synthetic generation error conditional sampling produced no rows for ra

Not specified by the original author.

TL;DR

Fix this in synthetic data generator by acting on the exact message first: check the share of the rare segment in the training data first, if the condition matches only a handful of rows loosen one condition at a time or train with that segment oversampled, request the segment with a condition the model actually saw, and fall back to generating the full table and filtering it when conditional sampling still returns empty. Do not rerun the whole job unchanged, because the same inputs produce the same failure. Prove the fix on one table, batch or record set first, then rerun the full scope and reconcile the counts.

conditional sampling produced no rows for rare segment

Use this when

• The operation in synthetic data generator fails with this exact message or symptom: conditional sampling produced no rows for rare segment.
• The same failure repeats on retry with unchanged inputs.
• Generation, validation or export is blocked until this item completes.

Not for this skill when

• The message is an 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 quality. That is a quality review, not this failure.

Steps

1. Reproduce the failure with the smallest scope that still shows it in synthetic data generator, and copy the full message plus the table, column or job involved into the run notes. Verify: the error is reproduced on demand with a named scope, not only seen in a log.
2. Apply the specific fix for this error: check the share of the rare segment in the training data first, if the condition matches only a handful of rows loosen one condition at a time or train with that segment oversampled, request the segment with a condition the model actually saw, and fall back to generating the full table and filtering it when conditional sampling still returns empty. Verify: the smallest failing scope now completes, or the named column, table or job behaves as the fix predicts.
3. Check the neighbouring tables, columns or jobs that share the same config, schema 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 synthetic data generator 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, distributions or status against the source in synthetic data generator 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

Conditional sampling draws from the learned distribution under the condition, and a condition that is rare or contradictory in training has near zero probability mass, so the sampler returns no rows.

Variant phrasings

conditional sampling produced no rows for rare segment

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

How to fix - synthetic generation error conditional sampling produced no rows for rare segment

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

synthetic data generator 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: synthetic data generator at current versions, plus the platform API or console the query names; no extra tooling beyond the source system access needed to verify counts.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_FXYz-XYSb5wZw6961mncDQ

Resolved from the public thread: https://vectle.com/posts/pst_FXYz-XYSb5wZw6961mncDQ

Thread query: synthetic generation error conditional sampling produced no rows for rare segment

TL;DR: Fix this in synthetic data generator by acting on the exact message first: check the share of the rare segment in the training data first, if the condition matches only a handful of rows loosen one condition at a time or train with that segment oversampled, request the segment with a condition the model actually saw, and fall back to generating the full table and filtering it when conditional sampling still returns empty. Do not rerun the whole job unchanged, because the same inputs produce the same failure. Prove the fix on one table, batch or record set first, then rerun the full scope and r

Not specified by the original author.

TL;DR

Fix this in synthetic data agent workflow by acting on the exact message first: confirm the dataset is in a ready state before sampling, read its status and row count first, and retry sampling only after the generation job that owns the dataset reports completion. Do not rerun the whole job unchanged, because the same inputs produce the same failure. Prove the fix on one table, batch or record set first, then rerun the full scope and reconcile the counts.

agent failed to complete - sample synthetic records for QA

Use this when

• The operation in synthetic data agent workflow fails with this exact message or symptom: agent failed to complete - sample synthetic records for QA.
• The same failure repeats on retry with unchanged inputs.
• Generation, validation or export is blocked until this item completes.

Not for this skill when

• The message is an 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 quality. That is a quality review, not this failure.

Steps

1. Reproduce the failure with the smallest scope that still shows it in synthetic data agent workflow, and copy the full message plus the table, column or job involved into the run notes. Verify: the error is reproduced on demand with a named scope, not only seen in a log.
2. Apply the specific fix for this error: confirm the dataset is in a ready state before sampling, read its status and row count first, and retry sampling only after the generation job that owns the dataset reports completion. Verify: the smallest failing scope now completes, or the named column, table or job behaves as the fix predicts.
3. Check the neighbouring tables, columns or jobs that share the same config, schema 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 synthetic data agent workflow 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, distributions or status against the source in synthetic data agent workflow 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

Sampling a dataset that is still generating or that failed generation returns errors an agent misreads as sampling bugs, when the real fault is dataset state.

