Ingestion behavior of invalid data

Data that is malformed, unparsable, too large, or doesn't conform to the schema may fail to be ingested properly. The following tables describe what to expect when ingesting invalid data into Azure Data Explorer.

Note

For more information about why ingestion might fail, see Ingestion failures and Ingestion error codes in Azure Data Explorer.

Failure with error code

The following table shows cases where ingestion of invalid data fails with an error code:

Ingestion problem	Error code
Invalid or corrupted format (actual data does not match the specified format)	BadRequest_InvalidBlob
Empty Data	BadRequest_NoRecordsOrWrongFormat
Malformed records in JSON data ingested with format="multijson" (e.g. missing braces or quotes)	BadRequest_InvalidBlob
CSV lines with inconsistent number of fields	Stream_WrongNumberOfFields

Failure without error code

The following table shows cases where ingestion succeeds without an error, silently handling the invalid data:

Ingestion problem	Notes
Malformed records in JSON data ingested with format="json". For example: unexpected newlines, missing braces or quotes.	Malformed records are ignored and not ingested
Value larger than 1MB ingested into a string column	Value truncated up to 1MB
Value larger than 1MB (default, see Encoding policy) ingested into a dynamic column	NULL value filled
Value not matching the table schema data type. For example: floating point value ingested into an int column.	NULL value filled
Mapped fields are missing from the data	NULL value filled

Related content

• Data ingestion
• Ingestion failures
• Encoding policy
• Duplicate next ingestion failure