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Category Archives: consistency

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 »

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.

CopyIf you follow this blog, you know that my BOOM group has spent a lot of time in the past couple years formalizing eventual consistency (EC) for distributed programs, via the CALM theorem and practical tools for analyzing Bloom programs.

In recent months, my student Peter Bailis and his teammate Shivaram Venkataraman took a different tack on the whole EC analysis problem which they call PBS: Probabilistically Bounded Staleness. The results are interesting, and extremely relevant to current practice.  (See, for example, the very nice blog post by folks at DataStax).

Many people today deal with EC in the specific context of replica consistency, particularly in distributed NoSQL-style Key-Value Stores (KVSs). It is typical to configure these stores with so-called “partial” quorum replication, to get a comfortable mix of low latency with reasonable availability. The term “partial” signifies that you are not guaranteed consistency of writes by these configurations — at best they guarantee a form of eventual consistency of final writes, but readers may well read stale data along the way. Lots of people are deploying these configurations in the field, but there’s little information on how often the approach messes up, and how badly.

Jumping off from earlier theoretical work on probabilistic quorum systems, Peter and Shivaram answered two natural questions about how these systems should perform in current practice:

  1. How many versions ago?  On expectation, if you do a read in a partial-quorum KVS, how many versions behind are you? Peter and Shivaram answer this one definitively, via a closed-form mathematical analysis.
  2. How stale on the (wall-)clock?  On expectation, if you do a read in a partial-quorum KVS, how out-of-date will your version be in terms of wall-clock time? Answering this one requires modeling a read/write workload in wall-clock time, as well as system parameters like replica propagation (“anti-entropy”). Peter and Shivaram address this with a Monte Carlo model, and run the model with parameters grounded in real-world performance numbers generously provided by two of our most excellent colleagues: Alex Feinberg at LinkedIn and Coda Hale at Yammer (both of whom also guest-lectured in my Programming the Cloud course last fall.)  Peter and Shivaram validated their models in practice using Cassandra, a widely-used KVS.

On the whole, PBS shows that being sloppy about consistency doesn’t bite you often or badly — especially if you’re in a single datacenter and you use SSDs. But things get more complex with magnetic disks, garbage collection delays (grr), and wide-area replication.

Interested in more detail?  You can check out two things: