apache/iceberg · error · UnsupportedOperationException

Unsupported type: ${primitive}

Error message

Unsupported type: ${primitive}

What it means

SparkParquetWriters.primitive() maps Spark/Iceberg primitive types to Parquet column writers and falls through to UnsupportedOperationException('Unsupported type: ...') when the physical Parquet type has no writer case, aborting the write task.

Source

Thrown at spark/v4.2/spark/src/main/java/org/apache/iceberg/spark/data/SparkParquetWriters.java:326

                        "Unsupported logical type: " + primitive.getLogicalTypeAnnotation()));
      }

      switch (primitive.getPrimitiveTypeName()) {
        case FIXED_LEN_BYTE_ARRAY:
        case BINARY:
          return byteArrays(desc);
        case BOOLEAN:
          return ParquetValueWriters.booleans(desc);
        case INT32:
          return ints(sType, desc);
        case INT64:
          return ParquetValueWriters.longs(desc);
        case FLOAT:
          return ParquetValueWriters.floats(desc);
        case DOUBLE:
          return ParquetValueWriters.doubles(desc);
        default:
          throw new UnsupportedOperationException("Unsupported type: " + primitive);
      }
    }
  }

  private static PrimitiveWriter<?> ints(DataType type, ColumnDescriptor desc) {
    if (type instanceof ByteType) {
      return ParquetValueWriters.tinyints(desc);
    } else if (type instanceof ShortType) {
      return ParquetValueWriters.shorts(desc);
    }
    return ParquetValueWriters.ints(desc);
  }

  private static PrimitiveWriter<UTF8String> utf8Strings(ColumnDescriptor desc) {
    return new UTF8StringWriter(desc);
  }

  private static PrimitiveWriter<UTF8String> uuids(ColumnDescriptor desc) {

View on GitHub (pinned to 86d9c8fc54)

Solutions

1. Upgrade the Iceberg Spark runtime to match your data types.
2. Cast unsupported columns to supported types before writing.
3. Use a write format (Avro/ORC) or writer path that supports the type.

Example fix

// before
df.writeTo("tbl") // column with unsupported physical mapping
// after
df.withColumn("c", col("c").cast("string")).writeTo("tbl")

Schema schema = SparkSchemaUtil.convert(df.schema());
for (Types.NestedField f : schema.columns()) {
  // verify types are supported by the Parquet writer for your Iceberg version
  System.out.println(f.fieldId() + " -> " + f.type());
}

try {
  df.writeTo("tbl").append();
} catch (UnsupportedOperationException e) {
  if (e.getMessage().startsWith("Unsupported type:")) {
    // cast offending column before writing
  }
}

When it happens

Trigger: Writing a Spark DataFrame to an Iceberg Parquet table with a column whose Parquet physical type falls outside BOOLEAN/INT32/INT64/FLOAT/DOUBLE handled in this switch (e.g. unexpected FIXED_LEN_BYTE_ARRAY or BINARY reached here via an unexpected path).

Common situations: Custom write paths or older writer code paths meeting newer types; version mismatch between Spark connector and Iceberg core.

Related errors

• Unsupported type: ${primitive}
• Unsupported type:
• Unsupported type: %s
• Unsupported type:
• Not a boolean column

AI-assisted analysis of apache/iceberg@86d9c8fc54 (2026-09-12). Data as JSON: /api/errors/62f188e68f77348c. Report an issue: GitHub.