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 |