Benchmarking on intensive transaction processing

Benchmarks play a crucial role in database performance evaluation,and have been effectively promoting the development of database management systems.With critical transaction processing requirements of new applications,we see an explosion of innovative database technologies for dealing with highly intensive transaction workloads(OLTP)with the obvious characteristics of sharp dynamics,terrificskewness,high contention,or high concurrency(abbr.DSC^2),which can not be well described or evaluated by current standard benchmarks.In this paper,based on the representative SecKill applications,we define a pacakge of workloads simulating intensive transactional processing requirements.And we create a general and flexible benchmark framework PeakBench for evaluating intensive OLTP workloads on databases.We are the first work to have full control on simulating DSC^2,especially for the fine granularity control for contention generation.With a comprehensive set of experiments conducted on popular open sourced DBMSs compared with the other representative OLTP benchmarks,we completely demonstrate the usefulness of PeakBench.

Pessimistic Predicate/Transform Model for Long Running Business Processes

Many business processes in enterprise applications are both long running and transactional in nature. However, no current transaction model can provide full transaction support for such long running business processes. This paper proposes a new transaction model, the pessimistic predicate/transform (PP/T) model, which can provide full transaction support for long running business processes. A framework was proposed on the enterprise JavaBeans platform to implement the PP/T model. The framework enables application developers to focus on the business logic, with the underlying platform providing the required transactional semantics. The development and maintenance effort are therefore greatly reduced. Simulations show that the model has a sound concurrency management ability for long running business processes.

Plover:parallel logging for replication systems

Logging and replication are commonly used recovery approaches in database systems.To guarantee that the database state is not corrupted due to system crash,database systems rely on a centralized logging method to persist log entries into a stable storage device;to prevent data loss due to device failure,a primary server in the database system periodically replicates its state to backup servers by copying log entries over networks.As the transaction execution in a modern database system is highly parallelized,the centralized logging with a single I/O channel tends to inhibit the scalability of the system.Meanwhile,log entries generated at high speed make a network with limited bandwidth a potential bottleneck for replication.In this paper,we propose an in-memory transaction engine named Plover with parallel logging and speedy replication for primary-backup replication systems.The parallel logging enables concurrent execution of logging by utilizing multiple log buffers associated with multiple stable storages.All log entries in the log buffers maintain a global sequence number(GSN),which ensures a partial order among transactions.The kernel of the speedy replication is an adaptive shipping method,which allows to transfer data increments instead of log entries to backups under heavy workloads.Experimental results using the YCSB and TPC-C benchmarks show that Plover scales well with the increasing number of worker threads and stable storage devices.And our adaptive shipping requires only one fifth network bandwidth of the conventional log shipping.

Agent-based simulation platform for cloud manufacturing

Cloud manufacturing is a new manufacturing paradigm which creates an open environment for transactions among the enterprises.Research on transaction modes and regularities in a cloud manufacturing environment is important for promoting the applications of cloud manufacturing.To this end,we design and implement a simulation platform according to the typical transaction processes of enterprises in the cloud manufacturing environment.In the simulation platform,enterprises are encapsulated into Service Agents,and thus the activities of service agents can be used to describe enterprise behaviors.By defining different rules,simulations for different business models can be conducted.Detailed descriptions of the platform architecture,functions,and key technologies are presented.The feasibility of the simulation platform is verified through a case study.

Incremental join view maintenance on distributed log-structured storage

Modern database systems desperate for the ability to support highly scalable transactions and efficient queries simultaneously for real-time applications.One solution is to utilize query optimization techniques on the on-line transaction processing(OLTP)systems.The materialized view is considered as a panacea to decrease query latency.However,it also involves the significant cost of maintenance which trades away transaction performance.In this paper,we examine the design space and conclude several design features for the implementation of a view on a distributed log-structured merge-tree(LSMtree),which is a well-known structure for improving data write performance.As a result,we develop two incremental view maintenance(IVM)approaches on LSM-tree.One avoids join computation in view maintenance transactions.Another with two optimizations is proposed to decouple the view maintenance with the transaction process.Under the asynchronous update,we also provide consistency queries for views.Experiments on TPC-H benchmark show our methods achieve better performance than straightforward methods on different workloads.