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Posted to reviews@spark.apache.org by "amaliujia (via GitHub)" <gi...@apache.org> on 2023/02/16 21:35:45 UTC
[GitHub] [spark] amaliujia opened a new pull request, #40057: [SPARK-42468][CONNECT] Implement agg by (String, String)*
amaliujia opened a new pull request, #40057:
URL: https://github.com/apache/spark/pull/40057
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### What changes were proposed in this pull request?
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Starting to support basic aggregation in Scala client. The first step is to support aggregation by strings.
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API coverage
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[GitHub] [spark] hvanhovell commented on a diff in pull request #40057: [SPARK-42468][CONNECT] Implement agg by (String, String)*
Posted by "hvanhovell (via GitHub)" <gi...@apache.org>.
hvanhovell commented on code in PR #40057:
URL: https://github.com/apache/spark/pull/40057#discussion_r1109162209
##########
connector/connect/client/jvm/src/main/scala/org/apache/spark/sql/RelationalGroupedDataset.scala:
##########
@@ -0,0 +1,162 @@
+/*
+ * 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
+
+import java.util.Locale
+
+import scala.collection.JavaConverters._
+
+import org.apache.spark.connect.proto
+
+/**
+ * A set of methods for aggregations on a `DataFrame`, created by [[Dataset#groupBy groupBy]],
+ * [[Dataset#cube cube]] or [[Dataset#rollup rollup]] (and also `pivot`).
+ *
+ * The main method is the `agg` function, which has multiple variants. This class also contains
+ * some first-order statistics such as `mean`, `sum` for convenience.
+ *
+ * @note
+ * This class was named `GroupedData` in Spark 1.x.
+ *
+ * @since 3.4.0
+ */
+class RelationalGroupedDataset protected[sql] (
+ private[sql] val df: DataFrame,
+ private[sql] val groupingExprs: Seq[proto.Expression]) {
+
+ private[this] def toDF(aggExprs: Seq[proto.Expression]): DataFrame = {
+ // TODO: support other GroupByType such as Rollup, Cube, Pivot.
+ df.session.newDataset { builder =>
+ builder.getAggregateBuilder
+ .setGroupType(proto.Aggregate.GroupType.GROUP_TYPE_GROUPBY)
+ .setInput(df.plan.getRoot)
+ .addAllGroupingExpressions(groupingExprs.asJava)
+ .addAllAggregateExpressions(aggExprs.asJava)
+ }
+ }
+
+ /**
+ * (Scala-specific) Compute aggregates by specifying the column names and aggregate methods. The
+ * resulting `DataFrame` will also contain the grouping columns.
+ *
+ * The available aggregate methods are `avg`, `max`, `min`, `sum`, `count`.
+ * {{{
+ * // Selects the age of the oldest employee and the aggregate expense for each department
+ * df.groupBy("department").agg(
+ * "age" -> "max",
+ * "expense" -> "sum"
+ * )
+ * }}}
+ *
+ * @since 3.4.0
+ */
+ def agg(aggExpr: (String, String), aggExprs: (String, String)*): DataFrame = {
+ toDF((aggExpr +: aggExprs).map { case (colName, expr) =>
+ strToExpr(expr)(df(colName).expr)
+ })
+ }
+
+ /**
+ * (Scala-specific) Compute aggregates by specifying a map from column name to aggregate
+ * methods. The resulting `DataFrame` will also contain the grouping columns.
+ *
+ * The available aggregate methods are `avg`, `max`, `min`, `sum`, `count`.
+ * {{{
+ * // Selects the age of the oldest employee and the aggregate expense for each department
+ * df.groupBy("department").agg(Map(
+ * "age" -> "max",
+ * "expense" -> "sum"
+ * ))
+ * }}}
+ *
+ * @since 3.4.0
+ */
+ def agg(exprs: Map[String, String]): DataFrame = {
+ toDF(exprs.map { case (colName, expr) =>
+ strToExpr(expr)(df(colName).expr)
+ }.toSeq)
+ }
+
+ /**
+ * (Java-specific) Compute aggregates by specifying a map from column name to aggregate methods.
+ * The resulting `DataFrame` will also contain the grouping columns.
