Showing posts with label SQL. Show all posts
Showing posts with label SQL. Show all posts

Monday, July 28, 2014

BUCKET_WIDTH: Calculating the size of the bucket

Some time ago I had some blog posts introducing some of the basic Statistical function available in Oracle. Here are the links to these.

Most people do not realise that Oracle has over 250+ statistical functions that are available (no addition cost) in all the database versions.

I've had a query about one of the functions BUCKET_WIDTH. The question was wondering if it was possible to get the width of the bucket in each case. There does not seem to be a build in feature to get this value, so we have to calculate this ourselves.

Here is an example of how to calculate the bucket width, as on the example I used in my previous blog post.

SELECT bucket, max(age)-min(age) BUCKET_WIDTH, count(*)

FROM (SELECT cust_id,

                       age,

                      width_bucket(age,

                         (SELECT min(age) from mining_data_build_v),

                         (select max(age)+1 from mining_data_build_v),

                      10) bucket

          FROM mining_data_build_v

          GROUP BY cust_id, age )

GROUP BY bucket

ORDER BY bucket;

Bucket width

What this query gives is an approximate value of the size of the Bucket Width based on the values/records that are in a bucket. The actual values used cannot be determined exactly as there is not function/value in SQL that tells us the actual value.

Thursday, July 24, 2014

Tokenizing a String

Over the past while I've been working a lot with text strings. Some of these have been short in length like tweets from Twitter, or longer pieces of text like product reviews. Plus others of various lengths.

In all these scenarios I have to break up the data into individual works or Tokens.

The examples given below illustrate how you can take a string and break it into its individual tokens. In addition to tokenising the string I've also included some code to remove any special characters that might be included with the string.

These include ? # $ . ; : &

This list of special characters to ignore are just an example and is not an exhaustive list. You can add whatever characters to the list yourself. To remove these special characters I've used regular expressions as this seemed to be the easiest way to do this.

Using PL/SQL

The following example shows a simple PL/SQL unit that will tokenise a string.

DECLARE

    vDelimiter VARCHAR2(5) := ' ';

    vString    VARCHAR2(32767) := 'Hello Brendan How are you today?'||vDelimiter;

    vPosition   PLS_INTEGER;

    vToken     VARCHAR2(32767);

    vRemove    VARCHAR2(100) := '\?|\#|\$|\.|\,|\;|\:|\&';

    vReplace   VARCHAR2(100) := '';

BEGIN

    dbms_output.put_line('String = '||vString);

    dbms_output.put_line('');

    dbms_output.put_line('Tokens');

    dbms_output.put_line('------------------------');

   

    vPosition := INSTR(vString, vDelimiter);

   

    WHILE vPosition > 0 LOOP

   

       vToken := LTRIM(RTRIM(SUBSTR(vString, 1, vPosition-1)));

       vToken := regexp_replace(vToken, vRemove, vReplace);

   

       vString := SUBSTR(vString, vPosition + LENGTH(vDelimiter));

       dbms_output.put_line(vPosition||': '||vToken);

       vPosition := INSTR(vString, vDelimiter);

   

    END LOOP;

END;

/

When we run this (with Serveroutput On) we get the following output.

Toke PLSQL

A slight adjustment is needed to the output of this code to remove the numbers or positions of the token separator/delimiter.

Tokenizer using a Function

To make this more usable we will really need to convert this into an iterative function. The following code illustrates this, how to call the function and what the output looks like.

CREATE OR replace TYPE token_list

AS TABLE OF VARCHAR2(32767);

/

 

CREATE OR replace FUNCTION TOKENIZER(pString IN VARCHAR2,

                                     pDelimiter IN VARCHAR2)

RETURN token_list pipelined

AS

    vPosition       INTEGER;

    vPrevPosition   INTEGER := 1;

    vRemove         VARCHAR2(100) := '\?|\#|\$|\.|\,|\;|\:|\&';

    vReplace        VARCHAR2(100) := '';

    vString         VARCHAR2(32767) := regexp_replace(pString, vRemove, vReplace);

BEGIN

    LOOP

       vPosition := INSTR (vString, pDelimiter, vPrevPosition);

       IF vPosition = 0 THEN

          pipe ROW (SUBSTR(vString, vPrevPosition ));

          EXIT;

       ELSE

          pipe ROW (SUBSTR(vString, vPrevPosition, vPosition - vPrevPosition ));

          vPrevPosition := vPosition + 1;

       END IF;

    END LOOP;

END TOKENIZER;

/

Here are a couple of examples to show how it works and returns the Tokens.

