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package org.apache.spark.sql.execution.datasources

import org.apache.iceberg.spark.SparkFilters
import org.apache.spark.sql.AnalysisException
import org.apache.spark.sql.SparkSession
import org.apache.spark.sql.catalyst.expressions.Expression
import org.apache.spark.sql.catalyst.plans.logical.Filter

object SparkExpressionConverter {

  def convertToIcebergExpression(sparkExpression: Expression): org.apache.iceberg.expressions.Expression = {
    // Currently, it is a double conversion as we are converting Spark expression to Spark filter
    // and then converting Spark filter to Iceberg expression.
    // But these two conversions already exist and well tested. So, we are going with this approach.
    SparkFilters.convert(DataSourceStrategy.translateFilter(sparkExpression, supportNestedPredicatePushdown = true).get)
  }

  @throws[AnalysisException]
  def collectResolvedSparkExpression(session: SparkSession, tableName: String, where: String): Expression = {
    var expression: Expression = null
    // Add a dummy prefix linking to the table to collect the resolved spark expression from optimized plan.
    val prefix = String.format("SELECT 42 from %s where ", tableName)
    val logicalPlan = session.sessionState.sqlParser.parsePlan(prefix + where)
    val optimizedLogicalPlan = session.sessionState.executePlan(logicalPlan).optimizedPlan
    optimizedLogicalPlan.collectFirst {
      case filter: Filter =>
        expression = filter.expressions.head
    }
    expression
  }
}




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