On the Semantics of Snapshot Isolation

Snapshot isolation (SI) is a standard transactional consistency model used in databases, distributed systems and software transactional memory (STM). Its semantics is formally defined both declaratively as an acyclicity axiom, and operationally as a concurrent algorithm with memory bearing timestamps. We develop two simpler equivalent operational definitions of SI as lock-based reference implementations that do not use timestamps. Our first locking implementation is prescient in that requires a priori knowledge of the data accessed by a transaction and carries out transactional writes eagerly (in-place). Our second implementation is non-prescient and performs transactional writes lazily by recording them in a local log and propagating them to memory at commit time. Whilst our first implementation is simpler and may be better suited for developing a program logic for SI transactions, our second implementation is more practical due to its non-prescience. We show that both implementations are sound and complete against the declarative SI specification and thus yield equivalent operational definitions for SI. We further consider, for the first time formally, the use of SI in a context with racy non-transactional accesses, as can arise in STM implementations of SI. We introduce robust snapshot isolation (RSI), an adaptation of SI with similar semantics and guarantees in this mixed setting. We present a declarative specification of RSI as an acyclicity axiom and analogously develop two operational models as lock-based reference implementations (one eager, one lazy). We show that these operational models are both sound and complete against the declarative RSI model

SEUSS: rapid serverless deployment using environment snapshots

Modern FaaS systems perform well in the case of repeat executions when function working sets stay small. However, these platforms are less effective when applied to more complex, large-scale and dynamic workloads. In this paper, we introduce SEUSS (serverless execution via unikernel snapshot stacks), a new system-level approach for rapidly deploying serverless functions. Through our approach, we demonstrate orders of magnitude improvements in function start times and cacheability, which improves common re-execution paths while also unlocking previously-unsupported large-scale bursty workloads.Published versio

Dynamic Package Interfaces - Extended Version

A hallmark of object-oriented programming is the ability to perform computation through a set of interacting objects. A common manifestation of this style is the notion of a package, which groups a set of commonly used classes together. A challenge in using a package is to ensure that a client follows the implicit protocol of the package when calling its methods. Violations of the protocol can cause a runtime error or latent invariant violations. These protocols can extend across different, potentially unboundedly many, objects, and are specified informally in the documentation. As a result, ensuring that a client does not violate the protocol is hard. We introduce dynamic package interfaces (DPI), a formalism to explicitly capture the protocol of a package. The DPI of a package is a finite set of rules that together specify how any set of interacting objects of the package can evolve through method calls and under what conditions an error can happen. We have developed a dynamic tool that automatically computes an approximation of the DPI of a package, given a set of abstraction predicates. A key property of DPI is that the unbounded number of configurations of objects of a package are summarized finitely in an abstract domain. This uses the observation that many packages behave monotonically: the semantics of a method call over a configuration does not essentially change if more objects are added to the configuration. We have exploited monotonicity and have devised heuristics to obtain succinct yet general DPIs. We have used our tool to compute DPIs for several commonly used Java packages with complex protocols, such as JDBC, HashSet, and ArrayList.Comment: The only changes compared to v1 are improvements to the Abstract and Introductio

06472 Abstracts Collection - XQuery Implementation Paradigms

From 19.11.2006 to 22.11.2006, the Dagstuhl Seminar 06472 ``XQuery Implementation Paradigms'' was held in the International Conference and Research Center (IBFI), Schloss Dagstuhl. During the seminar, several participants presented their current research, and ongoing work and open problems were discussed. Abstracts of the presentations given during the seminar as well as abstracts of seminar results and ideas are put together in this paper. The first section describes the seminar topics and goals in general. Links to extended abstracts or full papers are provided, if available

Semantics Through Pictures: towards a diagrammatic semantics for object-oriented modelling notations

An object-oriented (OO) model has a static component, the set of allowable snapshots or system states, and a dynamic component, the set of filmstrips or sequences of snapshots. Diagrammatic notations, such as those in UML, each places constraints on the static and/or dynamic models. A formal semantics of OO modeling notations can be constructed by providing a formal description of (i) sets of snapshots and filmstrips, (ii) constraints on those sets, and (iii) the derivation of those constraints from diagrammatic notations. In addition, since constraints are contributed by many diagrams for the same model, a way of doing this compositionally is desirable. One approach to the semantics is to use first-order logic for (i) and (ii), and theory inclusion with renaming, as in Larch, to characterize composition. A common approach to (iii) is to bootstrap: provide a semantics for a kernel of the notation and then use the kernel to give a semantics to the other notations. This only works if a kernel which is sufficiently expressive can be identified, and this is not the case for UML. However, we have developed a diagrammatic notation, dubbed constraint diagrams, which seems capable of expressing most if not all static and dynamic constraints, and it is proposed that this be used to give a diagrammatic semantics to OO models

S-Store: Streaming Meets Transaction Processing

Stream processing addresses the needs of real-time applications. Transaction processing addresses the coordination and safety of short atomic computations. Heretofore, these two modes of operation existed in separate, stove-piped systems. In this work, we attempt to fuse the two computational paradigms in a single system called S-Store. In this way, S-Store can simultaneously accommodate OLTP and streaming applications. We present a simple transaction model for streams that integrates seamlessly with a traditional OLTP system. We chose to build S-Store as an extension of H-Store, an open-source, in-memory, distributed OLTP database system. By implementing S-Store in this way, we can make use of the transaction processing facilities that H-Store already supports, and we can concentrate on the additional implementation features that are needed to support streaming. Similar implementations could be done using other main-memory OLTP platforms. We show that we can actually achieve higher throughput for streaming workloads in S-Store than an equivalent deployment in H-Store alone. We also show how this can be achieved within H-Store with the addition of a modest amount of new functionality. Furthermore, we compare S-Store to two state-of-the-art streaming systems, Spark Streaming and Storm, and show how S-Store matches and sometimes exceeds their performance while providing stronger transactional guarantees