apache/iceberg · error · IllegalArgumentException

Unsupported type:

Error message

Unsupported type: 

What it means

SparkPlannedAvroReader.primitive() switches on the Avro primitive schema type (NULL, BOOLEAN, INT, LONG, FLOAT, DOUBLE, STRING, FIXED, BYTES, ENUM). Any Avro primitive not covered — such as RECORD or ARRAY appearing where a primitive is expected, or future Avro types — reaches the default branch and throws IllegalArgumentException with the schema. Reader construction fails for the file.

Source

Thrown at spark/v4.0/spark/src/main/java/org/apache/iceberg/spark/data/SparkPlannedAvroReader.java:194

        case LONG:
          return ValueReaders.longs();
        case FLOAT:
          if (partner != null && partner.typeId() == Type.TypeID.DOUBLE) {
            return ValueReaders.floatsAsDoubles();
          }
          return ValueReaders.floats();
        case DOUBLE:
          return ValueReaders.doubles();
        case STRING:
          return SparkValueReaders.strings();
        case FIXED:
          return ValueReaders.fixed(primitive.getFixedSize());
        case BYTES:
          return ValueReaders.bytes();
        case ENUM:
          return SparkValueReaders.enums(primitive.getEnumSymbols());
        default:
          throw new IllegalArgumentException("Unsupported type: " + primitive);
      }
    }
  }
}

View on GitHub (pinned to 86d9c8fc54)

Solutions

1. Print the Avro file schema and verify the field types; ensure nested types are handled by the reader factory, not primitive()
2. Rewrite the data files with a schema that matches the Iceberg table schema
3. Refresh/repair the table metadata so Iceberg's projected schema matches the files' Avro schema
4. Upgrade Iceberg if a newly supported Avro type is involved

for (Field f : avroSchema.getFields()) {
  Schema.Type t = f.schema().getType();
  if (!(t == Schema.Type.NULL || t == Schema.Type.BOOLEAN || t == Schema.Type.INT
      || t == Schema.Type.LONG || t == Schema.Type.FLOAT || t == Schema.Type.DOUBLE
      || t == Schema.Type.STRING || t == Schema.Type.FIXED || t == Schema.Type.BYTES
      || t == Schema.Type.ENUM)) {
    throw new IllegalStateException("Unexpected Avro type at primitive level: " + t);
  }
}

boolean isSupportedAvroPrimitive(Schema s) {
  switch (s.getType()) {
    case NULL: case BOOLEAN: case INT: case LONG: case FLOAT: case DOUBLE:
    case STRING: case FIXED: case BYTES: case ENUM:
      return true;
    default:
      return false;
  }
}

try {
  spark.read().format("iceberg").load("db.tbl");
} catch (IllegalArgumentException e) {
  if (e.getMessage().startsWith("Unsupported type: ")) {
    // verify the Avro schema matches the Iceberg table schema and rewrite the file if needed
  } else {
    throw e;
  }
}

When it happens

Trigger: Planning an Avro read where a field's Avro schema type is not one of the handled primitive cases, e.g. a nested record/array reaching primitive() due to a schema-mapping mismatch, or an unrecognized Avro type name.

Common situations: Malformed or hand-edited Avro schemas; producer/consumer schema drift where a field changed from primitive to nested without updating the Iceberg schema; bugs in schema projection code passing the wrong schema node.

Related errors

• Unsupported type:
• Unsupported type:
• Unknown logical type: ${logicalType}
• Unsupported type: ${primitive}
• Unknown logical type:

AI-assisted analysis of apache/iceberg@86d9c8fc54 (2026-09-12). Data as JSON: /api/errors/abff61d68bd7e3c9. Report an issue: GitHub.