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Metabase queries can become expensive, especially on large datasets or distributed engines like Trino. The following best practices help reduce execution time, lower load on the engine, and improve dashboard responsiveness.

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Avoid full table scans

Large unfiltered queries are costly. Always filter or limit data early when possible. Filtering drastically reduces the data scanned and speeds up execution.
  • Bad example: Full table scan 
    SELECT *
FROM events;
  • Good example: Filter early 
    SELECT user_id, event_type
FROM events
WHERE event_type = 'purchase';

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Use date filters

Filtering by a recent time window reduces the amount of data scanned. For example 
WHERE event_date >= current_date - interval '30' day

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Prefer aggregated queries

Aggregations reduce the amount of data processed and returned.
  • Bad example: Returning raw rows 
    SELECT user_id, amount
FROM transactions
WHERE transaction_date >= current_date - interval '7' day;
  • Good example: Summary metric 
    SELECT
    date(transaction_date) AS day,
    sum(amount) AS total_revenue
FROM transactions
WHERE transaction_date >= current_date - interval '7' day
GROUP BY 1
ORDER BY 1;

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Use partitions

If you partition Iceberg tables, filtering on partitions, such as date or region, significantly improves performance.
  • Bad example: Filtering on a non-partition column. 
    SELECT *
FROM events
WHERE user_id = 123;
  • Good example: Filter using the partition column. 
    SELECT * FROM events
WHERE event_date = current_date
AND user_id = 123;

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Limit returned data

Visualization queries should return summaries or KPIs, not raw tables with large row counts.
  • Bad example: Returning millions of rows. 
    SELECT *
FROM page_views
WHERE view_date >= current_date - interval '90' day;
  • Good example: Return only needed fields, or aggregated data. 
    SELECT user_id, page_url, view_time
FROM page_views
WHERE view_time >= current_date - interval '7' day
LIMIT 5000;

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Additional resources

  • To get an overview of Metabase, refer to the Metabase in NexusOne page.
  • To learn practical ways to use Metabase in the NexusOne environment, refer to the Metabase hands-on examples page.
  • For more details about Metabase, refer to the Metabase official documentation.
  • If you are using the NexusOne portal to visualize your dataset, refer to the Discover page.