NoSQL vs RDBMS: Why and why not to use NoSQL over RDBMS?
NoSQL (not only SQL) is not a relational database management system (RDBMS). We will discuss what is the difference between NoSQL databases and Relational Databases and then why and why not to use NoSQL database model over traditional and relational database model (RDBMS) in detail. As NoSQL is the new technology, it is also facing many challenges, so will also have a look upon them.
Today, the internet world has billions of users. Big Data, Big Users, and Cloud Computing are the big technologies which every major internet application is using or preparing to use because internet application users are growing day by day and data is becoming more and more complex and unstructured which is very hard to manage using traditional relational database management system (RDBMS). NoSQL technology has the answer to all these problems. NoSQL is meant for Unstructured Big Data and Cloud Computing. A NoSQL database is exactly the type of database that can handle the all sort of unstructured, messy and unpredictable data that our system of engagement requires. NoSQL is a whole new way of thinking about a database.
Difference between NoSQL and Relational Data Models (RDBMS)
Relational and NoSQL data models are very different.
The relational model takes data and separates it into many interrelated tables that contain rows and columns. Tables reference each other through foreign keys that are stored in columns as well. When looking up data, the desired information needs to be collected from many tables (often hundreds in today’s enterprise applications) and combined before it can be provided to the application. Similarly, when writing data, the write needs to be coordinated and performed on many tables.
NoSQL databases have a very different model. For example, a document-oriented NoSQL database takes the data you want to store and aggregates it into documents using the JSON format. Each JSON document can be thought of as an object to be used by your application. A JSON document might, for example, take all the data stored in a row that spans 20 tables of a relational database and aggregate it into a single document/object. Aggregating this information may lead to duplication of information, but since storage is no longer cost prohibitive, the resulting data model flexibility, ease of efficiently distributing the resulting documents and read and write performance improvements make it an easy trade-off for web-based applications.
Another major difference is that relational technologies have rigid schemas while NoSQL models are schemaless. Relational technology requires strict definition of a schema prior to storing any data into a database. Changing the schema once data is inserted is a big deal, extremely disruptive and frequently avoided – the exact opposite of the behavior desired in the Big Data era, where application developers need to constantly – and rapidly – incorporate new types of data to enrich their apps.
It also may not provide full ACID (atomicity, consistency, isolation, durability) guarantees, but still has a distributed and fault tolerant architecture.
The NoSQL taxonomy supports key-value stores, document store, BigTable, and graph databases.
In comparison, document databases are schemaless, allowing you to freely add fields to JSON documents without having to first define changes. The format of the data being inserted can be changed at any time, without application disruption.
Examples: MongoDB, Cassandra, CouchDB, HBase are the examples of NoSQL.
NoSQL Database Types
Document databases pair each key with a complex data structure known as a document. Documents can contain many different key-value pairs, or key-array pairs, or even nested documents.
Graph stores are used to store information about networks, such as social connections. Graph stores include Neo4J and HyperGraphDB.
Key-value stores are the simplest NoSQL databases. Every single item in the database is stored as an attribute name (or "key"), together with its value. Examples of key-value stores are Riak and Voldemort. Some key-value stores, such as Redis, allow each value to have a type, such as "integer", which adds functionality.
Wide-column stores such as Cassandra and HBase are optimized for queries over large datasets, and store columns of data together, instead of rows.
Why to use NoSQL Databases?
1. NoSQL has Flexible Data Model to Capture Unstructured / Semi-structured Big Data
Data is becoming easier to capture and access through third parties such as Facebook, D&B, and others. Personal user information, geo location data, social graphs, user-generated content, machine logging data, and sensor-generated data are just a few examples of the ever-expanding array of data being captured. It’s not surprising that developers want to enrich existing applications and create new ones made possible by it. And the use of the data is rapidly changing the nature of communication, shopping, advertising, entertainment, and relationship management. Apps that don’t leverage it quickly will quickly fall behind.
Developers want a very flexible database that easily accommodates new data types and isn’t disrupted by content structure changes from third-party data providers. Much of the new data is unstructured and semi-structured, so developers also need a database that is capable of efficiently storing it. Unfortunately, the rigidly defined, schema-based approach used by relational databases makes it impossible to quickly incorporate new types of data, and is a poor fit for unstructured and semi-structured data. NoSQL provides a data model that maps better to these needs.