SELECT column_value TOKEN

FROM TABLE(tokenizer('It is a hot and sunny day in Ireland.', ' '))

, dual;

Token fun 1

How if we add in some of the special characters we should see a cleaned up set of tokens.

SELECT column_value TOKEN

FROM TABLE(tokenizer('$$$It is a hot and sunny day in #Ireland.', ' '))

, dual;

Token fun 2

Wednesday, December 11, 2013

Running PL/SQL Procedures in Parallel

As your data volumes increase, particularly as you evolve into the big data world, you will be start to see that your Oracle Data Mining scoring functions will start to take longer and longer.  To apply an Oracle Data Mining model to new data is a very quick process. The models are, what Oracle calls, first class objects in the database. This basically means that they run Very quickly with very little overhead.

But as the data volumes increase you will start to see that your Apply process or scoring the data will start to take longer and longer. As with all OLTP or OLAP environments as the data grows you will start to use other in-database features to help your code run quicker. One example of this is to use the Parallel Option.

You can use the Parallel Option to run your Oracle Data Mining functions in real-time and in batch processing mode. The examples given below shows you how you can do this.

Let us first start with some basics. What are the typical commands necessary to setup our schema or objects to use Parallel. The following commands are examples of what we can use

ALTER session enable parallel dml;
ALTER TABLE table_name PARALLEL (DEGREE 8);
ALTER TABLE table_name NOPARALLEL;
CREATE TABLE … PARALLEL degree …
ALTER  TABLE … PARALLEL degree …
CREATE INDEX … PARALLEL degree …
ALTER  INDEX … PARALLEL degree …

You can force parallel operations for tables that have a degree of 1 by using the force option.

ALTER SESSION ENABLE PARALLEL DDL;
ALTER SESSION ENABLE PARALLEL DML;
ALTER SESSION ENABLE PARALLEL QUERY;

alter session force parallel query PARALLEL 2

You can disable parallel processing with the following session statements.

ALTER SESSION DISABLE PARALLEL DDL;
ALTER SESSION DISABLE PARALLEL DML;
ALTER SESSION DISABLE PARALLEL QUERY;

We can also tell the database what degree of Parallelism to use


ALTER SESSION FORCE PARALLEL DDL PARALLEL 32;
ALTER SESSION FORCE PARALLEL DML PARALLEL 32;
ALTER SESSION FORCE PARALLEL QUERY PARALLEL 32;


 


Using your Oracle Data Mining model in real-time using Parallel


When you want to use your Oracle Data Mining model in real-time, on one record or a set of records you will be using the PREDICTION and PREDICTION_PROBABILITY function. The following example shows how a Classification model is being applied to some data in a view called MINING_DATA_APPLY_V.


column prob format 99.99999
SELECT cust_id,
       PREDICTION(DEMO_CLASS_DT_MODEL USING *)  Pred,
       PREDICTION_PROBABILITY(DEMO_CLASS_DT_MODEL USING *) Prob
FROM   mining_data_apply_v
WHERE  rownum <= 18
/


   CUST_ID       PRED      PROB
---------- ---------- ---------
    100574          0    .63415
    100577          1    .73663
    100586          0    .95219
    100593          0    .60061
    100598          0    .95219
    100599          0    .95219
    100601          1    .73663
    100603          0    .95219
    100612          1    .73663
    100619          0    .95219
    100621          1    .73663
    100626          1    .73663
    100627          0    .95219
    100628          0    .95219
    100633          1    .73663
    100640          0    .95219
    100648          1    .73663
    100650          0    .60061


If the volume of data warrants the use of the Parallel option then we can add the necessary hint to the above query as illustrated in the example below.


SELECT /*+ PARALLEL(mining_data_apply_v, 4) */
       cust_id,
       PREDICTION(DEMO_CLASS_DT_MODEL USING *)  Pred,
       PREDICTION_PROBABILITY(DEMO_CLASS_DT_MODEL USING *) Prob
FROM   mining_data_apply_v
WHERE  rownum <= 18
/


If you turn on autotrace you will see that Parallel was used. So you should now be able to use your Oracle Data Mining models to work on a Very large number of records and by adjusting the degree of parallelism you can improvements.


