yihua commented on code in PR #13736:
URL: https://github.com/apache/hudi/pull/13736#discussion_r2292427332
##########
hudi-spark-datasource/hudi-spark/src/main/scala/org/apache/spark/sql/hudi/command/procedures/ShowCleansProcedure.scala:
##########
@@ -35,7 +35,8 @@ class ShowCleansProcedure(includePartitionMetadata: Boolean)
extends BaseProcedu
private val PARAMETERS = Array[ProcedureParameter](
ProcedureParameter.required(0, "table", DataTypes.StringType),
ProcedureParameter.optional(1, "limit", DataTypes.IntegerType, 10),
- ProcedureParameter.optional(2, "showArchived", DataTypes.BooleanType,
false)
+ ProcedureParameter.optional(2, "showArchived", DataTypes.BooleanType,
false),
+ ProcedureParameter.optional(3, "filter", DataTypes.StringType, "")
Review Comment:
Same on adding docs on `filter`
##########
hudi-spark-datasource/hudi-spark/src/main/scala/org/apache/spark/sql/hudi/command/procedures/ShowCleansPlanProcedure.scala:
##########
@@ -184,7 +199,8 @@ object ShowCleansPlanProcedure {
private val PARAMETERS = Array[ProcedureParameter](
ProcedureParameter.required(0, "table", DataTypes.StringType),
ProcedureParameter.optional(1, "limit", DataTypes.IntegerType, 10),
- ProcedureParameter.optional(2, "showArchived", DataTypes.BooleanType,
false)
+ ProcedureParameter.optional(2, "showArchived", DataTypes.BooleanType,
false),
+ ProcedureParameter.optional(3, "filter", DataTypes.StringType, "")
Review Comment:
Add docs on how this `filter` should be used with examples (the details can
be added to scaladocs of `ShowCleansPlanProcedure` class)?
##########
hudi-spark-datasource/hudi-spark/src/main/scala/org/apache/spark/sql/hudi/command/procedures/HoodieProcedureFilterUtils.scala:
##########
@@ -0,0 +1,531 @@
+/*
+ * 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.
+ */
+
+package org.apache.spark.sql.hudi.command.procedures
+
+import org.apache.spark.sql.{Row, SparkSession}
+import org.apache.spark.sql.catalyst.analysis.UnresolvedAttribute
+import org.apache.spark.sql.catalyst.expressions.{Expression,
GenericInternalRow}
+import org.apache.spark.sql.catalyst.util.DateTimeUtils
+import org.apache.spark.sql.types.{DataType, StructType}
+import org.apache.spark.unsafe.types.UTF8String
+
+import scala.collection.JavaConverters._
+import scala.util.{Failure, Success, Try}
+
+/**
+ * Utility object for filtering procedure results using SQL expressions.
+ *
+ * Supports all Spark SQL data types including:
+ * - Primitive types: Boolean, Byte, Short, Int, Long, Float, Double, String,
Binary
+ * - Date/Time types: Date, Timestamp, Instant, LocalDate, LocalDateTime
+ * - Decimal types: BigDecimal with precision/scale
+ * - Complex types: Array, Map, Struct (Row)
+ * - Nested combinations of all above types
+ */
+object HoodieProcedureFilterUtils {
+
+ /**
+ * Evaluates a SQL filter expression against a sequence of rows.
+ *
+ * @param rows The rows to filter
+ * @param filterExpression SQL expression string
+ * @param schema The schema of the rows
+ * @param sparkSession Spark session for expression parsing
+ * @return Filtered rows that match the expression
+ */
+ def evaluateFilter(
+ rows: Seq[Row],
Review Comment:
nit: in Hudi repo, these are usually put into one line
##########
hudi-spark-datasource/hudi-spark/src/main/scala/org/apache/spark/sql/hudi/command/procedures/HoodieProcedureFilterUtils.scala:
##########
@@ -0,0 +1,531 @@
+/*
+ * 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.