+ *
+ * The available aggregate methods are `avg`, `max`, `min`, `sum`, `count`.
+ * {{{
+ * // Selects the age of the oldest employee and the aggregate expense for each department
+ * import com.google.common.collect.ImmutableMap;
+ * df.groupBy("department").agg(ImmutableMap.of("age", "max", "expense", "sum"));
+ * }}}
+ *
+ * @since 3.4.0
+ */
+ def agg(exprs: java.util.Map[String, String]): DataFrame = {
+ agg(exprs.asScala.toMap)
+ }
+
+ private[this] def strToExpr(expr: String): (proto.Expression => proto.Expression) = {
+ val exprToFunc: (proto.Expression => proto.Expression) = {
+ val builder = proto.Expression.newBuilder()
+
+ (inputExpr: proto.Expression) =>
+ expr.toLowerCase(Locale.ROOT) match {
+ // We special handle a few cases that have alias that are not in function registry.
+ case "avg" | "average" | "mean" =>
+ builder.getUnresolvedFunctionBuilder
+ .setFunctionName("avg")
+ .addArguments(inputExpr)
+ .setIsDistinct(false)
+ case "stddev" | "std" =>
+ builder.getUnresolvedFunctionBuilder
+ .setFunctionName("stddev")
+ .addArguments(inputExpr)
+ .setIsDistinct(false)
+ // Also special handle count because we need to take care count(*).
+ case "count" | "size" =>
+ // Turn count(*) into count(1)
+ inputExpr match {
+ case s if s.hasUnresolvedStar =>
+ val exprBuilder = proto.Expression.newBuilder
+ exprBuilder.getLiteralBuilder.setInteger(1)
+ builder.getUnresolvedFunctionBuilder
+ .setFunctionName("count")
+ .addArguments(exprBuilder)
+ .setIsDistinct(false)
+ case _ =>
+ builder.getUnresolvedFunctionBuilder
+ .setFunctionName("count")
+ .addArguments(inputExpr)
+ .setIsDistinct(false)
+ }
+ case name =>
+ builder.getUnresolvedFunctionBuilder
+ .setFunctionName(name)
+ .addArguments(inputExpr)
+ .setIsDistinct(false)
+ }
+ builder.build()
+ }
+ (inputExpr: proto.Expression) => exprToFunc(inputExpr)
+ }
+}
+
+private[sql] object RelationalGroupedDataset {
+ def apply(df: DataFrame, groupingExprs: Seq[proto.Expression]): RelationalGroupedDataset = {
Review Comment:
Yeah, you need to use `new`. Given that this method is not public we don't need to implement it.
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[GitHub] [spark] amaliujia commented on a diff in pull request #40057: [SPARK-42468][CONNECT] Implement agg by (String, String)*
Posted by "amaliujia (via GitHub)" <gi...@apache.org>.
amaliujia commented on code in PR #40057:
URL: https://github.com/apache/spark/pull/40057#discussion_r1109163806
##########
connector/connect/client/jvm/src/main/scala/org/apache/spark/sql/RelationalGroupedDataset.scala:
##########
@@ -0,0 +1,162 @@
+/*
+ * 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
+
+import java.util.Locale
+
+import scala.collection.JavaConverters._
+
+import org.apache.spark.connect.proto
+
+/**
+ * A set of methods for aggregations on a `DataFrame`, created by [[Dataset#groupBy groupBy]],
+ * [[Dataset#cube cube]] or [[Dataset#rollup rollup]] (and also `pivot`).
+ *
+ * The main method is the `agg` function, which has multiple variants. This class also contains
+ * some first-order statistics such as `mean`, `sum` for convenience.
+ *
+ * @note
+ * This class was named `GroupedData` in Spark 1.x.
+ *
+ * @since 3.4.0
+ */
+class RelationalGroupedDataset protected[sql] (
+ private[sql] val df: DataFrame,
+ private[sql] val groupingExprs: Seq[proto.Expression]) {
+
+ private[this] def toDF(aggExprs: Seq[proto.Expression]): DataFrame = {
+ // TODO: support other GroupByType such as Rollup, Cube, Pivot.
