How To Compare Long Datatype In Oracle Effectively

How To Compare Long Datatype In Oracle effectively is a crucial skill for database developers and administrators. COMPARE.EDU.VN offers in-depth analysis and comparisons of various methods to help you master this task. This guide explores the nuances of comparing LONG data types in Oracle, providing practical examples and best practices for optimal performance. Discover techniques for effective long data comparison, leveraging insights from advanced comparison strategies.

1. Understanding the `LONG` Data Type in Oracle

The LONG data type in Oracle is used to store variable-length character strings containing up to 2 gigabytes of information. It is often used for storing large amounts of text, such as documents, articles, or program source code. However, the LONG data type has several restrictions compared to other character data types like VARCHAR2 and CLOB:

Limited Functionality: You cannot use LONG columns in WHERE clauses for most comparison operations, in GROUP BY clauses, or with many built-in functions.
Single LONG Column per Table: A table can contain only one LONG column.
No Indexing: LONG columns cannot be indexed, making full-text searches inefficient.

Despite these limitations, understanding how to compare LONG data is essential, especially when dealing with legacy systems or data structures.

2. Challenges in Comparing `LONG` Data Types

Comparing LONG data types in Oracle presents several challenges due to their size and the restrictions imposed on them. Here are some common issues:

Direct Comparison Not Possible: You cannot directly compare LONG values using standard SQL comparison operators (e.g., =, !=, >, <) in WHERE clauses.
Length Limitations: Comparing LONG data based on length can be misleading, as the actual content might vary significantly even if the lengths are similar.
Performance Issues: Extracting and comparing LONG data can be resource-intensive, leading to performance bottlenecks in large databases.

Given these challenges, it’s crucial to explore alternative methods to effectively compare LONG data.

3. Techniques for Comparing `LONG` Data Types in Oracle

3.1. Using PL/SQL for Comparison

PL/SQL provides a way to compare LONG data by reading the data into variables and then comparing those variables. Here’s an example:

DECLARE
    long_data1 LONG;
    long_data2 LONG;
    are_equal BOOLEAN;
BEGIN
    SELECT text INTO long_data1 FROM article_text WHERE id = 1;
    SELECT text INTO long_data2 FROM article_text WHERE id = 2;

    IF long_data1 = long_data2 THEN
        are_equal := TRUE;
    ELSE
        are_equal := FALSE;
    END IF;

    IF are_equal THEN
        DBMS_OUTPUT.PUT_LINE('The LONG data values are equal.');
    ELSE
        DBMS_OUTPUT.PUT_LINE('The LONG data values are not equal.');
    END IF;
END;
/

Explanation:

The PL/SQL block declares two LONG variables, long_data1 and long_data2.
It selects LONG data from the article_text table into these variables.
It compares the variables and sets the are_equal flag accordingly.
Finally, it outputs whether the LONG data values are equal or not.

Advantages:

Allows for direct comparison of LONG data within PL/SQL.
Useful for performing complex logic based on the comparison.

Disadvantages:

Requires PL/SQL knowledge and syntax.
Might not be suitable for large-scale comparisons due to performance considerations.

3.2. Comparing Hash Values of `LONG` Data

One efficient way to compare LONG data is to generate hash values for the data and compare the hash values instead. This approach is faster and more efficient than comparing the entire LONG strings directly.

SELECT
    id,
    DBMS_OBFUSCATION_TOOLKIT.MD5(INPUT_STRING => text) AS hash_value
FROM
    article_text;

Explanation:

The query uses the DBMS_OBFUSCATION_TOOLKIT.MD5 function to generate an MD5 hash value for the LONG data in the text column.
It returns the id and the corresponding hash value for each row in the article_text table.

To compare two LONG values, you can compare their hash values:

SELECT
    CASE
        WHEN DBMS_OBFUSCATION_TOOLKIT.MD5(INPUT_STRING => (SELECT text FROM article_text WHERE id = 1)) =
             DBMS_OBFUSCATION_TOOLKIT.MD5(INPUT_STRING => (SELECT text FROM article_text WHERE id = 2))
        THEN 'Equal'
        ELSE 'Not Equal'
    END AS comparison_result
FROM
    dual;

Advantages:

Efficient comparison of large LONG data.
Hash values are much smaller and faster to compare than the full text.

