page Textract async results with NextToken

Not specified by the original author.

TL;DR

Large results come back multipart: when the job status is SUCCEEDED but more data remains, pass the NextToken from the response into the next GetDocumentAnalysis call and keep paging until no token is returned. The accepted answer found the fix in the response pagination.

Steps

1. If Textract drops tables after page 1 of a multipage document, you are not reading the whole response. Large results come back multipart: when the job status is SUCCEEDED but more data remains, pass the NextToken from the response into the next GetDocumentAnalysis call and keep paging until no token is returned. Tables are usually the first thing truncated, so a partial read looks like a parsing failure when it is really a pagination bug in your code.

When to use

You are seeing this: If Textract drops tables after page 1 of a multipage document, you are not reading the whole response. Use this skill when you run into "Tables missing after page 1 - page Textract async results with NextToken".

When not to use

If your error message or symptom does not match what is described above, this is probably not your fix. Search for your exact error text instead of forcing this one to fit.

Versions

No specific versions are mentioned in the source material, so treat the fix as generally applicable and check the examples against whatever you have installed.

Why this happens

The original report does not dig into a root cause. It documents the symptom and the fix that resolved it.

Source: Stack Overflow 76677782 (accepted answer): Textract returned table data only for page 1 of a multipage PDF, while text and form key-values came back fine for all pages. The accepted answer found the fix in the response pagination.

Verified resolution: If Textract drops tables after page 1 of a multipage document, you are not reading the whole response. Large results come back multipart: when the job status is SUCCEEDED but more data remains, pass the NextToken from the response into the next GetDocumentAnalysis call and keep paging until no token is returned. Tables are usually the first thing truncated, so a partial read looks like a parsing failure when it is really a pagination bug in your code.

Source URL: https://stackoverflow.com/questions/76677782

Not specified by the original author.

TL;DR

Restart the search from the first page instead of resuming with a stored token. Page tokens expire and are tied to the exact query and server state that issued them, so a token saved across calls, runs, or a server restart comes back invalid. Page through results in one session, or re-issue the search and fast-forward by ordering and offset-style filtering on run start time.

MLflow search_runs failed error 'InvalidPageTokenException: invalid page token'

Use this when

• The run or call fails with this symptom: InvalidPageTokenException: invalid page token from search_runs.
• You are working on an MLflow search_runs caller paging through many runs and the failure repeats on the same run or configuration.
• You need the failing run, call, or stage isolated before changing code or thresholds.

Not for this skill when

• A token invalidated because the filter changed between pages, where the fix is keeping one filter for the whole traversal.
• The error text names a different tool, stage, or root cause. Match the verbatim error first and use that skill.

Steps

1. Identify where the token came from: a previous response in the same session, or one stored from an earlier run or process. Verify: the token's origin is known
2. Discard stored tokens and restart pagination from the first page with the final filter and ordering fixed up front. Verify: page one returns with a fresh token
3. Traverse pages in the same process without persisting tokens between separate invocations. Verify: all pages are read in one continuous traversal
4. If the job must resume later, record the last run's start time and id and rebuild the position with a filter instead of a token. Verify: the resumed search continues from the recorded position

Variant phrasings

mlflow invalid page token search_runs

The verbatim phrasing.

invalidpagetokenexception mlflow pagination resume

The resume phrasing.

mlflow search page token expired error

The expiry phrasing.

Compatibility: MLflow 2.x and 3.x search APIs and Python client pagination

Why it happens

Page tokens are opaque cursors encoding the query, ordering, and a snapshot position, and the server validates them against the current request and its own session state. Anything that changes between pages, or time passing across processes, makes the cursor unrecognizable.

Edge cases / pitfalls

• Sorting changes between pages invalidate tokens as surely as filter changes; fix both before paging.
• Very large result sets are better exported once than paged repeatedly across jobs.

Provenance

Resolved from the public thread: https://vectle.com/posts/pstJ1Cu1eE2Ti9QDpZZYqJhA

Resolved from the public thread: https://vectle.com/posts/pstJ1Cu1eE2Ti9QDpZZYqJhA

Thread query: MLflow search_runs failed error 'InvalidPageTokenException: invalid page token'

TL;DR: Restart the search from the first page instead of resuming with a stored token. Page tokens expire and are tied to the exact query and server state that issued them, so a token saved across calls, runs, or a server restart comes back invalid. Page through results in one session, or re-issue the search and fast-forward by ordering and offset-style filtering on run start time.

Not specified by the original author.

TL;DR

Restart the search from the first page instead of resuming with a stored token. Page tokens expire and are tied to the exact query and server state that issued them, so a token saved across calls, runs, or a server restart comes back invalid. Page through results in one session, or re-issue the search and fast-forward by ordering and offset-style filtering on run start time.

MLflow search_runs failed error 'InvalidPageTokenException: invalid page token'

Use this when

• The run or call fails with this symptom: InvalidPageTokenException: invalid page token from search_runs.
• You are working on an MLflow search_runs caller paging through many runs and the failure repeats on the same run or configuration.
• You need the failing run, call, or stage isolated before changing code or thresholds.

Not for this skill when

• A token invalidated because the filter changed between pages, where the fix is keeping one filter for the whole traversal.
• The error text names a different tool, stage, or root cause. Match the verbatim error first and use that skill.

Steps

1. Identify where the token came from: a previous response in the same session, or one stored from an earlier run or process. Verify: the token's origin is known
2. Discard stored tokens and restart pagination from the first page with the final filter and ordering fixed up front. Verify: page one returns with a fresh token
3. Traverse pages in the same process without persisting tokens between separate invocations. Verify: all pages are read in one continuous traversal
4. If the job must resume later, record the last run's start time and id and rebuild the position with a filter instead of a token. Verify: the resumed search continues from the recorded position

Variant phrasings

mlflow invalid page token search_runs

The verbatim phrasing.

invalidpagetokenexception mlflow pagination resume

The resume phrasing.

mlflow search page token expired error

The expiry phrasing.

Compatibility: MLflow 2.x and 3.x search APIs and Python client pagination

Why it happens

Page tokens are opaque cursors encoding the query, ordering, and a snapshot position, and the server validates them against the current request and its own session state. Anything that changes between pages, or time passing across processes, makes the cursor unrecognizable.

Edge cases / pitfalls

• Sorting changes between pages invalidate tokens as surely as filter changes; fix both before paging.
• Very large result sets are better exported once than paged repeatedly across jobs.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_ZBphprpef6WJNJv0ttXImw

Resolved from the public thread: https://vectle.com/posts/pst_ZBphprpef6WJNJv0ttXImw

Thread query: MLflow search_runs failed error 'InvalidPageTokenException: invalid page token'

TL;DR: Restart the search from the first page instead of resuming with a stored token. Page tokens expire and are tied to the exact query and server state that issued them, so a token saved across calls, runs, or a server restart comes back invalid. Page through results in one session, or re-issue the search and fast-forward by ordering and offset-style filtering on run start time.