Tuesday, October 28, 2014

Part 4: DBAs guide to managing sandboxes

This is the next part in my on-going series of posts on the topic of how to successfully manage sandboxes within an Oracle data warehouse environment. In Part 1 I provided an overview of sandboxing (key characteristics, deployment models) and introduced the concept of a lifecycle called BOX’D (Build, Observe, X-Charge and Drop). In Part 2 I briefly explored the key differences between data marts and sandboxes. Part 3 explored the Build-phase of our lifecycle.

Now, in this post I am going to focus on the Observe-phase. At this stage in the lifecycle we are concerned with managing our sandboxes. Most modern data warehouse environments will be running hundreds of data discovery projects so it is vital that the DBA can monitor and control the resources that each sandbox consumes by establishing rules to control the resources available to each project both in general terms and specifically for each project.  

In most cases, DBAs will setup a sandbox with dedicated resources. However, this approach does not create an efficient use of resources since sharing of unused resources across other projects is just not possible. The key advantage of Oracle Multitenant is its unique approach to resource management. The only realistic way to support thousands of sandboxes, which in today’s analytical driven environments is entirely possible if not inevitable, is to allocate one chunk of memory and one set of background processes for each container database. This provides much greater utilisation of existing IT resources and greater scalability as multiple pluggable sandboxes are consolidated into the multitenant container database.

Resources

 

Using multitenant we can now expand and reduce our resources as required to match our workloads. In the example below we are running an Oracle RAC environment, with two nodes in the cluster. You can see that only certain PDBs are open on certain nodes of the cluster and this is achieved by opening the corresponding services on these nodes as appropriate. In this way we are partitioning the SGA across the various nodes of the RAC cluster. This allows us to achieve the scalability we need for managing lots of sandboxes. At this stage we have a lot of project teams running large, sophisticated workloads which is causing the system to run close to capacity as represented by the little resource meters.

 

Expand 1

 

It would be great if our DBA could add some additional processing power to this environment to handle this increased workload. With 12c what we can do is simply drop another node into the cluster which allows us to spread the processing of the various sandbox workloads loads out across the expanded cluster. 

Expand 2

Now our little resource meters are showing that the load on the system is a lot more comfortable. This shows that the new multitenant feature integrates really well with RAC. It’s a symbiotic relationship whereby Multitenant makes RAC better and RAC makes Multitenant better.

So now we can add resources to the cluster how do we actually manage resources across each of our sandboxes? As a DBA I am sure that you are familiar with the features in Resource Manager that allow you to control system resources: CPU, sessions, parallel execution servers, Exadata I/O. If you need a quick refresher on Resource Manager then check out this presentation by Dan Norris “Overview of Oracle Resource Manager on Exadata” and the chapter on resource management in the 12c DBA guide.

With 12c Resource Manager is now multitenant-aware. Using Resource Manager we can configure policies to control how system resources are shared across the sandboxes/projects. Policies control how resources are utilised across PDBs creating hard limits that can enforce a “get what you pay for” model which is an important point when we move forward to the next phase of the lifecycle: X-Charge. Within Resource Manager we have adopted an “industry standard” approach to controlling resources based on two notions:

  1. a number of shares is allocated to each PDB
  2. a maximum utilization limit may be applied to each PDB

To help DBAs quickly deploy PDBs with a pre-defined set of shares and utilisation limits there is a “Default” configuration that works, even as PDBs are added or removed. How would this work in practice? Using a simple example this is how we could specify resource plans for the allocation of CPU between three PDBs:

RM 1

 

As you can see, there are four total shares, 2 for the data warehouse and one each for our two sandboxes. This means that our data warehouse is guaranteed 50% of the CPU whatever else is going on in the other sandboxes (PDBs). Similarly each of our sandbox projects is guaranteed at least 25%. However, in this case we did not specify settings for maximum utilisation. Therefore, our marketing sandbox could use 100% of the CPU if both the data warehouse and the sales sandbox were idle.

By using the “Default” profile we can simplify the whole process of adding and removing sandboxes/PDBS. As we add and remove sandboxes, the system resources are correctly rebalanced, by using the settings specific default profile, across all the plugged-in sandboxes/PDBs as shown below.

RM 2

 

Summary

In this latest post on sandboxing I have examined the “Observe” phase of our BOX’D sandbox lifecycle. With the new  multitenant-aware Resource Manager we can configure policies to control how system resources are shared across sandboxes. Using Resource Manager it is possible to configure a policy so that the first tenant in a large, powerful server experiences a realistic share of the resources that will eventually be shared as other tenants are plugged in.

In the next post I will explore the next phase of our sandbox lifecycle, X-charge, which will cover the metering and chargeback services for pluggable sandboxes. 

