Dynamic Action Scheduling in a Parallel Database System

This paper describes a scheduling technique for parallel database systems to obtain high performance, both in terms of response time and throughput. The technique enables both intra- and inter-transaction parallelism while controlling concurrency between transactions correctly. Scheduling is performed dynamically at transaction execution time, taking into account dynamic aspects of the execution and allowing parallelism between the scheduling and transaction execution processes. The technique has a solid conceptual background, based on a simple graph-based approach. The usability and effectiveness of the technique are demonstrated by implementation in and measurements on the parallel PRISMA database system

Low overhead concurrency control for partitioned main memory databases

Database partitioning is a technique for improving the performance of distributed OLTP databases, since "single partition" transactions that access data on one partition do not need coordination with other partitions. For workloads that are amenable to partitioning, some argue that transactions should be executed serially on each partition without any concurrency at all. This strategy makes sense for a main memory database where there are no disk or user stalls, since the CPU can be fully utilized and the overhead of traditional concurrency control, such as two-phase locking, can be avoided. Unfortunately, many OLTP applications have some transactions which access multiple partitions. This introduces network stalls in order to coordinate distributed transactions, which will limit the performance of a database that does not allow concurrency. In this paper, we compare two low overhead concurrency control schemes that allow partitions to work on other transactions during network stalls, yet have little cost in the common case when concurrency is not needed. The first is a light-weight locking scheme, and the second is an even lighter-weight type of speculative concurrency control that avoids the overhead of tracking reads and writes, but sometimes performs work that eventually must be undone. We quantify the range of workloads over which each technique is beneficial, showing that speculative concurrency control generally outperforms locking as long as there are few aborts or few distributed transactions that involve multiple rounds of communication. On a modified TPC-C benchmark, speculative concurrency control can improve throughput relative to the other schemes by up to a factor of two.National Science Foundation (U.S.). (Grant number IIS-0704424)National Science Foundation (U.S.). (Grant number IIS-0845643

Fault-tolerant distributed transactions for partitioned OLTP databases

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2012.Cataloged from PDF version of thesis.Includes bibliographical references (p. 103-112).This thesis presents Dtxn, a fault-tolerant distributed transaction system designed specifically for building online transaction processing (OLTP) databases. Databases have traditionally been designed as general purpose data processing tools. By being designed only for OLTP workloads, Dtxn can be more efficient. It is designed to support very large databases by partitioning data across a cluster of commodity servers in a data center. Combining multiple servers together allows systems built with Dtxn to be cost effective, highly available, scalable, and fault-tolerant. Dtxn provides three novel features. First, it provides reusable infrastructure for building a distributed OLTP database out of single machine databases. This allows developers to take a specialized backend storage engine and use it across multiple machines, without needing to re-implement the distributed transaction infrastructure. We used Dtxn to build four different applications: a simple key/value store, a specialized TPC-C implementation, a main-memory OLTP database, and a traditional disk-based OLTP database. Second, Dtxn provides a novel concurrency control mechanism called speculative concurrency control, designed for main memory OLTP workloads that are primarily composed of transactions with a single round of communication between the application and database. Speculative concurrency control executes one transaction at a time, with no concurrency control overhead. In cases where there may be stalls due to network communication, it speculates future transactions. Our results show that this provides significantly better throughput than traditional two-phase locking, outperforming it by a factor of two on the TPC-C benchmark. Finally, Dtxn supports live migration, allowing part of the data on one server to be moved to another server while processing transactions. Our experiments show that our approach has nearly no visible impact on throughput or latency when moving data under moderate to high loads. It has significantly less impact than the best commercially available systems when the database is overloaded. The period of time where the throughput is reduced is less than half as long as failing over to another replica or using virtual machine migration.by Evan Philip Charles Jones.Ph.D