+ df.session.newDataset { builder =>
+ builder.getAggregateBuilder
+ .setGroupType(proto.Aggregate.GroupType.GROUP_TYPE_GROUPBY)
+ .setInput(df.plan.getRoot)
+ .addAllGroupingExpressions(groupingExprs.asJava)
+ .addAllAggregateExpressions(aggExprs.asJava)
+ }
+ }
+
+ /**
+ * (Scala-specific) Compute aggregates by specifying the column names and aggregate methods. The
+ * resulting `DataFrame` will also contain the grouping columns.
+ *
+ * The available aggregate methods are `avg`, `max`, `min`, `sum`, `count`.
+ * {{{
+ * // Selects the age of the oldest employee and the aggregate expense for each department
+ * df.groupBy("department").agg(
+ * "age" -> "max",
+ * "expense" -> "sum"
+ * )
+ * }}}
+ *
+ * @since 3.4.0
+ */
+ def agg(aggExpr: (String, String), aggExprs: (String, String)*): DataFrame = {
+ toDF((aggExpr +: aggExprs).map { case (colName, expr) =>
+ strToExpr(expr)(df(colName).expr)
+ })
+ }
+
+ /**
+ * (Scala-specific) Compute aggregates by specifying a map from column name to aggregate
+ * methods. The resulting `DataFrame` will also contain the grouping columns.
+ *
+ * The available aggregate methods are `avg`, `max`, `min`, `sum`, `count`.
+ * {{{
+ * // Selects the age of the oldest employee and the aggregate expense for each department
+ * df.groupBy("department").agg(Map(
+ * "age" -> "max",
+ * "expense" -> "sum"
+ * ))
+ * }}}
+ *
+ * @since 3.4.0
+ */
+ def agg(exprs: Map[String, String]): DataFrame = {
+ toDF(exprs.map { case (colName, expr) =>
+ strToExpr(expr)(df(colName).expr)
+ }.toSeq)
+ }
+
+ /**
+ * (Java-specific) Compute aggregates by specifying a map from column name to aggregate methods.
+ * The resulting `DataFrame` will also contain the grouping columns.
+ *
+ * The available aggregate methods are `avg`, `max`, `min`, `sum`, `count`.
+ * {{{
+ * // Selects the age of the oldest employee and the aggregate expense for each department
+ * import com.google.common.collect.ImmutableMap;
+ * df.groupBy("department").agg(ImmutableMap.of("age", "max", "expense", "sum"));
+ * }}}
+ *
+ * @since 3.4.0
+ */
+ def agg(exprs: java.util.Map[String, String]): DataFrame = {
+ agg(exprs.asScala.toMap)
+ }
+
+ private[this] def strToExpr(expr: String): (proto.Expression => proto.Expression) = {
+ val exprToFunc: (proto.Expression => proto.Expression) = {
+ val builder = proto.Expression.newBuilder()
+
+ (inputExpr: proto.Expression) =>
+ expr.toLowerCase(Locale.ROOT) match {
+ // We special handle a few cases that have alias that are not in function registry.
+ case "avg" | "average" | "mean" =>
+ builder.getUnresolvedFunctionBuilder
+ .setFunctionName("avg")
+ .addArguments(inputExpr)
+ .setIsDistinct(false)
+ case "stddev" | "std" =>
+ builder.getUnresolvedFunctionBuilder
+ .setFunctionName("stddev")
+ .addArguments(inputExpr)
+ .setIsDistinct(false)
+ // Also special handle count because we need to take care count(*).
+ case "count" | "size" =>
+ // Turn count(*) into count(1)
+ inputExpr match {
+ case s if s.hasUnresolvedStar =>
+ val exprBuilder = proto.Expression.newBuilder
+ exprBuilder.getLiteralBuilder.setInteger(1)
+ builder.getUnresolvedFunctionBuilder
+ .setFunctionName("count")
+ .addArguments(exprBuilder)
+ .setIsDistinct(false)
+ case _ =>
+ builder.getUnresolvedFunctionBuilder
+ .setFunctionName("count")
+ .addArguments(inputExpr)
+ .setIsDistinct(false)
+ }
+ case name =>
+ builder.getUnresolvedFunctionBuilder
+ .setFunctionName(name)
+ .addArguments(inputExpr)
+ .setIsDistinct(false)
+ }
+ builder.build()
+ }
+ (inputExpr: proto.Expression) => exprToFunc(inputExpr)
+ }
+}
+
+private[sql] object RelationalGroupedDataset {
+ def apply(df: DataFrame, groupingExprs: Seq[proto.Expression]): RelationalGroupedDataset = {
Review Comment:
Removed this one and just use `new`.