 

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Tuesday, March 4, 2014

The OLAP Extension is now available in SQL Developer 4.0



The OLAP Extension is now in SQL Developer 4.0.

See
http://www.oracle.com/technetwork/developer-tools/sql-developer/downloads/sqldev-releasenotes-v4-1925251.html for the details.

The OLAP functionality is mentioned toward the bottom of the web page.
You will still need AWM 12.1.0.1.0 to
  • Manage and enable cube and dimension MV's.
  • Manage data security.
  • Create and edit nested measure folders (i.e. measure folders that are children of other measure folders)
  • Create and edit Maintenance Scripts
  • Manage multilingual support for OLAP Metadata objects
  • Use the OBIEE plugin or the Data Validation plugin
What is new or improved:
  • New Calculation Expression editor for calculated measures.  This allows the user to nest different types to calculated measures easily.  For instance a user can now create a Moving Total of a Prior Period as one calculated measure.  In AWM, it would have required a user to create a Prior Period first and then create a Moving Total calculated measure which referred to the Prior Period measure.  Also the new Calculation Expression editor displays hypertext helper templates when the user selects the OLAP API syntax in the editor.
  • Support for OLAP DML command execution in the SQL Worksheet.  Simply prefix OLAP DML commands by a '~' and then select the execute button to execute them on the SQL Worksheet.  The output of the command will appear in the DBMS Output Window if it is opened, or the Script Output Window if the user has executed 'set serveroutput on' before executing the DML command.
  • Improved OLAP DML Program Editor integrated within the SQL Developer framework.
  • New diagnostic reports in the SQL Developer Report navigator.
  • Ability to create a fact view with a measure dimension (i.e. "pivot cube").  This functionality is accessible from the SQL Developer Tools-OLAP menu option.
  • Cube scripts have been renamed to Build Specifications and are now accessible within the Create/Edit Cube dialog.  The Build Specifications editor there, is similar to the calculation expression editor as far as functionality.

Thursday, March 28, 2013

Oracle SQL Developer Data Modeler 3.3 now available

http://www.oracle.com/technetwork/developer-tools/datamodeler/overview/index.html

Attention all data modellers - we are pleased to announce the release of SQL Developer Data Modeler 3.3. This release includes a new search, reports can be generated from search results, extended Excel import and export capabilities and more control and flexibility in generating your DDL. Here are a few links to get you started:


For data warehouse data modellers there are some very important new features around logical models, multi-dimensional models and physical models. For example:

  • Support for surrogate keys during engineering to relational model which can be set on each entity. 
  • More flexible transformation to relational model with mixed engineering strategies based on “engineer” flag and subtypes setting for each entity in the hierarchy
  • Export to “Oracle AW” now supports Oracle 11g OLAP
  • Support for role playing dimensions in export to Oracle AW.
  • Level descriptive attributes can be created without mapping to attribute in logical model.
  • Multidimensional model can be bound directly to relational model. 
  • Support EDITIONING option on views, and support for invisible indexes in Oracle 11g physical model.


Lots of great features that will make life a lot easier for data warehouse teams.

Tuesday, March 12, 2013

Starbucks 1TB cube in production

Check out the customer snapshot Oracle has published which describes the success Starbucks Coffee has achieved by moving their data warehouse to the Exadata platform, leveraging the Oracle Database OLAP Option and Oracle BIEE at the front end.    10,000 users in HQ and across thousands of store locations now have timely accurate and calculation rich information at their fingertips.


Starbucks Coffee Company Delivers Daily, Actionable Information to Store Managers, Improves Business Insight with High Performance Data Warehouse


( http://www.oracle.com/us/corporate/customers/customersearch/starbucks-coffee-co-1-exadata-ss-1907993.html )

By delivering extreme performance combined with the architectural simplicity and sophisticated multidimensional calculation power of the in-database analytics of the Database, Starbucks use of OLAP has enabled some outstanding results. Together with the power of other Oracle Database and Exadata benefits such as Partitioning, Hybrid Columnar Compression, Storage Indexes and Flash Memory, Starbucks is able to handle the constant growth in data volumes and end-user demands with ease.

A great example of the power of the "Disk To Dashboard" capability of Oracle Business Analytics.

Tuesday, January 29, 2013

Webcast: Using Oracle OLAP data in multiple BI clients. Feb. 5 at 9:00 PST


Want to learn how to connect multiple BI clients such as SAP BusinessObjects Analysis, Cognos and Microsoft Excel to Oracle OLAP, quickly and easily, using the XMLA version of the MDX Provider for Oracle OLAP?  Check out the Oracle webcast on February 5 at 9:00 AM PST.

