the accompanying table “ Computational effects”).

Services as a Service

When developing code, programmers rely on not only programming languages and libraries, but also various tools such as debuggers and profilers. When talking about moving existing programming idioms to the cloud, we tend to sweep a lot of details under the rug that are necessary for building enterprise-grade services, such as monitoring, tracing, distributed locking, fault detection, replication, and fail-over. Interestingly, many of these can be exposed as objects implemented by a service. Implementing the building blocks for debugging, understanding, and monitoring distributed systems is no panacea, but it is a necessary condition to make programmers successful in the new world of distributed applications.

Other aspects that become relevant when composing larger services from smaller services are colocation and efficient service resolution. Even on a single Windows machine, it is necessary to reason about thread affinity—for example, in order to update the UI, you must run on the UI thread. A practical substrate for building distributed applications must provide abstractions that make location and placement boundaries explicit—and perhaps provide local atomicity or reliability guarantees. Within the confines of a single machine, programmers often gloss over such details (just as modelers do), but in a distributed world, exposing them becomes relevant and important to acknowledge for both programmers and modelers.

all Data is a monad

Even though I have argued that models are not the right operational basis for cloud computing, let there be absolutely no mistake that I do acknowledge the enormous economic and intellectual value of (relational) database technology, and programmers are eager to use the data that modelers have curated, cleansed, normalized, and refined.

Fortunately, models can be embed-
ded into programs by appealing to a
generalization of Codd’s theory7—
namely, Category Theory. As it turns
out, most of the notions of “collec-
tions” or “tables” in various data-
base implementations are actually
instances of a mathematical concept,
really just a kind of interface, called
monads. Queries can be translated
into the underlying monadic opera-
tors implemented as code.

the Revenge of object Databases

Both relational databases and distributed actors shine in the right context. To paraphrase Pat Helland, 6 SQL is clearly the undefeated leader for transactional processing of data on the inside. The object-oriented databases from the 1980s tried too much to be like databases and too little like objects, and they did not play to their strengths. In the small closed world of tables, declarative queries, and sophisticated optimizers2 it is difficult to beat RDMSs at their own game.

In the big open world of the cloud however, highly available, inconsistency robust, Web services exposed as standard imperative objects/actors will rule. The cloud era is one of ( monadic and comonadic) computation and verbs, as opposed of to data and nouns. To ease the transition for programmers to the cloud, we have identified the following requisites:

˲ Expose every data source created by modelers to programmers using monads/LINQ.

˲ Create a class library of distributed collections implemented as highly available and scalable services but exposed using standard programming language bindings and interfaces.

˲ Give programmers access to the ocean of concurrency in the cloud via comonads/actors.

˲ Expose tracing, monitoring, debugging, diagnostics infrastructure as another service in the cloud.

Many of the required materials are
available today. What is missing is a
tasteful assembly of all these pieces
into a set of elegant and functional
packages that target the challenges
developers face when migrating into
the cloud.

acknowledgments

I would like to thank Brian Beckman, Terry Coatta, Gavin Bierman, Joe Hoag, Brian Grunkemeyer, and Rafael Fernandez Moctezuma for improving both the style and substance of this article.

 

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7. Meijer, e. and Bierman, g. a co-relational model of data for large shared data banks. ACM Queue 9 3 (2011); http://queue.acm.org/detail.cfm?id=1961297.

8. Microsoft research. Concurrent revisions; http:// research.microsoft.com/en-us/projects/revisions/ default.aspx

9. shapiro, M., preguica, n., Baquero, C. and Zawirski, M. a comprehensive study of convergent and commutative replicated data types. Inria no. rr-7506 (2011); http://hal.inria.fr/inria-00555588/en/.

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Erik Meijer ( [e-mail], emeijer@microsoft. com) has been working on “democratizing the cloud” for the past 15 years. he is perhaps best known for his work on the haskell language and his contributions to lInQ and rx (reactive framework). he is a part-time professor of cloud programming at tuDelft and runs the cloud programmability team in Microsoft’s server and tools Business.

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