Performance Tuning with Tableau – Computing Layout
Performance Tuning with Tableau – Computing Layout
This inaugural entry in our performance tuning series empowers you to actively optimize computation layouts within Tableau, a pivotal aspect of ensuring your workbooks perform at peak efficiency. We will not only dissect the theoretical foundations but also equip you with practical, hands-on examples to demonstrate effective optimization techniques.
Harness Tableau Performance Recording:
Tableau’s performance recording tool actively logs key events as you interact with your workbook. These events include query execution, geocoding, data source connections, layout computations, extract generation, and data blending. By analyzing these recordings, you actively pinpoint performance bottlenecks and implement targeted improvements.
Just as a music director orchestrates a recording session, you, as a Tableau developer, actively orchestrate your workbooks for optimal performance. You use the performance recording tool as your studio, gaining insights into the “musicality” of your workbook’s operations.
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Upon completion of a performance recording, Tableau actively presents a comprehensive worksheet detailing the timing of various events. This worksheet, typically integrated into a dashboard, allows you to actively identify processes consuming excessive time. Notably, the “computing layout” event often reveals areas for optimization.
Specifically, if the computation layout event exhibits prolonged execution times, you must actively simplify your workbooks. This is because parameter and calculation validation processes consume significant resources in complex workbooks laden with numerous worksheets, dashboards, calculations, filters, and parameters.
Implement Practical Optimization: Computation Layouts and Query Performance:
To illustrate these concepts, consider a scenario involving two large tables: a source and a destination. You aim to display message log text from the destination table based on selections made in the source table. Initially, you achieved this using a filter that involved a “contains” string operation, leading to inefficient query execution and substantial sorting overhead.
Through performance recording, you actively identified these inefficiencies. Consequently, you revised the approach to leverage action filters and relationships between the source and destination tables. This strategic shift eliminated the need for the inefficient “contains” string query and the costly sorting operation.
By implementing these changes, the performance recording output actively demonstrated a significant reduction in execution times, effectively optimizing both query performance and computation layout efficiency.
Implement Key Optimization Strategies:
- Use Action Filters: Actively utilize action filters and relationships to improve query efficiency.
- Minimize sorting of large datasets: Actively eliminate unnecessary sorting operations to reduce processing time.
- Simplify complex workbooks: Actively reduce the number of worksheets, dashboards, calculations, filters, and parameters.
- Use relevant values: When using filters, actively show only the relevant values instead of all values.
