluster replication. While relational databases like Oracle require separate software for replication e. In addition, automatic failover ensures that if a node fails, the database can continue to perform reads and writes by sending the requests to a different node.

While deploying a database to multiple datacenters increases availability and helps with disaster recovery, it also has the benefit of increasing performance, because all reads and writes can be executed on the nearest datacenter, thereby reducing latency.

Ensuring global availability is difficult for relational databases in cases where the requirement of separate add-ons increases complexity e. Also, when replicating between datacenters, applications built on relational databases can experience performance degradation or find that the datacenters are severely out of sync.

A distributed, NoSQL database includes built-in replication between datacenters — no separate software is required. So what are NoSQL databases and why do they matter now? As enterprises shift to the Digital Economy — enabled by cloud, mobile, social media, and big data technologies — developers and operations teams have to build and maintain web, mobile, and IoT applications faster and faster, and at greater scale. Hundreds of Global enterprises, along with tens of thousands smaller businesses and startups, have adopted NoSQL.

For many, the use of NoSQL started with a cache, proof of concept, or a small application, then expanded to targeted mission-critical applications, and is now the foundation for all application development.

With NoSQL, enterprises are better able to both develop with agility and operate at any scale — and deliver the performance and availability required to meet the demands of Digital Economy businesses. Why use NoSQL? How does NoSQL work? Conclusion NoSQL databases store data in documents rather than relational tables. The following defines the four most-popular types of NoSQL database: Document databases are primarily built for storing information as documents, including, but not limited to, JSON documents.

These systems can also be used for storing XML documents, for example. Key-value stores group associated data in collections with records that are identified with unique keys for easy retrieval. Key-value stores have just enough structure to mirror the value of relational databases while still preserving the benefits of NoSQL. Wide-column databases use the tabular format of relational databases yet allow a wide variance in how data is named and formatted in each row, even in the same table.

Like key-value stores, wide-column databases have some basic structure while also preserving a lot of flexibility Graph databases use graph structures to define the relationships between stored data points.

Graph databases are useful for identifying patterns in unstructured and semi-structured information. They need to: Support large numbers of concurrent users tens of thousands, perhaps millions Deliver highly responsive experiences to a globally distributed base of users Be always available — no downtime Handle semi- and unstructured data Rapidly adapt to changing requirements with frequent updates and new features Building and running these massively interactive applications has created a new set of technology requirements.

Five trends creating new technical challenges that NoSQL DBs address These companies and hundreds more like them are turning to NoSQL because of five trends that present technical challenges that are too difficult for most relational databases. What about SQL? These databases were engineered to meet a new generation of enterprise requirements: The need to develop with agility , to meet changing requirements , and to eliminate data transformation.

Eliminate data transformation Applications and services model data as objects e. Database Icon. Analytics Icon. Code Icon. Book Icon. Dollar Sign Icon. User Circle Icon. Arrow Right Icon. Cloud Icon. Stream Icon. Tools Icon.

Lock Icon. Sync Icon. Lightbulb Icon. Users Icon. Briefcase Icon. Industry Icon. Sitemap Icon. Check Icon. Life Ring Icon. Comments Icon.

List Icon. Laptop Icon. File Download Icon. Calendar Icon. Rss Icon. Newspaper Icon. Presentation Icon. Video Icon.

Users Class Icon. Contact Us. The rise of this new generation of data services solved many of the problems of web scale and rapidly growing data sets when it was created more than a decade ago. However, in the rush to solve for the challenges of big data and large numbers of concurrent users, NoSQL abandoned some of the core features of databases that make them highly performant and easy to use. It forced an evolution, combining the best of the big data capabilities with the structure and flexibility of the proven relational model to produce a scalable relational database.

Relational databases evolved to create an entirely new generation of systems that can handle nearly all of the workloads, with the scalability, reliability, and availability requirements that modern applications demand. From traditional workloads such as transactional applications and business analytics, to newer workloads such as multi-tenant services and operational analytics. The rise of new databases such as Google Spanner, Azure Data Warehouse, and our eponymous database, SingleStore, have proven that, for the majority of use cases, relational databases are easier to use and generally perform better than the NoSQL systems.

