What causes "${this.getClass()} does not implement getMap" in apache/iceberg?

Move map-typed constant projections out of the scan (apply withColumn after reading the table)

apache/iceberg · error · java.lang.UnsupportedOperationException

${this.getClass()} does not implement getMap

Error message

${this.getClass()} does not implement getMap

What it means

ConstantColumnVector (vectorized Spark reads) returns one constant value for all rows and implements only scalar accessors plus getDecimal; getMap is intentionally not implemented, so calling it throws UnsupportedOperationException. A map-valued constant column cannot be represented by this vector.

Source

Thrown at spark/v3.5/spark/src/main/java/org/apache/iceberg/spark/data/vectorized/ConstantColumnVector.java:107

  @Override
  public float getFloat(int rowId) {
    return (float) constant;
  }

  @Override
  public double getDouble(int rowId) {
    return (double) constant;
  }

  @Override
  public ColumnarArray getArray(int rowId) {
    throw new UnsupportedOperationException(this.getClass() + " does not implement getArray");
  }

  @Override
  public ColumnarMap getMap(int ordinal) {
    throw new UnsupportedOperationException(this.getClass() + " does not implement getMap");
  }

  @Override
  public Decimal getDecimal(int rowId, int precision, int scale) {
    return (Decimal) constant;
  }

  @Override
  public UTF8String getUTF8String(int rowId) {
    return (UTF8String) constant;
  }

  @Override
  public byte[] getBinary(int rowId) {
    return (byte[]) constant;
  }

  @Override

View on GitHub (pinned to 86d9c8fc54)

Solutions

Move map-typed constant projections out of the scan (apply withColumn after reading the table)
Disable vectorized reads: spark.sql.iceberg.vectorization.enabled=false
Upgrade the Iceberg runtime to a version with broader constant-vector support
Rewrite the query so the map column originates from data files, not a constant

Example fix

// before: SELECT map('k',1) AS m, * FROM iceberg_table
// after:
val df = spark.read.format("iceberg").load("db.table").withColumn("m", map(lit("k"), lit(1)))

Defensive patterns

Strategy: try-catch

Validate before calling

// Ensure projected constants are scalar-only; move map-typed constants outside the scan

Try / catch

try {
  df.collect();
} catch (UnsupportedOperationException e) {
  if (e.getMessage().endsWith("does not implement getMap")) {
    // recompute the map column post-scan or disable vectorization
  } else throw e;
}

Prevention

Add map literals with withColumn after reading, not inside the same projection as the Iceberg scan
Enable vectorization in CI to catch unsupported constant column types early
Upgrade Iceberg when adding map-typed metadata projections

When it happens

Trigger: A vectorized Spark read where Spark calls getMap on a column materialized as ConstantColumnVector — e.g. a constant-folded map literal or a map-typed metadata column pushed into the constant vector path.

Common situations: Queries projecting map-typed literals or constant map columns alongside an Iceberg scan with vectorization enabled; optimizer folding map expressions into the scan's constant columns.

Related errors

AI-assisted analysis of apache/iceberg@86d9c8fc54 (2026-09-12). Data as JSON: /api/errors/84298f38e8db5644. Report an issue: GitHub.