A lot of applications might gain from this unstructured data model: tools like CRM, ERP, BPM, etc, could use this flexibility to store their data without performing changes on tables or creating generic columns in a database. These databases are also good to create prototypes or fast applications, because this flexibility provides a tool to develop new features very easily.
2. NoSQL is highly and easily scalable (Scale up vs Scale out)
If millions of users are using your app frequently and concurrently, you need to think about the scalable database technology instead of traditional RDBMS. With relational technologies, many application developers find it difficult, or even impossible, to get the dynamic scalability and level of scale they need while also maintaining the performance users demand. You need to switch to NoSQL databases.
For the cloud applications, relational databases were originally the popular choice. Their use was increasingly problematic however, because they are a centralized, share-everything technology that scales up rather than out. This made them a poor fit for applications that require easy and dynamic scalability. NoSQL databases have been built from the ground up to be distributed, scale-out technologies and therefore fit better with the highly distributed nature of the three-tier Internet architecture.
Scale up vs Scale out
To deal with the increase in concurrent users (Big Users) and the amount of data (Big Data), applications and their underlying databases need to scale using one of two choices: scale up or scale out. Scaling up implies a centralized approach that relies on bigger and bigger servers. Scaling out implies a distributed approach that leverages many standard, commodity physical or virtual servers.
Scale up with relational technology: limitations at the database tier
At the web/application tier of the three-tier Internet architecture, a scale out approach has been the default for many years and worked extremely well. As more people use an application, more commodity servers are added to the web/application tier, performance is maintained by distributing load across an increased number of servers, and the cost scales linearly with the number of users.
Prior to NoSQL databases, the default scaling approach at the database tier was to scale up. This was dictated by the fundamentally centralized, shared-everything architecture of relational database technology. To support more concurrent users and/or store more data, you need a bigger and bigger server with more CPUs, more memory, and more disk storage to keep all the tables. Big servers tend to be highly complex, proprietary, and disproportionately expensive, unlike the low-cost, commodity hardware typically used so effectively at the web/application server tier.
Scale out with NoSQL technology at the database tier
NoSQL databases were developed from the ground up to be distributed, scale out databases. They use a cluster of standard, physical or virtual servers to store data and support database operations. To scale, additional servers are joined to the cluster and the data and database operations are spread across the larger cluster. Since commodity servers are expected to fail from time-to-time, NoSQL databases are built to tolerate and recover from such failure making them highly resilient.
NoSQL databases provide a much easier, linear approach to database scaling. If 10,000 new users start using your application, simply add another database server to your cluster. Add ten thousand more users and add another server. There’s no need to modify the application as you scale since the application always sees a single (distributed) database.
At scale, a distributed scale out approach also usually ends up being cheaper than the scale up alternative. This is a consequence of large, complex, fault tolerant servers being expensive to design, build and support. Licensing costs of commercial relational databases can also be prohibitive because they are priced with a single server in mind. NoSQL databases on the other hand are generally open source, priced to operate on a cluster of servers, and relatively inexpensive.
While implementations differ, NoSQL databases share some characteristics with respect to scaling and performance:
Relational databases require that schemas be defined before you can add data. For example, you might want to store data about your customers such as phone numbers, first and last name, address, city and state – a SQL database needs to know what you are storing in advance.
This fits poorly with agile development approaches, because each time you complete new features, the schema of your database often needs to change. So if you decide, a few iterations into development, that you'd like to store customers' favorite items in addition to their addresses and phone numbers, you'll need to add that column to the database, and then migrate the entire database to the new schema.
If the database is large, this is a very slow process that involves significant downtime. If you are frequently changing the data your application stores – because you are iterating rapidly – this downtime may also be frequent. There's also no way, using a relational database, to effectively address data that's completely unstructured or unknown in advance.
NoSQL databases are built to allow the insertion of data without a predefined schema. That makes it easy to make significant application changes in real-time, without worrying about service interruptions – which means development is faster, code integration is more reliable, and less database administrator time is needed.
Because of the way they are structured, relational databases usually scale vertically – a single server has to host the entire database to ensure reliability and continuous availability of data. This gets expensive quickly, places limits on scale, and creates a relatively small number of failure points for database infrastructure. The solution is to scale horizontally, by adding servers instead of concentrating more capacity in a single server.