Disadvantages:

Hash collisions can occur, meaning different LONG values might produce the same hash value (though this is rare with MD5).
Does not provide detailed information about differences between the LONG values.

3.3. Using `DBMS_LOB` Package for Partial Comparison

The DBMS_LOB package provides functions to work with large objects, including LONG data. You can use it to read portions of the LONG data and compare those portions.

DECLARE
    long_data1 LONG;
    long_data2 LONG;
    buffer1    VARCHAR2(32767);
    buffer2    VARCHAR2(32767);
    amount     INTEGER := 32767;
    offset     INTEGER := 1;
    are_equal  BOOLEAN := TRUE;
BEGIN
    SELECT text INTO long_data1 FROM article_text WHERE id = 1;
    SELECT text INTO long_data2 FROM article_text WHERE id = 2;

    WHILE offset <= LEAST(LENGTH(long_data1), LENGTH(long_data2)) LOOP
        buffer1 := SUBSTR(long_data1, offset, amount);
        buffer2 := SUBSTR(long_data2, offset, amount);

        IF buffer1 != buffer2 THEN
            are_equal := FALSE;
            EXIT;
        END IF;

        offset := offset + amount;
    END LOOP;

    IF are_equal THEN
        DBMS_OUTPUT.PUT_LINE('The LONG data values are equal.');
    ELSE
        DBMS_OUTPUT.PUT_LINE('The LONG data values are not equal.');
    END IF;
END;
/

Explanation:

The PL/SQL block declares two LONG variables and two VARCHAR2 buffers.
It reads portions of the LONG data into the buffers using SUBSTR.
It compares the buffers and sets the are_equal flag accordingly.
The loop continues until the end of the shortest LONG data value is reached.

Advantages:

Allows for partial comparison of LONG data.
Useful for identifying differences between LONG values.

Disadvantages:

More complex than hash-based comparison.
Performance can be an issue for very large LONG data values.

3.4. Converting `LONG` to `CLOB` for Easier Comparison

Since CLOB data types have fewer restrictions than LONG, converting LONG data to CLOB can simplify the comparison process. However, this requires creating a new table or modifying an existing one.

-- Create a new table with a CLOB column
CREATE TABLE article_text_clob (
    id   NUMBER PRIMARY KEY,
    text CLOB
);

-- Insert data from the LONG column to the CLOB column
INSERT INTO article_text_clob (id, text)
SELECT id, TO_LOB(text) FROM article_text;

-- Now you can compare the CLOB data
SELECT
    CASE
        WHEN (SELECT text FROM article_text_clob WHERE id = 1) =
             (SELECT text FROM article_text_clob WHERE id = 2)
        THEN 'Equal'
        ELSE 'Not Equal'
    END AS comparison_result
FROM
    dual;

Explanation:

A new table article_text_clob is created with a CLOB column.
Data from the LONG column in article_text is inserted into the CLOB column using TO_LOB.
The CLOB data can now be compared using standard SQL comparison operators.

Advantages:

Leverages the greater functionality of CLOB data types.
Simplifies comparison operations.

Disadvantages:

Requires modifying the database schema.
Involves data migration, which can be time-consuming.

3.5. Using Full-Text Search with Oracle Text

Oracle Text allows you to create indexes on text columns, including LONG columns, and perform full-text searches. This can be useful for identifying similar documents or articles based on their content.

-- Create a text index on the LONG column
CREATE INDEX article_text_index ON article_text(text)
INDEXTYPE IS CTXSYS.CONTEXT;

-- Perform a full-text search
SELECT id
FROM article_text
WHERE CONTAINS(text, 'keyword1 AND keyword2') > 0;

Explanation:

A text index is created on the LONG column using CTXSYS.CONTEXT.
The CONTAINS function is used to perform a full-text search for specific keywords.

Advantages:

Enables advanced text search capabilities.
Useful for identifying documents with similar content.

Disadvantages:

Requires setting up and configuring Oracle Text.
Can be resource-intensive for large-scale indexing and searching.

4. Best Practices for Comparing `LONG` Data Types in Oracle

When comparing LONG data types in Oracle, consider the following best practices to ensure accuracy and efficiency:

Use Hash Values for Quick Comparison: Generate and compare hash values for quick identification of differences.
Employ PL/SQL for Complex Logic: Use PL/SQL blocks to perform complex comparison logic and handle exceptions.
Convert to CLOB When Possible: Migrate LONG data to CLOB data types to leverage greater functionality and simplify comparisons.
Utilize Oracle Text for Full-Text Search: Implement Oracle Text for advanced text search capabilities and content-based comparisons.
Profile Performance: Profile the performance of different comparison methods to identify bottlenecks and optimize queries.

