Your SlideShare is downloading. ×
OPTIMIZATION IN ESSBASE:
Application Performance Optimization can be done by the following techniques
1.

Designing of The...
3.

Add and Delete One Dummy Member in the Dense Dimension .

Restructuring: There are 3 types of Restructure.
1.

Outline...
5.

Dynamic Calculator Cache

Index Cache: Index Cache is a buffer in a memory that holds Index Files (.IND). Index cache ...
Data Load Optimization: Data load optimization can be achieved by the following.
1.

Always load the data from the Server ...


Positioning Data in the Same Order As the Outline



Loading from the Essbase OLAP Server



Making the Data Source A...
Report Script Optimization:
1) In the report script first specify the sparse dimensions and then dense dimensions, why
mea...
Upcoming SlideShare
Loading in...5
×

Optimization in essbase

1,691

Published on

Published in: Technology
0 Comments
2 Likes
Statistics
Notes
  • Be the first to comment

No Downloads
Views
Total Views
1,691
On Slideshare
0
From Embeds
0
Number of Embeds
0
Actions
Shares
0
Downloads
138
Comments
0
Likes
2
Embeds 0
No embeds

No notes for slide

Transcript of "Optimization in essbase"

  1. 1. OPTIMIZATION IN ESSBASE: Application Performance Optimization can be done by the following techniques 1. Designing of The Outline using Hour Glass Model 2. Defragmentation 3. Restructuring 4. Compression Techniques 5. Cache Settings 6. Intelligent Calculation 7. Uncommitted Access 8. Data Load Optimization Designing of The Outline using Hour Glass Model: Outline should be designed in such a way that dimensions are placed in the following order - largest dense to smallest dense, smallest sparse to largest sparse followed by Attribute Dimensions. Using hourglass model improves 10% of calculation Performance of the cube. Defragmentation: Fragmentation is caused due to the following 1. Frequent Data Load 2. Frequent Retrieval 3. Frequent Calculation 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 3 ways of doing Defragmentation: 1. Export Data of the application in to text file, then clear data and reload the data using text file without using Rules file. 2. Using MAXL Command: Maxl>Alter Database Appname.DB name Force Restructure
  2. 2. 3. Add and Delete One Dummy Member in the Dense Dimension . Restructuring: There are 3 types of Restructure. 1. Outline Restructure 2. Sparse Restructure 3. Dense Restructure/Full Restructure Outline Restructure: When we rename any member or add Alias to any member then outline Restructure would Happen. .OTL file is converted to .OTN which in turn converts in to .OTL again. .OTN file is a temp file deleted by default after restructure Dense Restructure(Full Restructure): If a member of a dense dimension is moved, deleted, or added, Essbase restructures the blocks in the data files and creates new data files. When Essbase restructures the data blocks, it regenerates the index automatically so that index entries point to the new data blocks. Empty blocks are not removed. Essbase marks all restructured blocks as dirty, so after a dense restructure you must recalculate the database. Dense Restructuring, the most time-consuming of the restructures, can take a long time to complete for large databases. Sparse Restructure: If a member of a sparse dimension is moved, deleted, or added, Essbase restructures the index and creates new index files. Restructuring the index is relatively fast; the time required depends on the index size. Compression Techniques: There are 4 types of Compressions. They are 1. Bitmap Compression 2. RLE – Run length Encoding 3. ZLIB 4. No Compression. Caches: There are 5 types of caches. 1. Index cache 2. Data Cache 3. Data File Cache 4. Calculator Cache
  3. 3. 5. Dynamic Calculator Cache Index Cache: Index Cache is a buffer in a memory that holds Index Files (.IND). Index cache should be set equal to the size of the index file. Note: Restart the database in order to make the new cache settings come in to effect. Data Cache: Data cache is a buffer in a memory that holds Uncompressed Data Blocks. Data cache should be 12.5% of the PAG file memory, by default it is set to 3MB. Data File Cache: Data file cache is a buffer in memory that holds compressed data blocks. Size of the Data file cache should be size of the PAG File memory. It is set to 32MB by default. Max. Size for data file cache is is 2GB We can use only either Data cache/ Data file cache most of the developers prefer Data cache in Real time. Calculator Cache: It is basically used to improve the performance of calculation. WE set the calculator cache in calculation scripts. 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. Dynamic Calculator Cache: 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). 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; in scripts . 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.
