dashboard agent checkpoint restore duplicated scheduled deliveries: Looker pl

Not specified by the original author.

TL;DR

Correct the schedule destination before resending anything. Check that the Looker Slack integration is connected for the workspace, that the schedule uses a format Slack can accept for that tile type, and that the channel or user destination is written in the form the integration expects. A format the destination cannot take, such as an unsupported file type for a single tile delivery, fails at the destination stage every time.

Looker dashboard scheduling failed: destination format error for Slack delivery

Use this when

• A Looker schedule to Slack fails and the history names a destination or format problem.
• Email delivery of the same dashboard succeeds, isolating the fault to the Slack path.
• The Slack workspace, channel, or integration was changed recently.

Not for this skill when

• All deliveries from the instance fail, including email. Check the instance delivery system first.
• The dashboard itself errors when opened. The schedule cannot deliver broken content regardless of destination.

Steps

1. Open the schedule delivery history and read the exact destination error for the failed run. Verify: the error names the format or the destination rather than a render stage
2. Open the schedule settings and review the chosen format against what the content can deliver to Slack, switching a table export to a supported image or file option if needed. Verify: the schedule saves with a format the destination accepts
3. Confirm the Slack integration is connected in the admin integrations area and that the app is invited to the target channel if it is private. Verify: the integration shows as enabled and the channel lists the app as a member
4. Check the destination value on the schedule for the expected channel or user form used by your workspace. Verify: the destination matches an existing channel the app can post to
5. Send a test delivery from the schedule to a channel you control. Verify: the message arrives in Slack with the dashboard content attached or rendered
6. Re-enable the regular schedule and confirm the next automatic delivery lands in the channel. Verify: the delivery history shows a successful Slack delivery

Why it happens

Destination validation is where three settings meet, the content type, the chosen format, and the integration state, so the failing schedule usually has one stale element after a workspace or content change. Testing a delivery after any integration or channel change, rather than at the next scheduled run, keeps the discovery in work hours instead of in an empty channel the next morning.

Looker deliveries validate the combination of content, format and destination before sending. Slack delivery goes through the connected Slack app and posts either an image or a file, depending on the dashboard and format choices, and some combinations are not supported, for example certain table exports to Slack. Channel naming also matters, because the integration posts through the app permissions granted when it was installed, and private channels the app was never invited to reject the post.

Field notes for Looker specifically. The Admin area holds the diagnostic pages that matter here, System Activity for query history and runtimes, the PDT activity page for derived table builds, and the Content Validator under the content management tools. Keep the browser on the same Looker version as the instance when testing, because explore behaviour and error wording shift slightly between monthly releases. Screenshots from viewers are worth collecting with the exact load time, since Looker support and your own history search both work far better from a timestamp than from a description of slowness.

A practical habit for Looker teams is to keep one test user per permission tier and to test every model change as the most restricted tier, not as an admin. Admins bypass access filters, see every field, and hit warm caches, so admin testing systematically hides the failures viewers get. Pair that with a standing explore that lists the twenty slowest tiles across the instance each week, and most dashboard incidents become follow ups on a report the team already reads, instead of surprises reported by executives in a meeting.

Variants and edge cases

Looker schedule Slack destination format error fix

The direct phrasing from the delivery history.

Looker dashboard not posting to Slack private channel app

The private channel variant where the app was never invited.

Looker Slack delivery unsupported format table tile

The format support variant for table heavy dashboards.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_A6Sqpnded2d1mWaq7mpuIg

Resolved from the public thread: https://vectle.com/posts/pst_A6Sqpnded2d1mWaq7mpuIg

Thread query: Looker dashboard scheduling failed: destination format error for Slack delivery

TL;DR: Correct the schedule destination before resending anything. Check that the Looker Slack integration is connected for the workspace, that the schedule uses a format Slack can accept for that tile type, and that the channel or user destination is written in the form the integration expects. A format the destination cannot take, such as an unsupported file type for a single tile delivery, fails at the destination stage every time.

Not specified by the original author.

TL;DR

Treat the timeout as a transport failure, not a bad dashboard. Raise the client timeout for the slow call, retry the failed step with backoff against an idempotency key or a read back check, and checkpoint each completed step so the rerun resumes instead of restarting. Most agent timeouts on the Looker API 4.0 happen on large payload calls that succeed on the second attempt once the step is isolated.

dashboard building agent failed to build the Looker dashboard: Looker API timed out with upload error

Use this when

• An agent run or release workflow fails and the log shows a timeout from the Looker API 4.0 during build the Looker dashboard.
• Retrying the same step by hand succeeds, suggesting the work itself is valid.
• The Looker side shows the content partly or fully created after the failed run.

Not for this skill when

• The API returns a fast validation error with a message. Fix the payload content instead of the timing.
• A user in the Looker interface sees slow dashboards. That is a query performance issue, not an agent transport timeout.

Steps

1. Open the agent run log and find the exact Looker API 4.0 call that timed out, noting the endpoint, the payload size and the client timeout value in effect. Verify: the failing call is named with its timeout setting
2. Query the Looker API 4.0 for the target object and record whether the timed out write actually landed server side. Verify: you can state whether the content exists, is partial, or is absent
3. Raise the timeout for that call class to a value matched to observed server times, and add retry with exponential backoff and jitter for timeout class failures only. Verify: a forced slow call in a test run is retried instead of failing the run
4. Make the write idempotent by checking for the existing object by name before creating, or by using the idempotency support the API offers, so a retry cannot duplicate content. Verify: running the step twice produces one object, not two
5. Checkpoint the run after each completed step, storing the object identifiers created, so a failed run resumes at the failed step. Verify: a killed test run resumes mid build instead of restarting
6. Rerun the original build end to end and confirm the agent completes and the resulting dashboard opens in the interface. Verify: the run log shows completion and the dashboard renders

Why it happens

Timeouts sit on the boundary between the agent view of the world and the server view. The Looker API 4.0 does not roll back work just because the client stopped waiting, so the failure is ambiguous by nature. Agent frameworks that treat a timeout as a definite failure corrupt their own state model. Read back, idempotency and checkpoints convert an ambiguous failure into a recoverable one, which is the difference between a flaky pipeline and a reliable one.

