Some questions are easier to answer than others. In response to the question, “Is Hadoop ready for production?,” the answer is, simply, “yes.”
This answer may surprise you, given how young the Apache Hadoop technology actually is. You may wonder on what basis we offer this definitive response to the question of Hadoop’s readiness for production. The key reason we say that it is ready is simply because so many organizations are already using Hadoop in production and doing so successfully. Of course, being ready for production is not the same thing as being a mature technology.
Will Hadoop-based technologies change over the next few years? Of course they will. This is a rapidly expanding new arena, with continual improvements in the underlying technology and the appearance of innovative new tools that run in this ecosystem. The level of experience and understanding among Hadoop users is also rapidly increasing. As Hadoop and its related technologies continue progress toward maturity, there will be a high rate of change. Not only will new features and capabilities be added, these technologies will generally become easier to use as they become more refined.
Are these technologies a good choice for you? The answer to that question is more complicated, as it depends on your own project goals, your resources, and your willingness to adopt new approaches. Even with a mature technology, there would be a learning curve to account for in planning the use of something different; with a maturing technology you also have to account for a cost of novelty and stay adaptable to rapid change in the technology. Hadoop and NoSQL solutions are still young, so not only are the tools themselves still somewhat short of maturity, there is also a more limited pool of experienced users from which to select when building out your own team than with some older approaches.
Even so, Hadoop adoption is widespread and growing rapidly. For many, the question is no longer whether or not to turn to Hadoop and NoSQL solutions for their big data challenges but rather, “What are the best ways to use Hadoop and NoSQL to carry our projects successfully?” and “When should we start?”
This book aims to help answer these questions by providing a conversation around the choices that drive success in big data projects, by sharing tips that others have found useful, and by examining a selection of use cases and stories from successful Hadoop-based projects. What makes this collection of use cases different is that we include examples of how people are already using Hadoop in production and in near-production settings. We base our stories and recommendations on the experiences of real teams running real workloads. Of course, we do not focus only on Hadoop in production—we also provide advice to help you get started and to use Hadoop successfully in development.
When is the time right to give Hadoop a try? There is no “right” answer to that question, as each situation is different, but now may be a good time to give Hadoop a try even if you’re not yet ready to consider it in a production setting for your own projects. If you start now, you will not be a Hadoop pioneer—the true pioneers are the ones already using it in production. But there is still an early-mover advantage to be had for those starting now. For one thing, you will find out if this technology holds promise for your situation. For another, you will begin building Hadoop expertise within your organization, which may prove very valuable. Even if you do not at present have an urgent need for a Hadoop-based solution, it’s very likely you will need it or a solution similar to it soon. Having teams who are savvy about using Hadoop is an investment in the future.
Before we look in detail at what Hadoop is and how you might use it, let’s start with a look at an unusual Hadoop-based project that is changing society in fundamental ways. The story begins with this challenge: suppose you need to be able to identify every person in India, uniquely and reliably—all 1.2 billion of them. And suppose you need to be able to authenticate this identification for any individual who requests it, at any time, from any place in India, in less than a second. Does this sound sufficiently challenging?
That description is the central mission for India’s Aadhaar project, the Unique Identification Authority of India (UIDAI). The project provides a unique 12-digit, government-issued identification number that is tied to biometric data to verify the identity and address for each person in India. The biometric data includes an iris scan of both eyes plus multipoint data from the fingerprint pattern of all 10 fingers, as suggested by the illustration in Figure 1-1. The unique Aadhaar ID number is a random number, and it is assigned without classification based on caste, religion, or creed, assuring an openness and equality to the project.
The need for such an identification program and its potential impact on society is enormous. In India, there is no social security card, and much of the population lacks a passport. Literacy rates are relatively low, and the population is scattered across hundreds of thousands of villages. Without adequately verifiable identification, it has been difficult for many citizens to set up a bank account or otherwise participate in a modern economy.
For India’s poorer citizens, this problem has even more dire consequences. The government has extensive programs to provide widespread relief for the poor—for example, through grain subsidies to those who are underfed and through government-sponsored work programs for the unemployed. Yet many who need help do not have access to benefit programs, in part because of the inability to verify who they are and whether they qualify for the programs. In addition, there is a huge level of so-called “leakage” of government aid that disappears to apparent fraud. For example, it has been estimated that over 50% of funds intended to provide grain to the poor goes missing, and that fraudulent claims for “ghost workers” siphon off much of the aid intended to create work for the poor.
The Aadhaar program is poised to change this. It is in the process of creating the largest biometric database in the world, one that can be leveraged to authenticate identities for each citizen, even on site in rural villages. A wide range of mobile devices from cell phones to micro scanners can be used to enroll people and to authenticate their identities when a transaction is requested. People will be able to make payments at remote sites via micro-ATMs. Aadhaar ID authentication will be used to verify qualification for relief food deliveries and to provide pension payments for the elderly. Implementation of this massive digital identification system is expected to save the equivalent of millions and perhaps billions of dollars each year by thwarting efforts at fraud. While the UIDAI project will have broad benefits for the Indian society as a whole, the greatest impact will be for the poorest people.
