essbase11_loadingdata

© 2005 Hyperion Solutions Corporation. All Rights Reserved.

Hyperion Essbase 7X: New Features

Loading, Aggregating, and Retrieving Data

3


Objectives

At the end of this lesson, you should be able to:

• Explain the aggregate storage data load process

• Perform aggregate storage data loads using different methods

• Apply the Aggregation Design Wizard for calculation and data storage

• Retrieve data with Hyperion Visual Explorer


Aggregate Storage Load Data Process

Data sources

ASO MDB

Administration Console

Load buffer

Essbase Server

Rules files

MaxL DDL scripts

MaxL shell


Preparing Data Load

• Data sources −Field values that apply only to block storage outlines are ignored.−Only level-0 values can be loaded.−Specifying #MISSING or #MI as a value removes the associated cell if it is

present in the database.−Sorting data sources is unnecessary.−Currency name and currency category field types are not supported.

• Rules files−Properties that apply only to block storage outlines are ignored.−Data loads that use the load buffer utilize the same option to determine how

values are loaded−Building dimensions clears the data


Loading Data with Administration Services

Multiple data sources and rules files

Single option of handling the existing values for the loaded data


Loading Data with MaxL: Single Data Source

Loading a single data file does not involve the load buffer.

import database ASO.sample datafrom server excel data_file 'dataload'using server rules_file 'dataload' on error write to 'dataload.err';

ASO MDB Essbase Server MaxL DDL script

Data sources

Rules files


Loading Data with MaxL: Multiple Data Sources

• Using the load buffer during the incremental data load improves performance.

• To use the load buffer, specify a series of separate MaxL DDL statements that perform the following tasks: 1. Initialize the load buffer to accumulate the data2. Read the data sources into the load buffer 3. Load the data from the buffer into the database

Data sources

Rules files

ASO MDB Essbase Server MaxL DDL script

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1

Load buffer


Loading Data with MaxL: Multiple Data Sources Example

The following MaxL statements load two data sources into the databaseusing the load buffer:

alter database ASO.sample datainitialize load buffer with buffer_id 1;

import database ASO.sample datafrom server excel data_file 'dataload'to load_buffer with buffer_id 1;

import database ASO.sample datafrom server text data_file 'dataload'to load_buffer with buffer_id 1;

import database ASO.sample datafrom load_buffer with buffer_id 1;

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2

1


Aggregating Data

• Data consolidation and calculation is performed upon retrieval.

• Retrieval performance can be improved by aggregating and storing selected level 0 data.

Aggregated data

ReportLevel 0 data


Aggregation Terminology: Input Cells and Aggregate Cells

• Input cells are level 0 intersections across dimensions for members without formulas.

• Aggregate cells are high-level intersections other than the Accounts dimension or dynamic hierarchies.

Input cellsAggregate cells


Aggregation Terminology: Aggregate Views

An aggregate view is

• a collection of aggregate cells.

• based on the order of dimensions in the outline.

187

Transaction TypeOther SaleNo SaleSale

Payment Type

Jan Feb Mar Qtr1

Cash

ATM

Check

Credit Card 67

23

237

96

50

109

31

15

31 49 20

Months

66 82 43

433

194

88

100

191

Dimension Level

Measures 0

Years 0

Months 1

Transaction Type 1

Payment Type 1

Promotions 0

Age 0

Income Level 1

Products 0

Stores 2

Geography 3 [0,0,1,1,1,0,0,1,0,2,3]


Aggregation Terminology: Aggregation and Materialization

• An aggregation consists of one or more aggregate views.

• Materialization is a phase of the database aggregation process where calculation and storage of values for aggregate views takes place.

[0,0,2,0,1,0,1,1,3,2,3] 0.002630753 [0,0,2,0,0,1,1,1,2,2,4] 0.0026840358 [0,0,1,0,1,1,0,0,2,2,4] 0.012621903[0,0,1,1,0,0,2,1,1,3,4] 0.0024503034[0,0,1,0,1,0,2,1,2,1,3] 0.010172968[0,0,2,0,1,0,0,0,1,2,3] 0.040834382[0,0,2,0,0,0,2,1,2,2,2] 0.023270618[0,0,0,1,1,0,1,1,2,3,4] 0.0022258225[0,0,1,0,1,1,2,1,0,3,3] 0.0024801623[0,0,0,0,0,0,0,1,3,2,4] 0.028946253[0,0,1,0,1,0,1,1,3,0,2] 0.13215604[0,0,2,0,0,0,1,1,0,2,4] 0.020983158

Number of aggregate views selected: 12Total size of all aggregate views: 7.7911587 MB


Performing Database Aggregations

The aggregation process

• can be performed by using− Administration Services Aggregation Design Wizard− MaxL DDL statements

• has two phases: 1. Aggregate view selection based. 2. Calculation and storage of values for the selected aggregate views

(materialization).

Aggregated dataLevel 0 data

21

Aggregation scripts


Aggregation Design Wizard

The wizard performs database aggregation in the following steps:

1. Select an aggregation task

2. Specify aggregate view selection criteria

3. Display the selected aggregate views

4. Save and/or materialize the aggregation


Selecting an Aggregation Task

You select from the following options:

• Use recommended views to materialize aggregation

• Design, materialize, and save aggregation−Consider Existing Aggregate Views−Specify Stop Criteria for Selection Process−Start Aggregate View Selection−Save and Materialize Aggregation

• Use saved aggregation


Considering Existing Aggregate Views

In this step of the wizard you decide to

• Replace existing aggregate view selection

• Add to existing aggregate view selection


Specifying Stop Criteria

The aggregate view selection is based on:

• Outline design

• Specified storage limit (optional)


Select the aggregate views that you want to materialize based on thetrade-off between the database size and query cost.

Selecting Aggregate Views

Start Aggregate View selection process

Aggregate View selection statistics


Selecting Aggregate Views Based on Database Usage

1. Enable query tracking for the database to collect retrieval statistics.

2. Select the Use query tracking data during view selection check box in the wizard.


Saving and Materializing the Aggregation

• The aggregation can be saved (optional) as an aggregation script.

• The aggregation script can be executed immediately to materialize the aggregation.


Performing Aggregations Using MaxL

• The execute aggregate process MaxL DDL statement performs aggregation and materialization in a single phase.

• You can materialize saved aggregation by executing the corresponding aggregation scripts

execute aggregate process on database ASO.sample stopping when total_size exceeds 1.5;

execute aggregate build on database 'ASOsamp'.'Sample' using views 14154 0.002630752873648, 17810 0.002684035878964, 17689 0.012621902820468, 18597 0.002450303414580, 13033 0.010172967476241, 13642 0.040834380889012, 10146 0.023270618344429, 18764 0.002225822589757, 14585 0.002480162371676, 17952 0.028946252143793, 8393 0.202080686103553 with outline_id 1875372779


Retrieving Data

The programs and tools for retrieving aggregate storage data:

• Spreadsheet Add-in (without write-back capability)

• Essbase Spreadsheet Services

• MDX Queries

• Report Writer (commands that apply only to block storage databases can not be used)

• Attribute Calculations with the Sum member

• Hyperion Visual Explorer


Visual Explorer Architecture


Starting Visual Explorer

Select Essbase > Visualize & Explorein the Spreadsheet Add-in for MSExcel:


Visual Explorer Help


Summary

In this lesson, you should havelearned how to:

• Explain the aggregate storage data load process

• Perform aggregate storage data loads using different methods

• Apply the Aggregation Design Wizard for calculation and data storage

• Retrieve data with Hyperion Visual Explorer

essbase11_loadingdata

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