Column statistics are an important element of cardinality estimation frameworks. More accurate estimates allow the optimizer of a RDBMS to generate better plans and improve the overall system's efficiency. This paper introduces filtered statistics, which model value distribution over a set of rows restricted by a predicate. This feature, available in Microsoft SQL Server, can be used to handle column correlation, as well as focus on interesting data ranges. In particular, it fits well for scenarios with logical subtables, like flexible schema or multi-tenant applications. Integration with the existing cardinality estimation infrastructure is presented.

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.

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.

Efficient support for applications that deal with data heterogeneity, hierarchies and schema evolution is an important challenge for relational engines. In this paper we show how this flexibility can be handled in Microsoft SQL Server. For this purpose, the engine has been equipped in an integrated package of relational extensions. The package includes sparse storage, column set operations, filtered indices, filtered statistics and hierarchy querying with OrdPath labeling. In addition, economical loading of metadata allow us to answer queries independently of the number of columns in a table and drastically improve scaling capabilities. The design of a prototypical content and collaboration application based on a wide table is described, along with experiments validating its performance.

Accurate cardinality estimation is critically important to high-quality query optimization. It is well known that conventional cardinality estimation based on histograms or similar statistics may produce extremely poor estimates in a variety of situations, for example, queries with complex predicates, correlation among columns, or predicates containing user-defined functions. In this paper, we propose a new, general cardinality estimation technique that combines random sampling and materialized view technology to produce accurate estimates even in these situations. As a major innovation, we exploit feedback information from query execution and process control techniques to assure that estimates remain statistically valid when the underlying data changes. Experimental results based on a prototype implementation in Microsoft SQL Server demonstrate the practicality of the approach and illustrate the dramatic effects improved cardinality estimates may have.

Good cardinality estimates are critical for generating good execution plans during query optimization. Complex predicates, correlations between columns, and user-defined functions are extremely hard to handle when using the traditional histogram approach. This demo illustrates the use of sample views for cardinality estimations as prototyped in Microsoft SQL Server. We show the creation of sample views, discuss how they are exploited during query optimization, and explain their potential effect on query plans. In addition, we also show our implementation of maintenance policies using statistical quality control techniques based on query feedback.

We present a model for data reorganization in parallel disk systems that is geared toward load balancing in an environment with periodic access patterns. Data reorganization is performed by disk cooling, i.e., migrating files or extents from the hottest disks to the coldest ones. We develop an approximate queueing model for determining the effective arrival rates of cooling requests and discuss its use in assessing the costs versus benefits of cooling actions.

Disk Arrays or RAIDs are a widely accepted I/O system architecture, useful for a wide range of applications. Before a RAID can be used, one needs to configure the RAID to select parameters such as the RAID level to use, the stripe unit to use, how large a cache to use, and so on. Selecting these configuration parameters can be quite complex, yet no aids are available today to help the user configure his RAID. The optimal selection of the parameters strongly depends on the specific workload characteristics of the application. In this paper we describe a configuration tool called Raidtool which is intended to support the systems designer in the selection of the configuration parameters. Our approach consists of three basic steps. The first step is to collect a trace of I/Os while running one or more typical applications. In the second step, this trace data is analyzed to determine the workload characteristics of the applications. In the third and final step, we use a simulator to evaluate the different RAID controller configurations.

No abstract available.

Large arrays of small disks are being considered as a promising approach to high performance 1/0 architectures.In this paper we deal with the problem of data placement in such a disk array.The prevalent approach is to decluster large files across a number of disks so as to minimize the access time to a file and balance the 1/0 load across the disks.The data placement problem entails determining the number of disks and the set of disks across which a file is declustered.Unlike previous work, this paper does not assume that all files are allocated at the same time but rather considers dynamic file creations, This makes the placement problem considerably harder because each placement decision has to take into account the current allocation state and the access frequencies of the disks and the existing files.As a result, file creation may involve partial reorganization on one or more disks.The paper proposes heuristic algorithms for the placement of dynamically created files.The algorithms provide a good compromise between maximizing 1/0 performance of the disk array and minimizing the work invested in partial reorganizations.The paper presents preliminary performance results of various alternative algorithms under a synthetic workload.