5. Practical Examples of `LONG` Data Type Comparison

5.1. Comparing Article Content Using Hash Values

Consider a scenario where you need to identify duplicate articles in a database table. You can use hash values to quickly compare the content of the articles.

SELECT
    a1.id AS article1_id,
    a2.id AS article2_id
FROM
    article_text a1,
    article_text a2
WHERE
    a1.id != a2.id
    AND DBMS_OBFUSCATION_TOOLKIT.MD5(INPUT_STRING => a1.text) = DBMS_OBFUSCATION_TOOLKIT.MD5(INPUT_STRING => a2.text);

Explanation:

The query compares the MD5 hash values of the text column in the article_text table.
It returns the IDs of articles that have the same hash value, indicating potential duplicates.

5.2. Identifying Differences in Configuration Files

Suppose you have a table that stores configuration files as LONG data. You can use PL/SQL and partial comparison to identify differences between different versions of the configuration files.

DECLARE
    config1    LONG;
    config2    LONG;
    buffer1    VARCHAR2(32767);
    buffer2    VARCHAR2(32767);
    amount     INTEGER := 32767;
    offset     INTEGER := 1;
    difference_found BOOLEAN := FALSE;
BEGIN
    SELECT config_data INTO config1 FROM config_table WHERE id = 1;
    SELECT config_data INTO config2 FROM config_table WHERE id = 2;

    WHILE offset <= LEAST(LENGTH(config1), LENGTH(config2)) LOOP
        buffer1 := SUBSTR(config1, offset, amount);
        buffer2 := SUBSTR(config2, offset, amount);

        IF buffer1 != buffer2 THEN
            DBMS_OUTPUT.PUT_LINE('Difference found at offset: ' || offset);
            difference_found := TRUE;
            EXIT;
        END IF;

        offset := offset + amount;
    END LOOP;

    IF NOT difference_found THEN
        DBMS_OUTPUT.PUT_LINE('No differences found.');
    END IF;
END;
/

Explanation:

The PL/SQL block compares portions of the configuration files stored in the config_table.
If a difference is found, it outputs the offset at which the difference occurs.

6. Alternatives to Using `LONG` Data Types

Given the limitations of the LONG data type, it’s often better to use alternatives like CLOB or BLOB for storing large amounts of text or binary data. Here are some reasons to consider these alternatives:

Greater Functionality: CLOB and BLOB data types support a wider range of functions and operations compared to LONG.
No Size Restrictions: CLOB and BLOB data types can store up to 4 gigabytes of data.
Multiple Columns: Tables can contain multiple CLOB or BLOB columns.
Indexing: You can create indexes on CLOB and BLOB columns using Oracle Text or other indexing techniques.

7. Comparing `LONG` Data with External Tools

In some cases, it might be beneficial to extract LONG data and compare it using external tools. This can be useful for performing more advanced analysis or comparison tasks.

7.1. Extracting `LONG` Data to Files

You can use SQLPlus or other database tools to extract LONG data to files. Here’s an example using SQLPlus:

SET LONG 100000 -- Set LONG length to a large value
SET LINESIZE 200
SET PAGESIZE 0
SET FEEDBACK OFF
SET TRIMSPOOL ON

SPOOL article1.txt
SELECT text FROM article_text WHERE id = 1;
SPOOL OFF

SPOOL article2.txt
SELECT text FROM article_text WHERE id = 2;
SPOOL OFF

Explanation:

The script sets SQL*Plus parameters to handle LONG data.
It spools the LONG data to text files.

7.2. Comparing Files Using External Tools

Once you have extracted the LONG data to files, you can use external tools like diff (on Unix-like systems) or comparison tools like Beyond Compare to identify differences between the files.

diff article1.txt article2.txt

Advantages:

Leverages powerful external comparison tools.
Useful for performing detailed analysis of differences.

Disadvantages:

Requires extracting data to files.
Involves using external tools, which might require additional setup or configuration.

