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Levels of Abstraction in Big Data

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Levels of Abstraction in Big Data

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Recently we’ve spoken to a number of people to find out how our real-time stuff could be of use to them. Those were all very interesting conversations, but sometimes they were more about how to fit our approach into an existing infrastructure than the merits of our approach for a given application.

When discussing software, the question of fitting within an existing framework is often important, but for two reasons I think that’s even more the case with Big Data.

In the end, it’s a question about what kind of abstraction a piece of software provides. You might be familiar with the idea that the language used to describe something influences the way you think about a problem. That's even more true in programming, where the way a certain piece of software models some part of reality is not just theoretical, but puts hard constraints on what you can do. Some things are easier to express than others, some things might require workarounds, while other things might be downright impossible.

While this is true of any type of software, the consequences are even more pronounced in the area of Big Data, because abstractions there are often quite new and potentially imperfect, and because there is considerably more technical lock-in to a given solution.

Many tools like Hadoop or NoSQL databases are quite new, still exploring concepts and ways to describe operations well. It’s not like the interface has been honed and polished for years to converge to a sweet spot. For example, secondary indices have been missing from Cassandra for quite some time. Likewise, the inclusion or exclusion of a feature is more driven by technical feasibility than whether it would make sense. But this often means that you're forced to model your problems in ways which might be inflexible and unsuited to the problem at hand. (Of course, this is not particular to Big Data. Implementing neural networks on a SQL database might be feasible, but it's probably not the most practical way to do it.)

In Big Data, especially, exchanging one solution for another can be quite hard. You’ve already invested in your infrastructure; all your data is there; your monitoring infrastructure is tailored to your compute cluster. And so on. Few have the resources to run two different clusters in parallel. Also, since the field is quite new and there is little standardization, switching between frameworks is impossible without rewriting core functionalities.

You might ask why you should care. After all, your software's technical properties are a reality you have to deal with. But I think being too focused on how your current solution views the world might be a problem when you try to explore other approaches to do interesting stuff with your data, and ways to scale your computations.

So what can we do about this? Not much, but we can try to keep in mind that there are different ways to think about algorithms and represent them. Some classical examples:

  • “Normal” theoretical computer science is often pretty low-level with objects (more in the sense of C structs), arrays, and pointers. You can also throw standard data structures like trees, hash maps, linked list, etc. in there. Not all of these might be easily mapped to your favorite Big Data tool, but this is a very flexible way to think about algorithms with state.
  • Machine learning encodes a lot of stuff in either linear algebra or probability theory. Matrices and vectors are pretty boring data structures, but linear algebra gives you a very geometric way of thinking about things. Probability theory again comes with its own ideas and concepts, and many of the operations can be expressed as matrix operations (at least when the underlying set of events is finite).
  • Stream processing in the sense of worker threads which pass messages around is also a common way to think about algorithms. Stream processing frameworks and actor-based concurrency allow you to express such concepts naturally.
  • Recursion is also a standard way to think about algorithms which again might be hard to map into a MapReduce framework.

In the end, I think you should choose the abstraction that allows you to express the algorithm in the simplest way to think about it, instead of letting your existing installation tell you how to do it. A nice thing about software is that you can actually map between abstractions by writing a bit of interface code. It might not always be painless, and certain operations might be more expensive than you’d expect, but what you gain is flexibility in thinking about your algorithms.

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