You will see how it works and how customers in different industries are using this solution.

Just sign up here and join us on February 5th.

Monday, December 10, 2012

Webinar: Using XMLA with Cognos and Oracle OLAP Cubes

When:  Thursday, Dec 13, 2012 at 9:00am PST / 12:00pm EST / 6:00pm CET.

To attend:    Sign up here.
 
 
If you use a business intelligence tool such as IBM Cognos, Microstrategy or SPA BusinessObjects Analysis that uses XMLA to connect to multidimensional data sources, check out a free webinar by Simba Technologies which offers a "sneak peak" of the Simba XMLA Provider for Oracle OLAP.  The Simba XMLA Provider for Oracle OLAP is an XMLA version for the Simba MDX Provider for Oracle OLAP, the gold standard in MDX connectivity to Oracle OLAP.   (The Simba MDX Provider for Oracle OLAP allows MDX based clients such as Microsoft Excel PivotTables to query Oracle OLAP cubes.  The XMLA version allows clients that use XMLA rather than ODBO to connect to Oracle OLAP.)

Simba will demonstrate using IBM Cognos using the XMLA provider to query Oracle OLAP cubes.  Here's a brief outline of the session.

See how:
  • Familiar business intelligence applications such as IBM Cognos can connect to an Oracle OLAP cube.
  • Ad-hoc querying and data analysis can be performed directly in IBM Cognos on your OLAP data.
  • The most advanced application that responds to XMLA requests available enables users to interactively build reports, drill into details and slice and dice data
  • Connectivity can be established without the need to install any software on the client machine.
    Simply connect to the XMLA service and everything works!
See you there!


Thursday, June 14, 2012

Creating Oracle BI Presentation Layers from Oracle Cubes

Beginning with Oracle BI 11.1.1.5, the Oracle BI Administration tool has the ability to import Oracle cubes, dimensions and hierarchies into the Physical layer of the Oracle BI repository.  This provides an alternative to the Oracle BI plug-in for Analytic Workspace Manager.  Each method works somewhat differently and each has certain advantages that might make it the best choice for different situations.

The Oracle BI plug-in for Analytic Workspace Manager pushes metadata into the  Oracle BI repository using UDML code.  The result is a ready to use subject area with the Physical, Business Model and Presentation layers fully built for you.  It creates hierarchies in the Business Model layer, but it does not add hierarchical columns to the Presentation layer.  You can easily add hierarchical columns to the Physical layer by dragging hierarchies from the Business Model layer.

Hierarchical column in the channel dimension.

The Oracle BI plug-in for Analytic Workspace Manager creates a Physical layer that maps to hierarchy and cube views (these views make the dimensions, hierarchies and cubes look like a star schema to SQL).  In the Business Model, it assigns the SUM aggregation operator to measures selected from the cube view.  This approach allows SQL to aggregate data above the cube.  This is useful for queries that first filter and then aggregate and attribute break outs.  For example:

  • Report by Year where Quarter of Year is 3 and 4.  Data at the Year level will be the aggregate of child quarters 3 and 4. 
  • Report by Year, Color and Size where Color and Size are attributes of Item.  Oracle will SELECT year, color, size, SUM(sales) .... GROUP BY year, color, size.

In both cases, Oracle BI will get the highest aggregate level data it can from the cube and do some additional aggregation in SQL.

When cubes are imported using the Oracle BI Administration tool the Physical layer is mapped directly to the cube and dimensions, bypassing the cube and hierarchy views.  The SQL created in this case selects from the OLAP_TABLE table function.  Oracle BI has optimized this SQL to take advantage of some features in OLAP_TABLE, which offers better query performance with hierarchical columns in some situations.

When the cube is imported with the Oracle BI Administration tool, aggregation is set to 'External Aggregation'.  This will disable the ability to create queries that require aggregation above the cube.  This is good in that it prevents users creating queries that might not work as they expect in some cases, but it prevents filter before aggregate and attribute break out queries.

Also, the import method only creates the Physical layer.  It does not create the Business Model and Presentation layers.  You do that by dragging the Physical layer to the Business Model layer and then dragging the Business Model to the Presentation layer.  That's all quick and easy, but there will be some things you will want to update in the Business Model and Presentation layers.  For example, deleting some columns you don't really need and arranging columns in hierarchical order.  I've posted the paper Creating an Oracle BI Presentation Layer from Oracle Cubes on OTN.  This paper will walk you through the process of refining the Business Model and Presentation layers.

My recommendation is to try each method, learn about the strengths of each and choose which is best for any particular application.  Since all the data comes from the same cube, there's nothing wrong with using both methods.