In Command Manager 9.3.1, you can use scripts to manage Intelligent Cube Caches.
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In my last blog post, I blogged about the new MicroStrategy Community. Jaime Perez, VP of Worldwide Customer Services, and his crew have come up with a better way for us to engage with MicroStrategy as well as his team.
Speaking of Jaime, last June, he posted this great tip on the MicroStrategy Knowledgebase site as a TechNote. I am reblogging it since it is one of the most frequent questions I get asked and I find it an extremely useful Tip & Trick. Also, this will give you an idea of the great stuff being posted in the MicroStrategy Community.
MicroStrategy and Cross Joins
In some scenarios, one may encounter cross joins in the SQL View of a standard, SQL Report in MicroStrategy. Cross joins appear when two tables do not have any common key attributes between them in which they can inner join. As a result, the two tables essentially combine together to create one table that has all the data from both tables, but this results in poorer performance with a common effect of increased execution times. Sometimes these execution times, and performance hits, can be very severe. Therefore, it is important to understand some simple steps that can be performed to resolve a cross join, as well as some steps to understand why it may be appearing in the SQL View of the report.
One common occurrence of a cross join is when a report contains at least two unrelated attributes in the grid, and no metrics are present in order to relate the unrelated attributes via a fact table. Such a occurrence can be resolved in a few ways:
- Create a relationship filter, set the output level as the unrelated attributes (or the entire report level), and then relate these by a Logical Table object
- Create a relationship filter, set the output level as the unrelated attributes (or the entire report level), and then relate these by a Fact object
- Add a metric to the report that uses a fact from a table in which both attributes can inner join to
This provides a pathway from the fact table to the lookup tables in which the unrelated attributes are sourced from. The result is an inner join between the fact table and the lookup tables, which resolves the cross join between the two unrelated lookup tables.
Options 1 and 2 provide a means in which the report template can remain as only attributes, whereas Option 3 would have a metric on the report. Option 3 may not be desired if a metric does not want to be placed on the report. Keep in mind that other techniques can also be employed to have the metric on the report, but formatted to be hidden from display.
More common scenarios include cross joins between a fact table and a lookup table, and are typically surprising to a developer. These situations can be a bit more tricky to troubleshoot and resolve, but here are a few techniques that can be employed to try to resolve the issue:
- In SQL View look at where the cross join appears, and between which tables the cross join appears
- Open up those tables in the Table Editor by navigating to the Schema Objects\Tables folder, and double-clicking the tables
- Select the Logical View Tab of both tables to see all the logical objects mapped to the table
- Take note of which attributes have a key icon beside them
- These key attributes denote attributes at the lowest level of their hierarchy presently mapped to the table and/or attributes that are in their own hierarchy (meaning they have no parents or children)
- The SQL Engine will join 2 tables on common key attributes only, so if none of the key attributes on either table exist on both tables, then a cross join should appear
This means that just because a Region attribute exists on Table_A and a Region attribute exists on Table_B does not necessarily mean that the SQL Engine will join on Region. If Region has its child attribute on the table, then that attribute should be the key as it is the lowest level attribute of its particular hierarchy mapped to the table. If Region exists on both tables, and is also a key attribute on both tables, then an inner join should take place on Region.
This essentially means that one can find a cross join, investigate the tables in which it appears, and verify if at least 1 common key attribute exists between the tables. If not, then that should be the first path to investigate because a cross join is correct in that scenario.
You can find a detailed video on how this issue is reproduced and resolved here: Tech Note 71019 . Steps to reproduce and resolve
MicroStrategy Technical Support can assist with resolving cross joins in a specific report, however caution should be taken when resolving such issues. In some scenarios, the cross join is resolved through modifications to the schema objects, which can have a ripple effect to all other reports in an environment. For example, if a relationship is changed in the Region attribute to resolve a cross join in one report, this change will be reflected in all other reports that use Region, and potentially the hierarchy in which Region belongs. As a result, the SQL View of one report will have the cross join resolved, but the SQL may have changed in other reports using Region or its related attributes. This may or may not be desired. MicroStrategy Technical Support may not be able to fully understand the impact of such a schema change to the data model, so before a change is made to the data model the consequences of such a change should be fully understood by the developer, and any changes made to the schema should be recorded.
 Jaime Perez, TN47356: How to troubleshoot cross joins in SQL Reports for the SQL Generation Engine 9.x, MicroStrategy Community, 06/24/2014, http://community.microstrategy.com/t5/Architect/TN47356-How-to-troubleshoot-cross-joins-in-SQL-Reports-for-the/ta-p/196989.
 MicroStrrategy Knowledgebase, Tech Note 71019 . Steps to reproduce and resolve,
This is from a blog post by Thomas Greco.
Thomas is a web developer / graphic designer living in New York City. When Thomas isn’t striving towards frontend perfection, he enjoys hanging with friends, going to concerts, and exploring through the wilderness!
The Dygraphs.js library allows developers to create interactive charts using the X and Y axis to display powerful diagrams. The more data being parsed, the higher the functionality of the graph. That being said, Dygraphs was built for these visualizations to contain a multitude of views. For example, Dygraphs.js makes it capable to analyze separate portions of a data-set, such as specific months, in addition to the timeframe in its entirety. Also, the Dygraphs.js library is compatible across all major web browsers, and can responds to touch sensitivity, making it a thoroughougly solid choice as a data visualization framework.
Eventually becoming the successor to Protovis.js, D3 is capable of creating stunning graphics via dynamically updating the DOM. An acronym for Data-Driven Document, D3.js makes use of chained methods when scripting visualizations, subsequently creating dynamic code that is also reusable. Due to its reliance on the DOM, D3 has been created in accordance with W3C web standards so that the library may render correctly across web browsers. Lastly, D3′s path generator function, defined as
d3.svg.line(), gives developers the capability to produce a handful of SVGs by defining different paths, and their properties.
The Google Visualization API
Hailing from the Google Developers Console (GDC), Google’s Visualization API can be called with barely any code. In addition to easy DOM modification, this Google API makes it easy for its user to easily define custom modifier functions that can then be placed into custom groups. Furthermore, this interface’s usability, matched with its support from the GDC’s open source network, place it among the top of the list of data visualization tools.
Polymaps.js makes use of SVGs to generate interactive web maps with cross browser compatibility in mind. At the heart of Polymaps lies vector tiles, which help ensure both optimal load speeds and optimal zoom functionality. Although it may come configured with components, Polymaps.js is easily customized, and is able to read data in the form of vector geometry, GeoJSON Files, and more. Check out the graph below of the U.S. created by the U.S. Census borough.
The Raphael.js library was created with an emphasis on browser compatibility. The framework follows the SVG W3C Recommendation, which is a set of standards that ensure images are completely scalable and without pixelation. In addition to the use of SVGs, Raphael.js even reverts to the Vector Model Language (VML) if rendered in Internet Explorer browsers prior to IE9. Although VML is very rarely used today, the support for it does a great job of showing the attention to detail that the Raphael.js team placed on this project when developing the library.
For those who already use the juggernaut that is Ember.js, the developers at Addepar Open Source have created a few add-on libraries to extend the Ember experience: Ember Table, Ember Widgets, and Ember Charts. A child of Ember.js and D3.js, Ember Charts utilizes the properties of flat-design. Although limited, the library does have a handful of options that deal with properties such as color and size, making it fairly simple to create impressive visualizations. Nonetheless, Ember’s presence in the front end could really help Ember Chart’s popularity in the future.