You can modify the Integration Service properties in the Informatica Administrator Console.

Check session configuration: Confirm that the session properties are set correctly. Specifically, verify that the "Sorted Input" option is enabled for the source qualifier transformation connected to your source file.

As you mentioned, enabling the "Sorted Input" option in the Joiner transformation is a good step. However, make sure that the ports you are using for joining are indeed sorted. Double-check the sorting options for both the source qualifier transformation and the sorter transformation.

Verify sort orders: Ensure that the sort orders of the sorted ports in both the source qualifier transformation and the sorter transformation are the same. If the sort orders differ, you may encounter the "no ports are sorted" error.

Increase buffer sizes: Modify the buffer size properties for the Joiner transformation to accommodate the larger dataset. Increasing the buffer sizes can help handle the larger volume of data efficiently.

If you're encountering an error when joining 100 million source records in Informatica PowerCenter, here's a concise approach to address the issue:

Verify sorting order: Confirm that the sorting order is correctly specified for sorted ports in both the joiner and sorter transformations. Ensure consistency and matching sorting orders for the ports being sorted.

Increase memory resources: Allocate sufficient memory resources to the PowerCenter Integration Service to handle large datasets. Adjust the memory settings in the PowerCenter Administration Console.

Partition the data: Divide the source data into smaller partitions to enable parallel processing. Use the Partitioning option in the Source Qualifier transformation or a Partitioning object.

Use incremental joins: Consider performing incremental joins instead of joining all records at once. Incremental joins involve joining smaller subsets of data gradually, reducing memory and processing requirements.

Optimize the mapping: Streamline the mapping by removing unnecessary transformations, minimizing data flow, or aggregating data before the join if applicable.

Consider database join: If the source data is in a database, leverage database query optimization by performing the join within the database instead of using PowerCenter.

Enable high-performance mode: Activate the high-performance mode in the session properties to optimize performance for large data volumes.

Monitor session logs: Check session logs for detailed error messages or warnings that can provide insights into the join operation issue.

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Please insure the physical connection

When working with larger datasets, it's important to optimize your Informatica PowerCenter workflow to handle the increased data volume efficiently.

Here are some steps you can take to address the issue and fix the error:

• Increase Buffer Size: Adjust the buffer size for the joiner transformation to accommodate the larger dataset. You can increase the buffer size in the joiner transformation properties to improve performance. Experiment with different buffer sizes to find the optimal setting for your specific scenario.
• Enable Sorted Input: Ensure that the input ports for the joiner transformation are sorted. You mentioned that you have enabled the sorted port in the joiner, but you're still getting an error. Make sure that the input ports of the joiner transformation are explicitly marked as sorted by selecting the "Sorted Input" option in the joiner transformation properties.
• Verify Sorter Transformation: Double-check the configuration of the sorter transformation before the joiner. Ensure that the ports you're using for sorting in the sorter transformation match the corresponding ports in the other sorter transformation. The sort order and data types should also be consistent.
• Memory Considerations: Large datasets require sufficient memory resources. Check if your system has enough memory allocated for PowerCenter to process the larger dataset. Adjust the memory settings in the PowerCenter Integration Service configuration accordingly to avoid any memory-related issues.
• Partitioning and Pushdown Optimization: If possible, consider partitioning the join operation to distribute the data processing across multiple nodes or partitions. This can improve performance by utilizing parallel processing. Additionally, evaluate the possibility of leveraging database-specific optimizations, such as pushdown optimization, to offload the join operation to the database engine.
• Performance Monitoring: Monitor the performance of the join operation using PowerCenter's performance monitoring tools. Analyze the session logs and statistics to identify any bottlenecks or areas for improvement. Look for any warnings or errors that can provide insights into the specific issues encountered during the join operation.
  
  

It's worth noting that the optimal solution may vary depending on your specific environment, data characteristics, and hardware resources