Using your Oracle Data Mining model in Batch mode using Parallel


When you want to perform some batch scoring of your data using your Oracle Data Mining model you will have to use the APPLY procedure that is part of the DBMS_DATA_MINING package. But the problem with using a procedure or function is that you cannot give it a hint to tell it to use the parallel option. So unless you have the tables(s) setup with parallel and/or the session to use parallel, then you cannot run your Oracle Data Mining model in Parallel using the APPLY procedure.


So how can you get the DBMA_DATA_MINING.APPLY procedure to run in parallel?


The answer is that you can use the DBMS_PARALLEL_EXECUTE package. The following steps walks you through what you need to do to use the DMBS_PARALLEL_EXECUTE package to run your Oracle Data Mining models in parallel.


The first step required is for you to put the DBMS_DATA_MINING.APPLY code into a stored procedure. The following code shows how our DEMO_CLASS_DT_MODEL can be used by the APPLY procedure and how all of this can be incorporated into a stored procedure called SCORE_DATA.


create or replace procedure score_data
is
begin


dbms_data_mining.apply(
  model_name => 'DEMO_CLAS_DT_MODEL',
  data_table_name => 'NEW_DATA_TO_SCORE',
  case_id_column_name => 'CUST_ID',
  result_table_name => 'NEW_DATA_SCORED');


end;
/


Next we need to create a Parallel Task for the DBMS_PARALLEL_EXECUTE package. In the following example this is called ODM_SCORE_DATA.


-- Create the TASK
  DBMS_PARALLEL_EXECUTE.CREATE_TASK ('ODM_SCORE_DATA');


Next we need to define the Parallel Workload Chunks details

 -- Chunk the table by ROWID
DBMS_PARALLEL_EXECUTE.CREATE_CHUNKS_BY_ROWID('ODM_SCORE_DATA', 'DMUSER', 'NEW_DATA_TO_SCORE', true, 100);
The scheduled jobs take an unassigned workload chunk, process it and will then move onto the next unassigned chunk. 
 
Now you are ready to execute the stored procedure for your Oracle Data Mining model, in parallel by 10.

DECLARE
   l_sql_stmt   varchar2(200);
BEGIN
   -- Execute the DML in parallel
   l_sql_stmt := 'begin score_data(); end;';
  
   DBMS_PARALLEL_EXECUTE.RUN_TASK('ODM_SCORE_DATA', l_sql_stmt, DBMS_SQL.NATIVE,
                                  parallel_level => 10);
END;
/


When every thing is finished you can then clean up and remove the task using



BEGIN
   dbms_parallel_execute.drop_task('ODM_SCORE_DATA');
END;
/


 

NOTE: The schema that will be running the above code will need to have the necessary privileges to run DBMS_SCHEDULER, for example


grant create job to dmuser;

Thursday, September 19, 2013

Nested Tables (and Data) in Oracle & ODM

Oracle Data Mining uses Nested data types/tables to store some of its data. Oracle Data Mining creates a number of tables/objects that contain nested data when it is preparing data for input to the data mining algorithms and when outputting certain results from the algorithms.  In Oracle 11.2g there are two nested data types used and in Oracle 12.1c we get an additional two nested data types. These are setup when you install the Oracle Data Miner Repository. If you log into SQL*Plus or SQL Developer you can describe them like any other table or object.

DM_NESTED_NUMERICALS

image

DM_NESTED_CATEGORICALS

image

The following two Nested data types are only available in 12.1c

DM_NESTED_BINARY_DOUBLES

image

DM_NESTED_BINARY_FLOATS

image

These Nested data types are used by Oracle Data Miner in preparing data for input to the data mining algorithms and for producing the some of the outputs from the algorithms.

Creating your own Nested Tables

To create your own Nested Data Types and Nested Tables you need to performs steps that are similar to what is illustrated in the following steps. These steps show you how to define a data type, how to create a nested table, how to insert data into the nested table and how to select the data from the nested table.

