apache/iceberg · error · java.lang.IllegalArgumentException

Avro does not support TIMESTAMP type with precision

Error message

Avro does not support TIMESTAMP type with precision: ${precision}, it only supports precision less than 6.

What it means

TIMESTAMP WITH LOCAL TIME ZONE without legacy mapping supports up to microsecond precision (timestampMicros). Precision 7-9 has no corresponding Avro logical type, so the converter throws IllegalArgumentException rather than truncating.

Solutions

Declare the column as TIMESTAMP_LTZ(6) or lower
Cast the column to TIMESTAMP(3)/TIMESTAMP(6) before Avro serialization
Use a non-Avro format if nanosecond precision is mandatory

Example fix

// before
columns: TIMESTAMP_LTZ(9)
Schema avro = AvroSchemaConverter.convertToSchema(rowType, "rec", false);
// after
columns: TIMESTAMP_LTZ(6)
Schema avro = AvroSchemaConverter.convertToSchema(rowType, "rec", false);

Defensive patterns

Strategy: validation

Validate before calling

if (localZonedTimestampType.getPrecision() > 6) {
  throw new IllegalArgumentException("TIMESTAMP_LTZ precision must be <= 6 for Avro; got " + localZonedTimestampType.getPrecision());
}

Prevention

Use TIMESTAMP_LTZ(3) or TIMESTAMP_LTZ(6) only
Never accept framework-default TIMESTAMP_LTZ(9) into Avro sinks
Validate column precisions at job-configuration time, not at runtime

When it happens

Trigger: Calling AvroSchemaConverter.convertToSchema with legacyTimestampMapping=false on a row containing TIMESTAMP_LTZ(7..9).

Common situations: TIMESTAMP_LTZ(9) columns in Flink DDL sent through Avro format sinks; schema precision bumped during evolution without checking Avro limits.

Understand the failure class

Background: "value must be between 0 and 1" / "out of range" / "must not be negative" errors: fixing range-validation failures across open-source libraries — this error's family across 42 libraries.

Related errors

AI-assisted analysis of apache/iceberg@86d9c8fc54 (2026-09-12). Data as JSON: /api/errors/1185a2238eaefb4d. Report an issue: GitHub.

Appendix: source

Thrown at flink/v2.1/flink/src/main/java/org/apache/iceberg/flink/formats/avro/typeutils/AvroSchemaConverter.java:522

                    + ", it only supports precision less than 6.");
          }
        }
        Schema timestamp = avroLogicalType.addToSchema(SchemaBuilder.builder().longType());
        return nullable ? nullableSchema(timestamp) : timestamp;
      case TIMESTAMP_WITH_LOCAL_TIME_ZONE:
        if (legacyTimestampMapping) {
          throw new UnsupportedOperationException(
              "Unsupported to derive Schema for type: " + logicalType);
        } else {
          final LocalZonedTimestampType localZonedTimestampType =
              (LocalZonedTimestampType) logicalType;
          precision = localZonedTimestampType.getPrecision();
          if (precision <= 3) {
            avroLogicalType = LogicalTypes.timestampMillis();
          } else if (precision <= 6) {
            avroLogicalType = LogicalTypes.timestampMicros();
          } else {
            throw new IllegalArgumentException(
                "Avro does not support TIMESTAMP type "
                    + "with precision: "
                    + precision
                    + ", it only supports precision less than 6.");
          }
          timestamp = avroLogicalType.addToSchema(SchemaBuilder.builder().longType());
          return nullable ? nullableSchema(timestamp) : timestamp;
        }
      case DATE:
        // use int to represents Date
        Schema date = LogicalTypes.date().addToSchema(SchemaBuilder.builder().intType());
        return nullable ? nullableSchema(date) : date;
      case TIME_WITHOUT_TIME_ZONE:
        precision = ((TimeType) logicalType).getPrecision();
        if (precision > 3) {
          throw new IllegalArgumentException(
              "Avro does not support TIME type with precision: "
                  + precision

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