essbase qa

7/31/2019 Essbase QA

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1) Difference between ASO & BSO?

ASO is Essbase's alternative to the sometimes cumbersome BSO method of storing data in an Essbase

database. The differences between the two are as follows:

BSO ASO

Essbase creates a data block for each unique

combination of sparse standard dimension

members (providing that at least one data value

exists for the sparse dimension member

combination). The data block represents all the

dense dimension members for its combination of

sparse dimension members.

Essbase creates an index entry for each data block.

The index represents the combinations of sparse

standard dimension members. It contains an entry

for each unique combination of sparse standard

dimension members for which at least one data

value exists.

You manage the show with Tablespaces. You got

to accept/alter the allocation of default & temp

storage areas & disk space, as per the

requirements.

All data is stored, except for dynamically calculatedmembers. All data consolidations and parent-child

relationships in the database outline are stored as

well.

While the block storage method is quite efficient

from a data to size ratio perspective, a BSO

database can require large amounts of overhead to

deliver the retrieval performance demanded by the

business customer.

The ASO database efficiently stores not only zerolevel data, but can also store aggregated

hierarchical data with the understandings that

stored hierarchies can only have the no-

consolidation (~) or the addition (+) operator

assigned to them and the no-consolidation (~)

operator can only be used underneath Label Only

members. Outline member consolidations are

performed on the fly using dynamic calculations

and only at the time of the request for data. This

is the main reason why ASO is a valuable option

worth consideration when building an Essbasesystem for your customer.

The database outline must be loaded into memory

as a single element

The database outlines are created and stored in

what can be considered a page-able format.

This means that instead of Essbase loading the

entire database outline into memory, the page-

able outline can be loaded into memory either

one page or section at a time. This can free up

resources and can help make data retrieval and

data aggregations faster by reducing the amount

of memory consumed by large database outlines.

- Several databases stored in one application.

- No reserved names for application and database

names

- Account dimension supports all types of

calculations and attribute members.

- Calculation scripts are supported.

- Uncomplicated write back ability (before v 9)

- Formulas are allowed in all dimensions with no

restrictions.

- Aggregate storage applications have some

limitations that do not apply to block storage

applications with regard to consolidations,

calculations, and overall robust functionality.

- Can store only one database per application.

- Names reserved for table spaces cannot be used

as application or database names.

- Accounts dimension does not support time

balance members and association of attribute


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- Outline changes do not automatically clear data

values, even if a data source is used to both modify

members and load values. Therefore, incremental

data loads are supported for all outlines. (before

version 9)

- Currency conversion is supported.

dimensions.

- On non-account dimensions, there are

restrictions on label only members and dynamic

time series members. Members tagged as

dynamic hierarchies have no restrictions on the

consolidation settings. Stored hierarchy members

can only be tagged as label only or (+) addition.

- Non-account dimensions support only

consolidation operator (+) addition.

- Calculation scripts are not supported.

- Formulas are allowed only on account

dimension members and allowed with certain

restrictions.

- Only Level 0 cells whose values do not depend

on formulas in the outline are loaded.

- Data values are cleared each time the outline is

structurally changed. Therefore, incremental data

loads are only supported for outlines that do notchange.

- Currency conversion is not supported without

the use of special MDX queries. This method can

have a negative effect on performance.

- For better performance, the outline dimensions

must be defined as Dense or Sparse, based on data

density, which can sometimes be difficult to get

exactly right.

- Database calculationcalculation script or outline

consolidation.

- Calculation order will need to be defined in thecalc scripts and is predetermined in a default

outline calculation.

- Unrestricted write back ability which can be

dangerous if care is not exercised.

- No automatic update to values after data load.

- Necessary calculation scripts need to be specially

executed, including any default calculations.

- Sometimes requires large amounts of resources.

- Easy optimization, massive data scalability,

reduced disk space, and up to 100 times faster.

- Database creation is accomplished by either

migrating a BSO outline or defined as new after

application creation.

- Outline dimensions will not need to be

designated as dense or sparse.- Outline is validated every time a database is

started.

