Torsten Grabs

Microsoft CEP server and online behavioral targeting

In this demo, we present the Microsoft Complex Event Processing (CEP) Server, Microsoft CEP for short. Microsoft CEP is an event stream processing system featured by its declarative query language and its multiple consistency levels of stream query processing. Query composability, query fusing, and operator sharing are key features in the Microsoft CEP query processor. Moreover, the debugging and supportability tools of Microsoft CEP provide visibility of system internals to users. Web click analysis has been crucial to behavior-based online marketing. Streams of web click events provide a typical workload for a CEP server. Meanwhile, a CEP server with its processing capabilities plays a key role in web click analysis. This demo highlights the features of Microsoft CEP under a workload of web click events.

Intelligent Search on XML Data

Recently, we have seen a steep increase in the popularity and adoption of XML, in areas such as traditional databases, e-business, the scientific environment, and on the web. Querying XML documents and data efficiently is a challenging issue; this book approaches search on XML data by combining content-based methods from information retrieval and structure-based XML query methods and presents the following parts: applications, query languages, retrieval models, implementing intelligent XML systems, and evaluation. To appreciate the book, basic knowledge of traditional database technology, information retrieval, and XML is needed. The book is ideally suited for courses or seminars at the graduate level as well as for education of research and development professionals working on Web applications, digital libraries, database systems, and information retrieval.

Execution strategies for SQL subqueries

Optimizing SQL subqueries has been an active area in database research and the database industry throughout the last decades. Previous work has already identified some approaches to efficiently execute relational subqueries. For satisfactory performance, proper choice of subquery execution strategies becomes even more essential today with the increase in decision support systems and automatically generated SQL, e.g., with ad-hoc reporting tools. This goes hand in hand with increasing query complexity and growing data volumes, which all pose challenges for an industrial-strength query optimizer. This current paper explores the basic building blocks that Microsoft SQL Server utilizes to optimize and execute relational subqueries. We start with indispensable prerequisites such as detection and removal of correlations for subqueries. We identify a full spectrum of fundamental subquery execution strategies such as forward and reverse lookup as well as set-based approaches, explain the different execution strategies for subqueries implemented in SQL Server, and relate them to the current state of the art. To the best of our knowledge, several strategies discussed in this paper have not been published before. An experimental evaluation complements the paper. It quantifies the performance characteristics of the different approaches and shows that indeed alternative execution strategies are needed in different circumstances, which make a cost-based query optimizer indispensable for adequate query performance.

Measuring performance of complex event processing systems

Complex Event Processing (CEP) or stream data processing are becoming increasingly popular as the platform underlying event-driven solutions and applications in industries such as financial services, oil & gas, smart grids, health care, and IT monitoring. Satisfactory performance is crucial for any solution across these industries. Typically, performance of CEP engines is measured as (1) data rate, i.e., number of input events processed per second, and (2) latency, which denotes the time it takes for the result (output events) to emerge from the system after the business event (input event) happened. While data rates are typically easy to measure by capturing the numbers of input events over time, latency is less well defined. As it turns out, a definition becomes particularly challenging in the presence of data arriving out of order. That means that the order in which events arrive at the system is different from the order of their timestamps. Many important distributed scenarios need to deal with out-of-order arrival because communication delays easily introduce disorder. With out-of-order arrival, a CEP system cannot produce final answers as events arrive. Instead, time first needs to progress enough in the overall system before correct results can be produced. This introduces additional latency beyond the time it takes the system to perform the processing of the events. We denote the former as information latency and the latter as system latency. This paper discusses both types of latency in detail and defines them formally without depending on particular semantics of the CEP query plans. In addition, the paper suggests incorporating these definitions as metrics into the benchmarks that are being used to assess and compare CEP systems.

Optimizing Star Join Queries for Data Warehousing in Microsoft SQL Server

As mainstream data warehouses are growing into the multi-terabyte range, adequate performance for decision support queries remains challenging for database query processors. Proper choice of query plan is essential in data warehouses where fact tables often store billions of rows. This paper discusses query optimization and execution strategies that Microsoft SQL Server employs for decision support queries in dimensionally modeled relational data warehouses. Our approach is based on pattern matching to detect typical star query patterns. When matching the pattern, the optimizer generates additional query plan alternatives specifically optimized for data warehouse performance. For high selectivity queries, the plans use nested loops joins and seeks. Medium selectivity queries in turn rely on right-deep hash joins with bitmap filters. Bitmap filters perform semi-join reductions to efficiently prune out non-qualifying rows early. Final plan choice is left for cost-based optimization which also compares the data warehouse specific plans against conventional query plans. We conducted an extensive experimental investigation using both synthetic workloads and several customer workloads. As our results show, the new plan shapes and execution strategies yield significant performance improvements across the targeted workloads as compared to earlier versions of Microsoft SQL Server.

Evaluating the Coordination Overhead of Replica Maintenance in a Cluster of Databases

We investigate the design of a coordinator for a cluster of databases. We consider the following alternatives: TP-Heavy using the TUXEDO TP-monitor, TP-Lite with the ORACLE8 database system, and a TP-Less coordinator implemented in Embedded SQL/C++. In particular, we investigate the scalability of full replication. We assume that update actions on all replica are executed either synchronously or asynchronously. It turns out that the TP-Less approach outperforms commercial TP-middleware for small cluster sizes already. Another observation is that asynchronous updates are the preferred option compared to synchronous updates. The conclusion is that a transaction protocol at the second layer must be replication-aware.