1. Set up the Object Type

Create a Type object that will defines the structure of the data. In these examples we want to capture the products and quantity purchased by a customer.

create type CUST_ORDER as object
(product_id     varchar2(6),
quantity_sold  number(6));
/

2. Create a Type as a Table

Now you need to create a Type as a table.

create type cust_orders_type as table of CUST_ORDER;
/

3. Create the table using the Nested Data

Now you can create the nested table.

create table customer_orders_nested (
cust_id       number(6) primary key,
order_date    date,
sales_person  varchar2(30),
c_order       CUST_ORDERS_TYPE)
NESTED TABLE c_order STORE AS c_order_table;

4. Insert a Record and Query

This insert statement shows you how to insert one record into the nested column.

insert into customer_orders_nested
values (1, sysdate, 'BT', CUST_ORDERS_TYPE(cust_order('P1', 2)) );

When we select the data from the table we get

select * from customer_orders_nested;

   CUST_ID ORDER_DAT SALES_PERSON
---------- --------- ------------------------------
C_ORDER(PRODUCT_ID, QUANTITY_SOLD)
-----------------------------------------------------
         1 19-SEP-13 BT
CUST_ORDERS_TYPE(CUST_ORDER('P1', 2))

It can be a bit difficult to read the data in the nested column so we can convert the nested column into a table to display the results in a better way

select cust_id, order_date, sales_person, product_id, quantity_sold
from customer_orders_nested, table(c_order)


   CUST_ID ORDER_DAT SALES_PERSON                   PRODUC QUANTITY_SOLD
---------- --------- ------------------------------ ------ -------------
         1 19-SEP-13 BT                             P1                 2


5. Insert many Nested Data items & Query

To insert many entries into the nested column you can do this

insert into customer_orders_nested
values (2, sysdate, 'BT2', CUST_ORDERS_TYPE(CUST_ORDER('P2', 2), CUST_ORDER('P3',3)));

When we do a Select * we get

   CUST_ID ORDER_DAT SALES_PERSON
---------- --------- ------------------------------
C_ORDER(PRODUCT_ID, QUANTITY_SOLD)
-------------------------------------------------------------
         1 19-SEP-13 BT
CUST_ORDERS_TYPE(CUST_ORDER2('P1', 2))

         2 19-SEP-13 BT2
CUST_ORDERS_TYPE(CUST_ORDER2('P2', 2), CUST_ORDER2('P3', 3))

Again it is not easy to ready the data in the nested column, so if we convert it to a table again we now get a row being displayed for each entry in the nested column.

select cust_id, order_date, sales_person, product_id, quantity_sold
from customer_orders_nested, table(c_order);

   CUST_ID ORDER_DAT SALES_PERSON                   PRODUC QUANTITY_SOLD
---------- --------- ------------------------------ ------ -------------
         1 19-SEP-13 BT                             P1                 2
         2 19-SEP-13 BT2                            P2                 2
         2 19-SEP-13 BT2                            P3                 3

Thursday, July 25, 2013

12c New Data Mining functions

With the release of Oracle 12c we get new functions/procedures and some updated ones for Oracle Data Miner that is part of the Advanced Analytics option.

The following are the new functions/procedures and the functions/procedures that have been updated in 12c, with a link to the 12c Documentation that explains what they do.

  • CLUSTER_DETAILS is a new function that predicts cluster membership for each row. It can use a pre-defined clustering model or perform dynamic clustering. The function returns an XML string that describes the predicted cluster or a specified cluster.

  • CLUSTER_DISTANCE is a new function that predicts cluster membership for each row. It can use a pre-defined clustering model or perform dynamic clustering. The function returns the raw distance between each row and the centroid of either the predicted cluster or a specified.

  • CLUSTER_ID has been enhanced so that it can either use a pre-defined clustering model or perform dynamic clustering.

  • CLUSTER_PROBABILITY has been enhanced so that it can either use a pre-defined clustering model or perform dynamic clustering. The data type of the return value has been changed from NUMBER to BINARY_DOUBLE.

  • CLUSTER_SET has been enhanced so that it can either use a pre-defined clustering model or perform dynamic clustering. The data type of the returned probability has been changed from NUMBER to BINARY_DOUBLE

  • FEATURE_DETAILS is a new function that predicts feature matches for each row. It can use a pre-defined feature extraction model or perform dynamic feature extraction. The function returns an XML string that describes the predicted feature or a specified feature.

  • FEATURE_ID has been enhanced so that it can either use a pre-defined feature extraction model or perform dynamic feature extraction.

  • FEATURE_SET has been enhanced so that it can either use a pre-defined feature extraction model or perform dynamic feature extraction. The data type of the returned probability has been changed from NUMBER to BINARY_DOUBLE.

  • FEATURE_VALUE has been enhanced so that it can either use a pre-defined feature extraction model or perform dynamic feature extraction. The data type of the return value has been changed from NUMBER to BINARY_DOUBLE.

  • PREDICTION has been enhanced so that it can either use a pre-defined predictive model or perform dynamic prediction.

