Multi-Version Concurrency Control (MVCC) is a widely employed concurrency control mechanism, as it allows for execution modes where readers never block writers. However, most systems implement only snapshot isolation (SI) instead of full serializability. Adding serializability guarantees to existing SI implementations tends to be prohibitively expensive.We present a novel MVCC implementation for main-memory database systems that has very little overhead compared to serial execution with single-version concurrency control, even when maintaining serializability guarantees. Updating data in-place and storing versions as before-image deltas in undo buffers not only allows us to retain the high scan performance of single-version systems but also forms the basis of our cheap and fine-grained serializability validation mechanism. The novel idea is based on an adaptation of precision locking and verifies that the (extensional) writes of recently committed transactions do not intersect with the (intensional) read predicate space of a committing transaction. We experimentally show that our MVCC model allows very fast processing of transactions with point accesses as well as read-heavy transactions and that there is little need to prefer SI over full serializability any longer.

Abstract. Availability is an important security property for Internet services and a key ingredient of most service level agreements. It can be compromised by distributed Denial of Service (DoS) attacks. In this work we propose a formal pattern-based approach to study defense mechanisms against DoS attacks. We enhance pattern descriptions with formal models that allow the designer to give guarantees on the behavior of the proposed solution. The underlying executable specification formalism we use is the rewriting logic language Maude and its real-time and probabilistic extensions. We introduce the notion of stable availability, which means that with very high probability service quality remains very close to a threshold, regardless of how bad the DoS attack can get. Then we present two formal patterns which can serve as defenses against DoS attacks: the Adaptive Selective Verification (ASV) pattern, which enhances a communication protocol with a defense mechanism, and the Server Replicator (SR) pattern, which provisions additional resources on demand. However, ASV achieves availability without stability, and SR cannot achieve stable availability at a reasonable cost. As a main result we show, by statistical model checking with the PVeStA tool, that the composition of both patterns yields a new improved pattern which guarantees stable availability at a reasonable cost.

eScience and big data analytics applications are facing the challenge of efficiently evaluating complex queries over vast amounts of structured text data archived in network storage solutions. To analyze such data in traditional disk-based database systems, it needs to be bulk loaded, an operation whose performance largely depends on the wire speed of the data source and the speed of the data sink, i.e., the disk. As the speed of network adapters and disks has stagnated in the past, loading has become a major bottleneck. The delays it is causing are now ubiquitous as text formats are a preferred storage format for reasons of portability.But the game has changed: Ever increasing main memory capacities have fostered the development of in-memory database systems and very fast network infrastructures are on the verge of becoming economical. While hardware limitations for fast loading have disappeared, current approaches for main memory databases fail to saturate the now available wire speeds of tens of Gbit/s. With Instant Loading, we contribute a novel CSV loading approach that allows scalable bulk loading at wire speed. This is achieved by optimizing all phases of loading for modern super-scalar multi-core CPUs. Large main memory capacities and Instant Loading thereby facilitate a very efficient data staging processing model consisting of instantaneous load -work-unload cycles across data archives on a single node. Once data is loaded, updates and queries are efficiently processed with the flexibility, security, and high performance of relational main memory databases.

Abstract-The growth in compute speed has outpaced the growth in network bandwidth over the last decades. This has led to an increasing performance gap between local and distributed processing. A parallel database cluster thus has to maximize the locality of query processing. A common technique to this end is to co-partition relations to avoid expensive data shuffling across the network. However, this is limited to one attribute per relation and is expensive to maintain in the face of updates. Other attributes often exhibit a fuzzy co-location due to correlations with the distribution key but current approaches do not leverage this.In this paper, we introduce locality-sensitive data shuffling, which can dramatically reduce the amount of network communication for distributed operators such as join and aggregation. We present four novel techniques: (i) optimal partition assignment exploits locality to reduce the network phase duration; (ii) communication scheduling avoids bandwidth underutilization due to cross traffic; (iii) adaptive radix partitioning retains locality during data repartitioning and handles value skew gracefully; and (iv) selective broadcast reduces network communication in the presence of extreme value skew or large numbers of duplicates. We present comprehensive experimental results, which show that our techniques can improve performance by up to factor of 5 for fuzzy co-location and a factor of 3 for inputs with value skew.

Modern database clusters entail two levels of networks: connecting CPUs and NUMA regions inside a single server in the small and multiple servers in the large. The huge performance gap between these two types of networks used to slow down distributed query processing to such an extent that a cluster of machines actually performed worse than a single many-core server. The increased main-memory capacity of the cluster remained the sole benefit of such a scale-out.The economic viability of high-speed interconnects such as InfiniBand has narrowed this performance gap considerably. However, InfiniBand's higher network bandwidth alone does not improve query performance as expected when the distributed query engine is left unchanged. The scalability of distributed query processing is impaired by TCP overheads, switch contention due to uncoordinated communication, and load imbalances resulting from the inflexibility of the classic exchange operator model. This paper presents the blueprint for a distributed query engine that addresses these problems by considering both levels of networks holistically. It consists of two parts: First, hybrid parallelism that distinguishes local and distributed parallelism for better scalability in both the number of cores as well as servers. Second, a novel communication multiplexer tailored for analytical database workloads using remote direct memory access (RDMA) and low-latency network scheduling for high-speed communication with almost no CPU overhead. An extensive evaluation within the HyPer database system using the TPC-H benchmark shows that our holistic approach indeed enables high-speed query processing over high-speed networks.

Abstract. In this paper we propose the so-called composite actor model for specifying composed entities such as the Internet. This model extends the actor model of concurrent computation so that it follows the "Reflective Russian Dolls" pattern and supports an arbitrary hierarchical composition of entities. To enable statistical model checking we introduce a new scheduling approach for composite actor models which guarantees the absence of unquantified nondeterminism. The underlying executable specification formalism we use is the rewriting logic-based semantic framework Maude, its probabilistic extension PMaude, and the statistical model checker PVeStA. We formalize a model transformation which-given certain formal requirements-generates a scheduled specification. We prove the correctness of the scheduling approach and the soundness of the transformation by introducing the notions of strong zero-time rule confluence and time-passing bisimulation and by showing that the transformation is a time-passing bisimulation for strongly zero-time rule confluent composite actor specifications.

This work aims at reducing the main-memory footprint in high performance hybrid OLTP & OLAP databases, while retaining high query performance and transactional throughput. For this purpose, an innovative compressed columnar storage format for cold data, called Data Block s is introduced. Data Blocks further incorporate a new lightweight index structure called Positional SMA that narrows scan ranges within Data Blocks even if the entire block cannot be ruled out. To achieve highest OLTP performance, the compression schemes of Data Blocks are very lightweight , such that OLTP transactions can still quickly access individual tuples. This sets our storage scheme apart from those used in specialized analytical databases where data must usually be bit-unpacked. Up to now, high-performance analytical systems use either vectorized query execution or "just-in-time" (JIT) query compilation. The fine-grained adaptivity of Data Blocks necessitates the integration of the best features of each approach by an interpreted vectorized scan subsystem feeding into JIT-compiled query pipelines. Experimental evaluation of HyPer, our full-fledged hybrid OLTP & OLAP database system, shows that Data Blocks accelerate performance on a variety of query workloads while retaining high transaction throughput.

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