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Need Help Understanding Stale Data After Changing Conditions
can someone explain this stale data? all I did was change some conditions -- prompt and data is the same. even tho i reran the row it still show stale.
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can someone explain this stale data? all I did was change some conditions -- prompt and data is the same. even tho i reran the row it still show stale.
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This is Classic stale data problem.
For an example of stale or outdated data, check out this example video: https://www.loom.com/share/1a9204dc588f42efa6fc84e26fcf0d13?sid=e6b02ed6-a2b5-4922-8b0d-2c8d2a7a0596.
nice - thanks for that. Any way you can know what cause that stale data?
Yes, please see the video:
it only show the stale on the column you are running not the original
Hi, happy to provide additional context. Stale data occurs when the information displayed in a system or application is outdated because it hasn't been refreshed to reflect recent changes. In the context Clay, stale data is caused by changes in inputs or configurations that are not propagated to dependent outputs due to settings like auto-update being turned off. Here are the main factors that cause stale data: 1. Changes in Input Data: When the source data or initial inputs are modified, any cells or columns that depend on this data may become outdated if they are not recalculated. For example, if you alter a value in the first column of a table, any subsequent columns that rely on this value need to be refreshed to reflect the change. 2. Disabled Auto-Update Settings: If auto-update is turned off at the table or column level, dependent cells will not automatically refresh when the input data changes. This means any modifications to the data require manual intervention to update the outputs, leading to stale data until the refresh is triggered. 3. Modifications to API Calls or Prompts: In systems that make API calls to external providers (like ChatGPT), even minor changes to the structured calls or prompts can cause the system to treat the request as new. This requires reprocessing by the provider, and if the system isn't set to auto-update, the data remains stale until the new call is executed. 4. Dependencies Between Columns: When you have a sequence of columns where each one depends on the previous, a change in an upstream column can render downstream data stale. Unless each dependent column is updated to reflect the new input, the data will not be current. 5. Manual Intervention Required for Updates: In scenarios where auto-update is disabled, users need to manually trigger the refresh of data. Forgetting to do so or not realizing that a change has caused data to become stale can result in outdated information being displayed. Best Practices to Avoid Stale Data: * Enable Auto-Update When Appropriate: If you want changes to propagate automatically, ensure that auto-update is enabled for your tables and columns. * Be Cautious with Prompt Changes: Recognize that altering prompts or API call structures can cause all related data to reprocess. This is especially important when working with large datasets, as it can consume additional resources or credits. * Test with a Subset of Data: When experimenting with changes, apply them to a small number of rows (e.g., 10-30) to observe the effects before scaling up. This helps prevent unintended widespread stale data and resource usage. * Monitor Dependencies: Keep track of how your data columns are interrelated. Understanding the dependencies helps in knowing which columns need to be refreshed when changes are made. * Manual Refresh: If auto-update is turned off, remember to manually refresh dependent columns after making changes to ensure all data is up-to-date.