+ */
+
+package org.apache.spark.sql.hudi.command.procedures
+
+import org.apache.spark.sql.{Row, SparkSession}
+import org.apache.spark.sql.catalyst.analysis.UnresolvedAttribute
+import org.apache.spark.sql.catalyst.expressions.{Expression,
GenericInternalRow}
+import org.apache.spark.sql.catalyst.util.DateTimeUtils
+import org.apache.spark.sql.types.{DataType, StructType}
+import org.apache.spark.unsafe.types.UTF8String
+
+import scala.collection.JavaConverters._
+import scala.util.{Failure, Success, Try}
+
+/**
+ * Utility object for filtering procedure results using SQL expressions.
+ *
+ * Supports all Spark SQL data types including:
+ * - Primitive types: Boolean, Byte, Short, Int, Long, Float, Double, String,
Binary
+ * - Date/Time types: Date, Timestamp, Instant, LocalDate, LocalDateTime
+ * - Decimal types: BigDecimal with precision/scale
+ * - Complex types: Array, Map, Struct (Row)
+ * - Nested combinations of all above types
+ */
+object HoodieProcedureFilterUtils {
+
+ /**
+ * Evaluates a SQL filter expression against a sequence of rows.
+ *
+ * @param rows The rows to filter
+ * @param filterExpression SQL expression string
+ * @param schema The schema of the rows
+ * @param sparkSession Spark session for expression parsing
+ * @return Filtered rows that match the expression
+ */
+ def evaluateFilter(
+ rows: Seq[Row],
+ filterExpression: String,
+ schema: StructType,
+ sparkSession: SparkSession
+ ): Seq[Row] = {
+
+ if (filterExpression == null || filterExpression.trim.isEmpty) {
+ rows
+ } else {
+ Try {
+ val parsedExpr =
sparkSession.sessionState.sqlParser.parseExpression(filterExpression)
+
+ rows.filter { row =>
+ evaluateExpressionOnRow(parsedExpr, row, schema)
+ }
+ } match {
+ case Success(filteredRows) => filteredRows
+ case Failure(exception) =>
+ throw new IllegalArgumentException(
+ s"Failed to parse or evaluate filter expression
'$filterExpression': ${exception.getMessage}",
+ exception
+ )
+ }
+ }
+ }
+
+ private def evaluateExpressionOnRow(
+ expression: Expression,
+ row: Row,
+ schema: StructType
+ ): Boolean = {
+
+ val internalRow = convertRowToInternalRow(row, schema)
+
+ Try {
+ // First pass: bind attributes
+ val attributeBound = expression.transform {
+ case attr: org.apache.spark.sql.catalyst.analysis.UnresolvedAttribute
=>
+ try {
+ val fieldIndex = schema.fieldIndex(attr.name)
+ val field = schema.fields(fieldIndex)
+
org.apache.spark.sql.catalyst.expressions.BoundReference(fieldIndex,
field.dataType, field.nullable)
+ } catch {
+ case _: IllegalArgumentException => attr
+ }
+ }
+
+ // Second pass: resolve functions
+ val functionResolved = attributeBound.transform {
+ case unresolvedFunc:
org.apache.spark.sql.catalyst.analysis.UnresolvedFunction =>
+ unresolvedFunc.nameParts.head.toLowerCase match {
+ case "upper" =>
+ if (unresolvedFunc.arguments.length == 1) {
+
org.apache.spark.sql.catalyst.expressions.Upper(unresolvedFunc.arguments.head)
+ } else {
+ unresolvedFunc
+ }
+ case "lower" =>
+ if (unresolvedFunc.arguments.length == 1) {
+
org.apache.spark.sql.catalyst.expressions.Lower(unresolvedFunc.arguments.head)
+ } else {
+ unresolvedFunc
+ }
+ case "length" | "len" =>
+ if (unresolvedFunc.arguments.length == 1) {
+
org.apache.spark.sql.catalyst.expressions.Length(unresolvedFunc.arguments.head)
+ } else {
+ unresolvedFunc
+ }
+ case "trim" =>
Review Comment:
The function is manually mapped to Spark expression. Is it possible to make
it go through Spark's own logic of resolving the function? @jonvex do you know?
##########
hudi-spark-datasource/hudi-spark/src/main/scala/org/apache/spark/sql/hudi/command/procedures/HoodieProcedureFilterUtils.scala:
##########
@@ -0,0 +1,531 @@
+/*
+ * 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.
