Research on transaction processing has made significant progress towards improving performance of main memory multicore OLTP systems under low contention. However, these systems struggle on workloads with lots of conflicts. Partitioned databases (and variants) perform well on high contention workloads that are statically partitionable, but time-varying workloads often make them impractical. Towards addressing this, we propose Strife-a novel transaction processing scheme that clusters transactions together dynamically and executes most of them without any concurrency control. Strife executes transactions in batches, where each batch is partitioned into disjoint clusters without any cross-cluster conflicts and a small set of residuals. The clusters are then executed in parallel with no concurrency control, followed by residuals separately executed with concurrency control. Strife uses a fast dynamic clustering algorithm that exploits a combination of random sampling and concurrent union-find data structure to partition the batch online, before executing it. Strife outperforms lock-based and optimistic protocols by up to 2× on high contention workloads. While Strife incurs about 50% overhead relative to partitioned systems in the statically partitionable case, it performs 2× better when such static partitioning is not possible and adapts to dynamically varying workloads.

Acyclic schemes have numerous applications in databases and in machine learning, such as improved design, more efficient storage, and increased performance for queries and machine learning algorithms. Multivalued dependencies (MVDs) are the building blocks of acyclic schemes. The discovery from data of both MVDs and acyclic schemes is more challenging than other forms of data dependencies, such as Functional Dependencies, because these dependencies do not hold on subsets of data, and because they are very sensitive to noise in the data; for example a single wrong or missing tuple may invalidate the schema. In this paper we present Maimon, a system for discovering approximate acyclic schemes and MVDs from data. We give a principled definition of approximation, by using notions from information theory, then describe the two components of Maimon: mining for approximate MVDs, then reconstructing acyclic schemes from approximate MVDs. We conduct an experimental evaluation of Maimon on 20 real-world datasets, and show that it can scale up to 1M rows, and up to 30 columns.

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Over the last decade, there has been a tremendous growth in data-intensive applications and services in the cloud. Data is created on a variety of edge sources such as devices, and is processed by cloud applications to gain insights or make decisions. These applications are typically update intensive and involve a large amount of state beyond what can fit in main memory. However, they display significant temporal locality in their access pattern. We demonstrate F aster , a new key-value store that combines a latch-free concurrent hash index with a hybrid log : a concurrent log-structured record store that spans main memory and storage, while supporting fast in-place updates in memory. F aster achieves up to orders-of-magnitude better throughput than systems deployed widely today. It is built as an embedded high-level language component using dynamic code generation, and can work with any storage back-end such as local SSD or cloud storage. Our demonstration focuses on: (1) the ease with which cloud applications and state stores can deeply integrate state management into their high-level language logic at low overhead; and (2) the innovative system design and the resulting high performance, adaptability to varying memory capacities, durability, and natural caching properties of our system.