8. Performance Considerations

Comparing LONG data types can be resource-intensive, especially for large data values. Consider the following performance considerations:

Minimize Data Transfer: Avoid transferring large amounts of LONG data unnecessarily.
Use Indexes: Create indexes on related columns to speed up queries.
Profile Queries: Profile the performance of comparison queries to identify bottlenecks.
Optimize PL/SQL Code: Optimize PL/SQL code to minimize overhead.
Consider Materialized Views: Use materialized views to precompute and store comparison results.

9. Using Regular Expressions for Pattern Matching

Regular expressions can be useful for identifying patterns or specific content within LONG data. Oracle supports regular expressions through the REGEXP_LIKE, REGEXP_INSTR, REGEXP_SUBSTR, and REGEXP_REPLACE functions.

SELECT id
FROM article_text
WHERE REGEXP_LIKE(text, 'pattern');

Explanation:

The query uses the REGEXP_LIKE function to find rows where the text column matches a regular expression pattern.

Advantages:

Enables advanced pattern matching and text analysis.
Useful for identifying specific content within LONG data.

Disadvantages:

Regular expressions can be complex and resource-intensive.
Requires knowledge of regular expression syntax.

10. Security Considerations

When working with LONG data types, consider the following security considerations:

Data Masking: Mask sensitive data stored in LONG columns to protect against unauthorized access.
Access Control: Implement strict access control policies to limit who can access LONG data.
Encryption: Encrypt LONG data to protect it from unauthorized access.
Auditing: Enable auditing to track access to LONG data.

11. Automating `LONG` Data Comparisons

To streamline the process of comparing LONG data, consider automating the comparison process using scripts or scheduled jobs.

11.1. Creating a Script for Hash-Based Comparison

You can create a script that generates hash values for LONG data and compares the hash values to identify differences.

-- Script to compare LONG data using hash values
DECLARE
    cursor c_articles IS
        SELECT id, DBMS_OBFUSCATION_TOOLKIT.MD5(INPUT_STRING => text) AS hash_value
        FROM article_text;
    article1_id   NUMBER;
    article1_hash VARCHAR2(32);
    article2_id   NUMBER;
    article2_hash VARCHAR2(32);
BEGIN
    OPEN c_articles;
    LOOP
        FETCH c_articles INTO article1_id, article1_hash;
        EXIT WHEN c_articles%NOTFOUND;

        DECLARE
            cursor c_compare IS
                SELECT id, DBMS_OBFUSCATION_TOOLKIT.MD5(INPUT_STRING => text) AS hash_value
                FROM article_text
                WHERE id != article1_id;
        BEGIN
            OPEN c_compare;
            LOOP
                FETCH c_compare INTO article2_id, article2_hash;
                EXIT WHEN c_compare%NOTFOUND;

                IF article1_hash = article2_hash THEN
                    DBMS_OUTPUT.PUT_LINE('Article ' || article1_id || ' and Article ' || article2_id || ' have the same content.');
                END IF;
            END LOOP;
            CLOSE c_compare;
        END;
    END LOOP;
    CLOSE c_articles;
END;
/

Explanation:

The script uses nested cursors to compare the hash values of all articles in the article_text table.
It outputs the IDs of articles that have the same content.

11.2. Scheduling the Script Using DBMS_SCHEDULER

You can schedule the script to run automatically using the DBMS_SCHEDULER package.

BEGIN
    DBMS_SCHEDULER.CREATE_JOB (
        job_name        => 'long_data_comparison_job',
        job_type        => 'PLSQL_BLOCK',
        job_action      => 'BEGIN ... END;', -- Replace with the PL/SQL script
        start_date      => SYSTIMESTAMP,
        repeat_interval => 'FREQ=DAILY',
        enabled         => TRUE
    );
END;
/

Explanation:

The code creates a scheduled job that runs the PL/SQL script daily.

12. Addressing Specific Use Cases

12.1. Comparing Code Blocks

When comparing code blocks stored as LONG data, consider using specialized tools for code comparison. These tools can identify differences in code structure, formatting, and logic.

12.2. Comparing Log Files

For comparing log files stored as LONG data, use log analysis tools that can parse and analyze log data based on specific patterns and criteria.

