43,675 research outputs found
Compiling Pattern Matching in Join-Patterns
We propose an extension of the join-calculus with pattern matching on algebraic data types. Our initial motivation is twofold: to provide an intuitive semantics of the interaction between concurrency and pattern matching; to define a practical compilation scheme from extended join-definitions into ordinary ones plus (ML) pattern matching. To assess the correctness of our compilation scheme, we develop a theory of the applied join-calculus, a calculus with value-passing and value matching
AIOCJ: A Choreographic Framework for Safe Adaptive Distributed Applications
We present AIOCJ, a framework for programming distributed adaptive
applications. Applications are programmed using AIOC, a choreographic language
suited for expressing patterns of interaction from a global point of view. AIOC
allows the programmer to specify which parts of the application can be adapted.
Adaptation takes place at runtime by means of rules, which can change during
the execution to tackle possibly unforeseen adaptation needs. AIOCJ relies on a
solid theory that ensures applications to be deadlock-free by construction also
after adaptation. We describe the architecture of AIOCJ, the design of the AIOC
language, and an empirical validation of the framework.Comment: Technical Repor
Building Efficient Query Engines in a High-Level Language
Abstraction without regret refers to the vision of using high-level
programming languages for systems development without experiencing a negative
impact on performance. A database system designed according to this vision
offers both increased productivity and high performance, instead of sacrificing
the former for the latter as is the case with existing, monolithic
implementations that are hard to maintain and extend. In this article, we
realize this vision in the domain of analytical query processing. We present
LegoBase, a query engine written in the high-level language Scala. The key
technique to regain efficiency is to apply generative programming: LegoBase
performs source-to-source compilation and optimizes the entire query engine by
converting the high-level Scala code to specialized, low-level C code. We show
how generative programming allows to easily implement a wide spectrum of
optimizations, such as introducing data partitioning or switching from a row to
a column data layout, which are difficult to achieve with existing low-level
query compilers that handle only queries. We demonstrate that sufficiently
powerful abstractions are essential for dealing with the complexity of the
optimization effort, shielding developers from compiler internals and
decoupling individual optimizations from each other. We evaluate our approach
with the TPC-H benchmark and show that: (a) With all optimizations enabled,
LegoBase significantly outperforms a commercial database and an existing query
compiler. (b) Programmers need to provide just a few hundred lines of
high-level code for implementing the optimizations, instead of complicated
low-level code that is required by existing query compilation approaches. (c)
The compilation overhead is low compared to the overall execution time, thus
making our approach usable in practice for compiling query engines
Code Generation for Efficient Query Processing in Managed Runtimes
In this paper we examine opportunities arising from the conver-gence of two trends in data management: in-memory database sys-tems (IMDBs), which have received renewed attention following the availability of affordable, very large main memory systems; and language-integrated query, which transparently integrates database queries with programming languages (thus addressing the famous âimpedance mismatch â problem). Language-integrated query not only gives application developers a more convenient way to query external data sources like IMDBs, but also to use the same querying language to query an applicationâs in-memory collections. The lat-ter offers further transparency to developers as the query language and all data is represented in the data model of the host program-ming language. However, compared to IMDBs, this additional free-dom comes at a higher cost for query evaluation. Our vision is to improve in-memory query processing of application objects by introducing database technologies to managed runtimes. We focus on querying and we leverage query compilation to im-prove query processing on application objects. We explore dif-ferent query compilation strategies and study how they improve the performance of query processing over application data. We take C] as the host programming language as it supports language-integrated query through the LINQ framework. Our techniques de-liver significant performance improvements over the default LINQ implementation. Our work makes important first steps towards a future where data processing applications will commonly run on machines that can store their entire datasets in-memory, and will be written in a single programming language employing language-integrated query and IMDB-inspired runtimes to provide transparent and highly efficient querying. 1
Forecasting the cost of processing multi-join queries via hashing for main-memory databases (Extended version)
Database management systems (DBMSs) carefully optimize complex multi-join
queries to avoid expensive disk I/O. As servers today feature tens or hundreds
of gigabytes of RAM, a significant fraction of many analytic databases becomes
memory-resident. Even after careful tuning for an in-memory environment, a
linear disk I/O model such as the one implemented in PostgreSQL may make query
response time predictions that are up to 2X slower than the optimal multi-join
query plan over memory-resident data. This paper introduces a memory I/O cost
model to identify good evaluation strategies for complex query plans with
multiple hash-based equi-joins over memory-resident data. The proposed cost
model is carefully validated for accuracy using three different systems,
including an Amazon EC2 instance, to control for hardware-specific differences.
Prior work in parallel query evaluation has advocated right-deep and bushy
trees for multi-join queries due to their greater parallelization and
pipelining potential. A surprising finding is that the conventional wisdom from
shared-nothing disk-based systems does not directly apply to the modern
shared-everything memory hierarchy. As corroborated by our model, the
performance gap between the optimal left-deep and right-deep query plan can
grow to about 10X as the number of joins in the query increases.Comment: 15 pages, 8 figures, extended version of the paper to appear in
SoCC'1
The High Court on constitutional law: the 2013 statistics
Introduction: This article presents statistical information about the High Courtâs decision making for 2013 at both an institutional and individual level, with an emphasis on constitutional cases as a subset of the total. The results have been compiled using the same methodology employed in studies of earlier years. Likewise, we maintain our customary advice to readers about the inevitable limitations that must apply to the results of an empirical study of the decisions of any final court over the space of a single calendar year. In particular, the fact that constitutional cases comprise just a portion of the High Courtâs annual work means that we strive to make observations leaning more towards circumspection than boldness. Nevertheless, taking stock of the cases handed down by the Court in the preceding year and the way in which the justices have, as individuals within the institution, decided their outcomes is illuminating. As ever, we are careful not to attribute greater or lesser âinfluenceâ to particular individuals simply through tracking the rates of consensus, dissent and co-authorship experienced within the Court
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