[HUDI-3594] Supporting Composite Expressions over Data Table Columns in Data Skipping flow #4996
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| /* | ||
| * Licensed to the Apache Software Foundation (ASF) under one or more | ||
| * contributor license agreements. See the NOTICE file distributed with | ||
| * this work for additional information regarding copyright ownership. | ||
| * The ASF licenses this file to You under the Apache License, Version 2.0 | ||
| * (the "License"); you may not use this file except in compliance with | ||
| * the License. You may obtain a copy of the License at | ||
| * | ||
| * http://www.apache.org/licenses/LICENSE-2.0 | ||
| * | ||
| * Unless required by applicable law or agreed to in writing, software | ||
| * distributed under the License is distributed on an "AS IS" BASIS, | ||
| * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
| * See the License for the specific language governing permissions and | ||
| * limitations under the License. | ||
| */ | ||
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| package org.apache.spark.sql | ||
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| import org.apache.spark.sql.catalyst.analysis.{UnresolvedAttribute, UnresolvedFunction} | ||
| import org.apache.spark.sql.catalyst.expressions.{AttributeReference, Expression, SubqueryExpression} | ||
| import org.apache.spark.sql.catalyst.plans.logical.{Filter, LocalRelation, LogicalPlan} | ||
| import org.apache.spark.sql.types.StructType | ||
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| trait HoodieCatalystExpressionUtils { | ||
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| /** | ||
| * Parses and resolves expression against the attributes of the given table schema. | ||
| * | ||
| * For example: | ||
| * <pre> | ||
| * ts > 1000 and ts <= 1500 | ||
| * </pre> | ||
| * will be resolved as | ||
| * <pre> | ||
| * And(GreaterThan(ts#590L > 1000), LessThanOrEqual(ts#590L <= 1500)) | ||
| * </pre> | ||
| * | ||
| * Where <pre>ts</pre> is a column of the provided [[tableSchema]] | ||
| * | ||
| * @param spark spark session | ||
| * @param exprString string representation of the expression to parse and resolve | ||
| * @param tableSchema table schema encompassing attributes to resolve against | ||
| * @return Resolved filter expression | ||
| */ | ||
| def resolveExpr(spark: SparkSession, exprString: String, tableSchema: StructType): Expression = { | ||
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| val expr = spark.sessionState.sqlParser.parseExpression(exprString) | ||
| resolveExpr(spark, expr, tableSchema) | ||
| } | ||
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| /** | ||
| * Resolves provided expression (unless already resolved) against the attributes of the given table schema. | ||
| * | ||
| * For example: | ||
| * <pre> | ||
| * ts > 1000 and ts <= 1500 | ||
| * </pre> | ||
| * will be resolved as | ||
| * <pre> | ||
| * And(GreaterThan(ts#590L > 1000), LessThanOrEqual(ts#590L <= 1500)) | ||
| * </pre> | ||
| * | ||
| * Where <pre>ts</pre> is a column of the provided [[tableSchema]] | ||
| * | ||
| * @param spark spark session | ||
| * @param expr Catalyst expression to be resolved (if not yet) | ||
| * @param tableSchema table schema encompassing attributes to resolve against | ||
| * @return Resolved filter expression | ||
| */ | ||
| def resolveExpr(spark: SparkSession, expr: Expression, tableSchema: StructType): Expression = { | ||
| val analyzer = spark.sessionState.analyzer | ||
| val schemaFields = tableSchema.fields | ||
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| val resolvedExpr = { | ||
| val plan: LogicalPlan = Filter(expr, LocalRelation(schemaFields.head, schemaFields.drop(1): _*)) | ||
| analyzer.execute(plan).asInstanceOf[Filter].condition | ||
| } | ||
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| if (!hasUnresolvedRefs(resolvedExpr)) { | ||
| resolvedExpr | ||
| } else { | ||
| throw new IllegalStateException("unresolved attribute") | ||
| } | ||
| } | ||
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| /** | ||
| * Split the given predicates into two sequence predicates: | ||
| * - predicates that references partition columns only(and involves no sub-query); | ||
| * - other predicates. | ||
| * | ||
| * @param sparkSession The spark session | ||
| * @param predicates The predicates to be split | ||
| * @param partitionColumns The partition columns | ||
| * @return (partitionFilters, dataFilters) | ||
| */ | ||
| def splitPartitionAndDataPredicates(sparkSession: SparkSession, | ||
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| predicates: Array[Expression], | ||
| partitionColumns: Array[String]): (Array[Expression], Array[Expression]) = { | ||
| // Validates that the provided names both resolve to the same entity | ||
| val resolvedNameEquals = sparkSession.sessionState.analyzer.resolver | ||
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| predicates.partition(expr => { | ||
| // Checks whether given expression only references partition columns(and involves no sub-query) | ||
| expr.references.forall(r => partitionColumns.exists(resolvedNameEquals(r.name, _))) && | ||
| !SubqueryExpression.hasSubquery(expr) | ||
| }) | ||
| } | ||
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| /** | ||
| * Matches an expression iff | ||
| * | ||
| * <ol> | ||
| * <li>It references exactly one [[AttributeReference]]</li> | ||
| * <li>It contains only whitelisted transformations that preserve ordering of the source column [1]</li> | ||
| * </ol> | ||
| * | ||
| * [1] Preserving ordering is defined as following: transformation T is defined as ordering preserving in case | ||
| * values of the source column A values being ordered as a1, a2, a3 ..., will map into column B = T(A) which | ||
| * will keep the same ordering b1, b2, b3, ... with b1 = T(a1), b2 = T(a2), ... | ||
| */ | ||
| def tryMatchAttributeOrderingPreservingTransformation(expr: Expression): Option[AttributeReference] | ||
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| private def hasUnresolvedRefs(resolvedExpr: Expression): Boolean = | ||
| resolvedExpr.collectFirst { | ||
| case _: UnresolvedAttribute | _: UnresolvedFunction => true | ||
| }.isDefined | ||
| } | ||
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is this code adapted from somewhere? if so, can you please add source attribution
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Nope, this is our code. Had to place it in
spark.sqlto access package-private API