Yearly Archives: 2023

Record Triggered Automation – Key Takeaways: Additional Insights: By adhering to these takeaways and insights, organizations can streamline their automation processes and enhance overall efficiency within the Salesforce platform. High-Performance Batch Processing For intricate batch processing requiring high performance and complex logic evaluation, Apex stands out with its configurable nature and robust debugging capabilities. Here’s why Apex is recommended for such scenarios: While before-save flow triggers might not match the performance of Apex triggers in simplistic speed comparisons, their overhead impact is mitigated within broader transactions. Before-save flow triggers still offer significant performance advantages over Workflow Rules, making them suitable for most non-complex batch scenarios, especially for same-record field updates. For batch processing that doesn’t require immediate execution during the initial transaction, Flow provides some capabilities, albeit less feature-rich compared to Apex. Scheduled Flows and Scheduled Paths in record-triggered flows offer options for batch operations, albeit with certain limitations. However, for extensive batch processing needs, Apex remains the preferred choice due to its flexibility and performance capabilities. Cross-Object CRUD Performing CRUD operations on different records necessitates database operations, making Apex triggers the go-to option for such cross-object operations. While Flow triggers support cross-object CRUD post-save, they lack support in the before-save context. Apex outperforms Flow in raw database operation speed, ensuring quicker preparation, execution, and processing of database calls. However, optimizing user implementations, such as consolidating multiple DML statements, often yields more significant performance improvements than solely focusing on lower-level operations. Efficiently managing DML operations and minimizing unnecessary database transactions are crucial for optimizing performance. Strategies like consolidating DML operations, using temporary record variables, and adhering to design standards aid in enhancing performance and maintaining data integrity across complex use cases. Complex List Processing Flow presents limitations in list processing capabilities, hindering tasks like in-place data transforms, sorts, and filters, which are more efficiently achieved in Apex. Flow’s basic list processing operations and serial execution of loops contribute to challenges in handling complex lists effectively. Extending flows with invocable Apex allows leveraging Apex’s robust list processing functionalities while retaining the usability of Flow for business-facing users. Apex developers can create efficient, modular list processing methods, making them accessible to Flow users through invocable actions. When implementing invocable Apex methods, ensuring proper bulkification and handling multiple invocations in a batch scenario is essential. Leveraging generic sObject inputs and dynamic Apex facilitates reusable and elegant implementations across multiple triggers and sObjects. While Flow has introduced some list processing capabilities, it still lags behind Apex in functionality, emphasizing the continued relevance of Apex for complex list processing tasks. Asynchronous Processing Asynchronous processing offers flexibility in executing logic outside the initial transaction context, providing benefits like minimal database transactions and consistent rollbacks. Both Flow and Apex offer solutions for asynchronous processing, catering to use cases requiring separate transactions, external callouts, or prolonged processing times. Flow’s After-Save Flow Trigger and Apex triggers support asynchronous processing, enabling the execution of logic independently from the initial transaction. While Flow offers a low-code solution through the Run Asynchronously path, Apex provides more control and customization options, especially regarding callouts and error handling. When choosing between Flow and Apex for asynchronous processing, consider factors like control over callouts, error handling customization, and the complexity of the solution. Apex’s more extensive error handling capabilities and direct control over callouts may be advantageous for complex scenarios requiring meticulous error management. Testing asynchronous solutions, particularly those involving callouts, necessitates careful consideration of error handling and edge cases. Apex’s advanced error handling features and flexibility in managing callouts make it suitable for handling complex scenarios effectively. In summary, both Flow and Apex offer viable options for asynchronous processing, with the choice depending on factors like control requirements, error handling preferences, and the complexity of the solution. Record Triggered Automation Exploring Other Solutions In the realm of low-code administration, administrators have historically employed various methods, often considered “hacks,” to achieve asynchronous processing. One such method involved setting up time-based workflows in Workflow Rules, scheduled actions in Process Builder, or Scheduled Paths in Flow to run immediately after a trigger execution. While effective, these approaches had their limitations. The introduction of dedicated paths, like the Run Asynchronously path in Flow, offers distinct advantages over these makeshift solutions. The “zero-wait pause” technique, another common workaround, entailed adding a Pause element with a zero-minute wait time through an autolaunched subflow, triggered by Process Builder. Although this method effectively separated transactions, its scalability and performance were compromised, leading to potential issues with flow interview limits and performance degradation over time. Consequently, it’s not recommended, especially for subflows called from record-triggered flows. Data Transfer and State Management Between Processes An enticing aspect of the “zero-wait pause” approach was the perceived ability to maintain a stateful relationship between synchronous and asynchronous processing. However, this method contradicts the underlying principles of asynchronous processing, which aim for flexibility and performance control by separating processes. While flow variables may persist before and after the pause, relying on them for extended periods risks data inconsistency. To ensure data integrity, particularly across independent processes, it’s advisable to store relevant information in persistent storage, such as custom fields on the triggering record. Summary Asynchronous processing introduces complexities in designing record-triggered automation, especially concerning external callouts or state persistence between processes. While the Run Asynchronously path in Flow addresses many low-code requirements, scenarios involving custom errors or configurable retries may necessitate Apex. It’s crucial to consider the specific needs of your organization and weigh the benefits of each solution accordingly. Custom Validation Errors Flow currently lacks native support for preventing DML operations or throwing custom errors, although upcoming releases are expected to address this limitation. Until then, Validation Rules and Apex triggers remain viable options for handling complex validation scenarios. Designing Record-Triggered Automation Designing effective record-triggered automation involves considering various best practices and organizational needs. Performance, troubleshooting, ordering, and orchestration are key aspects to address, each offering its own set of challenges and solutions. Striking a balance between consolidation and modularization of automation components is essential