- Database calculation or aggregation of the

database can be predefined by defining

aggregate views.

- Calculation order is not relevant for database

calculation, but is relevant for dynamic

calculation formulas.

- Limited write back ability

- At the end of a data load, if aggregation exists,

the values in aggregation are recalculated and

updated automatically.

- Aggregate storage database outlines are page-

able. This feature significantly reduces memory

usage for very large database outlines.

As you can see, there are some substantial differences and some very good reasons to use one type of

database over another. To give you our idea of the ideal application of ASO and BSO, read below:


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ASO Database: The ASO database is ideal for dynamically built Essbase cubes that are usually

Read Only and used for reporting, presentation, and analysis. This type of database would also tend to

have a rather large outline where at least one dimension has a significant amount of members. A parts

dimension or product dimension comes to mind.

Behind this ASO database would be a large BSO parent Essbase database, from which the dynamic ASO

databases are built on the fly.

BSO Database: The BSO database is ideal for virtually any size cube, but where performance is

not necessarily the number one priority. Accuracy and completeness of data would be the main

consideration. The BSO database is ideal as the large parent database where users from many different

departments can trigger jobs which will dynamically build ASO reporting cubes on an as needed basis.

The typical BSO database is ideally suited for financial analysis applications.

Of course, these are just one possibility or scenario. The beauty of Essbase is that you can do most

anything with it. Heck, you could easily have a large Oracle relational database as the backend data

source for your ASO cubes. The possibilities are endless!

2) What is Incremental Dimensional Build and one time Dimension Build?

Incremental dimension building is deferred-restructure dimension build process. Particularly insituations where you want to build an outline from multiple data sources and save time by deferring

restructure until after all files have been processed. When we used to build one or more dimensions

from a data file, without restructuring the database we use incremental dimensional build.

Incremental Dimensional Build:

Builds and reads from different data sources for dimension builds and delay restructuring until all data

sources have been processed. If you make frequent changes to a database outline, consider enabling

incremental restructuring. When incremental restructuring is enabled, Essbase defers restructuring so

that a change to the database outline or to a dimension does not cause structural change. Essbase

restructures the index and, if necessary, the affected block the next time the block is accessed.

3) What are the different types of log files?1. Separate log file for all applications 2. Essbase.log for the Essbase server

3. Essbase _service.log 4. Shared_Services_Client.log

5. EssbasePlugin.log in the lcm folder 6. Configtool.log all config logs

7. EssbaseExternalizationTask.log 8. easserver.log

9. eas_install.log 10. essbaseserver-install.log

11.essbaseclient_install.log 12. error logs

4) What is the Extension of Cal Scripts and Rule File? .csc and .rul

5) Why are Filters used?

Filters are used for data level security. If we want to grant access to all dimensions then we wouldn't use

a filter. Just grant read access for the database to the user and/or group. The only reason you would use

a filter is if you wanted to restrict access of the data for example, to restrict access to a particular

dimension member.

6) What are dense and sparse dimensions?

Dense: A dimension which has the high probability that data exists for every combination of dimension

members


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Sparse: A dimension which has low probability that data exists for every combination of dimension

members

7) What are Filters? Data level security

Method of controlling access to database cells in Essbase. A filter is the most detailed level of security,

allowing you to define varying access levels different users can have to individual database values.

8) What are Attributes?

Classification of a member in a dimension. You can select and group members based on their associated

attributes. You can also specify an attribute when you perform calculations and use calculation

functions. Eg: The database in Sample Basic which has product dimension has some attributes like size,

package type, and flavor. We can add these attributes to the dimensions where we can retrieve the data

like for example to retrieve coke with 8 Oz with bottles, this is useful for generating reports.

9) What are different types of attributes?

Essbase supports two different types of attributes.

1. User-Defined attributes: The attributes that are defined by the user.

2. Simple attributes: Essbase supports some attributes, they are: Boolean, date, number, and string. (asin Attribute Dimensions)

10) What is Substitution Variable?

