How to use Boto3 to paginate through all tables present in AWS Glue

AWS Glue Data Catalog stores metadata about your data sources. When you have many tables, pagination helps retrieve them efficiently without overwhelming your application. The boto3 library provides built-in pagination support for AWS Glue operations.

Understanding Pagination Parameters

The pagination configuration accepts three optional parameters:

• max_items: Total number of records to return across all pages
• page_size: Number of records per page (default is 100)
• starting_token: Token from previous response to resume pagination

Setting up AWS Glue Client

First, create a boto3 session and Glue client. Ensure your AWS credentials and region are configured properly ?

import boto3
from botocore.exceptions import ClientError

def paginate_through_tables(database_name, max_items=None, page_size=None, starting_token=None):
    session = boto3.session.Session()
    glue_client = session.client('glue')
    
    try:
        paginator = glue_client.get_paginator('get_tables')
        response = paginator.paginate(
            DatabaseName=database_name,
            PaginationConfig={
                'MaxItems': max_items,
                'PageSize': page_size,
                'StartingToken': starting_token
            }
        )
        return response
    except ClientError as e:
        raise Exception(f"boto3 client error in paginate_through_tables: {str(e)}")
    except Exception as e:
        raise Exception(f"Unexpected error in paginate_through_tables: {str(e)}")

# Example usage
paginator = paginate_through_tables("test_db", max_items=2, page_size=5)

Processing Paginated Results

The paginator returns an iterator that yields pages of results. You can process each page individually ?

def process_all_tables(database_name):
    paginator = paginate_through_tables(database_name, page_size=10)
    
    table_count = 0
    for page in paginator:
        tables = page.get('TableList', [])
        
        for table in tables:
            table_count += 1
            print(f"Table {table_count}: {table['Name']}")
            print(f"Location: {table['StorageDescriptor']['Location']}")
            print(f"Created: {table['CreateTime']}")
            print("-" * 50)
    
    return table_count

# Process all tables
total_tables = process_all_tables("test_db")
print(f"Total tables processed: {total_tables}")

Example Output

The output shows table metadata including name, location, and creation time ?

Table 1: temp_table
Location: s3://test/
Created: 2020-09-10 20:44:29+05:30
--------------------------------------------------
Table 2: test_3
Location: s3://test3/
Created: 2020-09-10 21:54:39+05:30
--------------------------------------------------
Total tables processed: 2

Best Practices

• Use appropriate page sizes: Start with 100 (default) and adjust based on your needs
• Handle pagination tokens: Store NextToken to resume pagination in subsequent calls
• Error handling: Always wrap API calls in try-catch blocks
• Resource management: Close sessions properly to avoid connection leaks

Conclusion

AWS Glue pagination with boto3 efficiently handles large numbers of tables by breaking results into manageable chunks. Use appropriate page sizes and always handle pagination tokens for seamless data retrieval.