NoSQL

Goodbye DBAs see you later

Not Your Father’s Transaction Processing

 Three trends disrupting the database status quo

Interactive applications have changed dramatically over the last 15 years. In the late ‘90s, large web companies emerged with dramatic increases in scale on many dimensions:

·         The number of concurrent users skyrocketed as applications increasingly became accessible via the web (and later on mobile devices).

·         The amount of data collected and processed soared as it became easier and increasingly valuable to capture all kinds of data.

·         The amount of unstructured or semi-structured data exploded and its use became integral to the value and richness of applications.

Dealing with these issues was more and more difficult using relational database technology. The key reason is that relational databases are essentially architected to run a single machine and use a rigid, schema-bas approach to modelling data.

Google, Amazon, Facebook, and LinkedIn were among the first companies to discover the serious limitations of relational database technology for supporting these new application requirements. Commercial alternatives didn’t exist, so they invented new data management approaches themselves. Their pioneering work generated tremendous interest because a growing number of companies faced similar problems. Open source NoSQL database projects formed to leverage the work of the pioneers, and commercial companies associated with these projects soon followed.

Today, the use of NoSQL technology is rising rapidly among Internet companies and the enterprise. It’s increasingly considered a viable alternative to relational databases, especially as more organizations recognize that operating at scale is more effectively achieved running on clusters of standard, commodity servers, and a schema-less data model is often a better approach for handling the variety and type of data most often captured and processed today.

What’s causing the move to NoSQL?

Three interrelated megatrends –Big Users, Big Data, and Cloud Computing – are driving the adoption of NoSQL technology.

Big Users

Not that long ago, 1,000 daily users of an application was a lot and 10,000 was an extreme case. Today, most new applications are hosted in the cloud and available over the Internet, where they must support global users 24 hours a day, 365 days a year. More than 2 billion people are connected to the Internet worldwide – and the amount time they spend online each day is steadily growing – creating an explosion in the number of concurrent users. Today, it’s not uncommon for apps to have millions of different users a day.

Supporting large numbers of concurrent users is important but, because application usage requirements are difficult to predict, it’s just as important to dynamically support rapidly growing (or shrinking) numbers of concurrent users:

·         A newly launched app can go viral, growing from zero to a million users overnight – literally.

·         Some users are active frequently, while others use an app a few times, never to return.

·         Seasonal swings like those around Christmas or Valentine’s Day create spikes for short periods.

·         New product releases or promotions can spawn dramatically higher application usage.

Big Data (Four V’s)

To help us talk about "big data," IBM data scientists break it down into four dimensions: volume, velocity, variety, and veracity. Here's some information about each so you can better understand the fundamentals.

 Volume: Scale of Data

Big data is big. It's estimated that 2.5 quintillion bytes (2.3 trillion gigabytes) of data are created every day. By 2020, we are expected to create 40 zettabytes (that's 43 trillion gigabytes) of information, an increase of 300 times the amount of data in existence in 2005. Why are we producing so much data? For starters, 6 of the world's 7 billion people now have cell phones. As infrastructure becomes increasingly available and affordable, cell phone use such as text messaging is bound to increase exponentially.

The amount of information being collected is so huge that modern database management tools are becoming overloaded and therefore obsolete. The need to find new ways of supporting big data helps explain the need for more data scientists. By 2015, the U.S. will see 1.9 million new IT jobs; 4.4 million will be created globally.

Velocity: Analysis of Streaming Data

The sheer velocity at which we are creating data today is a huge cause of big data. The New York Stock Exchange alone captures one terabyte of trade information during each session. Each time you drive, sensors in your car monitor items like fuel level and tire pressure; modern cars use close to 100 such sensors. For this reason computer systems within cars are becoming more advanced in be able to process the large amounts of data collected by cars' sensors every minute. By 2016, it is projected there will be 18.9 billion network connections – that's almost 2.5 connections per person on Earth. As we continue to create more data, we will use more methods to monitor the information, too.

Variety: Different Forms of Data

As tech moves into more realms of our lives, big data is taking on a larger variety of forms. As of 2011, the global size of data in healthcare alone was estimated to be 150 exabytes (161 billion gigabytes). As hospitals continue to adopt systems for electronic medical records, this number can only increase. By 2014, there will be an estimated 420 million wearable, wireless health monitors in use, storing constant data about our bodies that was never monitored so extensively before. And then there's common internet consumption: currently, we watch over 4 billion hours of video on YouTube and share 30 billion pieces of content on Facebook each month. Remember, only a portion of the world currently has reliable internet access. Imagine how heavy our internet use will be when more of the world gains steady internet access.

Veracity: Uncertainty of Data

Data scientists will also have their work cut out keeping big data organized. As data currently stands, it's hard to know which information is accurate and which is out of date. This is why one in three business leaders do not trust the information they use to make decisions. What's more, poor data quality costs the U.S. economy around $3.1 trillion each year, giving scientists a huge incentive for establishing systems that maintain the veracity of data.

If organized and used correctly, big data can help us spot business trends, prevent diseases, and combat crime, among other things. As humans continue to create more data in their daily lives, the work of data scientists will become that much more important and useful.

Cloud Computing

Not long ago, most consumer and many business applications were single-user applications that ran on your PC. Most data-intensive, multi-user business applications used a two-tier, client-server architecture that ran inside the firewall and supported a limited number of users. Today, most new applications (both consumer and business) use three-tier Internet architecture, run in a public or private cloud, and support large numbers of users. Along with this shift in software architecture, new business models like software-as-a-service (SaaS) and advertising based models have become more prevalent.

