ORACLE_23-03-31_en.pdf

Oracle APEX & Oracle Analytic Views
Ing. Roberto Capancioni
31st March 2023

Chi Sono
Ing. Roberto Capancioni
–Sviluppo Oracle APEX
–Board Member ITOUG
Email: sviluppo@capancioni.com
Linkedin: https://www.linkedin.com/in/robertocapancioni
Web: https://capancioni.com

Group by
Amount and quantity per
year, region and category

Group by
select d.year,
c.region,
p.category,
sum(s.quantity)quantity,
sum(s.amount)amount
from ex2_sales s
join ex2_product p on s.product = p.product
join ex2_customer c on s.customer = c.customer
join ex2_date d on s.the_date = d.the_date
group by d.year,
c.region,
p.category
This group by is static!
What if instead of by year you want it by month
I need another query!

13 © 2020 Oracle Corporation.
I Dati di Vendita
Analytic Views
• Create attribute dimension (Date, Product, Customer)
• Create hyerarchy (Date, Product, Customer)
• Create analytic view(s) (Sales)

Date Attribute Dimension
create or replace attribute dimension ex2_date_attr_dim
using ex2_date
attributes (
the_date,
month,
quarter,
year
)
level the_date
key the_date
order by the_date
determines (month)
level month
key month
order by month
determines (quarter)
level quarter
key quarter
order by quarter
determines (year)
level year
key year
order by year;
Date Level
Month Level
Quarter Level
Year Level
create or replace hierarchy ex2_date_hier
using ex2_date_attr_dim
(
the_date
child of month
child of quarter
child of year
);
Attribute Dimension Hierarchy
Levels

Customer Attribute Dimension
create or replace attribute dimension ex2_customer_attr_dim
using ex2_customer
attributes (
customer,
city,
region
)
level customer
key customer
order by customer
determines (city)
level city
key city
order by city
determines (region)
level region
key region
order by region;
Customer Level
City Level
Region Level
create or replace hierarchy ex2_customer_hier
using ex2_customer_attr_dim
(
customer
child of city
child of region
);
Levels

Product Attribute Dimension
create or replace attribute dimension ex2_product_attr_dim
using ex2_product
attributes (
product,
sub_category,
category
)
level product
key product
order by product
determines (sub_category)
level sub_category
key sub_category
order by sub_category
determines (category)
level category
key category
order by category;
Product Level
Sub Category Level
Category Level
create or replace hierarchy ex2_product_hier
using ex2_product_attr_dim
(
product
child of sub_category
child of category
);
Livelli

Sales Analytic View
(One of many we could do …)
create or replace analytic view ex2_sales_av
using ex2_sales
dimension by ( ex2_date_attr_dim
key the_date
references the_date
hierarchies (ex2_date_hier default),
ex2_product_attr_dim
key product
references product
hierarchies (ex2_product_hier default),
ex2_customer_attr_dim
key customer
references customer
hierarchies (ex2_customer_hier default)
)
measures (
quantity fact quantity,
quantity_year_prev AS (LAG(quantity) OVER (HIERARCHY ex2_date_hier OFFSET 1 ACROSS ANCESTOR AT LEVEL year)),
quantity_diff_year_prev AS (LAG_DIFF(quantity) OVER (HIERARCHY ex2_date_hier OFFSET 1 ACROSS ANCESTOR AT LEVEL year)),
quantity_diff_perc_year_prev AS (ROUND(100*LAG_DIFF_PERCENT(quantity) OVER (HIERARCHY ex2_date_hier OFFSET 1 ACROSS ANCESTOR AT LEVEL
year),2)),
--
amount fact amount,
amount_year_prev AS (LAG(amount) OVER (HIERARCHY ex2_date_hier OFFSET 1 ACROSS ANCESTOR AT LEVEL year)),
amount_diff_year_prev AS (LAG_DIFF(amount) OVER (HIERARCHY ex2_date_hier OFFSET 1 ACROSS ANCESTOR AT LEVEL year)),
amount_diff_perc_year_prev AS (ROUND(100*LAG_DIFF_PERCENT(amount) OVER (HIERARCHY ex2_date_hier OFFSET 1 ACROSS ANCESTOR AT LEVEL year),2))
)
default measure amount;
Date Hierarchy
Analytic View Hierarchy
Measures
Product Hierarchy
Customer Hierarchy

