Tag Archives: TM1 Performance
A simple solution to enabling and disabling rules for different versions (or scenarios) using either an attribute on the Version dimension or a simple cube, intersecting the }Cube and Version dimensions.
Dimension order within cubes in TM1 is important not only for performance, but also for usability. When should you set the order for usability and when for performance? And how do you optimise the dimension order for performance? All these questions and more are addressed in this post from ExploringTM1.
There are a few settings in Cognos BI that can dramatically improve performance in Cognos BI when using TM1 as a data source. The first of these is to stop Cognos from using the old Bluenose method of querying TM1 and force it to use the native TM1 engine. The next setting that we need to configure is the Java Virtual Machine, or JVM. Wikipedia defines a Java Virtual Machine … Continue Reading
Cognos Business Intelligence and TM1 now work well together, however performance can be dramatically improved by changing the out of the box settings. These tuning changes are not difficult and can significantly enhance both the speed and usability of both tools. How can BI and TM1 be Integrated When used together we can use Framework Manager to prepare data from a database before it is loaded into TM1, we can … Continue Reading
There are a few settings in Cognos BI that can dramatically improve performance in Cognos BI when using TM1 as a data source. The first of these is to stop Cognos from using the old Bluenose method of querying TM1 and force it to use the native TM1 engine. Bluenose is the default and it is extremely slow on large queries. Anecdotally it appears to want TM1 to return the … Continue Reading
Much Faster Processing with MTQ in TM1 TM1 traditionally uses a single core or thread per task. What this means is that when a user opens a view for a cube (or a TI or Cognos BI opens a view), that view is created by a single core. Recently IBM changed TM1 so that it now can use multi threaded queries and it is almost a linear improvement in performance, roughly … Continue Reading
One of our major customers was facing severe performance problems. The problems related to 2 cubes, in which we wanted to execute a rather tricky elimination. Please follow along while we discuss the cube architecture. We have an allocation cube with the following dimensions: Financial code (10 n elements) Year (2008, 2009, 2010, 2011, 2012) Month (12 months plus YTD consolidations) Scenario (actuals, budget rounds) Allocation phase (7 phases, populated … Continue Reading