@SagarS98

You can go thru the below write up to understand better on thread level data page which will solve your problem

In Pega, thread-level data pages (TLPs) are used to store and manage data that is specific to a particular thread or requestor session. They play a crucial role in maintaining data consistency and optimizing performance in a multi-threaded environment like Pega's platform. Here's how they work:

1. **Data Isolation**: Each thread or requestor in Pega can have its own instance of a thread-level data page. This ensures that data used by one thread does not interfere with or get overwritten by another thread, providing isolation and consistency.

2. **Scope Definition**: Thread-level data pages have a specific scope defined as "Thread" in their configuration. This means the data stored in these pages is accessible only within the context of the thread that created it. Once the thread's processing is complete, the data page and its data are typically discarded.

3. **Loading Data**: Thread-level data pages can be populated with data either automatically or on-demand. The data can be loaded using various data sources such as database queries, connectors, or activities. When a thread-level data page is referenced, Pega checks if the page is already populated; if not, it loads the data according to the defined data source.

4. **Declarative Caching**: Pega supports declarative caching for thread-level data pages. This means that once a data page is loaded, it can be reused within the same thread without reloading the data multiple times, improving performance by reducing redundant data retrieval operations.

5. **Automatic Cleanup**: Thread-level data pages are automatically cleaned up when the thread's processing is completed. This ensures that memory is efficiently managed and there are no stale data pages lingering around after they are no longer needed.

6. **Use Cases**: Thread-level data pages are commonly used in scenarios where data needs to be fetched and used within a single request or session. Examples include user-specific preferences, session-specific calculations, or any transient data required for processing a specific task within a requestor's session.

7. **Accessing Data Pages**: In Pega, thread-level data pages can be accessed and manipulated using the Data Page rules. These rules define the structure, sources, and behavior of the data pages, allowing developers to specify how and when the data should be loaded and refreshed.

8. **Performance Optimization**: By using thread-level data pages, Pega can optimize performance by reducing the need for repetitive data fetching and ensuring that data used within a thread is readily available. This reduces latency and improves the efficiency of request processing.

In summary, thread-level data pages in Pega provide a mechanism to manage and isolate data for individual threads or requestors, ensuring data consistency and optimizing performance. They are a key feature for handling session-specific data and improving the efficiency of multi-threaded processing in the Pega platform.

@SagarS98

You can go thru the below write up to understand better on thread level data page which will solve your problem

In Pega, thread-level data pages (TLPs) are used to store and manage data that is specific to a particular thread or requestor session. They play a crucial role in maintaining data consistency and optimizing performance in a multi-threaded environment like Pega's platform. Here's how they work:

1. **Data Isolation**: Each thread or requestor in Pega can have its own instance of a thread-level data page. This ensures that data used by one thread does not interfere with or get overwritten by another thread, providing isolation and consistency.

2. **Scope Definition**: Thread-level data pages have a specific scope defined as "Thread" in their configuration. This means the data stored in these pages is accessible only within the context of the thread that created it. Once the thread's processing is complete, the data page and its data are typically discarded.

3. **Loading Data**: Thread-level data pages can be populated with data either automatically or on-demand. The data can be loaded using various data sources such as database queries, connectors, or activities. When a thread-level data page is referenced, Pega checks if the page is already populated; if not, it loads the data according to the defined data source.

4. **Declarative Caching**: Pega supports declarative caching for thread-level data pages. This means that once a data page is loaded, it can be reused within the same thread without reloading the data multiple times, improving performance by reducing redundant data retrieval operations.

5. **Automatic Cleanup**: Thread-level data pages are automatically cleaned up when the thread's processing is completed. This ensures that memory is efficiently managed and there are no stale data pages lingering around after they are no longer needed.

6. **Use Cases**: Thread-level data pages are commonly used in scenarios where data needs to be fetched and used within a single request or session. Examples include user-specific preferences, session-specific calculations, or any transient data required for processing a specific task within a requestor's session.

7. **Accessing Data Pages**: In Pega, thread-level data pages can be accessed and manipulated using the Data Page rules. These rules define the structure, sources, and behavior of the data pages, allowing developers to specify how and when the data should be loaded and refreshed.

8. **Performance Optimization**: By using thread-level data pages, Pega can optimize performance by reducing the need for repetitive data fetching and ensuring that data used within a thread is readily available. This reduces latency and improves the efficiency of request processing.

In summary, thread-level data pages in Pega provide a mechanism to manage and isolate data for individual threads or requestors, ensuring data consistency and optimizing performance. They are a key feature for handling session-specific data and improving the efficiency of multi-threaded processing in the Pega platform.