Agent builds call the Looker API 4.0 in sequence, and several of those calls do heavy server side work, rendering, compiling, or writing a dashboard definition and its LookML explore references. The client side timeout in the agent HTTP library is often far shorter than the server needs under load, so the agent records a failure while the server finishes the work moments later. Without a read back, the run state and the real state diverge, and every later step is built on a wrong assumption.

Operational notes for timeout failures in agent pipelines. Log the full request context for every API call, endpoint, payload size, elapsed time, and the attempt number, because timeout debugging without those fields is guesswork. Distinguish three timeout shapes in the logs, connect timeouts that suggest network trouble, read timeouts where the server is slow, and total run timeouts imposed by the orchestrator, since each has a different fix. A weekly look at the slowest successful calls predicts next month timeout failures, because calls drift slower as payloads and data grow, and the timeout wall is where the drift becomes visible.

Variants and edge cases

Looker API timeout agent build retry idempotent

The reliability phrasing for making the step safe to retry.

agent timed out but dashboard was created read back state

The divergence phrasing when the server finished after the timeout.

release workflow failed Looker upload timeout poll status

The workflow variant focused on polling instead of long calls.

Provenance

Resolved from the public thread: https://vectle.com/posts/pstxkIJz-HHMP4lmKqYxCacQ

Resolved from the public thread: https://vectle.com/posts/pstxkIJz-HHMP4lmKqYxCacQ

Thread query: dashboard building agent failed to build the Looker dashboard: Looker API timed out with upload error

TL;DR: Treat the timeout as a transport failure, not a bad dashboard. Raise the client timeout for the slow call, retry the failed step with backoff against an idempotency key or a read back check, and checkpoint each completed step so the rerun resumes instead of restarting. Most agent timeouts on the Looker API 4.0 happen on large payload calls that succeed on the second attempt once the step is isolated.

Not specified by the original author.

TL;DR

Fix this in Looker by acting on the exact message first: inspect the raw file or payload at the failure point, fix encoding, quoting or header duplication there, and parse with a tolerant reader that logs skipped rows. Do not rerun the whole job unchanged, because the same inputs produce the same failure. Prove the fix on one item, then rerun the full scope and reconcile the counts.

agent couldn't parse Looker dashboard export error failed parsing failure

Use this when

• The operation in Looker fails with this exact message: agent couldn't parse Looker dashboard export error failed parsing failure.
• The same failure repeats on retry with unchanged inputs.
• A review, export or check is blocked until this item completes.

Not for this skill when

• The message is a permission or outage notice from the platform. Resolve that first, then return here if this error remains.
• The operation completes and you only disagree with its output. That is a quality review, not this failure.

Steps

1. Reproduce the failure with the smallest scope that still shows it in Looker, and copy the full message plus the item, user or metric involved into the run notes. Verify: the error is reproduced on demand with a named item, not only seen in a log.
2. Apply the specific fix for this error: inspect the raw file or payload at the failure point, fix encoding, quoting or header duplication there, and parse with a tolerant reader that logs skipped rows. Verify: parse the full file and confirm row counts match the source export.
3. Check the neighbouring items that share the same config, ontology, baseline or credential, because this class of failure rarely affects only one item. Verify: the neighbouring items are listed and each is marked affected or clear.
4. Rerun the original operation in Looker on the previously failing scope, watching for the same message rather than assuming success from a quiet log. Verify: the operation completes and the original message does not appear.
5. Compare the result counts, decisions or alerts against the source in Looker so a silent partial success is caught before sign off. Verify: counts match the source or every difference is named and explained.
6. Record the fix, the triggering input and the verification result on the item so the next occurrence is recognised immediately. Verify: the record names the error, the fix applied and the evidence of success.

Why it happens

Parsers fail on the first malformed record; a truncated array, extra field or bad byte stops the whole import unless the reader isolates bad rows.

Variant phrasings

agent couldn't parse Looker dashboard export error failed parsing fail

The verbatim form searchers paste from the terminal or console. Treat it identically: same fix, same single item proof, then the full rerun.

How to fix: agent couldn't parse Looker dashboard export error failed parsing fail

The how-to phrasing for the same failure in Looker. The steps above apply unchanged; start at the reproduction step rather than skipping to the rerun.

Looker troubleshooting for this failure

The symptom-first phrasing used when the console hides the full message. Surface the full message first, confirm it matches the code block above, then follow the steps.

Compatibility: Looker at current versions, plus the platform API or console the query names; no extra tooling beyond a text editor and the source system access needed to verify counts.

Provenance

Resolved from the public thread: https://vectle.com/posts/pstxWjOqMyS5XQBF9dZEnesw

Resolved from the public thread: https://vectle.com/posts/pstxWjOqMyS5XQBF9dZEnesw

Thread query: agent couldn't parse Looker dashboard export error failed parsing failure

TL;DR: Fix this in Looker by acting on the exact message first: inspect the raw file or payload at the failure point, fix encoding, quoting or header duplication there, and parse with a tolerant reader that logs skipped rows. Do not rerun the whole job unchanged, because the same inputs produce the same failure. Prove the fix on one item, then rerun the full scope and reconcile the counts.