Substitution variables act as global placeholders for information that changes regularly. Each variable

has a value assigned to it and can be changed at any time by the database administrator. The use of

substitution variables helps reduce maintenance of report scripts, eliminating the need for manual

changes to individual report scripts. For example, many report scripts refer to reporting periods, such as

current month or current quarter. By using substitution variables set such as CurrentMonth or

CurrentQuarter, you can change the assigned value in one place, and the appropriate report scripts are

dynamically updated when the report script is executed.

To refer to a substitution variable in your report script, place an ampersand (&) in front of the variable

name. For example, use &CurrentMonth in your report script to reference the substitution variable

CurrentMonth. When the query is executed, &CurrentMonth is substituted with the value defined in

the IBM DB2 OLAP Server or Hyperion Essbase server.

While substitution variables help reduce maintenance in report scripts, someone still has to manually

change the values in the IBM DB2 OLAP Server or Hyperion Essbase server. As an alternative in DB2

Alphablox applications, you could use Java methods in your JSP pages to automatically calculate a

value for the current month or other reporting period, then substitute that value in your report scripts.

11) How is Data Stored in Essbase?

BSO ASO

Essbase creates a data block for each unique

combination of sparse standard dimension

members (providing that at least one data value

exists for the sparse dimension member

combination). The data block represents all the

dense dimension members for its combination of

You manage the show with Tablespaces. You got

to accept/alter the allocation of default & temp

storage areas & disk space, as per the

requirements.


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sparse dimension members.

Essbase creates an index entry for each data block.

The index represents the combinations of sparse

standard dimension members. It contains an entry

for each unique combination of sparse standard

dimension members for which at least one data

value exists.

12) What is an hour glass model?

13) Types of Build Methods?

Generation Reference

Level Reference

Parent child Reference

14) What is Two Pass Calculation?

Property set for members to correct incorrect aggregations.

The members tagged as two pass calc are recalculated when the second pass is executed, overwriting

the incorrect summation with the correct calculation, which is defined on the member formulas or calc

scripts.

15) What is TB First and TB Last?

TB First: in the Sample.Basic database, the accounts member Opening Inventory is tagged as TB First.

Opening Inventory consolidates the value of the first month in each quarter and uses that value for that

months parent. For example, the value for Qtr1 is the same as the value for Jan.

TB Last: in the Sample.Basic database, the accounts member Ending Inventory is tagged as TB Last.Ending Inventory consolidates the value for the last month in each quarter and uses that value for that

months parent. For example, the value for Qtr1 is the same as the value for Mar.

16) How do you calculate the Size of the data block?

Essbase creates a data block for each unique combination of sparse standard dimension members

(providing that at least one data value exists for the sparse dimension member combination).


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The data block represents all the dense dimension members for its combination of sparse dimension

members.

For example, say Market & Scenario are Standard Sparse dimensions with EAST, WEST as children of Market andACTUAL,BUDGET as children of Scenario. Measures & Time are Standard Dense Dimensions with SALES, COGS as childrenof Measures and JAN,FEB as children of Time.

Combinations of Standard Sparse dimensions is

EAST - ACTUAL EAST - BUDGETWEST - ACTUAL WEST - BUDGET

Say the following unique combinations has data

EAST - ACTUALWEST - ACTUAL

Now, as per above information, there will be two Data blocks created.Now, Combinations of Standard Dense Dimensions is

SALES - JAN COGS - JAN

SALES - FEB COGS - FEB

Total is 4 combinations. Hence, there will be 4 cells will be created for each BLOCK.

Size of DATA BLOCK = 8 * (No. of cells in that block) (in bytes)

= 8 * 4 = 64

Size of Cube = (No. of Blocks) * (Block Size) (in bytes)= 2 * 64 = 128

17) How many data blocks are there in your cube?

18) What is meant by Descendents and can you give me the best example to describe it?

19) How do you do the Data Load?

20) Where is ISMember Command used?

22) What is intelligent Calculation?

Intelligent Calculation allows Essbase to remember which data-blocks in the database need to be

calculated based on new data coming in, and which havent been impacted (and dont need calculation).

Intelligent calculation is wonderful when you are running a default calc.