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[GitHub] [spark] hvanhovell commented on a diff in pull request #40057: [SPARK-42468][CONNECT] Implement agg by (String, String)*
Posted by "hvanhovell (via GitHub)" <gi...@apache.org>.
hvanhovell commented on code in PR #40057:
URL: https://github.com/apache/spark/pull/40057#discussion_r1109163416
##########
connector/connect/client/jvm/src/main/scala/org/apache/spark/sql/RelationalGroupedDataset.scala:
##########
@@ -0,0 +1,162 @@
+/*
+ * 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
+
+import java.util.Locale
+
+import scala.collection.JavaConverters._
+
+import org.apache.spark.connect.proto
+
+/**
+ * A set of methods for aggregations on a `DataFrame`, created by [[Dataset#groupBy groupBy]],
+ * [[Dataset#cube cube]] or [[Dataset#rollup rollup]] (and also `pivot`).
+ *
+ * The main method is the `agg` function, which has multiple variants. This class also contains
+ * some first-order statistics such as `mean`, `sum` for convenience.
+ *
+ * @note
+ * This class was named `GroupedData` in Spark 1.x.
+ *
+ * @since 3.4.0
+ */
+class RelationalGroupedDataset protected[sql] (
+ private[sql] val df: DataFrame,
+ private[sql] val groupingExprs: Seq[proto.Expression]) {
+
+ private[this] def toDF(aggExprs: Seq[proto.Expression]): DataFrame = {
+ // TODO: support other GroupByType such as Rollup, Cube, Pivot.
+ df.session.newDataset { builder =>
+ builder.getAggregateBuilder
+ .setGroupType(proto.Aggregate.GroupType.GROUP_TYPE_GROUPBY)
+ .setInput(df.plan.getRoot)
+ .addAllGroupingExpressions(groupingExprs.asJava)
+ .addAllAggregateExpressions(aggExprs.asJava)
+ }
+ }
+
+ /**
+ * (Scala-specific) Compute aggregates by specifying the column names and aggregate methods. The
+ * resulting `DataFrame` will also contain the grouping columns.
+ *
+ * The available aggregate methods are `avg`, `max`, `min`, `sum`, `count`.
+ * {{{
+ * // Selects the age of the oldest employee and the aggregate expense for each department
+ * df.groupBy("department").agg(
+ * "age" -> "max",
+ * "expense" -> "sum"
+ * )
+ * }}}
+ *
+ * @since 3.4.0
+ */
+ def agg(aggExpr: (String, String), aggExprs: (String, String)*): DataFrame = {
+ toDF((aggExpr +: aggExprs).map { case (colName, expr) =>
+ strToExpr(expr)(df(colName).expr)
+ })
+ }
+
+ /**
+ * (Scala-specific) Compute aggregates by specifying a map from column name to aggregate
+ * methods. The resulting `DataFrame` will also contain the grouping columns.
+ *
+ * The available aggregate methods are `avg`, `max`, `min`, `sum`, `count`.
+ * {{{
+ * // Selects the age of the oldest employee and the aggregate expense for each department
+ * df.groupBy("department").agg(Map(
+ * "age" -> "max",
+ * "expense" -> "sum"
+ * ))
+ * }}}
+ *
+ * @since 3.4.0
+ */
+ def agg(exprs: Map[String, String]): DataFrame = {
+ toDF(exprs.map { case (colName, expr) =>
+ strToExpr(expr)(df(colName).expr)
+ }.toSeq)
+ }
+
+ /**
+ * (Java-specific) Compute aggregates by specifying a map from column name to aggregate methods.
+ * The resulting `DataFrame` will also contain the grouping columns.
+ *
+ * The available aggregate methods are `avg`, `max`, `min`, `sum`, `count`.
