Storing data structures in high-capacity byte-addressable persistent memory instead of DRAM or a storage device offers the opportunity to (1) reduce cost and power consumption compared with DRAM, (2) decrease the latency and CPU resources needed for an I/O operation compared with storage, and (3) allow for fast recovery as the data structure remains in memory after a machine failure. The first commercial offering in this space is Intel® Optane™ Direct Connect (Optane™ DC) Persistent Memory. Optane™ DC promises access time within a constant factor of DRAM, with larger capacity, lower energy consumption, and persistence. We present an experimental evaluation of persistent transactional memory performance, and explore how Optane™ DC durability domains affect the overall results. Given that neither of the two available durability domains can deliver performance competitive with DRAM, we introduce and emulate a new durability domain, called PDRAM, in which the memory controller tracks enough information (and has enough reserve power) to make DRAM behave like a persistent cache of Optane™ DC memory.In this paper we compare the performance of these durability domains on several configurations of five persistent transactional memory applications. We find a large throughput difference, which emphasizes the importance of choosing the best durability domain for each application and system. At the same time, our results confirm that recently published persistent transactional memory algorithms are able to scale, and that recent optimizations for these algorithms lead to strong performance, with speedups as high as 6× at 16 threads.

Currently, we face the next major shift in processor designs that arose from the physical limitations known as the "dark silicon effect". Due to thermal limitations and shrinking transistor sizes, multi-core scaling is coming to an end. A major new direction that hardware vendors are currently investigating involves specialized and energy-efficient hardware accelerators (e.g., ASICs) placed on the same die as the normal CPU cores. In this paper, we present a novel query processing engine called SiliconDB that targets such heterogeneous processor environments. We leverage the Sparc M7 platform to develop and test our ideas. Based on the SSB benchmarks, as well as other micro benchmarks, we compare the efficiency of SiliconDB with existing execution strategies that make use of co-processors (e.g., FPGAs, GPUs) and demonstrate speed-up improvements of up to 2x.

With the addition of lambda expressions and the Stream API in Java 8, Java has gained a powerful and expressive query language that operates over in-memory collections of Java objects, making the transformation and analysis of data more convenient, scalable and efficient. In this paper, we build on Java 8 Stream and add a DistributableStream abstraction that supports federated query execution over an extensible set of distributed compute engines. Each query eventually results in the creation of a materialized result that is returned either as a local object or as an engine defined distributed Java Collection that can be saved and/or used as a source for future queries. Distinctively, DistributableStream supports the changing of compute engines both between and within a query, allowing different parts of a computation to be executed on different platforms. At execution time, the query is organized as a sequence of pipelined stages, each stage potentially running on a different engine. Each node that is part of a stage executes its portion of the computation on the data available locally or produced by the previous stage of the computation. This approach allows for computations to be assigned to engines based on pricing, data locality, and resource availability. Coupled with the inherent laziness of stream operations, this brings great flexibility to query planning and separates the semantics of the query from the details of the engine used to execute it. We currently support three engines, Local, Apache Hadoop MapReduce and Oracle Coherence, and we illustrate how new engines and data sources can be added.

The elapsed time of a parallel job depends on the completion time of its longest running constituent. We present a static load balancing algorithm that distributes work evenly across the reducers in a MapReduce job resulting in significant elapsed time reductions.

Big data is the tar sands of the data world: vast reserves of raw gritty data whose valuable information content can only be extracted at great cost. MapReduce is a popular parallel programming paradigm well suited to the programmatic extraction and analysis of information from these unstructured Big Data reserves. The Apache Hadoop implementation of MapReduce has become an important player in this market due to its ability to exploit large networks of inexpensive servers. The increasing importance of unstructured data has led to the interest in MapReduce and its Apache Hadoop implementation, which has led to the interest of data processing vendors in supporting this programming style.

