apache/iceberg · error

CREATE_VIEW_COLUMN_ARITY_MISMATCH.TOO_MANY_DATA_COLUMNS

CREATE_VIEW_COLUMN_ARITY_MISMATCH.TOO_MANY_DATA_COLUMNS

Error message

[CREATE_VIEW_COLUMN_ARITY_MISMATCH.TOO_MANY_DATA_COLUMNS]

What it means

Mirror case of the NOT_ENOUGH_DATA_COLUMNS check: if a CREATE VIEW statement's explicit column list has fewer names than the query's output columns, CheckViews throws Spark's CREATE_VIEW_COLUMN_ARITY_MISMATCH.TOO_MANY_DATA_COLUMNS. Each declared view column must map to exactly one query output column.

Source

Thrown at spark/v4.0/spark-extensions/src/main/scala/org/apache/spark/sql/catalyst/analysis/CheckViews.scala:84

      case _ => // OK
    }
  }

  private def verifyColumnCount(
      ident: ResolvedIdentifier,
      columns: Seq[String],
      query: LogicalPlan): Unit = {
    if (columns.nonEmpty) {
      if (columns.length > query.output.length) {
        throw new AnalysisException(
          errorClass = "CREATE_VIEW_COLUMN_ARITY_MISMATCH.NOT_ENOUGH_DATA_COLUMNS",
          messageParameters = Map(
            "viewName" -> String.format("%s.%s", ident.catalog.name(), ident.identifier),
            "viewColumns" -> columns.mkString(", "),
            "dataColumns" -> query.output.map(c => c.name).mkString(", ")))
      } else if (columns.length < query.output.length) {
        throw new AnalysisException(
          errorClass = "CREATE_VIEW_COLUMN_ARITY_MISMATCH.TOO_MANY_DATA_COLUMNS",
          messageParameters = Map(
            "viewName" -> String.format("%s.%s", ident.catalog.name(), ident.identifier),
            "viewColumns" -> columns.mkString(", "),
            "dataColumns" -> query.output.map(c => c.name).mkString(", ")))
      }
    }
  }

  private def checkCyclicViewReference(
      viewIdent: Seq[String],
      plan: LogicalPlan,
      cyclePath: Seq[Seq[String]]): Unit = {
    plan match {
      case sub @ SubqueryAlias(_, Project(_, _)) =>
        val currentViewIdent: Seq[String] = sub.identifier.qualifier :+ sub.identifier.name
        checkIfRecursiveView(viewIdent, currentViewIdent, cyclePath, sub.children)
      case v1View: View =>

View on GitHub (pinned to 86d9c8fc54)

Solutions

1. Add the missing column names to the explicit view column list
2. Reduce the SELECT to only the columns the view should expose
3. Drop the explicit column list entirely

Example fix

// before
CREATE VIEW v (a) AS SELECT x, y FROM t
// after
CREATE VIEW v (a, b) AS SELECT x, y FROM t

val queryOutput = spark.sql(viewQuery).schema.length
if (declaredColumns.length < queryOutput) {
  throw new IllegalArgumentException("Query output has more columns than declared view columns")
}

try {
  spark.sql(createViewDdl)
} catch {
  case e: AnalysisException if e.getErrorClass.contains("TOO_MANY_DATA_COLUMNS") =>
    logError(s"Declared too few view columns: $createViewDdl", e)
}

When it happens

Trigger: CREATE VIEW v (a) AS SELECT x, y FROM t — the query produces 2 columns but only 1 view column is declared.

Common situations: Forgetting to alias extra SELECT columns or omitting them from the column list; schema drift in the underlying query adding columns; templated DDL generated with a stale column list.

Understand the failure class

Background: Schema validation failed / invalid input schema: payload rejected because its shape doesn't match the expected schema — this error's family across 28 libraries.

Related errors

• CREATE_VIEW_COLUMN_ARITY_MISMATCH.NOT_ENOUGH_DATA_COLUMNS
• Renaming a view is not supported by catalog: ${catalogName}
• Unsupported Spark view dependency: ${dependency.getClass().g
• Failed to write Spark view dependencies
• Failed to parse Spark view dependencies

AI-assisted analysis of apache/iceberg@86d9c8fc54 (2026-09-12). Data as JSON: /api/errors/00247965e41db58b. Report an issue: GitHub.