+ * {{{
+ * // Selects the age of the oldest employee and the aggregate expense for each department
+ * import com.google.common.collect.ImmutableMap;
+ * df.groupBy("department").agg(ImmutableMap.of("age", "max", "expense", "sum"));
+ * }}}
+ *
+ * @since 3.4.0
+ */
+ def agg(exprs: java.util.Map[String, String]): DataFrame = {
+ agg(exprs.asScala.toMap)
+ }
+
+ private[this] def strToExpr(expr: String): (proto.Expression => proto.Expression) = {
Review Comment:
It is a layer of indirection you don't need; it is harder to follow for someone who is trying to understand what this code does. There is honestly also no good reason why the original code is like this.
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[GitHub] [spark] amaliujia commented on a diff in pull request #40057: [SPARK-42468][CONNECT] Implement agg by (String, String)*
Posted by "amaliujia (via GitHub)" <gi...@apache.org>.
amaliujia commented on code in PR #40057:
URL: https://github.com/apache/spark/pull/40057#discussion_r1109162607
##########
connector/connect/common/src/test/resources/query-tests/explain-results/groupby_agg.explain:
##########
@@ -0,0 +1,2 @@
+'Aggregate ['id], ['id, unresolvedalias('max('a), None), unresolvedalias('stddev('b), None), unresolvedalias('stddev('b), None), unresolvedalias('avg('b), None), unresolvedalias('avg('b), None), unresolvedalias('avg('b), None), unresolvedalias('count(1), None), unresolvedalias('count('a), None)]
Review Comment:
rebased and regenerated.
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[GitHub] [spark] amaliujia commented on pull request #40057: [SPARK-42468][CONNECT] Implement agg by (String, String)*
Posted by "amaliujia (via GitHub)" <gi...@apache.org>.
amaliujia commented on PR #40057:
URL: https://github.com/apache/spark/pull/40057#issuecomment-1433744359
@hvanhovell
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[GitHub] [spark] amaliujia commented on a diff in pull request #40057: [SPARK-42468][CONNECT] Implement agg by (String, String)*
Posted by "amaliujia (via GitHub)" <gi...@apache.org>.
amaliujia commented on code in PR #40057:
URL: https://github.com/apache/spark/pull/40057#discussion_r1109235158
##########
connector/connect/client/jvm/src/main/scala/org/apache/spark/sql/RelationalGroupedDataset.scala:
##########
@@ -0,0 +1,152 @@
+/*
+ * 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
+
+import java.util.Locale
+
+import scala.collection.JavaConverters._
+
+import org.apache.spark.connect.proto
+
+/**
+ * A set of methods for aggregations on a `DataFrame`, created by [[Dataset#groupBy groupBy]],
+ * [[Dataset#cube cube]] or [[Dataset#rollup rollup]] (and also `pivot`).
+ *
+ * The main method is the `agg` function, which has multiple variants. This class also contains
+ * some first-order statistics such as `mean`, `sum` for convenience.
+ *
+ * @note
+ * This class was named `GroupedData` in Spark 1.x.
+ *
+ * @since 3.4.0
+ */
+class RelationalGroupedDataset protected[sql] (
+ private[sql] val df: DataFrame,
+ private[sql] val groupingExprs: Seq[proto.Expression]) {
+
+ private[this] def toDF(aggExprs: Seq[proto.Expression]): DataFrame = {
+ // TODO: support other GroupByType such as Rollup, Cube, Pivot.
+ df.session.newDataset { builder =>
+ builder.getAggregateBuilder
+ .setGroupType(proto.Aggregate.GroupType.GROUP_TYPE_GROUPBY)
+ .setInput(df.plan.getRoot)
+ .addAllGroupingExpressions(groupingExprs.asJava)
+ .addAllAggregateExpressions(aggExprs.asJava)
+ }
+ }
+
+ /**
+ * (Scala-specific) Compute aggregates by specifying the column names and aggregate methods. The
+ * resulting `DataFrame` will also contain the grouping columns.
+ *
+ * The available aggregate methods are `avg`, `max`, `min`, `sum`, `count`.
