Microsoft SQL Server 2008 Analysis Services Unleashed Review Chapter 41
Conceptually, I consider this chapter similar to chapter 38 (on trace monitoring). This chapter introduces a new infrastructure for monitoring server resources called Dynamic Management Views (DMVs), and here are some resources:
The book provides a DMX query to start using the DMVs, and I will give that short script here:
select * from $system.discover_connections
select * from $system.dbschema_tables
select * from $system.dbschema_columns
However, in practical terms, though the book provides essential descriptive information on DMVs, the reality is that this area continues to be an active topic of discussion and practice. Continue reading “Resource Monitoring” »
Microsoft SQL Server 2008 Analysis Services Unleashed Review Chapter 38
This chapter descriptively provides an overview of SQL Server Profiler, the application which can be used to trace Analysis Services. The information is useful, but I will be offering a more prescriptive approach to tracing in this blog post.
First, acquire the downloadable Microsoft SQL Server Community Samples: Analysis Services (http://sqlsrvanalysissrvcs.codeplex.com/). Continue reading “Using Trace to Monitor and Audit Analysis Services” »
Microsoft SQL Server 2008 Analysis Services Unleashed Review Chapter 25
This chapter talks about the important leverage feature of SSAS, the ability to either scale-up or scale-out (or both):
- Scale-up means improving the power of a single machine, perhaps by increasing the number of processors or memory or dedicated storage capacity
- Scale-out means improving the overall power through multiple machines
This chapter includes important considerations on these features when looking solely at SSAS. However, I need to mention that Microsoft has a specific high-performance team with their own website. The team is referred to as SQLCAT (where CAT is an acronym for Customer Advisory Team), and on their homepage they say “Enabling SQL Server customers to navigate the most challenging frontiers of large scale data management”. They continue to publish guidelines on scalability and other topics related to large scale implementations. They want to know about your large scale challenge, and since they have a budget, they may be able to dedicate resources toward helping you solve some or all of your challenges, especially if they are novel and new based on the team’s past experience.
Continue reading “Building Scalable Analysis Services Applications” »