The UIDAI project is a Hadoop-based program that is well into production. At the time of this writing, over 700 million people have been enrolled and their identity information has been verified. The target is to reach a total of at least 100 crore (1 billion) enrollments during 2015. Currently the enrollment rate is about 10 million people every 10 days, so the project is well positioned to meet that target.
From a technical point of view, what are the requirements for such an impressive big data project? Scalability and reliability are among the most significant requirements, along with capability for very high performance. This challenge starts with the enrollment process itself. Once address and biometric data are collected for a particular individual, the enrollment must undergo deduplication. Deduplication for each new enrollment requires the processing of comparisons against billions of records. As the system grows, deduplication becomes an even greater challenge.
Meanwhile, the Aadhaar digital platform is also busy handling authentication for each transaction conducted by the millions of people already enrolled. Authentication involves a profile lookup, and it is required to support thousands of concurrent transactions with response times on the order of 100ms. The authentication system was designed to run on Hadoop and Apache HBase. It currently uses the MapR distribution for Hadoop. Rather than employ HBase, the authentication system uses MapR-DB, a NoSQL database that supports the HBase API and is part of MapR. We’ll delve more into how MapR-DB and other technologies interrelate later in this chapter and in Chapter 2. In addition to being able to handle the authentication workload, the Aadhaar authentication system also has to meet strict availability requirements, provide robustness in the face of machine failure, and operate across multiple datacenters.
Chief Architect of the Aadhaar project, Pramod Varma, has pointed out that the project is “built on sound strategy and a strong technology backbone.” The most essential characteristics of the technology involved in Aadhaar are to be highly available and to be able to deliver sub-second performance.
The Aadhaar project is not only an impressive example of vision and planning, it also highlights the ability of Hadoop and NoSQL solutions to meet the goals of an ambitious program. As unusual as this project seems—not everyone is trying to set up an identification program for a country the size of India—there are commonalities between this unusual project and more ordinary projects both in terms of the nature of the problems being addressed and the design of successful solutions. In other words, in this use case, as in the others we discuss, you should be able to see your own challenges even if you work in a very different sector.
One commonality is data size. As it turns out, while Aadhaar is a large-scale project with huge social impact and fairly extreme requirements for high availability and performance, as far as data volume goes, it is not unusually large among big data projects. Data volumes in the financial sector, for instance, are often this size or even larger because so many transactions are involved. Similarly, machine-produced data in the area of the industrial Internet can easily exceed the volumes of Aadhaar. The need for reliable scalability in India’s project applies to projects in these cases as well.
Other comparisons besides data volume can be drawn between Aadhaar and more conventional use cases. If you are involved with a large retail business, for instance, the idea of identity or profile lookup is quite familiar. You may be trying to optimize an advertising campaign, and as part of the project you need to look up the profiles of customers to verify their location, tastes, or buying behaviors, possibly at even higher rates than needed by Aadhaar. Such projects often involve more than simple verification, possibly relying on complex analytics or machine learning such as predictive filtering, but the need to get the individual profile data for a large number of customers is still an essential part of implementing such a solution. The challenging performance requirements of Aadhaar are also found in a wide range of projects. In these situations, a Hadoop-based NoSQL solution such as HBase or MapR-DB provides the ability to scale horizontally to meet the needs of large volume data and to avoid traffic problems that can reduce performance.
The most fundamental reason to turn to Hadoop is for the ability to handle very large-scale data at reasonable cost in a reasonable time. The same applies to Hadoop-based NoSQL database management solutions such as HBase and MapR-DB. There are other choices for large-scale distributed computing, including Cassandra, Riak, and more, but Hadoop-based systems are widely used and are the focus of our book. Figure 1-2 shows how interest in Hadoop has grown, based on search terms used in Google Trends.
In addition to the ability to store large amounts of data in a cost-effective way, Hadoop also provides mechanisms to greatly improve computation performance at scale. Hadoop involves a distributed storage layer plus a framework to coordinate that storage. In addition, Hadoop provides a computational framework to support parallel processing. In its original form, Hadoop was developed as an open source Apache Foundation project based on Google’s MapReduce paradigm. Today there are a variety of different distributions for Hadoop.
One of the key aspects of this distributed approach to computation involves dividing large jobs into a collection of smaller tasks that can be run in parallel, completely independently of each other. The outputs of these tasks are shuffled and then processed by other tasks. By running tasks in parallel, jobs can be completed more quickly, which allows the system to have very high throughput. The original Hadoop MapReduce provided a framework that allowed programs to be built in a relatively straightforward way that could run in this style and thus provided highly scalable computation. MapReduce programs run in batch, and they are useful for aggregation and counting at large scale.
