Troubleshooting Smart Ingest

This article provides solutions to common issues you might encounter when using Smart Ingest.

If you still need help, you can contact Iterable technical support or your customer success manager for assistance.

# In this article

# Errors from a data source

Troubleshooting information specific to your data source is available in its source connection guide.

# Errors from Iterable

# Fewer records in Iterable than synced

If you see a discrepancy between the number of records that have passed through your sync and the number of records displaying in Iterable, check your data set for duplicates. Smart Ingest automatically skips both duplicates when they share the same primary key, so the number of records in Iterable might be less than the number of records in your data model if your sync unintentionally contains duplicates.

# 413 - Request Entity Too Large

If you see this error, it means that there are so many records in your sync that the request size exceeds the maximum limit. To resolve this error, try lowering the batch size in your sync configuration.

This is possible for the following sync types:

• User
• Event

# 400 - Request does not have the same data types as the data previously submitted

Here’s an example error message where the field value being synced to a user’s profile doesn’t have a data type that’s compatible with the destination field.

{
  "msg": "Project 00000: The request does not have the same data types as the data previously submitted, or the input value is not within the correct range. Field 'date_field' already exists for type 'user' and has a data type of 'date' but possible types 'string, keyword' in the request.",
  "code": "RequestFieldsTypesMismatched",
  "params": {
    "validationErrors": {
      "date_field": {
        "incomingTypes": ["string", "keyword"],
        "expectedType": "date",
        "category": "user",
        "offendingValue": ["2023-04-19 12:15:30", "0000-00-00 00:00:00"],
        "_type": "UnexpectedType"
      }
    }
  }
}

The first value in the offendingValue array is associated with the first record in the batch sent to Iterable. The second value in the array is the value causing the issue. One instance of this value ("0000-00-00 00:00:00") causes the entire batch of records being sent to Iterable to fail.

To resolve the error, check for instances of ("0000-00-00 00:00:00") in the date_field column of your data model and replace these values with null or a valid date.