+ */
+
+package org.apache.spark.sql.hudi.command.procedures
+
+import org.apache.spark.sql.{Row, SparkSession}
+import org.apache.spark.sql.catalyst.analysis.UnresolvedAttribute
+import org.apache.spark.sql.catalyst.expressions.{Expression,
GenericInternalRow}
+import org.apache.spark.sql.catalyst.util.DateTimeUtils
+import org.apache.spark.sql.types.{DataType, StructType}
+import org.apache.spark.unsafe.types.UTF8String
+
+import scala.collection.JavaConverters._
+import scala.util.{Failure, Success, Try}
+
+/**
+ * Utility object for filtering procedure results using SQL expressions.
+ *
+ * Supports all Spark SQL data types including:
+ * - Primitive types: Boolean, Byte, Short, Int, Long, Float, Double, String,
Binary
+ * - Date/Time types: Date, Timestamp, Instant, LocalDate, LocalDateTime
+ * - Decimal types: BigDecimal with precision/scale
+ * - Complex types: Array, Map, Struct (Row)
+ * - Nested combinations of all above types
+ */
+object HoodieProcedureFilterUtils {
+
+ /**
+ * Evaluates a SQL filter expression against a sequence of rows.
+ *
+ * @param rows The rows to filter
+ * @param filterExpression SQL expression string
+ * @param schema The schema of the rows
+ * @param sparkSession Spark session for expression parsing
+ * @return Filtered rows that match the expression
+ */
+ def evaluateFilter(
+ rows: Seq[Row],
+ filterExpression: String,
+ schema: StructType,
+ sparkSession: SparkSession
+ ): Seq[Row] = {
+
+ if (filterExpression == null || filterExpression.trim.isEmpty) {
+ rows
+ } else {
+ Try {
+ val parsedExpr =
sparkSession.sessionState.sqlParser.parseExpression(filterExpression)
+
+ rows.filter { row =>
+ evaluateExpressionOnRow(parsedExpr, row, schema)
+ }
+ } match {
+ case Success(filteredRows) => filteredRows
+ case Failure(exception) =>
+ throw new IllegalArgumentException(
+ s"Failed to parse or evaluate filter expression
'$filterExpression': ${exception.getMessage}",
+ exception
+ )
+ }
+ }
+ }
+
+ private def evaluateExpressionOnRow(
+ expression: Expression,
+ row: Row,
+ schema: StructType
+ ): Boolean = {
+
+ val internalRow = convertRowToInternalRow(row, schema)
+
+ Try {
+ // First pass: bind attributes
+ val attributeBound = expression.transform {
+ case attr: org.apache.spark.sql.catalyst.analysis.UnresolvedAttribute
=>
+ try {
+ val fieldIndex = schema.fieldIndex(attr.name)
+ val field = schema.fields(fieldIndex)
+
org.apache.spark.sql.catalyst.expressions.BoundReference(fieldIndex,
field.dataType, field.nullable)
+ } catch {
+ case _: IllegalArgumentException => attr
+ }
+ }
+
+ // Second pass: resolve functions
+ val functionResolved = attributeBound.transform {
+ case unresolvedFunc:
org.apache.spark.sql.catalyst.analysis.UnresolvedFunction =>
+ unresolvedFunc.nameParts.head.toLowerCase match {
+ case "upper" =>
+ if (unresolvedFunc.arguments.length == 1) {
+
org.apache.spark.sql.catalyst.expressions.Upper(unresolvedFunc.arguments.head)
+ } else {
+ unresolvedFunc
+ }
+ case "lower" =>
+ if (unresolvedFunc.arguments.length == 1) {
+
org.apache.spark.sql.catalyst.expressions.Lower(unresolvedFunc.arguments.head)
+ } else {
+ unresolvedFunc
+ }
+ case "length" | "len" =>
+ if (unresolvedFunc.arguments.length == 1) {
+
org.apache.spark.sql.catalyst.expressions.Length(unresolvedFunc.arguments.head)
+ } else {
+ unresolvedFunc
+ }
+ case "trim" =>
Review Comment:
For now, as long as this util is only used by Spark procedures for debugging
purposes, it's OK.
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