Converting Comma Delimited Values to Records in Oracle SQL

Introduction

Oracle SQL provides several ways to manipulate and transform data, including converting comma delimited values to separate records. In this article, we will explore one such approach using regular expressions and the CONNECT BY clause.

Understanding the Problem

The problem at hand involves taking a comma delimited string as input and splitting it into individual records. The strings may contain spaces and consecutive commas, making them more challenging to process. We need to extract each record from the original string and store them in separate rows.

For example, consider the following input string:

AX, BC

We want to convert this string into separate records like so:

Column1
---------------
AX

BC

The Approach: Using Regular Expressions and CONNECT BY

One way to achieve this is by using regular expressions to split the comma delimited string. Oracle provides a built-in function called REGEXP_SUBSTR that can be used for this purpose.

WITH mydata AS (
  SELECT 'AX,BC' AS mycol FROM DUAL
)
SELECT REGEXP_SUBSTR(mycol, '[^,]+', 1, LEVEL) result
FROM mydata
CONNECT BY LEVEL <= LENGTH(REGEXP_REPLACE(mycol, '[^,]+', '')) + 1;

Let’s break down this code:

• We first create a temporary view mydata with the input string 'AX,BC'.
• The SELECT statement uses REGEXP_SUBSTR to extract each record from the input string.
  • The regular expression pattern [^,]+ matches any character that is not a comma. By using the ^ symbol at the beginning, we ensure that we match only characters that are not commas. By using the + symbol after the \, we match one or more occurrences of these characters.
  • The 1 in REGEXP_SUBSTR(mycol, '[^,]+', 1, LEVEL) specifies that we start searching for matches from the beginning of the string (i.e., level 1).
  • The LEVEL variable is used to keep track of the current position within the input string.
• In the CONNECT BY clause, we specify the upper bound for the level. This ensures that we process each record individually until there are no more matches left in the input string.

How it Works

Here’s a step-by-step explanation of how this code works:

1. Initialization: When the first record is processed (i.e., LEVEL equals 1), REGEXP_SUBSTR returns the entire input string 'AX,BC'. This is stored in the result column.
2. First Record Extraction: After processing the first record (LEVEL equals 2), REGEXP_SUBSTR returns the substring starting from the beginning of the original string and skipping over the comma before 'AX', i.e., 'AX'.
3. Second Record Extraction: For the next level (i.e., LEVEL equals 3), REGEXP_SUBSTR skips over the comma after 'BC' in the original string, leaving us with only 'BC'. This is now returned as part of the result.
4. Loop Continuation: As long as there are more characters to match (LENGTH(REGEXP_REPLACE(mycol, '[^,]+', '')) > LEVEL) and until all records have been processed (i.e., LEVEL equals or exceeds LENGTH(REGEXP_REPLACE(mycol, '[^,]+', '')) + 1), the loop continues with each iteration incrementing the level.

Example Walkthrough

To better understand this code, let’s walk through an example:

Suppose we have a table called mydata containing the following data:

The desired output should look like so:

Column1
---------------
AX

BC

Using our regular expression approach, here are the individual steps involved in extracting each record from the input string:

• Level 1: REGEXP_SUBSTR returns ‘AX,BC’ as part of the result.
  • Subsequent steps skip over the comma and return only ‘AX’.
• Level 2: REGEXP_SUBSTR skips over the comma after ‘X’, leaving us with ‘BC’. This is now returned as part of the result.

At this point, there are no more characters to match in the input string. As a result, LEVEL remains equal to the number of levels we’ve processed so far (2). The loop ends here, and all records have been extracted successfully.

Conclusion

In conclusion, converting comma delimited values to separate records in Oracle SQL can be achieved using regular expressions along with the CONNECT BY clause. By leveraging these tools, you can efficiently extract each record from your input string and store them as desired rows.

Last modified on 2024-05-02