SET UPDATECALC OFF command tells Essbase to ignore it during the calc script.

23) What is meant by Clean block and Dirty block?

With Intelligent Calculation on when a calculation is run Essbase flags blocks as 'clean', and when data is

loaded or changed blocks are flagged as 'dirty'. (It is important to notice that this is not done at the 'cell'

level but instead it is done at the block level.) This helps reduce calculation time by allowing the Essbase

Calculator to skip the blocks flagged as clean.

24) Commands of Intelligent calculation?

Set UpdateCalc On;

Set UpdateCalc Off;

Set ClearUpdateStatus After;

Set ClearUpdateStatus Only;

Set ClearUpdateStatus Off;


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25) How do you calculate the subset of a cube?

CALC DIM;

CALC ALL EXCEPT DIM(Product);

CALC ALL EXCEPT MBR (mbrlist);

26) Difference between standard dimension and attribute dimension?

Standard Dimensions represent the core components of a business plan and often relate to

departmental functions.

Attribute dimensions are a special type of dimension and are associated with standard dimensions.

Through attribute dimensions, you group and analyze members of your standard dimensions. It does not

associate any data.

27) Difference between standard dimension and attribute dimension?

A UDA is a user defined attribute; its basically a tag you can assign to a member that you can then

reference in calcs, security, and reporting to recall those members that have been tagged. UDA's are

fairly flexible as you can assign them across different hierarchy levels.

Attribute dimensions are more rigid, but provide more reporting functionality. Attributes have to be

consistent with the level they are applied to in the dim, in other words you can't have the same attribute

for a member at level 0 and another member at level 1. Attributes are part of a physical dimension that

users can render in a report as a replacement or augmentation to the base dimension they are

associated with. One powerful use is for cross tab reporting. Attribute dims are dynamically calculated

and provide summary level and/or subtotals for the attribute members.

So Attributes are more powerful than UDA's, but they take more work to set up and have more rigid

rules around using them. UDA's are easier to add on and more flexible, but not as robust for reporting.

The way I choose which one to use is based on the need. If the purpose is for end user reporting, I

usually try to use an Attribute dim in most cases. If the need is around identifying members for security

or calculations, I'll often lean towards UDA's. In many cases the two overlap and it just comes down to

preference or what the is best suited for the design and maintenance of the database.

One other point to remember is that you cannot assign an attribute to a dense dimension.

28) What is meant by XREF Function?

XREF function enables a database calculation to incorporate values from a different database.

Syntax: @XREF (locationAlias [, mbrList])

LocationAlias: A location alias for the data source. A location alias is a descriptor that identifies the data

source. The location alias must be set on the database on which the calculation script will be run. The

location alias is set by the database administrator and specifies a server, application, database,

username, and password for the data source.

29) Can you give the same name to different members in UDA?


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30) What is Data Cache?

Data blocks can reside on physical disk and in RAM. The amount of memory allocated for blocks is called

the data cache. When a block is requested, the data cache is searched. If the block is found in the data

cache, it is accessed immediately. If the block is not found in the data cache, the index is searched for

the appropriate block number. The block's index entry is then used to retrieve the block from the proper

data file on disk.

31) What is the Difference between the Data Cache and Data File Cache?

32) What is the Size of your cube?

33) What is shared members? Can shared members have children?

The data associated with the member comes from another member with the same name.

No. Shared members should always be level 0 members.

34) What are the different storage properties in Essbase?Store Data, Dynamic Calc, Dynamic Calc & Store, Never Share, Label Only, Shared Member

35) What is the difference between Dynamic calc and Dynamic Calc and Store?

36) How does Essbase consolidate Data?

37) Different types of Dimension Building?

38) What is Label only? Give the example of it?

Although a label only member has no data associated with it, it can still display a value. The label only

tag groups members and eases navigation and reporting. Typically, label only members are not

calculated.

39) Difference between calc all and Calc Dim?

40) Explain me about your project? And tell me any difficulties that you have faced and how did you

resolve it?

41) What is Commit Block?