  • PREDICTION_BOUNDS now returns the upper and lower bounds of the prediction as the BINARY_DOUBLE data type. It previously returned these values as the NUMBER data type.

  • PREDICTION_COST has been enhanced so that it can either use a pre-defined predictive model or perform dynamic prediction. The data type of the returned cost has been changed from NUMBER to BINARY_DOUBLE.

  • PREDICTION_DETAILS has been enhanced so that it can either use a pre-defined predictive model or perform dynamic prediction.

  • PREDICTION_PROBABILITY has been enhanced so that it can either use a pre-defined predictive model or perform dynamic prediction. The data type of the returned probability has been changed from NUMBER to BINARY_DOUBLE.

  • PREDICTION_SET has been enhanced so that it can either use a pre-defined predictive model or perform dynamic prediction. The data type of the returned probability has been changed from NUMBER to BINARY_DOUBLE.

Wednesday, June 12, 2013

Part 3–Getting start with Statistics for Oracle Data Science projects

This is the Part 3 blog post on getting started with Statistics for Oracle Data Science projects.

The table below is a collection of most of the statistical functions in Oracle 11.2. The links in the table bring you to the relevant section of the Oracle documentation where you will find a description of each function, the syntax and some examples of each.

ABS

LENGTH2

REGR_AVGX

ACOS

LENGTH4

REGR_ACGY

Aggregrate functions

LENGTHB

REGR_COUNT

Analytic functions

LENGTHC

REGR_INTERCEPT

Arithmetic operators

LN

REGR_R2

ASIN

LNNVL

REGR_SLOPE

ATAN

LOG

REGR_SXX

ATAN2

LOWER

REGR_SXY

AVG

LPAD

REGR_SYY

CAST

LTRIM

ROLLUP clause

Comparison functions

MAX

ROUND

CONCAT

MEDIAN

SAMPLE

CORR

MIN

SIN

CORR_K

MOD

SINH

CORR_S

MODEL clause

SQRT

COS

NTH_VALUE

STATS_BINOMIAL_TEST

COSH

Numeric Functions

STATS_CROSSTAB

COUNT

PERCENT_RANK

STATS_F_TEST

COVAR_POP

PERCENTILE_CONT

STATS_KS_TEST

COVAR_SAMP

PERCENTILE_DISC

STATS_MODE

CUBE clause

Pivot operations

STATS_MW_TEST

CUME_DIST

POWER

STATS_ONE_WAY_ANOVA

CV

PREDICTION

STATS_T_TEST_INDEP

Data functions

PREDICTION_BOUNDS

STATS_T_TEST_INDEPU

DENSE_RANK

PREDICTION_COST

STATS_T_TEST_ONE

EXP

PREDICTION_PROBABILITY

STATS_T_TEST_PAIRED

FLOOR

PREDICTION_SET

STATS_WSR_TEST

GREATEST

PRESENTNNV

STDDEV

Grouping Sets

PRESENTNTV

STDEEV_POP

INTERSECT

Prior clause

STDDEV_SAMP

Interval arithmetic

PRIOR

SUM

INTERVAL

RANK

TAN

Julian dates

RAWTOHEX

TANH

LAG

REGEXP_COUNT

t-test

LAST

REGEXP_INSTR

VAR_POP

LEAD

REGEXP_LIKE

VAR_SAMP

LEAST

REGEXP_REPLACE

VARIANCE

LENGTH

REGEXP_SUBSTR

WIDTH_BUCKET

The list about may not be complete (I’m sure it is not), but it will cover most of what you will need to use in your Oracle projects.

If you come across or know of other useful statistical functions in Oracle let me know the details and I will update the table above to include them.

Thursday, May 16, 2013

Outputting your data using inbuilt SQL Dev formatting

Oracle has build a number of formatting options into SQL Developer to allow you to output your data in some standard formats. This removes the need to use other tools or to write extra code or performs various follow up steps.
All you need to do is to add a comment and use the Scrip button
SELECT /*csv*/ * FROM scott.emp;
SELECT /*xml*/ * FROM scott.emp;
SELECT /*html*/ * FROM scott.emp;
SELECT /*delimited*/ * FROM scott.emp;
SELECT /*insert*/ * FROM SCOTT.EMP;
SELECT /*loader*/ * FROM scott.emp;
SELECT /*fixed*/ * FROM scott.emp;
SELECT /*text*/ * FROM scott.emp;

Hint: for some of these it is best to list the schema and table name in upper case
These are comments and not hints so they will not work in SQL*Plus.

