Learn More
MapReduce is a popular framework for data-intensive distributed computing of batch jobs. To simplify fault tolerance, many implementations of MapReduce materialize the entire output of each map and reduce task before it can be consumed. In this paper, we propose a modified MapReduce architecture that allows data to be pipelined between operators. This(More)
Recent research has explored using Datalog-based languages to express a distributed system as a set of logical invariants. Two properties of distributed systems proved difficult to model in Datalog. First, the state of any such system evolves with its execution. Second, deductions in these systems may be arbitrarily delayed, dropped, or reordered by the(More)
Distributed programming has become a topic of widespread interest, and many programmers now wrestle with tradeoffs between data consistency, availability and latency. Distributed transactions are often rejected as an undesirable tradeoff today, but in the absence of transactions there are few concrete principles or tools to help programmers design and(More)
As the cloud era begins and failures become commonplace , failure recovery becomes a critical factor in the availability, reliability and performance of cloud services. Unfortunately, recovery problems still take place, causing downtimes, data loss, and many other problems. We propose a new testing framework for cloud recovery: FATE (Failure Testing(More)
In recent years there has been interest in achieving application-level consistency criteria without the latency and availability costs of strongly consistent storage infrastructure. A standard technique is to adopt a vocabulary of commutative operations; this avoids the risk of inconsistency due to message reordering. Another approach was recently captured(More)
Building and debugging distributed software remains extremely difficult. We conjecture that by adopting a <i>data-centric</i> approach to system design and by employing <i>declarative</i> programming languages, a broad range of distributed software can be recast naturally in a data-parallel programming model. Our hope is that this model can significantly(More)
MapReduce is a popular framework for data-intensive distributed computing of batch jobs. To simplify fault tolerance, the output of each MapReduce task and job is <i>materialized</i> to disk before it is consumed. In this demonstration, we describe a modified MapReduce architecture that allows data to be <i>pipelined</i> between operators. This extends the(More)
The Paxos consensus protocol can be specified concisely, but is notoriously difficult to implement in practice. We recount our experience building Paxos in Overlog, a distributed declarative programming language. We found that the Paxos algorithm is easily translated to declarative logic, in large part because the primitives used in consensus protocol(More)
Building on recent interest in distributed logic programming, we take a model-theoretic approach to analyzing confluence of asynchronous distributed programs. We begin with a model-theoretic semantics for Dedalus and introduce the ultimate model, which captures non-deterministic eventual outcomes of distributed programs. After showing the question of(More)
—Distributed consistency is perhaps the most discussed topic in distributed systems today. Coordination protocols can ensure consistency, but in practice they cause undesirable performance unless used judiciously. Scalable distributed ar-chitectures avoid coordination whenever possible, but under-coordinated systems can exhibit behavioral anomalies under(More)