Storage-area networks are a popular and efficient way of building large storage systems both in an enterprise environment and for multi-domain storage service providers. In both environments the network and the storage has to be configured to ensure that the data is maintained securely and can be delivered efficiently. In this paper, we describe a model of mandatory security for SAN services that incorporates the notion of risk as a measure of the robustness of the SAN's configuration and that formally defines a vulnerability common in systems with mandatory security, i.e. cascaded threats. Our abstract SAN model is flexible enough to reflect the data requirements, tractable for the administrator, and can be implemented as part of an automatic configuration system. The implementation is given as part of a prototype written in OPL.

Query performance in current systems depends significantly on tuning: how well the query matches the available indexes, materialized views etc. Even in a well tuned system, there are always some queries that take much longer than others. This frustrates users who increasingly want consistent response times to ad hoc queries. We argue that query processors should instead aim for constant response times for all queries, with no assumption about tuning. We present Blink, our first attempt at this goal, that runs every query as a table scan over a fully denormalized database, with hash group-by done along the way. To make this scan efficient, Blink uses a novel compression scheme that horizontally partitions tuples by frequency, thereby compressing skewed data almost down to entropy, even while producing long runs of fixed-length, easily-parseable values. We also present a scheme for evaluating a conjunction of range and equality predicates in SIMD fashion over compressed tuples, and different schemes for efficient hash-based aggregation within the L2 cache. A experimental study with a suite of arbitrary single block SQL queries over a TPCH-like schema suggests that constant-time queries can be efficient.

Table scans have become more interesting recently due to greater use of ad-hoc queries and greater availability of multi-core, vector-enabled hardware. Table scan performance is limited by value representation, table layout, and processing techniques. In this paper we propose a new layout and processing technique for efficient one-pass predicate evaluation. Starting with a set of rows with a fixed number of bits per column, we append columns to form a set of banks and then pad each bank to a supported machine word length, typically 16, 32, or 64 bits. We then evaluate partial predicates on the columns of each bank, using a novel evaluation strategy that evaluates column level equality, range tests, IN-list predicates, and conjuncts of these predicates, simultaneously on multiple columns within a bank, and on multiple rows within a machine register. This approach outperforms pure column stores, which must evaluate the partial predicates one column at a time. We evaluate and compare the performance and representation overhead of this new approach and several proposed alternatives.

Two trends are converging to make the CPU cost of a table scan a more important component of database performance. First, table scans are becoming a larger fraction of the query processing workload, and second, large memories and compression are making table scans CPU, rather than disk bandwidth, bound. Data warehouse systems have found that they can avoid the unpredictability of joins and indexing and achieve good performance by using massive parallel processing to perform scans over compressed vertical partitions of a denormalized schema.

Storage-area networks are a popular and efficient way of building large storage systems both in an enterprise environment and for multi-domain storage service providers. In both environments the network and the storage has to be configured to ensure that the data is maintained securely and can be delivered efficiently. In this paper we describe a model of mandatory security for multi-domain storage services that is flexible enough to reflect the data requirements, tractable for the administrator, and implementable as part of an automatic configuration system. We describe the model abstractly, its implementation as part of a prototype SAN configuration system written in OPL, and illustrate its operation on a set of sample configurations. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

An informal look at some of the most annoying problems confronting users who attempt to manipulate data stored on the Web and a new update paradigm that handles many of these problems. This new approach, called Web workspaces, allows for collaboration, undo, 'what if' analysis, auditing and a form of long running transaction. The new approach builds on workspace versioning techniques long in use in CASE and CAD systems and on transaction coordination techniques, long used to turn disparate applications into cohesive systems. The ideas can be applied to Web services or to enterprise frameworks to build more robust and usable applications for manipulating data on the Web.

Extensive caching is a key feature of the Echo distributed file system. Echo client machines maintain coherent caches of file and directory data and properties, with write-behind (delayed write-back) ofallcached information. Echo specifies ordering constraints on this write-behind, enabling applications to store and maintain consistent data structures in the file system even when crashes or network faults prevent some writes from being completed. In this paper we describe the Echo cache's coherence and ordering semantics, show how they can improve the performance and consistency of applications, explain how they are implemented. We also discuss the general problem of reliably notifying applications and users when write-behind is lost; we addressed this problem as part of the Echo design, but did not find a fully satisfactory solution.