Wednesday, January 2, 2013

OUG Norway April 2013 - New Year’s News

I received an email at 23:24 on the 1st January from the OUG in Norway telling me that I’ve had two presentations accepted for the Annual OUG Norway seminar event. This will be on during the 17th-19th April.

The first day of this event (17th April) will be held in a hotel in Oslo. Then on the morning of 18th April we board the Color Magic cruise for the next two days of the conference. The ferry/cruise will go from Oslo to Kiel in Germany and then back again to Oslo, returning around 10am on Saturday 20th April.

I will be giving two presentations on the Oracle Advanced Analytics Option. The first presentation, ‘Using Predictive Analytics in Oracle’, will give an overview of the Oracle Advanced Analytics Option and will then focus on the Oracle Data Miner work-flow tool. This will presentation will include a live demo of using Oracle Data Miner to create some data mining models.

The second presentation, ‘How to Deploy and Use your Oracle Data Miner Models in Production’, builds on the examples given in the first presentation and will show how you can migrate, user and update your Oracle Data Miner models using the features available in SQL and PL/SQL. Again a demo will be given.

Friday, November 9, 2012

Update on : Adding numbers between

Over the past few days I’ve had a number of emails and comments based on my previous post.  My previous post was called ‘Adding numbers between two values’. I included some PL/SQL code that can be used to add up the numbers between two values. I mentioned that this was a question that my pre-teen son (a few year pre-teen) had asked me.

There are two main solutions to the same problem. One involves just using a SELECT and the other involves using recursion. I will come back the these alternative solutions below.

But let me start off with a bit more detail and background to why I approached the problem the way that I did. The main reason is that my son is a pre-teen. Over the past couple of years he as expressed an interest in what his daddy does. We even have matching ORACLENERD t-shirts Smile

When I was working through the problem with my son I wanted to show him how to take a problem and by breaking it down into its different parts we can work out an overall solution. We can then take each of these parts and translate them into code. In this case some PL/SQL, yes it is a bit nerdy and we do have the t-shirt. The code that I gave illustrates many different parts of the language and hopefully he will use some of these features as we continue on our learning experience.

It is good sometimes to break a problem down into smaller parts. That way we can understand it better, what works and what does not work, if something does not work then we will know what bit and also leads to easier maintenance. At a later point as you develop an in-depth knowledge of certain features of a language you can then rewrite what you have to be more efficient.

All part of the learning experience.

Ok lets take a look at the other ways to answer this problem. The first approach is to just use a single SELECT statement.

SELECT sum(rownum + &&Start_Number - 1)
FROM    dual
CONNECT by level <= &End_Number - &&Start_Number + 1;

An even simpler way is

SELECT sum(level)
FROM    dual
CONNECT BY level between &Start_Number and &End_Number;

These queries create a hierarchical query that produce all the numbers between the Start_Number parameter and the End_Number parameter. The SUM is needed to all all the numbers/rows produced.  This is nice and simple (but not that easy for by son at this point).

Thank you to everyone who contacted me about this. I really appreciated your feedback and please keep your comments coming for all my posts.

Tuesday, November 6, 2012

Adding numbers between two values

My son asked me the other day, what is the total number if you add all the numbers between Zero and 100.
We could have sat down to work it out with some paper and a pen, but instead I decided to introduce him to the world of Oracle, SQL and PL/SQL
The first step we took was to work out how you would do it on paper for some of the numbers. Then we translated this into some PL/SQL code. OK I did a lot this but he did seem to understand and follow what I was doing.
So the following Function is what we ended up with to add all the numbers between two numbers and return the answer.
CREATE or REPLACE function AddNumsBetween
       ( pStartNum IN NUMBER,
         pEndNum IN NUMBER)
   RETURN NUMBER
IS
   vSum   Number := 0;
BEGIN
   FOR i IN pStartNum .. pEndNum LOOP
      vSum := vSum + i;
   END LOOP;
   return vSum;
END;
/

The next step was to write some code to call this function. The code prompts the user to enter the Start number and End number.
set serveroutput on
DECLARE
   vStartNum  NUMBER := 0;
   vEndNum   NUMBER := 100;
   vAnswer    NUMBER := 0;
BEGIN
   vStartNum := &Start_Number;
   vEndNum := &End_Number;
   vAnswer := AddNumsBetween(vStartNum, vEndNum);
   dbms_output.put_line('The sum of numbers between '||vStartNum||' and '||vEndNum||' is '||vAnswer||'.');
END;
/

To answer by son’s original query, we used Zero and 100 as our inputs.
image
The answer to the question is 5,050.

