apache/iceberg · error · UnsupportedOperationException

Expected truncation col to be tinyint, shortint, int…

Error message

Expected truncation col to be tinyint, shortint, int, bigint, decimal, string, or binary

What it means

Iceberg's truncate(value, width) function supports value columns of tinyint, smallint, int, bigint, decimal, string, or binary only. Value columns of any other type (double, float, boolean, date, timestamp, complex types) have no truncation implementation, so bind() throws this UnsupportedOperationException during query analysis.

Solutions

Cast the value to a supported type, e.g. truncate(CAST(d AS DECIMAL(10,4)), 2) for doubles.
For temporal truncation use the dedicated functions: years(), months(), days(), hours().
Pick a supported column (numeric, decimal, string, binary) for the partition transform.
For doubles, consider scaling to bigint: truncate(CAST(d * 100 AS BIGINT), 1).

Example fix

// before
SELECT truncate(price, 2) FROM t  -- price is DOUBLE
// after
SELECT truncate(CAST(price AS DECIMAL(10,2)), 2) FROM t

Defensive patterns

Strategy: validation

Validate before calling

// Spark Scala
val dt = df.schema("value_col").dataType.catalogString.toLowerCase
val supported = Set("tinyint","smallint","int","bigint","decimal","string","binary")
require(supported.exists(s => dt.startsWith(s)), s"truncate() does not support value type: $dt")

Type guard

def isTruncatableType(dt: org.apache.spark.sql.types.DataType): Boolean = dt match {
  case _: org.apache.spark.sql.types.DecimalType => true
  case t => Set(
    org.apache.spark.sql.types.ByteType,
    org.apache.spark.sql.types.ShortType,
    org.apache.spark.sql.types.IntegerType,
    org.apache.spark.sql.types.LongType,
    org.apache.spark.sql.types.StringType,
    org.apache.spark.sql.types.BinaryType).contains(t)
}

Try / catch

try {
  df.select(expr("truncate(value_col, 5)"))
} catch {
  case e: UnsupportedOperationException if e.getMessage.contains("truncation col") =>
    throw new IllegalArgumentException("truncate() value column type unsupported; cast or use days()/hours() for temporal", e)
}

Prevention

Never truncate float/double — cast to DECIMAL or scale to BIGINT first
Use years()/months()/days()/hours() for date/timestamp truncation, not truncate()
Inspect column types with printSchema before building partition transforms
Validate partition specs against supported truncation types at table creation

When it happens

Trigger: Calling truncate(double_col, 2), truncate(bool_col, 1), truncate(ts_col, 10), or truncating struct/array/map columns.

Common situations: Truncating floats/doubles for bucket-like partitioning; attempting time truncation with truncate() instead of the dedicated years/months/days/hours functions; schema type drift to unsupported types.

Understand the failure class

Background: UnsupportedOperationException and "is not supported" errors: when a library deliberately refuses a call — this error's family across 30 libraries.

Related errors

AI-assisted analysis of apache/iceberg@86d9c8fc54 (2026-09-12). Data as JSON: /api/errors/da18cdb4417dce20. Report an issue: GitHub.

Appendix: source

Thrown at spark/v4.2/spark/src/main/java/org/apache/iceberg/spark/functions/TruncateFunction.java:93

    DataType valueType = valueField.dataType();
    if (valueType instanceof ByteType) {
      return new TruncateTinyInt();
    } else if (valueType instanceof ShortType) {
      return new TruncateSmallInt();
    } else if (valueType instanceof IntegerType) {
      return new TruncateInt();
    } else if (valueType instanceof LongType) {
      return new TruncateBigInt();
    } else if (valueType instanceof DecimalType) {
      return new TruncateDecimal(
          ((DecimalType) valueType).precision(), ((DecimalType) valueType).scale());
    } else if (valueType instanceof StringType) {
      return new TruncateString();
    } else if (valueType instanceof BinaryType) {
      return new TruncateBinary();
    } else {
      throw new UnsupportedOperationException(
          "Expected truncation col to be tinyint, shortint, int, bigint, decimal, string, or binary");
    }
  }

  @Override
  public String description() {
    return name()
        + "(width, col) - Call Iceberg's truncate transform\n"
        + "  width :: width for truncation, e.g. truncate(10, 255) -> 250 (must be an integer)\n"
        + "  col :: column to truncate (must be an integer, decimal, string, or binary)";
  }

  @Override
  public String name() {
    return "truncate";
  }

  public abstract static class TruncateBase<T> extends BaseScalarFunction<T> {

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