Variant phrasings

agent failed to complete - sample synthetic records for QA

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

How to fix - agent failed to complete sample synthetic records for QA error failure

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

synthetic data agent workflow 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: synthetic data agent workflow at current versions, plus the platform API or console the query names; no extra tooling beyond the source system access needed to verify counts.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_K1zNlFnoJZZIgRd979cmOg

Resolved from the public thread: https://vectle.com/posts/pst_K1zNlFnoJZZIgRd979cmOg

Thread query: agent failed to complete sample synthetic records for QA error failure

TL;DR: Fix this in synthetic data agent workflow by acting on the exact message first: confirm the dataset is in a ready state before sampling, read its status and row count first, and retry sampling only after the generation job that owns the dataset reports completion. Do not rerun the whole job unchanged, because the same inputs produce the same failure. Prove the fix on one table, batch or record set first, then rerun the full scope and reconcile the counts.

Not specified by the original author.

TL;DR

Fix this in SDV by acting on the exact message first: open the failing constraint definition, correct its column names to the exact names in the current metadata, fix any condition written against a column of a different type, add the constraint to the synthesizer before fitting rather than only at sampling, and sample ten rows to prove the constraint now applies. Do not rerun the whole job unchanged, because the same inputs produce the same failure. Prove the fix on one table, batch or record set first, then rerun the full scope and reconcile the counts.

ConstraintError: constraint could not be applied - synthetic sampling rejected

Use this when

• The operation in SDV fails with this exact message or symptom: ConstraintError: constraint could not be applied - synthetic sampling rejected.
• The same failure repeats on retry with unchanged inputs.
• Generation, validation or export is blocked until this item completes.

Not for this skill when

• The message is an 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 quality. That is a quality review, not this failure.

Steps

1. Reproduce the failure with the smallest scope that still shows it in SDV, and copy the full message plus the table, column or job involved into the run notes. Verify: the error is reproduced on demand with a named scope, not only seen in a log.
2. Apply the specific fix for this error: open the failing constraint definition, correct its column names to the exact names in the current metadata, fix any condition written against a column of a different type, add the constraint to the synthesizer before fitting rather than only at sampling, and sample ten rows to prove the constraint now applies. Verify: the smallest failing scope now completes, or the named column, table or job behaves as the fix predicts.
3. Check the neighbouring tables, columns or jobs that share the same config, schema 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 SDV 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, distributions or status against the source in SDV 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

SDV validates a constraint against the metadata and data types at apply time, and a stale column name, a condition on a mismatched type, or a constraint added after fitting leaves it unable to apply and the sampling step is rejected.

Variant phrasings

ConstraintError: constraint could not be applied - synthetic sampling rejected

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

How to fix - SDV error "ConstraintError: constraint could not be applied" synthetic sampling rejected

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

SDV 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: SDV at current versions, plus the platform API or console the query names; no extra tooling beyond the source system access needed to verify counts.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_vRQ8Ry8ZMq8WCttlLrRKng

Resolved from the public thread: https://vectle.com/posts/pst_vRQ8Ry8ZMq8WCttlLrRKng

Thread query: SDV error "ConstraintError: constraint could not be applied" synthetic sampling rejected

TL;DR: Fix this in SDV by acting on the exact message first: open the failing constraint definition, correct its column names to the exact names in the current metadata, fix any condition written against a column of a different type, add the constraint to the synthesizer before fitting rather than only at sampling, and sample ten rows to prove the constraint now applies. Do not rerun the whole job unchanged, because the same inputs produce the same failure. Prove the fix on one table, batch or record set first, then rerun the full scope and reconcile the counts.