In the three-tier architecture, applications are accessed through a web browser or mobile app that is connected to the Internet. In the cloud, a load balancer directs the incoming traffic to a scale-out tier of web/application servers that process the logic of the application. The scale-out architecture at the web/application tier works beautifully. For every 10,000 (or however many) new concurrent users, you simply add another commodity server to the web/ application tier to absorb the load.

At the database tier, 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 technologies 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.

Closer look at why developers are considering NoSQL databases

Big Users, Big Data, and Cloud Computing are changing the way many applications are being developed. The industry has been dominated by relational databases for 40 years, but application developers are increasingly turning to NoSQL databases to meet new challenges for three main reasons:

1.       Better application development productivity through a more flexible data model;

2.       Greater ability to scale dynamically to support more users and data;

3.       Improved performance to satisfy expectations of users wanting highly responsive applications and to allow more complex processing of data.

NoSQL’s more flexible data model

Relational and NoSQL data models are very different. The relational model takes data and separates it into many interrelated tables. Each table contains rows and columns where a row might contain lots of information about a person and each column might contain a value for a specific attribute associated with that person, like his age. Tables reference each other through foreign keys that are stored in columns as well.

The relational model minimizes the amount of storage space required, because each piece of data is only stored in one place – a key requirement during when relational databases were created and disk storage was very. However, space efficiency comes at expense of increased complexity 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.

Developers generally use object-oriented programming languages to build applications. It’s usually most efficient to work with data that’s in the form of an object with a complex structure consisting of nested data, lists, arrays, etc. The relational data model provides a very limited data structure that doesn’t map well to the object model. Instead data must be stored and retrieved from tens or even hundreds of interrelated tables. Object-relational frameworks provide some relief but the fundamental impedance mismatch still exists between the way an application would like to see its data and the way it’s actually stored in a relational database.

Document databases, on the other hand, can store an entire object in a single JSON document and support complex data structures. This makes it easier to conceptualize data as well as write, debug, and evolve applications, often with fewer lines of code.

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. Want to start capturing new information not previously considered? Want to make rapid changes to application behavior requiring changes to data formats and content? With relational technology, changes like these are extremely disruptive and frequently avoided, which is 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 applications.

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. This allows application developers to move quickly to incorporate new data into their applications.

The stark differences between relational and NoSQL data models have caught the attention of application development teams. Whether or not they ever scale beyond a single machine, growing numbers of development teams feel they can be far more productive using the data models embodied in NoSQL databases.

NoSQL’s scalability and performance advantage

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 out: an excellent approach at the web/application 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. (See Figure 6.) 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.

While the performance and cost curves of this approach are attractive, the flexibility of the approach is an equally big win. As users come and go, commodity servers (or virtual machines) can be quickly added or removed from the server pool, matching capital and operating costs to the difficult-to-predict size and activity level of the user population. And, by distributing the load across many servers (even across geographies) the system is inherently fault tolerant, supporting continuous operations.

Another advantage is that new software upgrades can be rolled out gradually across subsets of the overall server pool. Facebook, as an example, slowly dials up new functionality by rolling out new software to a subset of their entire application server tier (and user population) in a stepwise manner. If issues crop up, servers can be quickly reverted to the previous known good version. All this can be done without ever taking the application offline.

Scale up with relational technology: limitations at the database tier

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.

(Clustered relational databases, like Oracle RAC, work on the concept of a shared disk subsystem. They use a cluster-aware file system that writes to a highly available disk subsystem – but this means the cluster still has the disk sub-system as a single point of failure.)

With relational technology, upgrading a server is an exercise that requires planning, acquisition and application downtime to complete. Given the relatively unpredictable user growth rate of today’s interactive applications, it’s hard to avoid over- or under-provisioning resources. Too much and you’ve overspent; too little and users can have a bad experience or the application can outright fail. And, since all the eggs are in a single, centralized database basket, you have to get your fault tolerance and high-availability strategies exactly right.

Scale out with NoSQL technology at the database tier

Techniques used to extend the useful scope of relational technology – sharding, denormalizing, distributed caching – fight symptoms but not the disease itself In fact, they attempt to disguise one simple fact: relational technology is suboptimal for many modern interactive applications.

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:

·         Auto-sharding – A NoSQL database automatically spreads data across servers, without requiring applications to participate. Servers can be added or removed from the data layer without application downtime, with data (and I/O) automatically spread across the servers. Most NoSQL databases also support data replication, storing multiple copies of data across the cluster, and even across data centers, to ensure high availability and support disaster recovery. A properly managed NoSQL database system should never need to be taken offline, for any reason, supporting 24x365 continuous operation of applications.

·         Distributed query support – “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.

·         Integrated caching – To reduce latency and increase sustained data throughput, advanced NoSQL database technologies transparently cache data in system memory. This behavior is transparent to the application developer and the operations team, compared to relational technology where a caching tier is usually a separate infrastructure tier that must be developed to, deployed on separate servers, and explicitly managed by the ops team.

Most Popular NoSQL DB’s

Summary

Application needs have been changing dramatically, due in large part to three trends: growing numbers of users that applications must support (along with growing user expectations for how applications perform); growth in the volume and variety of data that developers must work with; and the rise of cloud computing, which relies on a distributed three-tier Internet architecture. NoSQL technology is rising rapidly among Internet companies and the enterprise because it offers data management capabilities that meet the needs of modern application:

·         Better application development productivity through a more flexible data model;

·         Greater ability to scale dynamically to support more users and data;

·         Improved performance to satisfy expectations of users wanting highly responsive applications and to allow more complex processing of data.

NoSQL is increasingly considered a viable alternative to relational databases, and should be considered particularly for interactive web and mobile applications.