Comparing Data Between Two Different Tables Using Oracle's DBMS

Comparing Data between Two Different Tables

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In this article, we will explore a common challenge in database development: comparing data between two different tables. With large datasets involved, traditional comparison methods can be slow and inefficient. We will discuss a solution that leverages Oracle’s DBMS_SQLHASH package to quickly generate hashes for chunks of data, reducing the need for full table comparisons.

Understanding the Problem

The problem is straightforward: we have two tables from different databases with similar columns but different data. The goal is to compare the data between these two tables efficiently, particularly when dealing with large datasets.

Traditional Comparison Methods

Traditional comparison methods involve querying both tables and comparing each row individually. This approach can be time-consuming and inefficient, especially for large datasets.

# Example traditional comparison code
EmplTbl = cur.execute("select A , B , C from EmployeeTable where EmplName in ('A','B')") 
for line in EmplTbl:
    EmplData.append(line)

DeptTbl = cur.execute("select A , B , C from DeptTable") 

for line in DeptTbl:
    DeptData.append(line)

for Empl in EmplData:
    DeptResult = all(Empl in DeptData for elm in DeptData)
    if DeptResult:
        print("Yes")
    else:
        print("No")

Oracle’s `DBMS_SQLHASH` Package

Oracle provides a package called DBMS_SQLHASH that can quickly generate hashes for chunks of data. This approach is particularly useful when dealing with large datasets where traditional comparison methods would be too slow.

# Example Oracle code using DBMS_SQLHASH
create table EmployeeTable1 as
select 1 a, 2 b, 3 c, 'abcdefg' EmplName from dual union all
select 1 a, 2 b, 3 c, 'bcdefg'  EmplName from dual union all
select 1 a, 2 b, 3 c, 'cdefg'   EmplName from dual;

create table EmployeeTable2 as
select 1 a, 2 b, 3 c, 'abcdefg' EmplName from dual union all
select 1 a, 2 b, 3 c, 'bcdefg'  EmplName from dual union all
select 9 a, 9 b, 9 c, 'cdefg'   EmplName from dual;

-- Generate hashes for each first-letter of the employee names
select 'a', dbms_sqlhash.gethash('select a,b,c,EmplName from EmployeeTable1 where EmplName like ''a%'' order by 1,2,3', 3) from dual union all
select 'b', dbms_sqlhash.gethash('select a,b,c,EmplName from EmployeeTable1 where EmplName like ''b%'' order by 1,2,3', 3) from dual union all
select 'c', dbms_sqlhash.gethash('select a,b,c,EmplName from EmployeeTable1 where EmplName like ''c%'' order by 1,2,3', 3) from dual;

a   923920839BFE25A44303718523CBFE1CEBB11053
b   355CB0FFAEBB60ECE2E81F3C9502F2F58A23F8BC
c   F2D94D7CC0C82329E576CD867CDC52D933C37C2C &lt;-- DIFFERENT

-- Generate hashes for each first-letter of the employee names (EmployeeTable2)
select 'a', dbms_sqlhash.gethash('select a,b,c,EmplName from EmployeeTable2 where EmplName like ''a%'' order by 1,2,3', 3) from dual union all
select 'b', dbms_sqlhash.gethash('select a,b,c,EmplName from EmployeeTable2 where EmplName like ''b%'' order by 1,2,3', 3) from dual union all
select 'c', dbms_sqlhash.gethash('select a,b,c,EmplName from EmployeeTable2 where EmplName like ''c%'' order by 1,2,3', 3) from dual;

a   923920839BFE25A44303718523CBFE1CEBB11053
b   355CB0FFAEBB60ECE2E81F3C9502F2F58A23F8BC
c   6B7B1D374568B353E9A37EB35B4508B6AE665F8A &lt;-- DIFFERENT

Python Program for Comparison

Using Oracle’s DBMS_SQLHASH package, we can quickly generate hashes for chunks of data. However, this approach requires more coding in Python, as we need to build loops and construct multiple SQL statements.

# Example Python code using DBMS_SQLHASH
import cx_Oracle

# Connect to the database
conn = cx_oracle.connect("username/password@host:port/dbname")

# Create a cursor object
cur = conn.cursor()

# Define the chunk size for comparison
chunk_size = 1000

# Generate hashes for each first-letter of the employee names
for i in range(3):
    cur.execute(f"select 'a', dbms_sqlhash.gethash('select a,b,c,EmplName from EmployeeTable1 where EmplName like ''a%'' order by {i+1},2,3', 3) from dual union all")
    results = cur.fetchall()
    for result in results:
        hash_value = result[0]
        # Compare the hashes
        if i == 0:
            print(f"Hash for 'a': {hash_value}")
        elif i == 1:
            print(f"Hash for 'b': {hash_value}")
        else:
            print(f"Hash for 'c': {hash_value}")

# Close the cursor and connection
cur.close()
conn.close()

Conclusion

Comparing data between two different tables can be a challenging task, especially when dealing with large datasets. Traditional comparison methods can be slow and inefficient, while Oracle’s DBMS_SQLHASH package provides a faster approach by generating hashes for chunks of data.

However, this solution requires more coding in Python, as we need to build loops and construct multiple SQL statements. Additionally, the solution will be slower if the tables are wildly different.

In conclusion, using Oracle’s DBMS_SQLHASH package can significantly improve the performance of comparing data between two different tables, especially when dealing with large datasets. However, it requires more coding in Python and may have limitations depending on the dataset size and complexity.

Next Steps

Experiment with different chunk sizes to find the optimal value for your specific use case.
Consider using other optimization techniques, such as caching or parallel processing, to further improve performance.
Explore other database features and tools that can help improve data comparison efficiency.

Last modified on 2024-04-09