+ * {{{
+ * // Selects the age of the oldest employee and the aggregate expense for each department
+ * df.groupBy("department").agg(
+ * "age" -> "max",
+ * "expense" -> "sum"
+ * )
+ * }}}
+ *
+ * @since 3.4.0
+ */
+ def agg(aggExpr: (String, String), aggExprs: (String, String)*): DataFrame = {
+ toDF((aggExpr +: aggExprs).map { case (colName, expr) =>
+ strToExpr(expr, df(colName).expr)
+ })
+ }
+
+ /**
+ * (Scala-specific) Compute aggregates by specifying a map from column name to aggregate
+ * methods. The resulting `DataFrame` will also contain the grouping columns.
+ *
+ * The available aggregate methods are `avg`, `max`, `min`, `sum`, `count`.
+ * {{{
+ * // Selects the age of the oldest employee and the aggregate expense for each department
+ * df.groupBy("department").agg(Map(
+ * "age" -> "max",
+ * "expense" -> "sum"
+ * ))
+ * }}}
+ *
+ * @since 3.4.0
+ */
+ def agg(exprs: Map[String, String]): DataFrame = {
+ toDF(exprs.map { case (colName, expr) =>
+ strToExpr(expr, df(colName).expr)
+ }.toSeq)
+ }
+
+ /**
+ * (Java-specific) Compute aggregates by specifying a map from column name to aggregate methods.
+ * The resulting `DataFrame` will also contain the grouping columns.
+ *
+ * The available aggregate methods are `avg`, `max`, `min`, `sum`, `count`.
+ * {{{
+ * // Selects the age of the oldest employee and the aggregate expense for each department
+ * import com.google.common.collect.ImmutableMap;
+ * df.groupBy("department").agg(ImmutableMap.of("age", "max", "expense", "sum"));
+ * }}}
+ *
+ * @since 3.4.0
+ */
+ def agg(exprs: java.util.Map[String, String]): DataFrame = {
+ agg(exprs.asScala.toMap)
+ }
+
+ private[this] def strToExpr(expr: String, inputExpr: proto.Expression): proto.Expression = {
+ val builder = proto.Expression.newBuilder()
+
+ expr.toLowerCase(Locale.ROOT) match {
+ // We special handle a few cases that have alias that are not in function registry.
+ case "avg" | "average" | "mean" =>
+ builder.getUnresolvedFunctionBuilder
+ .setFunctionName("avg")
+ .addArguments(inputExpr)
+ .setIsDistinct(false)
+ case "stddev" | "std" =>
+ builder.getUnresolvedFunctionBuilder
+ .setFunctionName("stddev")
+ .addArguments(inputExpr)
+ .setIsDistinct(false)
+ // Also special handle count because we need to take care count(*).
+ case "count" | "size" =>
+ // Turn count(*) into count(1)
Review Comment:
This is to match existing scala side Dataframe impl. @cloud-fan do you know if we need count(*) to count(1) conversion? If not we can both change here and existing DF.
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[GitHub] [spark] zhengruifeng commented on a diff in pull request #40057: [SPARK-42468][CONNECT] Implement agg by (String, String)*
Posted by "zhengruifeng (via GitHub)" <gi...@apache.org>.
zhengruifeng commented on code in PR #40057:
URL: https://github.com/apache/spark/pull/40057#discussion_r1109185992
##########
connector/connect/client/jvm/src/main/scala/org/apache/spark/sql/RelationalGroupedDataset.scala:
##########
@@ -0,0 +1,152 @@
+/*
+ * 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
+
+import java.util.Locale
+
+import scala.collection.JavaConverters._
+
+import org.apache.spark.connect.proto
+
+/**
+ * A set of methods for aggregations on a `DataFrame`, created by [[Dataset#groupBy groupBy]],
+ * [[Dataset#cube cube]] or [[Dataset#rollup rollup]] (and also `pivot`).
+ *
+ * The main method is the `agg` function, which has multiple variants. This class also contains
+ * some first-order statistics such as `mean`, `sum` for convenience.
+ *
+ * @note
+ * This class was named `GroupedData` in Spark 1.x.
