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Data stream processing applications are often expressed as data flow graphs, composed of operators connected via streams. This structured representation provides a simple yet powerful paradigm for building large-scale, distributed, high-performance applications. However, there are many tasks that require sharing data across operators, and across operators(More)
We define the Abortable Linearizable Module automaton (ALM for short) and prove its key composition property using the IOA theory of HOLCF. The ALM is at the heart of the Speculative Linearizabil-ity framework. This framework simplifies devising correct speculative algorithms by enabling their decomposition into independent modules that can be analyzed and(More)
Decades of research in distributed computing have led to a variety of perspectives on what it means for a concurrent algorithm to be efficient, depending on model assumptions, progress guarantees, and complexity metrics. It is therefore natural to ask whether one could compose algorithms that perform efficiently under different conditions, so that the(More)
Leaderless consensus algorithms in the vein of EPaxos have performance advantages, especially for geo-replication, but are also very intricate, making them hard to modify and adapt for specific use cases. In this paper we show that their core principle can be captured in a generic leaderless generalized-consensus algorithm that uses two new abstractions: a(More)
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