apache/iceberg · error · java.lang.UnsupportedOperationException

Cannot convert unsupported type to Spark:

Error message

Cannot convert unsupported type to Spark:

What it means

Fallback in TypeToSparkType.primitive for Iceberg primitive types that have no Spark representation and no dedicated case. Indicates the schema contains a primitive the Spark converter does not recognize — usually a spec-version or runtime-version mismatch.

Source

Thrown at spark/v4.2/spark/src/main/java/org/apache/iceberg/spark/TypeToSparkType.java:166

        return StringType$.MODULE$;
      case UUID:
        // use String
        return StringType$.MODULE$;
      case FIXED:
        return BinaryType$.MODULE$;
      case BINARY:
        return BinaryType$.MODULE$;
      case GEOMETRY:
        return geometryType((Types.GeometryType) primitive);
      case GEOGRAPHY:
        return geographyType((Types.GeographyType) primitive);
      case DECIMAL:
        Types.DecimalType decimal = (Types.DecimalType) primitive;
        return DecimalType$.MODULE$.apply(decimal.precision(), decimal.scale());
      case UNKNOWN:
        return NullType$.MODULE$;
      default:
        throw new UnsupportedOperationException(
            "Cannot convert unsupported type to Spark: " + primitive);
    }
  }

  private DataType geometryType(Types.GeometryType geometry) {
    // The spec lets a geometry CRS be any string identifying a CRS, but Spark recognizes only a
    // fixed set; a CRS Spark cannot resolve throws SparkIllegalArgumentException (an
    // IllegalArgumentException).
    return GeometryType$.MODULE$.apply(geometry.crs());
  }

  private DataType geographyType(Types.GeographyType geography) {
    // The spec requires a geography CRS to be geographic; Spark recognizes only OGC:CRS84, so any
    // other CRS throws SparkIllegalArgumentException (an IllegalArgumentException).
    return GeographyType$.MODULE$.apply(geography.crs(), convertAlgorithm(geography.algorithm()));
  }

  // Translates Iceberg's edge-interpolation algorithm to Spark's. Spark supports only the spherical

View on GitHub (pinned to 86d9c8fc54)

Solutions

  1. Align the Iceberg Spark runtime version with the version that wrote the table
  2. Inspect the offending field type (printed in the message) and rewrite it to a supported primitive
  3. Check for duplicate/mixed Iceberg jars on the classpath

Example fix

// before
// iceberg-spark-runtime 3.5_1.5.0 reading table written by 1.8.0 with new type
// after
// upgrade to iceberg-spark-runtime matching the writer version
Defensive patterns

Strategy: try-catch

Validate before calling

table.schema().columns().forEach(c -> {
  // ensure all primitives are standard spec types known to your runtime
});

Try / catch

try { spark.table("iceberg_table").schema(); }
catch (UnsupportedOperationException e) {
  if (e.getMessage().startsWith("Cannot convert unsupported type to Spark")) { /* upgrade runtime */ }
  else throw e;
}

Prevention

When it happens

Trigger: Converting an Iceberg schema containing a primitive type added in a newer Iceberg spec than the Spark runtime supports, or a corrupt/unknown type id reaching the switch's default branch.

Common situations: Newer Iceberg writer produced a table read by an older Spark runtime jar; custom Type implementations leaking into conversion; mixed Iceberg versions on the classpath.

Understand the failure class

Background: Type mismatch errors: IllegalArgumentException, TypeError and type guards across 150 open-source libraries — this error's family across 150 libraries.

Related errors

AI-assisted analysis of apache/iceberg@86d9c8fc54 (2026-09-12). Data as JSON: /api/errors/4b141b409de8c97b. Report an issue: GitHub.