Commit Block controls how often blocks in memory are written to disk while busy loading or calculating

a cube. You want to minimize disk writes, as this takes up a lot of processing time, so set this to be quite

high. The default setting is 3000 blocks; if your block size is relatively small (< 10KB) make this much

higher, 20000 to 50000. This setting alone can cause dramatic performance improvements specifically

on Calc All operations and cube loads.

42) What are the different steps so that you can optimize the performance your cube?

Step 1: The Starting Line: Model Analysis

- Minimize the number of dimensions. Do not ask for everything in one model


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- Minimize complexity of individual dimensions. Consider UDAs and Attribute Dimensions in order to

reduce the size of some of the dimensions

- Examine the level of granularity in the dimensions.

Step 2: Order The Outline: Hour-glass model

- Dense dimensions from largest to smallest. Small and large is measured simply by counting the number

of Stored members in a dimension. The effect of sparse dimension ordering is much greater than dense

dimension ordering.

- Sparse dimensions from smallest to largest. This relates directly to how the calculator cache functions

Step 3: Evaluate Dense/Sparse Settings

- Finding the optimal configuration for the Dense/sparse settings is the most important step in tuning a

database.

- Optimize the block size. This varies per operating system, but in choosing the best Dense/sparse

configuration keep in mind that blocks over 100k tend to yield poorer performance. In general, Analytic

Services runs optimally with smaller block sizes

Step 4: System Tuning: System tuning is dependent on the type of hardware and operating

- Keep memory size higher

- Ensure there is no conflict for resources with other applications

Step 5: Cache Settings

- The actual cache settings recommended is strongly dependent on your specific situation.

- To measure the effectiveness of the cache settings, keep track of the time taken to do a calculation and

examine the hit ratio statistics in your database information.

Step 6: Optimize Data Loads

- Know your database configuration settings (which dimensions are dense and sparse).

- Organize the data file so that it is sorted on sparse dimensions. The most effective data load is one

which makes the fewest passes on the database. Hence, by sorting on sparse dimensions, you are

loading a block fully before moving to the next one.

- Load data locally on the server. If you are loading from a raw data file dump, make sure the data file is

on the server. If it is on the client, you may bottleneck on the network

Step 7: Optimize Retrievals

- Increase the Retrieval Buffer size. This helps if retrievals are affected due to dynamic calculations and

attribute dimensions.

- Increase the Retrieval Sort Buffer size if you are performing queries involving sorting or ranking.

- Smaller block sizes tend to give better retrieval performance. Logically, this makes sense because it

usually implies less I/O.

- Smaller reports retrieve faster.

- Attribute may impact the calculation performance which usually has a higher importance from a

performance standpoint.

- If you have a lot of dynamic calculations or attribute dimensions

- Higher Index cache settings may help performance since blocks are found quicker

Step 8: Optimize Calculations


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- Unary calculations are the fastest. Try to put everything in the outline and perform a Calc All when

possible.

- You should FIX on sparse dimensions, IF on dense dimensions. FIX statements on sparse dimensions

only brings into memory blocks with those sparse combinations which the calc has focused on. If

statements on dense dimensions operate on blocks as they are brought into memory.

- Use the Two Pass Calculation tag. Try to avoid multiple passes on the database. In the case where the

calculation is a CALC

- Use Intelligent Calc in the case of simple calc scripts

Step 9: Defragmentation

Fragmentation occurs over time as data blocks are updated. As the data blocks are updated, they grow

(assuming you are using compression) and the updated blocks are appended to the page file. This tends

to leave small free space gaps in the page file.

Time - The longer you run your database without clearing and reloading the more likely it is that it has

become fragmented.

Incremental Loads - This usually leads to lots of updates for blocks.

Many Calculations/Many Passes On The Database - Incremental calculations or calculations that pass

through the data blocks multiple times leads to fragmentation.

Step 10: Partition

By breaking up one large database into smaller pieces, calculation performance may be optimized.

Because this adds a significant layer of complexity to administration, this is the last of the optimization

steps we list. However, this does not mean that has the least impact.

43) What is Partition?

44) What are different types of Partition? Have you ever worked on partitioning?

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