Tableau Server uses caching to speed up workbook loading by storing query results. After a scheduled extract refresh updates the data, the cache may need recomputing—let’s dive into the mechanics:
Caching Basics:
VizQL Cache: Stores rendered views and query results for faster access.
Refresh Trigger: A scheduled refresh updates the underlying extract (.hyper), but the cache isn’t automatically invalidated—it’s demand-driven.
Recompute Conditions: Tableau recomputes the cache when the workbook is accessed (viewed) and its data has changed (e.g., via refresh).
Evaluation:
Option B (The workbook has upcoming scheduled refresh tasks): Correct.
Why: An upcoming refresh task indicates the workbook relies on an extract with a schedule. After the refresh runs, the data changes, priming the cache for recomputation on next view. Without a schedule, no refresh occurs, so this is a prerequisite.
Detail: Schedules are set in Schedules > Tasks—e.g., "Daily at 2 AM."
Option D (The workbook has been viewed recently): Correct.
Why: Viewing triggers cache recomputation if the data has changed (e.g., post-refresh). Tableau uses a "lazy caching" model—cache updates only when a user loads the workbook, ensuring fresh results.
Detail: "Recently" isn’t strictly defined but implies post-refresh access.
Option A (Published in the last month): Incorrect.
Why: Publish date is irrelevant—cache recomputation ties to data changes and access, not publication timing.
Option C (All Users group has permission rule allowing access): Incorrect.
Why: Permissions enable viewing, but recomputation requires actual access (viewing) and a refresh event, not just potential access.
Why This Matters: Caching balances performance and freshness—understanding triggers prevents stale data surprises.
[Reference: Tableau Server Documentation - "Caching and Performance" (https://help.tableau.com/current/server/en-us/perf_cache.htm)., , ]
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