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Category Archives: bloom

computer on fireA major source of frustration in distributed programming is that contemporary software tools—think compilers and debuggers—have little to say about the really tricky bugs that distributed systems developers face.  Sure, compilers can find type and memory errors, and debuggers can single-step you through sequential code snippets. But how do they help with distributed systems issues?  In some sense, they don’t help at all with the stuff that matters—things like:

  • Concurrency: Does your code have bugs due to race conditions?  Don’t forget that a distributed system is a parallel system!
  • Consistency: Are there potential consistency errors in your program due to replicated state? Can you get undesirable non-deterministic outcomes based on network delays?  What about the potential for the awful “split-brain” scenario where the state of multiple machines gets irrevocably out of sync?
  • Coordination performance: Do you have performance issues due to overly-aggressive coordination or locking? Can you avoid expensive coordination without incurring bugs like the ones above?

These questions are especially tricky if you use services or libraries, where you don’t necessarily know how state and communication are managed.  What code can you trust, and what about that code do you need to know to trust it?

Peter Alvaro has been doing groundbreaking work in the space, and recently started taking the veil off his results.  This is a big deal. Read More »

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Photo Credit: Karthick R via Compfight cc

We just finished writing up an overview of our most recent thinking about distributed consistency. The paper is entitled Consistency Without Borders, and it’s going to appear in the ACM SoCC conference next month in Silicon Valley.

It starts with two things we all know:

  1. Strong distributed consistency can be expensive and dangerous. (My favorite exposition: the LADIS ’08 conference writeup by Birman, Chockler and van Renesse. See especially the quotes from James Hamilton and Randy Shoup. And note that recent work like Spanner changes little: throughput of 10’s to 100’s of updates per second is only useful at the fringes.)
  2. Managing coordination in application logic is fraught with software engineering peril: you have to spec, build, test and maintain special-case, cross-stack distributed reasoning over time. Here be dragons.

The point of the paper is to try to reorient the community to explore the design space in between these extremes. Distributed consistency is one of the biggest CS problems of our day, and the technical community is spending way too much of its energy at these two ends of the design space.

We’ll be curious to hear feedback here, and at the conference.

Hadoop is not healthy for children and other living things.I sat at Berkeley CS faculty lunch this past week with Brian Harvey and Dan Garcia, two guys who think hard about teaching computing to undergraduates.  I was waxing philosophical about how we need to get data-centric thinking embedded deep into the initial CS courses—not just as an application of traditional programming, but as a key frame of reference for how to think about computing per se.

Dan pointed out that he and Brian and others took steps in this direction years ago at Berkeley, by introducing MapReduce and Hadoop in our initial 61A course.  I have argued elsewhere that this is a Good Thing, because it gets people used to the kind of disorderly thinking needed for scaling distributed and data-centric systems.

But as a matter of both pedagogy and system design, I have begun to think that Google’s MapReduce model is not healthy for beginning students.  The basic issue is that Google’s narrow MapReduce API conflates logical semantics (define a function over all items in a collection) with an expensive physical implementation (utilize a parallel barrier). As it happens, many common cluster-wide operations over a collection of items do not require a barrier even though they may require all-to-all communication.  But there’s no way to tell the API whether a particular Reduce method has that property, so the runtime always does the most expensive thing imaginable in distributed coordination: global synchronization.

From an architectural point of view, a good language for parallelism should expose pipelining, and MapReduce hides it. Brian suggested I expand on this point somewhere so people could talk about it.  So here we go.

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Today was a big day in the BOOM group: we launched the alpha version of Bud: Bloom Under Development. If you’re new to this blog, Bloom is our new programming language for cloud computing and other distributed systems settings. Bud is the first fully-functional release of Bloom, implemented as a DSL in Ruby.

I’ve written a lot about Bloom in research papers and on the new Bloom website, and I have lots to say about distributed programming that I won’t recap. Instead, I want to focus here on the tangible: working code. If you’re looking for something serious, check out the walkthrough of the bfs distributed filesystem, a GFS clone. But to get the flavor, consider the following two lines of code, which implement what you might consider to be “hello, world” for distributed systems: a chat server.

nodelist <= connect.payloads
mcast <~ (mcast * nodelist).pairs { |m,n| [n.key, m.val] }

That’s it.

The first line says “if you get a message on a channel called ‘connect’, remember the payload in a table called ‘nodelist'”. The second says “if you get a message on the ‘mcast’ channel, then forward its contents to each address stored in ‘nodelist'”. That’s all that’s needed for a bare-bones chat server.  Nice, right?

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In today’s episode of the Twilight Zone, a young William Shatner stumbles into a time machine and travels back into the past. Cornered in a dark alley, he is threatened by a teenage hooligan waving a loaded pistol. A tussle ensues, and in trying to wrest the gun from his assailant, Shatner fires, killing him dead. Examining the contents of the dead youth’s wallet, Bill comes to a shocking conclusion: he has just killed his own grandfather. Tight focus: Shatner howling soundlessly as he stares at his own hand flickering in and out of view.

Shatner? Or Not(Shatner)? Having now changed history, he could not have been born, meaning he could not have traveled back in time and changed history, meaning he was indeed born, meaning…?

You see where this goes.  It’s the old grandfather paradox, a hoary chestnut of SciFi and AI.  Personally I side with Captain Kirk: I don’t like mysteries. They give me a bellyache. But whether or not you think a discussion of “p if Not(p)” is news that’s fit to print, it is something to avoid in your software.  This is particularly tricky in distributed programming, where multiple machines have different clock settings, and those clocks may even turn backward on occasion. The theory of Distributed Systems is built on the notion of Causality, which enables programmers and programs to avoid doing unusual things like executing instructions in orders that could not have been specified by the program that generated them. Causality is established by distributed clock protocols. These protocols are often used to enforce causal orderings–i.e. to make machines wait for messages. And waiting for messages, as we know, is bad.

So I’m here to tell you today that Causality is overrated, and we can often skip the wait. To hell with distributed clocks: time travel can be fine.  In many cases it’s even fine to change history. Here’s the thing: Casuality is Required Only to control Non-monotonicity. I call this the CRON principle.

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12/16/2010: final version of CALM/Bloom paper for CIDR now posted

Conventional Wisdom:
In large distributed systems, perfect data consistency is too expensive to guarantee in general. “Eventually consistent” approaches are often a better choice, since temporary inconsistencies work out in most cases. Consistency mechanisms (transactions, quorums, etc.) should be reserved for infrequent, small-scale, mission-critical tasks.

Most computer systems designers agree on this at some level (once you get past the NoSQL vs. ACID sloganeering). But like lots of well-intentioned design maxims, it’s not so easy to translate into practice — all kinds of unavoidable tactical questions pop up:

Questions:

  • Exactly where in my multifaceted system is eventual consistency “good enough”?
  • How do I know that my “mission-critical” software isn’t tainted by my “best effort” components?
  • How do I maintain my design maxim as software evolves? For example, how can the junior programmer in year n of a project reason about whether their piece of the code maintains the system’s overall consistency requirements?

If you think you have answers to those questions, I’d love to hear them. And then I’ll raise the stakes, because I have a better challenge for you: can you write down your answers in an algorithm?

Challenge:
Write a program checker that will either “bless” your code’s inconsistency as provably acceptable, or identify the locations of unacceptable consistency bugs.

The CALM Conjecture is my initial answer to that challenge.

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