org.apache.spark.sql.execution.datasources.SparkExpressionConverter.scala Maven / Gradle / Ivy
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A table format for huge analytic datasets
/*
* Licensed to the Apache Software Foundation (ASF) under one
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* to you under the Apache License, Version 2.0 (the
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*
* http://www.apache.org/licenses/LICENSE-2.0
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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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