  4. 4. 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) 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 loads. Optimization Techniques in Essbase  The best technique to make large data loads faster is to have the optimal order of dimensions in source file, and to sort this optimally, order the fields in your source file (or SQL statement) by having hourglass dimension order, you data file should have dimensions listed from the bottom dimension upwards. Your dense dimensions should always be first, and if you have multiple data columns these should be dense dimension members. This will cause blocks to be created and filled with data in sequence, making the data load faster and the cube less fragmented. As a part of Optimization we need to re-order the dimensions as follows Large members Dense dimension  Small members Dense dimension  Small members Sparse dimension  Large members Sparse dimension  Attribute dimensions.  Calculation order of the dimensions. Dimension tagged accounts if it is dense.  Dense dimensions in outline or CALC DIM statement order.  Dimensions tagged as Accounts if it is sparse.  Sparse dimensions in outline order or CALC DIM statement order.  Two-pass calculations on members in the Accounts tagged dimension. Here are some more optimization techniques used in Essbase For data loading:  Grouping Sparse Member Combinations
  5. 5.  Positioning Data in the Same Order As the Outline  Loading from the Essbase OLAP Server  Making the Data Source As Small As Possible  Making Source Fields As Small As Possible  Managing Parallel Data Load Processing  For Calculation: Using Parallel Calculation  Using Formulas  Managing Caches to Improve Performance  Using Two-Pass Calculation  Aggregating #MISSING Values  Removing #MISSSING Blocks Few Optimization Techniques in Essbase With the essential features available in Essbase you can load the huge data to the Essbase cubes, Run the reports and you can perform the complex calculations also, As you keep on adding the different features to your application the performance will get reduce. As i said Essbase came up with different features along with the different performance tuning techniques which makes the application best optimized. The optimization can be done at many places such as Outline Optimization: 1) Arrange the dimension in "Hour Glass Model" The Outline should starts with dense dimension with highest stored members and it keep going till the dense dimension with least stored members and then starts with sparse dimension with least stored members and it keep going till the sparse dimension with highest stored members. 2) Use the member storage properties efficiently. If the dimension is to just host the different types of data such as scenarios, here there is no point in rolling up the lower values to higher level, in this situation you can tag the dimension as "Label Only" and assign the no consolidation operator to the members under it. Some calculations really not required to stored the results in database at this point of time tag the concern members with "Dynamic Calc" property. Data Load Optimization: 1) In data file, the fields should starts with sparse dimension members and then dense dimension members and then the data field. 2) If the same field is repeating in all the records in the data file, then try to ignore that field from fetching itself and keep that member in the "Header Definition", why means to save the buffer memory and it will increase data load process.
  6. 6. Report Script Optimization: 1) In the report script first specify the sparse dimensions and then dense dimensions, why means :Sparse dimension creates the data blocks within which the data cells are available, so specifying the dense first does not make sense. So to speed up the process specify the data blocks first(Sparse dimension) and then data cells (Dense dimensions). 2) The dimensions which are not required to display in the report put them in the page. 3) Use the special commands to increase the report performance SUPMISSINGROWS : To Suppress the data missing rows. SUPHEADING : To Suppress the headings. SUPBRACKETS: To Suppress the brackets around the negative values. SUPEMPTYROWS: To Suppress the empty rows. Calculation Script Optimization: 1) Use the set commands to increase the calculation performance. SET MSG SUMMARY : Set the message level to summary. SET AGGMISSG ON : To avoid the aggregation of #missing values. SET CACHE HIGH : To increase the bugger size. SET NOTICE LOW : To set the notices to low. 2) Perform the calculations on only required part of database using FIX command.

×