Thursday, November 1, 2012

ASCII to character conversion in Oracle

Here is code code that will produce formatted output of the characters and their ascii values. The formatting is broken into lower case letters, uppercase letters, characters with an ascii value less than an ‘a’ and characters whose ascii characters are greater than a ‘z.

Code

set serveroutput on FORMAT WRAPPED
DECLARE
    vTab           VARCHAR2(5) := CHR(9);
    vNum          NUMBER := 0;
    vString       VARCHAR2(80) := '';
BEGIN  
   --
   -- Formatted lower case letter to ASCII values
   --
   dbms_output.put_line('Formatted Lower Case Letters to ASCII values');
   dbms_output.put_line('-------------------------------------------------------');
   FOR i IN ASCII('a') .. ASCII('z') LOOP
      IF vNum < 6 THEN
         vString := vString||CHR(i)||' : '||i||vTab;
         vNum := vNum + 1;
      ELSIF vNum = 6 then
         dbms_output.put_line(vString||CHR(i)||' : '||i);
         vNum := 0;
         vString := '';
      ELSE
         dbms_output.put_line('ERROR');
      END IF;
   END LOOP;
   dbms_output.put_line(vString);

   --
   -- Formatted upper case letter to ASCII values
   --
   vString := '';
   vNum := 0;

   dbms_output.new_line;
   dbms_output.new_line;
   dbms_output.put_line('Formatted Upper Case Letters to ASCII values');
   dbms_output.put_line('-------------------------------------------------------');
   FOR i IN ASCII('A') .. ASCII('Z') LOOP
      IF vNum < 6 THEN
         vString := vString||CHR(i)||' : '||i||vTab;
         vNum := vNum + 1;
      ELSIF vNum = 6 then
         dbms_output.put_line(vString||CHR(i)||' : '||i);
         vNum := 0;
         vString := '';
      ELSE
         dbms_output.put_line('ERROR');
      END IF;
   END LOOP;
   dbms_output.put_line(vString);

   --
   -- Formatted chars less than 'a' to ASCII values
   --
   vString := '';
   vNum := 0;

   dbms_output.new_line;
   dbms_output.new_line;
   dbms_output.put_line('Formatted Letters, less than a  to ASCII values');
   dbms_output.put_line('-------------------------------------------------------');
   FOR i in 0 .. ASCII('a')-1 LOOP
      IF vNum < 6 THEN
         vString := vString||CHR(i)||' : '||i||vTab;
         vNum := vNum + 1;
      ELSIF vNum = 6 then
         dbms_output.put_line(vString||CHR(i)||' : '||i);
         vNum := 0;
         vString := '';
      ELSE
         dbms_output.put_line('ERROR');
      END IF;
   END LOOP;
   dbms_output.put_line(vString);

   --
   -- Formatted chars greater than 'Z' to ASCII values
   --
   vNum := 0;
   vString := '';

   dbms_output.new_line;
   dbms_output.new_line;
   dbms_output.put_line('Formatted Letters, greater than z  to ASCII values');
   dbms_output.put_line('-------------------------------------------------------');
   FOR i IN ASCII('z') .. ASCII('z')+133 LOOP
      IF vNum < 6 THEN
         vString := vString||CHR(i)||' : '||i||vTab;
         vNum := vNum + 1;
      ELSIF vNum = 6 then
         dbms_output.put_line(vString||CHR(i)||' : '||i);
         vNum := 0;
         vString := '';
      ELSE
         dbms_output.put_line('ERROR');
      END IF;
   END LOOP;
   dbms_output.put_line(vString);

END;
/

Output

Formatted Lower Case Letters to ASCII values
-------------------------------------------------------
a : 97  b : 98  c : 99  d : 100 e : 101 f : 102 g : 103
h : 104 i : 105 j : 106 k : 107 l : 108 m : 109 n : 110
o : 111 p : 112 q : 113 r : 114 s : 115 t : 116 u : 117
v : 118 w : 119 x : 120 y : 121 z : 122