+ *
+ * @since 3.4.0
+ */
+class RelationalGroupedDataset protected[sql] (
+ private[sql] val df: DataFrame,
+ private[sql] val groupingExprs: Seq[proto.Expression]) {
+
+ private[this] def toDF(aggExprs: Seq[proto.Expression]): DataFrame = {
+ // TODO: support other GroupByType such as Rollup, Cube, Pivot.
+ df.session.newDataset { builder =>
+ builder.getAggregateBuilder
+ .setGroupType(proto.Aggregate.GroupType.GROUP_TYPE_GROUPBY)
+ .setInput(df.plan.getRoot)
+ .addAllGroupingExpressions(groupingExprs.asJava)
+ .addAllAggregateExpressions(aggExprs.asJava)
+ }
+ }
+
+ /**
+ * (Scala-specific) Compute aggregates by specifying the column names and aggregate methods. The
+ * resulting `DataFrame` will also contain the grouping columns.
+ *
+ * The available aggregate methods are `avg`, `max`, `min`, `sum`, `count`.
+ * {{{
+ * // Selects the age of the oldest employee and the aggregate expense for each department
+ * df.groupBy("department").agg(
+ * "age" -> "max",
+ * "expense" -> "sum"
+ * )
+ * }}}
+ *
+ * @since 3.4.0
+ */
+ def agg(aggExpr: (String, String), aggExprs: (String, String)*): DataFrame = {
+ toDF((aggExpr +: aggExprs).map { case (colName, expr) =>
+ strToExpr(expr, df(colName).expr)
+ })
+ }
+
+ /**
+ * (Scala-specific) Compute aggregates by specifying a map from column name to aggregate
+ * methods. The resulting `DataFrame` will also contain the grouping columns.
+ *
+ * The available aggregate methods are `avg`, `max`, `min`, `sum`, `count`.
+ * {{{
+ * // Selects the age of the oldest employee and the aggregate expense for each department
+ * df.groupBy("department").agg(Map(
+ * "age" -> "max",
+ * "expense" -> "sum"
+ * ))
+ * }}}
+ *
+ * @since 3.4.0
+ */
+ def agg(exprs: Map[String, String]): DataFrame = {
+ toDF(exprs.map { case (colName, expr) =>
+ strToExpr(expr, df(colName).expr)
+ }.toSeq)
+ }
+
+ /**
+ * (Java-specific) Compute aggregates by specifying a map from column name to aggregate methods.
+ * The resulting `DataFrame` will also contain the grouping columns.
+ *
+ * The available aggregate methods are `avg`, `max`, `min`, `sum`, `count`.
+ * {{{
+ * // Selects the age of the oldest employee and the aggregate expense for each department
+ * import com.google.common.collect.ImmutableMap;
+ * df.groupBy("department").agg(ImmutableMap.of("age", "max", "expense", "sum"));
+ * }}}
+ *
+ * @since 3.4.0
+ */
+ def agg(exprs: java.util.Map[String, String]): DataFrame = {
+ agg(exprs.asScala.toMap)
+ }
+
+ private[this] def strToExpr(expr: String, inputExpr: proto.Expression): proto.Expression = {
+ val builder = proto.Expression.newBuilder()
+
+ expr.toLowerCase(Locale.ROOT) match {
+ // We special handle a few cases that have alias that are not in function registry.
+ case "avg" | "average" | "mean" =>
+ builder.getUnresolvedFunctionBuilder
+ .setFunctionName("avg")
+ .addArguments(inputExpr)
+ .setIsDistinct(false)
+ case "stddev" | "std" =>
+ builder.getUnresolvedFunctionBuilder
+ .setFunctionName("stddev")
+ .addArguments(inputExpr)
+ .setIsDistinct(false)
+ // Also special handle count because we need to take care count(*).
+ case "count" | "size" =>
+ // Turn count(*) into count(1)
Review Comment:
Do we still need to take care of `count(*)`? we don't need it in python client https://github.com/apache/spark/pull/39622#issue-1535865007
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[GitHub] [spark] amaliujia commented on a diff in pull request #40057: [SPARK-42468][CONNECT] Implement agg by (String, String)*
Posted by "amaliujia (via GitHub)" <gi...@apache.org>.
amaliujia commented on code in PR #40057:
URL: https://github.com/apache/spark/pull/40057#discussion_r1109160796
##########
connector/connect/client/jvm/src/main/scala/org/apache/spark/sql/Dataset.scala:
##########
@@ -1035,6 +1035,29 @@ class Dataset[T] private[sql] (val session: SparkSession, private[sql] val plan:
}
}
+ /**
+ * Groups the Dataset using the specified columns, so we can run aggregation on them. See
+ * [[RelationalGroupedDataset]] for all the available aggregate functions.