13. Migrating from `LONG` to `CLOB`

Migrating from LONG to CLOB is a significant step in modernizing your database schema. Here’s a detailed guide:

Create a new table or add a CLOB column:

ALTER TABLE article_text ADD (text_clob CLOB);

Migrate the data:

UPDATE article_text SET text_clob = TO_LOB(text);

Drop the old LONG column:

ALTER TABLE article_text DROP COLUMN text;

Rename the CLOB column:

ALTER TABLE article_text RENAME COLUMN text_clob TO text;

14. Advanced Comparison Techniques

14.1. Using Edit Distance Algorithms

Edit distance algorithms, such as Levenshtein distance, can measure the similarity between two LONG strings. This is useful when you need to find strings that are similar but not identical.

14.2. Semantic Comparison

Semantic comparison involves understanding the meaning of the text and comparing based on that understanding. This is a more advanced technique that requires natural language processing (NLP) tools.

15. Common Mistakes and How to Avoid Them

Trying to Directly Compare LONG Columns:
- Mistake: Using WHERE long_column1 = long_column2.
- Solution: Use PL/SQL, hash values, or convert to CLOB.
Ignoring Performance Implications:
- Mistake: Running comparisons without profiling.
- Solution: Profile queries and optimize as needed.
Not Handling NULL Values:
- Mistake: Assuming all LONG columns have data.
- Solution: Use NVL or CASE statements to handle NULL values.

16. Using Third-Party Tools

Several third-party tools can assist in comparing LONG data types:

Toad for Oracle: Provides a graphical interface for comparing data.
SQL Developer: Offers data comparison features.
DBVisualizer: Supports data comparison across different databases.

17. The Future of `LONG` Data Type Comparison

As databases evolve, the need to store and compare large amounts of text data will continue to grow. Future trends include:

More Advanced NLP Tools: Making semantic comparison more accessible.
Better Support for CLOB and BLOB: Oracle will likely continue to enhance the functionality of these data types.
Improved Indexing Techniques: Allowing for more efficient full-text search.

18. Comparing `LONG` Data in Different Character Sets

When comparing LONG data in different character sets, ensure that you handle character set conversions correctly. Use the CONVERT function to convert data to a common character set before comparing.

SELECT
    CASE
        WHEN CONVERT(long_data1, 'US7ASCII', 'WE8ISO8859P1') = CONVERT(long_data2, 'US7ASCII', 'WE8ISO8859P1')
        THEN 'Equal'
        ELSE 'Not Equal'
    END AS comparison_result
FROM
    dual;

19. Troubleshooting Common Issues

ORA-00932: inconsistent datatypes:
- Cause: Attempting to directly compare LONG columns.
- Solution: Use PL/SQL, hash values, or convert to CLOB.
Performance issues with large LONG columns:
- Cause: Full table scans and inefficient queries.
- Solution: Use indexes, profile queries, and optimize PL/SQL code.

20. Frequently Asked Questions (FAQ)

Q1: Can I directly compare LONG data types in Oracle?
A: No, you cannot directly compare LONG data types using standard SQL comparison operators.

Q2: What are the alternatives to using LONG data types?
A: Alternatives include CLOB (Character Large Object) and BLOB (Binary Large Object).

Q3: How can I compare LONG data efficiently?
A: Use hash values to quickly compare the content of LONG data.

Q4: What is the best way to compare LONG data for equality?
A: Converting LONG to CLOB or using hash values are effective methods.

Q5: Can I use indexes with LONG data types?
A: No, LONG columns cannot be indexed. Consider using CLOB with Oracle Text for indexing.

Q6: What are the performance implications of comparing LONG data?
A: Comparing LONG data can be resource-intensive, especially for large data values. Profile your queries and optimize as needed.

Q7: How do I handle NULL values when comparing LONG data?
A: Use NVL or CASE statements to handle NULL values.

Q8: Is it possible to compare parts of LONG data?
A: Yes, you can use the DBMS_LOB package to read portions of the LONG data and compare those portions.

Q9: What are the security considerations when working with LONG data types?
A: Consider data masking, access control, encryption, and auditing to protect LONG data.

Q10: How can I automate the process of comparing LONG data?
A: Use scripts or scheduled jobs to automate the comparison process.

In conclusion, comparing LONG data types in Oracle requires understanding the limitations of the LONG data type and employing alternative methods such as PL/SQL, hash values, DBMS_LOB package, and converting to CLOB. By following the best practices and considering the performance and security implications, you can effectively compare LONG data and ensure the accuracy and efficiency of your database operations.