Formatted Upper Case Letters to ASCII values
-------------------------------------------------------
A : 65  B : 66  C : 67  D : 68  E : 69  F : 70  G : 71
H : 72  I : 73  J : 74  K : 75  L : 76  M : 77  N : 78
O : 79  P : 80  Q : 81  R : 82  S : 83  T : 84  U : 85
V : 86  W : 87  X : 88  Y : 89  Z : 90


Formatted Letters, less than a  to ASCII values
-------------------------------------------------------
  : 0   ☺ : 1   ☻ : 2   ♥ : 3   ♦ : 4   ♣ : 5   ♠ : 6
: 7 : 8                 : 9
: 13   ♂ : 11  ♀ : 12
♫ : 14  ☼ : 15  ► : 16  ◄ : 17  ↕ : 18  ‼ : 19  ¶ : 20
§ : 21  ▬ : 22  ↨ : 23  ↑ : 24  ↓ : 25  → : 26  ← : 27
∟ : 28  ↔ : 29  ▲ : 30  ▼ : 31    : 32  ! : 33  " : 34
# : 35  $ : 36  % : 37  & : 38  ' : 39  ( : 40  ) : 41
* : 42  + : 43  , : 44  - : 45  . : 46  / : 47  0 : 48
1 : 49  2 : 50  3 : 51  4 : 52  5 : 53  6 : 54  7 : 55
8 : 56  9 : 57  : : 58  ; : 59  < : 60  = : 61  > : 62
? : 63  @ : 64  A : 65  B : 66  C : 67  D : 68  E : 69
F : 70  G : 71  H : 72  I : 73  J : 74  K : 75  L : 76
M : 77  N : 78  O : 79  P : 80  Q : 81  R : 82  S : 83
T : 84  U : 85  V : 86  W : 87  X : 88  Y : 89  Z : 90
[ : 91  \ : 92  ] : 93  ^ : 94  _ : 95  ` : 96


Formatted Letters, greater than z  to ASCII values
-------------------------------------------------------
z : 122 { : 123 | : 124 } : 125 ~ : 126 ⌂ : 127 Ç : 128
ü : 129 é : 130 â : 131 ä : 132 à : 133 å : 134 ç : 135
ê : 136 ë : 137 è : 138 ï : 139 î : 140 ì : 141 Ä : 142
Å : 143 É : 144 æ : 145 Æ : 146 ô : 147 ö : 148 ò : 149
û : 150 ù : 151 ÿ : 152 Ö : 153 Ü : 154 ø : 155 £ : 156
Ø : 157 × : 158 ƒ : 159 á : 160 í : 161 ó : 162 ú : 163
ñ : 164 Ñ : 165 ª : 166 º : 167 ¿ : 168 ® : 169 ¬ : 170
½ : 171 ¼ : 172 ¡ : 173 « : 174 » : 175 ░ : 176 ▒ : 177
▓ : 178 │ : 179 ┤ : 180 Á : 181 Â : 182 À : 183 © : 184
╣ : 185 ║ : 186 ╗ : 187 ╝ : 188 ¢ : 189 ¥ : 190 ┐ : 191
└ : 192 ┴ : 193 ┬ : 194 ├ : 195 ─ : 196 ┼ : 197 ã : 198
à : 199 ╚ : 200 ╔ : 201 ╩ : 202 ╦ : 203 ╠ : 204 ═ : 205
╬ : 206 ¤ : 207 ð : 208 Ð : 209 Ê : 210 Ë : 211 È : 212
ı : 213 Í : 214 Î : 215 Ï : 216 ┘ : 217 ┌ : 218 █ : 219
▄ : 220 ¦ : 221 Ì : 222 ▀ : 223 Ó : 224 ß : 225 Ô : 226
Ò : 227 õ : 228 Õ : 229 µ : 230 þ : 231 Þ : 232 Ú : 233
Û : 234 Ù : 235 ý : 236 Ý : 237 ¯ : 238 ´ : 239 ­ : 240
± : 241 ‗ : 242 ¾ : 243 ¶ : 244 § : 245 ÷ : 246 ¸ : 247
° : 248 ¨ : 249 · : 250 ¹ : 251 ³ : 252 ² : 253 ■ : 254
  : 255

PL/SQL procedure successfully completed.

Observations

There are two things that stand out in this. The first is there is sound produced. This is because one of the characters is defined this way. It is ASCII number 7. This can be repeated using the following:

select chr(7) from dual.

The second is the formatting of the lines for ascii codes 8 to 12. We can see that one of the ascii codes does not get displayed and the ordering of this is not as expected. This is due to ascii 10 being a line feed.