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Posted Oct 20, 2003

Introduction to MSSQL Server 2000 Analysis Services: MSAS Administration and Optimization: Toward More Sophisticated Analysis

By William Pearson

About the Series ...

This is the sixteenth article of the series, Introduction to MSSQL Server 2000 Analysis Services. As I stated in the first article, Creating Our First Cube, the primary focus of this series is an introduction to the practical creation and manipulation of multidimensional OLAP cubes. The series is designed to provide hands-on application of the fundamentals of MS SQL Server 2000 Analysis Services ("MSAS"), with each installment progressively adding features and techniques designed to meet specific real-world needs. For more information on the series, as well as the hardware / software requirements to prepare for the exercises we will undertake, please see my initial article, Creating Our First Cube.

Note: Service Pack 3 updates are assumed for MSSQL Server 2000, MSSQL Server 2000 Analysis Services, and the related Books Online and Samples.

Introduction

We learned in our last lesson, MSAS Administration and Optimization: Simple Cube Usage Analysis, that Microsoft SQL Server 2000 Analysis Services ("MSAS") provides the Usage Analysis Wizard to assist us in the maintenance and optimization of our cubes. We noted that the Usage Analysis Wizard allows us to rapidly produce simple, on-screen reports that provide information surrounding a cube's query patterns, and that the cube activity metrics generated by the wizard have a host of other potential uses, as well, such as the provision of a "quick and dirty" means of trending cube processing performance over time after the cube has entered a production status.

As I stated in Lesson 15, however, I often receive requests from clients and readers, asking how they can approach the creation of more sophisticated reporting to assist in their usage analysis pursuits. This is sometimes based upon a need to create a report similar to the pre-defined, on-screen reports, but in a way that allows for printing, publishing to the web, or otherwise delivering report results to information consumers. Moreover, some users simply want to be able to design different reports that they can tailor themselves, to meet specific needs not addressed by the Usage Analysis Wizard's relatively simple offerings. Yet others want a combination of these capabilities, and / or simply do not like the rather basic user interface that the wizard presents, as it is relatively awkward, does not scale and so forth.

Each of these more sophisticated analysis and reporting needs can be met in numerous ways. In this lesson, we will we will examine the source of cube performance statistics, the Query Log, discussing its location and physical structure, how it is populated, and other characteristics. Next, we will discuss ways that we can customize the degree and magnitude of statistical capture in the Query Log to enhance its value with regard to meeting more precisely our local analysis and reporting needs; we will practice the process of making the necessary changes in settings to illustrate how this is done. Finally, we will discuss options for generating more in-depth, custom reports than the wizard provides, exposing ways that we can directly obtain detailed information surrounding cube processing events in a manner that allows more sophisticated selection, filtering and display, as well as more customized reporting of these important metrics.

At the Heart of Usage Analysis for the Analysis Services Cube: The Query Log

Along with an installation of MSSQL Server 2000 Analysis Services comes the installation of two independent MS Access databases, msmdqlog.mdb, and msmdrep.mdb. By default, the location in which these databases are installed is [Installation Drive]:\Program Files\Microsoft Analysis Services\Bin. The msmdrep.mdb database houses the repository, and will not be the focus of this lesson. We will be concentrating upon the msmdqlog.mdb database, the home of the Query Log where the source information for our usage analysis and reporting is stored.

Structure and Operation of the Query Log

A study of msmdqlog.mdb reveals that it consists of a single table, named, aptly enough, QueryLog. Illustration 1 depicts the table within the database, design view, so that we can see it's layout for purposes of discussion.


Illustration 1: The Query Log Table, Design View, within Msmdqlog.mdb

NOTE: Making a copy of msmdqlog.mdb before undertaking the steps of this lesson, including entering the database simply to view it, is highly recommended to avoid issues with an operating MSAS environment, damaging a production log, etc.

The Usage-Based Optimization Wizard and Usage Analysis Wizard (see Lesson 15 for a discussion) rely on the Query Log, as we learned in our last session. As we can see, the log is composed of several relatively straightforward fields. The fields, together with their respective descriptions, are summarized in Table 1.

Field

Description

MSOLAP_Database

The name of the database used in the query

MSOLAP_Cube

The name of the cube used in the query

MSOLAP_User

The name of the user that ran the query

Dataset

A numeric string indicating the level from each dimension used to satisfy the query

Slice

A string indicating the data slice for the query

StartTime

The time the query began

Duration

The length of time (in seconds) of the query execution

MOLAPPartitions

The number of different multidimensional OLAP (MOLAP) partitions that were used to satisfy the query

ROLAPPartitions

The number of different relational OLAP (ROLAP) partitions that were used to satisfy the query

SamplingRate

The sampling rate at the time the query was executed


Table 1: The Fields of the Query Log

In lockstep with a review of the fields from a description perspective, we can view sample data in Illustration 2, which depicts the same table in data view.

Click for larger image


Illustration 2: The Query Log Table, Data View, within Msmdqlog.mdb

Although each of the fields has a great deal of potential, with regard to analysis and reporting utility (the first three could be seen as dimensions), the fourth, Dataset, can be highly useful with regard to the information that it reveals about cube usage. The cryptic records within this column represent the associated levels accessed for each dimensional hierarchy within the query. An example of the Dataset field ("121411") appears in the sample row depicted in Illustration 3.


Illustration 3: Example of the Dataset Field

While we won't go into a detailed explanation in this lesson, I expect to publish an article in the near future that outlines the interpretation of the digits in the Dataset field. We will trace an example Dataset field's component digits to their corresponding components in the respective cube structure, along with more information regarding report writing based upon the Query Log in general. Our purpose here is more to expose general options for using the Query Log directly to generate customized usage analysis reports.

Additional fields provide rather obvious utility in analyzing cube usage, together with performance in general. The Slice field presents information, in combination with Dataset, which helps us to report precisely on the exact points at which queries interact with the cube. These combinations can provide excellent access and "audit" data. To some extent, they can confirm the validity of cube design if, say, a developer wants to verify which requests, collected during the business requirements phase of cube design, are actually valid, and which, by contrast, might be considered for removal from the cube structure based upon disuse, should the time arrive that we wish to optimize cube size and performance by jettisoning little-used data.

StartTime and Duration provide the ingredients for evolved performance trending, and act as useful statistics upon which to base numerous types of administrative reports, including information that will help us to plan for heavy reporting demands and other cyclical considerations. MOLAPPartitions and ROLAPPartitions, which provide counts on the different multidimensional OLAP or relational OLAP partitions, respectively, that were used to retrieve the specified query results, can also offer great advanced reporting options, particularly in the analysis / monitoring of partitioning scheme efficiency, and the related planning for adjustments and so forth.

Finally, SamplingRate displays the setting in effect for automatic logging of queries performed by MSAS. This appears in Illustration 2 at its default of 10. The setting can be changed, however, as we shall see in the next section.



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