I know this might be controversial. I also know that you might quickly dismiss my perspective as biased. But let me break down the history, architecture, and applications of these databases, then judge for yourself. This blog post, originally published in July , has been updated with references to newer SingleStore releases. NoSQL came full force onto the scene in the late s, although it started much further back. It was developed largely to address the scale problems of existing database systems.

It was clear that scale out was a more cost-effective model for building large systems. For the largest systems such as email and search built by Google, Facebook, Microsoft, and Yahoo, it was the only way to scale.

Scaling the application tier came first because it was easier to scale a stateless system. The storage layer was another story. By definition, databases are stateful, and maintaining the guarantees i. ACID on that state across a distributed system is really hard. The first example was internal at Microsoft where the company built Webstore, which was a sharding layer on top of SQL Server used by Hotmail and the associated services.

Webstore was clunky and lacked a lot of core functionality but it worked and gave Microsoft an ability both to scale to the size of data it needed and achieve high availability. But Webstore required an entire team of engineers to build and maintain.

The company was adding users so fast that new SQL Server boxes needed to be added every day. Running all those SQL Servers and querying across them was a massively complicated endeavor that took a large number of engineers to maintain. The same patterns were repeated at Facebook and others, because all of the burgeoning tech giants struggled with scale.

It became clear that, with their massive usage and growth, these new digital services demanded a new solution for ingesting, managing, and surfacing data. Ideally, we needed something that could natively present a single interface but scale out over many machines with built-in high availability. Eventually, the large scale cloud services Google, Facebook, Yahoo, Microsoft, and others all built their own custom systems to handle scale demand. NoSQL on the other hand is completely flexible. Instead of defining all the column names before inserting data, a NoSQL database has no data schema restrictions.

If your business demands a change or addition in the data from here-forth, no problem. Just add new entities inside new JSON documents. With the help of a NoSQL database, your data schema is flexible and that supports the speed that agile development demands.

Your application will accept new changes as required by the business without advanced definition. Storing data in the JSON format not only provides schema flexibility, it is also a more natural way to store data than in rows and columns. For example, a relational database stores data in pieces. The relational way to store such information would be as follows. This data is naturally meant to be stored together since the values are accessed, used and created together.

So it's safe to say that storing information in rows and columns is highly structured and therefore unnatural. The natural way would have been to store this information as one and group them together. Such flexibility with fluid data models allows you to mold your data according to your application needs and also helps you adapt to new changes per requirements of the business.

While you need to learn a few tricks about storing data in a way which bests suits your needs, why should you need to learn new tricks to retrieve it? This cuts down the learning curve for data retrieval by a huge margin. For example:.

Such a non-relational data model helps you to develop faster and more agile applications and also helps the NoSQL database to distribute data among multiple server machines. NoSQL databases break the traditional mindset of storing data at a single location. Instead, NoSQL distributes and stores data over a set of multiple servers. This distribution of data helps the NoSQL database server to distribute the load on the database tier.

This distributed data approach also means the system can scale out rather than just scale up. One of the main advantages of a NoSQL database over a relational database is the ability to scale out. Scaling out means adding more servers to your system to distribute the load. That is, when your current server machines application or database reach their maximum capacity, you do not stop operations to upgrade your server machines; instead you add more nodes.

Such scalability also saves money by using less-costly commodity hardware rather than expensive multi-core heavy duty machines.

As seen from the above figure, adding servers only when required helps to keep the operating cost low. NosDB can scale out to an infinitely large cluster and it provides linear scalability. Which means that as you increase your NoSQL database servers, your transaction capacity increases equally.

This helps you to handle huge amounts of data beyond the limits of a single machine. Other than providing scalability, NoSQL databases also provide high availability out of the box.

NoSQL architecture supports data replication that happens near real time. If a primary server goes down, the replica server automatically takes over and your client applications work seamlessly. There is practically no limit to the number of replicas a node can have.