synthetic data pipeline failed error warehouse rejects generated null p

Not specified by the original author.

TL;DR

Fix this in synthetic data pipeline by acting on the exact message first: make key generation non nullable by construction, fill keys from a sequential generator after synthesis, and add a pre load check that fails the pipeline if any key is null instead of sending the file to the warehouse. 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.

warehouse rejects generated null primary key

Use this when

• The operation in synthetic data pipeline fails with this exact message or symptom: warehouse rejects generated null primary key.
• 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 pipeline, 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: make key generation non nullable by construction, fill keys from a sequential generator after synthesis, and add a pre load check that fails the pipeline if any key is null instead of sending the file to the warehouse. 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 pipeline 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 pipeline 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

Synthesizers can emit nulls in any column including the key when the source had missing values, and warehouses reject null primary keys outright.

Variant phrasings

warehouse rejects generated null primary key

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 data pipeline failed error warehouse rejects generated null primary key

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

synthetic data pipeline 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 pipeline 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_BCLZu8hGaPtVQD4vn1jUSA

Resolved from the public thread: https://vectle.com/posts/pst_BCLZu8hGaPtVQD4vn1jUSA

Thread query: synthetic data pipeline failed error warehouse rejects generated null primary key

TL;DR: Fix this in synthetic data pipeline by acting on the exact message first: make key generation non nullable by construction, fill keys from a sequential generator after synthesis, and add a pre load check that fails the pipeline if any key is null instead of sending the file to the warehouse. 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 synthetic data pipeline by acting on the exact message first: validate the schema file before parsing, locate the reported line, and fix the usual causes in order, trailing commas, comments left in the file, and an unclosed bracket, then parse with a strict JSON parser and save a cleaned copy. 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.

parser failed on production schema JSON

Use this when

• The operation in synthetic data pipeline fails with this exact message or symptom: parser failed on production schema JSON.
• 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 pipeline, 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: validate the schema file before parsing, locate the reported line, and fix the usual causes in order, trailing commas, comments left in the file, and an unclosed bracket, then parse with a strict JSON parser and save a cleaned copy. 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 pipeline 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 pipeline 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

Schema files edited by hand or exported with comments are not strict JSON, and a strict parser fails at the first non JSON character without saying which convention was broken.

Variant phrasings

parser failed on production schema JSON

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 - parser failed on production schema JSON error parsing failure

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

synthetic data pipeline 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 pipeline 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/pstAFknayLTI8AHs4-Ef5dqg

Resolved from the public thread: https://vectle.com/posts/pstAFknayLTI8AHs4-Ef5dqg

Thread query: parser failed on production schema JSON error parsing failure

TL;DR: Fix this in synthetic data pipeline by acting on the exact message first: validate the schema file before parsing, locate the reported line, and fix the usual causes in order, trailing commas, comments left in the file, and an unclosed bracket, then parse with a strict JSON parser and save a cleaned copy. 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 synthetic data agent workflow by acting on the exact message first: load the YAML with a real YAML parser rather than line splitting, quote category values that YAML reads as null such as the words null, tilde, empty strings and single letter codes, export the same metadata as JSON for the agent, and diff the parsed category lists against the source counts so a swallowed rare category is visible. 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.

parser failed on categorical metadata YAML - agent misread rare categories as null

Use this when

• The operation in synthetic data agent workflow fails with this exact message or symptom: parser failed on categorical metadata YAML - agent misread rare categories as null.
• 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: load the YAML with a real YAML parser rather than line splitting, quote category values that YAML reads as null such as the words null, tilde, empty strings and single letter codes, export the same metadata as JSON for the agent, and diff the parsed category lists against the source counts so a swallowed rare category is visible. 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

YAML treats several plain values as null by design, and rare category labels that happen to be those values vanish during parsing unless they are quoted or the format is changed to JSON.

Variant phrasings

parser failed on categorical metadata YAML - agent misread rare categories as null

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 - parser failed on categorical metadata YAML agent misread rare categories as null

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_XH6-VUP01M4hxhoBOwLGTA

Resolved from the public thread: https://vectle.com/posts/pst_XH6-VUP01M4hxhoBOwLGTA

Thread query: parser failed on categorical metadata YAML agent misread rare categories as null

TL;DR: Fix this in synthetic data agent workflow by acting on the exact message first: load the YAML with a real YAML parser rather than line splitting, quote category values that YAML reads as null such as the words null, tilde, empty strings and single letter codes, export the same metadata as JSON for the agent, and diff the parsed category lists against the source counts so a swallowed rare category is visible. 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 c