When setting up an object, users may specify certain columns as Metrics or Dimensions to enhance query efficiency and data organization.
  • METRICS columns contain numeric measures. For example, TOTAL_ORDERS or AVERAGE_ORDER_REVENUE.
  • DIMENSIONS columns represent ways to slice or catagorize data. For example, REGION_NAME or CUSTOMER_AGE.

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Benefits of Defining Metrics and Dimensions

Specifying metrics and dimensions improves the performance and usability of queries by enabling faster identification of relevant data areas. For instance, if a user asks, “Split total orders by region and plot on a map,” the agent can leverage the object explorer to quickly surface the necessary data. When metrics and dimensions are pre-defined, query performance improves significantly, particularly for large datasets.

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Best Practices for Large Datasets

For users handling datasets with over 10 million rows, defining metrics and dimensions is strongly recommended. This approach:
  • Increases the reliability of the agent.
  • Reduces message latency.
  • Allows the agent to load more data efficiently into the Python sandbox.
By structuring datasets with clear metrics and dimensions, users can streamline data exploration and enhance the overall query experience.