"Sharding" a database across many server instances can be achieved with SQL databases, but usually is accomplished through SANs and other complex arrangements for making hardware act as a single server. Because the database does not provide this ability natively, development teams take on the work of deploying multiple relational databases across a number of machines. Data is stored in each database instance autonomously. Application code is developed to distribute the data, distribute queries, and aggregate the results of data across all of the database instances. Additional code must be developed to handle resource failures, to perform joins across the different databases, for data rebalancing, replication, and other requirements. Furthermore, many benefits of the relational database, such as transactional integrity, are compromised or eliminated when employing manual sharding.
NoSQL databases, on the other hand, usually support auto-sharding, meaning that they natively and automatically spread data across an arbitrary number of servers, without requiring the application to even be aware of the composition of the server pool. Data and query load are automatically balanced across servers, and when a server goes down, it can be quickly and transparently replaced with no application disruption.
Cloud computing makes this significantly easier, with providers such as Amazon Web Services providing virtually unlimited capacity on demand, and taking care of all the necessary database administration tasks. Developers no longer need to construct complex, expensive platforms to support their applications, and can concentrate on writing application code. Commodity servers can provide the same processing and storage capabilities as a single high-end server for a fraction of the price.
“Sharding” a relational database can reduce, or eliminate in certain cases, the ability to perform complex data queries. NoSQL database systems retain their full query expressive power even when distributed across hundreds of servers.
A number of products provide a caching tier for SQL database systems. These systems can improve read performance substantially, but they do not improve write performance, and they add complexity to system deployments. If your application is dominated by reads then a distributed cache should probably be considered, but if your application is dominated by writes or if you have a relatively even mix of reads and writes, then a distributed cache may not improve the overall experience of your end users.
Many NoSQL database technologies have excellent integrated caching capabilities, keeping frequently-used data in system memory as much as possible and removing the need for a separate caching layer that must be maintained.
Most NoSQL databases also support automatic replication, meaning that you get high availability and disaster recovery without involving separate applications to manage these tasks. The storage environment is essentially virtualized from the developer's perspective.
Challenges of NoSQL
The promise of the NoSQL database has generated a lot of enthusiasm, but there are many obstacles to overcome before they can appeal to mainstream enterprises. Here are a few of the top challenges.
RDBMS systems have been around for a long time. NoSQL advocates will argue that their advancing age is a sign of their obsolescence, but for most CIOs, the maturity of the RDBMS is reassuring. For the most part, RDBMS systems are stable and richly functional. In comparison, most NoSQL alternatives are in pre-production versions with many key features yet to be implemented.
Living on the technological leading edge is an exciting prospect for many developers, but enterprises should approach it with extreme caution.
Enterprises want the reassurance that if a key system fails, they will be able to get timely and competent support. All RDBMS vendors go to great lengths to provide a high level of enterprise support.
In contrast, most NoSQL systems are open source projects, and although there are usually one or more firms offering support for each NoSQL database, these companies often are small start-ups without the global reach, support resources, or credibility of an Oracle, Microsoft, or IBM.
3. Analytics and business intelligence
NoSQL databases have evolved to meet the scaling demands of modern Web 2.0 applications. Consequently, most of their feature set is oriented toward the demands of these applications. However, data in an application has value to the business that goes beyond the insert-read-update-delete cycle of a typical Web application. Businesses mine information in corporate databases to improve their efficiency and competitiveness, and business intelligence (BI) is a key IT issue for all medium to large companies.
NoSQL databases offer few facilities for ad-hoc query and analysis. Even a simple query requires significant programming expertise, and commonly used BI tools do not provide connectivity to NoSQL.
Some relief is provided by the emergence of solutions such as HIVE or PIG, which can provide easier access to data held in Hadoop clusters and perhaps eventually, other NoSQL databases. Quest Software has developed a product -- Toad for Cloud Databases -- that can provide ad-hoc query capabilities to a variety of NoSQL databases.
The design goals for NoSQL may be to provide a zero-admin solution, but the current reality falls well short of that goal. NoSQL today requires a lot of skill to install and a lot of effort to maintain.
There are literally millions of developers throughout the world, and in every business segment, who are familiar with RDBMS concepts and programming. In contrast, almost every NoSQL developer is in a learning mode. This situation will address naturally over time, but for now, it's far easier to find experienced RDBMS programmers or administrators than a NoSQL expert.
NoSQL databases are becoming an increasingly important part of the database landscape, and when used appropriately, can offer real benefits. However, enterprises should proceed with caution with full awareness of the legitimate limitations and issues that are associated with these databases.