Query an Analytic View
select ex2_date_hier.level_name date_type,
ex2_date_hier.member_name the_date,
ex2_date_hier.hier_order date_order,
amount,
amount_year_prev,
amount_diff_year_prev,
amount_diff_perc_year_prev
from ex2_sales_av
hierarchies (
ex2_date_hier
)
where ex2_date_hier.level_name in ('YEAR')
and ex2_date_hier.member_name in ('2021','2022')
order by ex1_data_hier.hier_order
Level Name , Member Name and Order
Measures
Only Date Hierarchy
Date Hier Order
For every Hierarchy at least a where condition

Interrogare una Vista Analitica Vendita
amount,
amount_year_prev
from ex2_sales_av
hierarchies (
ex2_date_hier
)
where ex2_date_hier.level_name in ('YEAR','QUARTER')
order by ex2_date_hier.hier_order
Raggruppamento Dinamico
Ho sia il totale per anno che per
trimestre
Nessuna aggregazione da scrivere qui, il group by non serve

21
Let’s simplify query with SQL Macro

SQL Macro
• Wrap code inside a function
• The code returned is «pure» SQL non PLSQL
• You can create real parametric query
Transforms this … in this
With same performances

create or replace package body ex2_pkg is
function sales_av_mcr1(
p_customer_level in varchar2 default null,
p_product_level in varchar2 default null,
p_date_level in varchar2 default null,
p_customer in varchar2 default null,
p_product in varchar2 default null,
p_date in varchar2 default null
) return clob sql_macro is
begin
RETURN q'{
ex2_date_hier.hier_order date_order,
ex2_customer_hier.level_name customer_type,
ex2_customer_hier.member_name customer,
ex2_customer_hier.hier_order customer_order,
ex2_product_hier.level_name product_type,
ex2_product_hier.member_name product,
ex2_product_hier.hier_order product_order,
amount,
amount_year_prev,
amount_diff_year_prev,
amount_diff_perc_year_prev
from ex2_sales_av
hierarchies (
ex2_date_hier,
ex2_customer_hier,
ex2_product_hier
)
where ( instr( ':' || nvl(p_customer_level,'ALL') || ':', ':' || ex2_customer_hier.level_name || ':' ) > 0 )
and ( instr( ':' || nvl(p_product_level,'ALL') || ':', ':' || ex2_product_hier.level_name || ':' ) > 0 )
and ( instr( ':' || nvl(p_date_level,'ALL') || ':', ':' || ex2_date_hier.level_name || ':' ) > 0 )
and ( instr( ':' || nvl(p_customer,'ALL') || ':', ':' || ex2_customer_hier.member_name || ':' ) > 0 )
and ( instr( ':' || nvl(p_product,'ALL') || ':', ':' || ex2_product_hier.member_name || ':' ) > 0 )
and ( instr( ':' || nvl(p_date,'ALL') || ':', ':' || ex2_date_hier.member_name || ':' ) > 0 )
}';
end sales_av_mcr1;
end ex2_pkg;
/
create or replace package ex2_pkg is
function sales_av_mcr1(
p_customer_level in varchar2 default null,
p_product_level in varchar2 default null,
p_date_level in varchar2 default null,
p_customer in varchar2 default null,
p_product in varchar2 default null,
p_date in varchar2 default null
) return clob sql_macro;
end ex2_pkg;
/

Query an Analytic view with SQL Macro
select *
from ex2_pkg.sales_av_mcr1();
select *
from ex2_pkg.sales_av_mcr1(
p_date_level =>'YEAR',
p_date =>'2021:2022'
);
select *
p_date_level =>'YEAR:QUARTER',
p_date =>'2022:2022-1:2022-2:2022-3:2022-4'
)
order by date_order;
select *
p_date =>'2021:2022',
p_customer_level =>'REGION',
p_customer =>'Central'
);
select *
p_date =>'2021:2022',
p_product_level =>'CATEGORY',
p_product =>'Office Supplies'
);
select *
p_date =>'2021:2022',
p_customer_level =>'REGION',
p_customer =>'ALL',
p_product_level =>'CATEGORY',
p_product =>'Office Supplies'
);
A Low-cod Developer can use a pre-built SQL Macro
written by a PL/SQL developer

Total Year
Total Quarter

Total Customer
Total Region

Total Product
Total Category

Sorgente della Griglia Interattiva

ORACLE_23-03-31_en.pdf

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