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How To Compare Long Datatype In Oracle Effectively

1. Understanding the `LONG` Data Type in Oracle

2. Challenges in Comparing `LONG` Data Types

3. Techniques for Comparing `LONG` Data Types in Oracle

3.1. Using PL/SQL for Comparison

3.2. Comparing Hash Values of `LONG` Data

3.3. Using `DBMS_LOB` Package for Partial Comparison

3.4. Converting `LONG` to `CLOB` for Easier Comparison

3.5. Using Full-Text Search with Oracle Text

4. Best Practices for Comparing `LONG` Data Types in Oracle

5. Practical Examples of `LONG` Data Type Comparison

5.1. Comparing Article Content Using Hash Values

5.2. Identifying Differences in Configuration Files

6. Alternatives to Using `LONG` Data Types

7. Comparing `LONG` Data with External Tools

7.1. Extracting `LONG` Data to Files

7.2. Comparing Files Using External Tools

8. Performance Considerations

9. Using Regular Expressions for Pattern Matching

10. Security Considerations

11. Automating `LONG` Data Comparisons

11.1. Creating a Script for Hash-Based Comparison

11.2. Scheduling the Script Using DBMS_SCHEDULER

12. Addressing Specific Use Cases

12.1. Comparing Code Blocks

12.2. Comparing Log Files

13. Migrating from `LONG` to `CLOB`

14. Advanced Comparison Techniques

14.1. Using Edit Distance Algorithms

14.2. Semantic Comparison

15. Common Mistakes and How to Avoid Them

16. Using Third-Party Tools

17. The Future of `LONG` Data Type Comparison

18. Comparing `LONG` Data in Different Character Sets

19. Troubleshooting Common Issues

20. Frequently Asked Questions (FAQ)

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Leave a Reply Cancel reply

1. Understanding the LONG Data Type in Oracle

2. Challenges in Comparing LONG Data Types

3. Techniques for Comparing LONG Data Types in Oracle

3.1. Using PL/SQL for Comparison

3.2. Comparing Hash Values of LONG Data

3.3. Using DBMS_LOB Package for Partial Comparison

3.4. Converting LONG to CLOB for Easier Comparison

3.5. Using Full-Text Search with Oracle Text

4. Best Practices for Comparing LONG Data Types in Oracle

5. Practical Examples of LONG Data Type Comparison

5.1. Comparing Article Content Using Hash Values

5.2. Identifying Differences in Configuration Files

6. Alternatives to Using LONG Data Types

7. Comparing LONG Data with External Tools

7.1. Extracting LONG Data to Files

7.2. Comparing Files Using External Tools

8. Performance Considerations

9. Using Regular Expressions for Pattern Matching

10. Security Considerations

11. Automating LONG Data Comparisons

11.1. Creating a Script for Hash-Based Comparison

11.2. Scheduling the Script Using DBMS_SCHEDULER

12. Addressing Specific Use Cases

12.1. Comparing Code Blocks

12.2. Comparing Log Files

13. Migrating from LONG to CLOB

14. Advanced Comparison Techniques

14.1. Using Edit Distance Algorithms

14.2. Semantic Comparison

15. Common Mistakes and How to Avoid Them

16. Using Third-Party Tools

17. The Future of LONG Data Type Comparison

18. Comparing LONG Data in Different Character Sets

19. Troubleshooting Common Issues

20. Frequently Asked Questions (FAQ)

Comments

Leave a Reply Cancel reply

1. Understanding the `LONG` Data Type in Oracle

2. Challenges in Comparing `LONG` Data Types

3. Techniques for Comparing `LONG` Data Types in Oracle

3.2. Comparing Hash Values of `LONG` Data

3.3. Using `DBMS_LOB` Package for Partial Comparison

3.4. Converting `LONG` to `CLOB` for Easier Comparison

4. Best Practices for Comparing `LONG` Data Types in Oracle

5. Practical Examples of `LONG` Data Type Comparison

6. Alternatives to Using `LONG` Data Types

7. Comparing `LONG` Data with External Tools

7.1. Extracting `LONG` Data to Files

11. Automating `LONG` Data Comparisons

13. Migrating from `LONG` to `CLOB`

17. The Future of `LONG` Data Type Comparison

18. Comparing `LONG` Data in Different Character Sets