Using sequence() between dates returns an array of timestamps rather than array of dates

Greeting

Hi Emm!

Thanks for bringing up this interesting question about sequence() behavior in Amazon Athena. It’s always great to explore how Athena handles data types. Let’s work through your issue together. 😊

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Clarifying the Issue

You’ve noticed that the sequence() function in Athena generates an array of timestamps (array(timestamp(0))) instead of an array of dates (array(date)) when provided two date inputs and an interval. You also noted that this behavior doesn’t occur in Trino version 464, suggesting it’s specific to Athena.

This behavior stems from Athena’s reliance on higher-precision types like timestamp to maintain compatibility across its ecosystem. While Trino’s implementation of sequence() may focus on maintaining the input type (date), Athena defaults to timestamp to ensure broader compatibility and precision, especially when integrating with AWS services like S3.

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Key Terms

• sequence() function: Generates an array of values between two bounds, spaced by a specified interval.
• timestamp: A data type that includes date and time information, down to fractional seconds.
• date: A simpler data type that only includes calendar date information, without time details.
• Explicit Casting: Using a CAST function to change the data type of a value or expression.
• Athena Integration: Athena is optimized for querying data stored in Amazon S3, leveraging timestamp precision to ensure compatibility across large-scale datasets.

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The Solution (Our Recipe)

Steps at a Glance:

1. Modify the query to explicitly cast the input bounds to date.
2. Use CAST on the sequence() output to enforce array(date) as the output type.
3. Test and verify the query results in Athena.
4. Compare outputs before and after applying the solution.
5. Evaluate performance trade-offs using provided optimization steps.

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Step-by-Step Guide:

1. Modify Input Bounds
   Ensure both input bounds are explicitly cast to date, even if they are already in this format. This step avoids potential implicit type handling by Athena:

   SELECT typeof(sequence(CAST(date '2023-10-20' AS date), CAST(date '2023-11-11' AS date), INTERVAL '7' DAY));

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2. Enforce Output Type
   If the output remains array(timestamp), wrap the sequence() function with a CAST to explicitly enforce the desired array(date) type:

   SELECT CAST(sequence(date '2023-10-20', date '2023-11-11', INTERVAL '7' DAY) AS array(date)) AS result_array;

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3. Test and Verify
   Run a query to check the data type of the result array. This verification ensures the casting worked as intended:

   SELECT typeof(CAST(sequence(date '2023-10-20', date '2023-11-11', INTERVAL '7' DAY) AS array(date))) AS result_type;

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4. Compare Outputs
   Examine the actual output of the sequence() function to verify the results:

   • Without Casting:

     SELECT sequence(date '2023-10-20', date '2023-11-11', INTERVAL '7' DAY) AS result_array;

     Output:

     ["2023-10-20T00:00:00.000", "2023-10-27T00:00:00.000", "2023-11-03T00:00:00.000"]
     

   • With Casting:

     SELECT CAST(sequence(date '2023-10-20', date '2023-11-11', INTERVAL '7' DAY) AS array(date)) AS result_array;

     Output:

     ["2023-10-20", "2023-10-27", "2023-11-03"]
     

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5. Evaluate Performance Trade-Offs
   Be mindful of performance trade-offs when using CAST with large datasets. Explicit type conversions can slightly increase query execution time, especially with high volumes of data. Here’s how to evaluate and optimize performance:

   • Use Sample Datasets: Start by testing your query on a representative subset of your data to measure execution time and identify bottlenecks.

     SELECT CAST(sequence(date '2023-10-20', date '2023-11-11', INTERVAL '7' DAY) AS array(date)) AS result_array
     WHERE your_partition_column = '2023-10';

   • Monitor Query Performance: Use Athena's query execution details in the console to measure runtime and resource usage, such as scanned data size.

   • Partition Data for Optimization: If you're querying large datasets in S3, ensure your data is partitioned effectively (e.g., by date). This reduces the volume of data scanned and offsets the cost of CAST.

   • Preprocess Data: Consider preprocessing your data to convert relevant columns to date during ETL stages, eliminating the need for CAST during querying.

   • Compare Execution Times: Run the same query with and without CAST on different datasets and note differences in execution time and scanned data.

   • Athena Query Optimization Resources: Refer to Athena query performance tuning tips for additional optimization strategies.

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Closing Thoughts

Athena defaults to timestamp to ensure precision and compatibility with its broader ecosystem, especially when integrating with AWS services like S3. Explicit casting provides a reliable solution when you need an array(date) output. By testing and optimizing your query as outlined above, you can balance precision with performance effectively. For more information, refer to the Athena SQL documentation. Let me know if you need further assistance or clarification. Happy querying! 🚀

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Farewell

Take care, Emm! I hope this resolves your issue. Wishing you smooth analytics and precise outputs ahead. 😊

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Cheers,

Aaron 😊