Essbase Application Performance Tuning

Essbasse system and application performance tuning are very important tasks for a successful implementation of Essbase or Hyperion Planning. Good application design and proper system administration include some of following items.

1) Design The Outline Hour Glass Model
2) Defragmentation
3) Database Restructuring
4) Compression Techniques
5) Cache Settings
6) Intelligent Calculation
7) Data Load Optimization
8) Uncommitted Access

Design the Outline using Hour Glass Model:

We have to build the Outline should be in a way that dimensions are placed in the following order
1) Dimension tagged as an Account Dimension Type
2)Dimension tagged as a Time Dimension Type
3) Largest dense
4)Smallest dense
5) Smallest sparse
6) Largest sparse

Attribute Dimensions.
Using hourglass model improves the calculation Performance of the cube.

Defragmentation:

Fragmentation is caused by any of the following activities:
1) Frequent data loads
2) Frequent data retrieval
3) Calculations

We can check whether the cube is fragmented or not by seeing its Average Clustering Ratio in the properties . The Optimum clustering value is 1. If the average clustering ratio is less than 1, then the cube is fragmented which degrades the performance of the cube.
There are Three ways of doing Defragmentation.
Export Data of the application in to text file, then clear data and reload the data using text file without using Rules file.
1)Using MAXL Command:
Alter Database App name .DB name Force Restructure;
2)Add and Delete One Dummy Member in the Dense Dimension to force a full restructure.

Database Restructuring:

There are 3 types of Restructure.
1)Outline Restructure
2)Sparse Restructure
3)Dense Restructure/Full Restructure

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Outline Restructure:
When we rename any member or add Alias to any member or member formula changes then outline Restructure would happen.
Dense Restructure:
If we want to moved, delete and add a member to a dense dimension then dense Restructure would happen.
Sparse Restructure:
when we moved, deleted, or added a member to a sparse dimension then sparse restructure would happen.

Compression Techniques:

There are 4 types of Compressions. They are
1)Bitmap Compression
2)RLE – Run length Encoding
3)ZLIB
4)No Compression.

Index Cache: Min -1024 KB (1048576 bytes) Default – Buffered I/O : 1024 KB (1048576 bytes);Direct I/O : 10240 KB (10485760 bytes) Opt -Combined size of all essn.ind files, if possible; as large as possible otherwise. Do not set this cache size higher than the total index size, as no performance improvement results.

Data File Cache:

Min – Direct I/O: 10240 KB(10485760 bytes) Default -Direct I/O: 32768 KB(33554432 bytes) Opt -Combined size of all essn.pag files, if possible; otherwise as large as possible. This cache setting not used if Essbase is set to use buffered I/O.
Data Cache: Min – 3072 KB (3145728 bytes) Default – 3072 KB (3145728 bytes) Opt -0.125 * the value of data file cache size.
Calculator Cache: Min – 4 bytes Max: 200,000,000 bytes Default – 200,000 bytes Opt -The best size for the calculator cache depends on the number and density of the sparse dimensions in your outline. The optimum size of the calculator cache depends on the amount of memory the system has available.Set cache High |Low |Off; —–à command used in calc scripts to set the cache.

We set cache value for calculator cache in Essbase.cfg file.

We need to restart the server to make the changes in calculator cache after setting it in config file.

Intelligent Calculation:

Whenever the Block is created for the 1st time Essbase would treat it as Dirty Block. When we run Calc all/Calc dim Essbase would calculate and mark all blocks as Clean blocks. Subsequently, when we change value in any block the block is marked as Dirty block. when we run calc scripts again only dirty blocks are calculated it is known as Intelligent Calculation.

By default Intelligent calculation is ON. To turn off the Intelligent Calculation use command
SET Update Calc OFF;

Data Load Optimization:

Data load optimization can be achieved by the following.
1)Always load the data from the Server than file system.
2)The data should be at last after the combinations.
3)Should use #MI instead of ‘0’s. If we use ‘0’ uses 8 bytes of memory for each cell.
4)Restrict max Decimal Points to ‘3’ –à 1.234
5)Data should be loaded in the form of Inverted Hourglass Model.(Largest sparse to Smallest Sparse followed by smallest Dense to Largest Dense data) .Sorting data prior to loading in this manner will allow a block to be fully populated prior to storing it on disk .thus eliminating the need to retrieve a previously created block from disk during the load.
6)Always Pre-Aggregate data before loading data in to Database
DL Threads write (4/8): Used for Parallel Data loads. Loads 4 records at a time for 32-Bit system and 8 records for 64-Bit system.

By default Essbase Loads data Record – by – Record which would consume more time resulting in consuming huge time for data load.

Uncommitted Access:

Under uncommitted access, Essbase locks blocks for write access until Essbase finishes updating the block. Under committed access, Essbase holds locks until a transaction completes. With uncommitted access, blocks are released more frequently than with committed access. The Essbase performance is better if we set uncommitted access. Besides, parallel calculation only works with uncommitted access.