for all Hyperion video tutorial/Training/Certification/Material Essbase Optimization Techniques by...

http://learnobiee.blogspot.com [email protected] for all Hyperion video tutorial/Training/Certification/Material

Essbase Optimization Techniquesby

Amit Sharma

http://learnobiee.blogspot.commail to [email protected]

http://learnobiee.blogspot.com/


Essbase Optimization

The first step of Essbase optimization is to monitor Essbase performance and identify performance bottleneck.

Viewing Essbase Server/Database InformationMaxL Display ApplicationESSCMD GETAPPSTATE, GETPERFSTATS ESSCMD GETAPPINFO,GETDBINFO

ListLocks, UNLOCKOBJECT

Monitoring User Sessions and RequestsMaxL display session, alter system

alter system logout session by user 'admin' on application sample force;Unlockobject 1 sample basic basic


Database Cache Settings Database Cache Settings describes database cache settings and lists the location of

the settings in Administration Services, MaxL, and ESSCMD.


Optimizing Database Caches

Analytic Services depends on caches for indexes, paging and calculating. Inadequate cache settings can significantly impact database processes

Index Cache

When you request a data block, the index is used to find its location on disk. If the block location is not found in the index cache, the index page that has the block entry is pulled into memory (into the index cache) from the disk. If the index cache is full, the least recently used index page in memory (in the index cache) is dropped to make room for the new index page.

alter database set index_cache_size


1

Block Numbering

Index:100-10, New York 100-20, New York100-30, New York100, New York200-20, New York200-30, New York200-40, New York200, New York

. . .

100-10, Massachusetts100-20, Massachusetts100-30, Massachusetts

. . .

2

4

5

6

20

21

7

22

8

33

1

3



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.

alter database set data_cache_size


Calculator Cache


The calculator cache is a buffer in memory that Essbase uses to create and track data blocks during calculation operations. Essbase can create a bitmap, whose size is controlled by the size of the calculator cache, to record and track data blocks during a calculation. Determining which blocks exist using the bitmap is faster than accessing the disk to obtain the information, particularly if calculating a database for the first time or calculating a database when the data is very sparse.

The dynamic calculator cache is a buffer in memory that Essbase uses to store all of the blocks needed for a calculation of a Dynamic Calc member in a dense dimension (for example, for a query). Essbase uses a separate dynamic calculator cache for each open database. The DYNCALCCACHEMAXSIZE setting in the essbase.cfg file specifies the maximum size of each dynamic calculator cache on the server.

Dynamic calculator cache


Preventing or Removing Fragmentation

You can prevent and remove fragmentation:

•To prevent fragmentation, optimize data loads by sorting load records based upon sparse dimension members. •To remove fragmentation, perform an export of the database, delete all data in the database with CLEARDATA, and reload the export file.

•To remove fragmentation, force a dense restructure of the database.

Types of Database Restructuring This section describes the two ways that a database restructure is triggered: Implicit RestructuresExplicit Restructures


Hit Ratio – Percentage of searches that did not involve retrieving from disk

Percentage Of Maximum Blocks Existing - Is A Percentage Comparison between existing blocks and potential blocks

Compression Ratio - Ratio of compressed block size to expanded block size

The average clustering ratio database statistic indicates the fragmentation level of the data (.pag) files. The

maximum value, 1, indicates no fragmentation


Others Database Settings

Miscellaneous Database Settings describes miscellaneous database settings and lists the location of the settings in Administration Services, MaxL, and ESSCMD.


Ten Steps To Optimization

Step 1: The Starting Line: Model Analysis

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

Sparse smallest to Largest

Dense LargestSmallest

Hourly 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 TuningSystem 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 SettingsThe 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 LoadsKnow 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 dimensionsHigher Index cache settings may help performance since blocks are found quicker



Step 8: Optimize Calculations

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.

for all Hyperion video tutorial/Training/Certification/Material Essbase Optimization Techniques by...

Documents

Transcript of for all Hyperion video tutorial/Training/Certification/Material Essbase Optimization Techniques by...