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Batch Processing

A system cannot be successful if it is too strongly influenced by a single person. Once the initial design is complete and fairly robust, the real test begins as people with many different viewpoints undertake their own experiments.

—Donald Knuth

In the first two parts of this book we talked a lot about requests and queries, and the corresponding responses or results. This style of data processing is assumed in many modern data systems: you ask for something, or you send an instruction, and some time later the system (hopefully) gives you an answer. Databases, caches, search indexes, web servers, and many other systems work this way.

In such online systems, whether it’s a web browser requesting a page or a service calling a remote API, we generally assume that the request is triggered by a human user, and that the user is waiting for the response. They shouldn’t have to wait too long, so we pay a lot of attention to the response time of these systems (see “Describing Performance” on page 13).

The web, and increasing numbers of HTTP/REST-based APIs, has made the request/ response style of interaction so common that it’s easy to take it for granted. But we should remember that it’s not the only way of building systems, and that other approaches have their merits too. Let’s distinguish three different types of systems:

Services (online systems)

A service waits for a request or instruction from a client to arrive. When one is received, the service tries to handle it as quickly as possible and sends a response back. Response time is usually the primary measure of performance of a service, and availability is often very important (if the client can’t reach the service, the user will probably get an error message).

Batch processing systems (offline systems)

A batch processing system takes a large amount of input data, runs a job to process it, and produces some output data. Jobs often take a while (from a few minutes to several days), so there normally isn’t a user waiting for the job to finish. Instead, batch jobs are often scheduled to run periodically (for example, once a day). The primary performance measure of a batch job is usually throughput (the time it takes to crunch through an input dataset of a certain size). We discuss batch processing in this chapter.

Stream processing systems (near-real-time systems)

Stream processing is somewhere between online and offline/batch processing (so it is sometimes called near-real-time or nearline processing). Like a batch processing system, a stream processor consumes inputs and produces outputs (rather than responding to requests). However, a stream job operates on events shortly after they happen, whereas a batch job operates on a fixed set of input data. This difference allows stream processing systems to have lower latency than the equivalent batch systems. As stream processing builds upon batch processing, we discuss it in Chapter 11.

As we shall see in this chapter, batch processing is an important building block in our quest to build reliable, scalable, and maintainable applications. For example, MapReduce, a batch processing algorithm published in 2004 [1], was (perhaps overenthusiastically) called “the algorithm that makes Google so massively scalable” [2]. It was subsequently implemented in various open source data systems, including Hadoop, CouchDB, and MongoDB.

MapReduce is a fairly low-level programming model compared to the parallel processing systems that were developed for data warehouses many years previously [3, 4], but it was a major step forward in terms of the scale of processing that could be achieved on commodity hardware. Although the importance of MapReduce is now declining [5], it is still worth understanding, because it provides a clear picture of why and how batch processing is useful.

In fact, batch processing is a very old form of computing. Long before programmable digital computers were invented, punch card tabulating machines—such as the Hollerith machines used in the 1890 US Census [6]—implemented a semi-mechanized form of batch processing to compute aggregate statistics from large inputs. And MapReduce bears an uncanny resemblance to the electromechanical IBM card-sorting machines that were widely used for business data processing in the 1940s and 1950s [7]. As usual, history has a tendency of repeating itself.

In this chapter, we will look at MapReduce and several other batch processing algorithms and frameworks, and explore how they are used in modern data systems. But first, to get started, we will look at data processing using standard Unix tools. Even if you are already familiar with them, a reminder about the Unix philosophy is worth?while because the ideas and lessons from Unix carry over to large-scale, heterogeneous distributed data systems.

 
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