Another key factor for performance is the ability to move the computation to where the data is stored rather than having to move data to the computation. In traditional computing systems, data storage is segregated from computational systems. With Hadoop, there is no such segregation, and programs can run on the same machines that store the data. The result is that you move only megabytes of program instead of terabytes of data in order to do a very large-scale computation, which results in greatly improved performance.
The original Hadoop MapReduce implementation was innovative but also fairly limited and inflexible. MapReduce provided a start and was good enough to set in motion a revolution in scalable computing. With recent additions to the Hadoop ecosystem, more advanced and more flexible systems are also becoming available. MapReduce is, however, still an important method for aggregation and counting, particularly in certain situations where the batch nature of MapReduce is not a problem. More importantly, the basic ideas on which MapReduce is based—parallel processing, data locality, and the shuffle and re-assembly of results—can also be seen underlying new computational tools such as Apache Spark, Apache Tez, and Apache Drill. Most likely, more tools that take advantage of these basic innovations will also be coming along.
All of these computational frameworks run on the Hadoop storage layer, as shown in Figure 1-3. An important difference in these new systems is that they avoid storing every intermediate result to disk, which in turn provides improved speed for computation. Another difference is that the new systems allow computations to be chained more flexibly. The effect of these differences is better overall performance in some situations and applicability to a wider range of problems.
In addition to the ability to scale horizontally at low cost and to perform large-scale computations very efficiently and rapidly, Hadoop-based technologies are also changing the game by encouraging the use of new types of data formats. Both files and NoSQL databases allow you to use a wide range of data formats, including unstructured or semistructured data. Concurrent with the development of Hadoop’s computational capabilities, there have been dramatic improvements in our understanding of how to store data in flat files and NoSQL databases. These new ways for structuring data greatly expand the options and provide a greater degree of flexibility than you may be used to. We say more about new data formats among the tips in Chapter 4.
NoSQL nonrelational databases can augment the capabilities of the flat files with the ability to access and update a subset of records, each identified by a unique key, rather than having to scan an entire file as you would with flat files.
With their ability to scale in a cost-effective way and to handle unstructured data, NoSQL databases provide a powerful solution for a variety of use cases, but they should not be thought of as a replacement for the function of traditional databases. This distinction is described in more detail in Chapter 2, but the essential point to note is that each of these technologies—NoSQL databases and traditional relational databases (RDBMS)—should be used for the things they do best. Some NoSQL databases have a better ability to scale and to do so while keeping costs down. They can handle raw, unstructured data that also affects use cases for which they are well suited. In contrast, there is a price to be paid with RDBMS (literally and in terms of the effort required for processing and structuring data for loading). The reward for this cost, however, can be extremely good performance with RDBMS for specific, highly defined tasks such as critical path billing and standardized reporting. However, to get these benefits, the data must already be prepared for the tasks.
The need to handle large amounts of data cost effectively has led to the development of scalable distributed computing systems such as those discussed in this book, based on Hadoop and on NoSQL databases. But these new technologies are proving so effective that they go beyond providing solutions for existing projects; they are inviting exploration into new problems that previously would have seemed impractical to consider.
A key distinction of these systems from previous ones is flexibility. This flexibility is manifested in the platform itself through the capability to handle new data types and multiple computational models, as well as to scale to very large data sizes. Hadoop adds flexibility in your choices by being able to store essentially “raw” data and make decisions about how to process and use it later. Flexibility is also manifested in the ways that developers are combining diverse data streams and new analysis techniques in a single platform. In the MapR distribution for Hadoop, there is further flexibility to use existing non-Hadoop applications side by side on the same systems and operate on the same data as Hadoop applications.
Chapter 2 provides you with an overview of the functions supported by Hadoop ecosystem tools, while Chapter 3 explains some of the extra capabilities of MapR’s distribution so that you will be better able to extrapolate from our examples to your own situation, whichever Hadoop distribution you choose to try. In Chapter 4, we provide a list of tips for success when working with Hadoop that offer help for newcomers and for experienced Hadoop users.
In order to help you better understand how these technologies may bring value to your own projects, this book also describes a selection of Hadoop use cases based on how MapR customers are using it. MapR currently has over 700 paying customers across a wide range of sectors including financial services, web-based businesses, manufacturing, media, telecommunications, and retail. Based on those, we have identified a variety of key usage patterns that we describe in Chapter 5 as well as telling example stories in Chapter 6 that show how customers combine these solutions to meet their own big data challenges.
Are the examples described in this book MapR specific? For the most part, no. There are some aspects of what people are doing that rely on specific MapR features or behaviors, but we’ve tried to call those to your attention and point out what the alternatives would be. The use cases and customer stories have been chosen to show the power of Hadoop and NoSQL solutions to provide new ways to solve problems at scale. Regardless of the Hadoop distribution you choose, the material in this book should help guide you in the decisions you’ll need to make in order to plan well and execute successful big data projects.