+ *
+ * {{{
+ * // Compute the average for all numeric columns grouped by department.
+ * ds.groupBy($"department").avg()
+ *
+ * // Compute the max age and average salary, grouped by department and gender.
+ * ds.groupBy($"department", $"gender").agg(Map(
+ * "salary" -> "avg",
+ * "age" -> "max"
+ * ))
+ * }}}
+ *
+ * @group untypedrel
+ * @since 3.4.0
+ */
+ @scala.annotation.varargs
+ def groupBy(cols: Column*): RelationalGroupedDataset = {
+ RelationalGroupedDataset(toDF(), cols.map(_.expr))
Review Comment:
This is about if we want to keep the same class signature of `RelationalGroupedDataset` as what it is in SQL. If such class as protected/private class is not needed to match SQL ones, then it is ok to passing in more closer classes.
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[GitHub] [spark] amaliujia commented on a diff in pull request #40057: [SPARK-42468][CONNECT] Implement agg by (String, String)*
Posted by "amaliujia (via GitHub)" <gi...@apache.org>.
amaliujia commented on code in PR #40057:
URL: https://github.com/apache/spark/pull/40057#discussion_r1109166322
##########
connector/connect/client/jvm/src/main/scala/org/apache/spark/sql/Dataset.scala:
##########
@@ -1035,6 +1035,29 @@ class Dataset[T] private[sql] (val session: SparkSession, private[sql] val plan:
}
}
+ /**
+ * Groups the Dataset using the specified columns, so we can run aggregation on them. See
+ * [[RelationalGroupedDataset]] for all the available aggregate functions.
+ *
+ * {{{
+ * // Compute the average for all numeric columns grouped by department.
+ * ds.groupBy($"department").avg()
+ *
+ * // Compute the max age and average salary, grouped by department and gender.
+ * ds.groupBy($"department", $"gender").agg(Map(
+ * "salary" -> "avg",
+ * "age" -> "max"
+ * ))
+ * }}}
+ *
+ * @group untypedrel
+ * @since 3.4.0
+ */
+ @scala.annotation.varargs
+ def groupBy(cols: Column*): RelationalGroupedDataset = {
+ RelationalGroupedDataset(toDF(), cols.map(_.expr))
Review Comment:
Yeah I guess the major thing probably is because this is not a public API.
How about let me follow up in future PRs on what is the final class signature for RelationalGroupedDataset? There are a lot more API to add in this class.
##########
connector/connect/client/jvm/src/main/scala/org/apache/spark/sql/RelationalGroupedDataset.scala:
##########
@@ -0,0 +1,162 @@
+/*
+ * 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
+
+import java.util.Locale
+
+import scala.collection.JavaConverters._
+
+import org.apache.spark.connect.proto
+
+/**
+ * A set of methods for aggregations on a `DataFrame`, created by [[Dataset#groupBy groupBy]],
+ * [[Dataset#cube cube]] or [[Dataset#rollup rollup]] (and also `pivot`).
+ *
+ * The main method is the `agg` function, which has multiple variants. This class also contains
+ * some first-order statistics such as `mean`, `sum` for convenience.
+ *
+ * @note
+ * This class was named `GroupedData` in Spark 1.x.
+ *
+ * @since 3.4.0
+ */
+class RelationalGroupedDataset protected[sql] (
+ private[sql] val df: DataFrame,
+ private[sql] val groupingExprs: Seq[proto.Expression]) {
+
+ private[this] def toDF(aggExprs: Seq[proto.Expression]): DataFrame = {
+ // TODO: support other GroupByType such as Rollup, Cube, Pivot.
+ df.session.newDataset { builder =>
+ builder.getAggregateBuilder
+ .setGroupType(proto.Aggregate.GroupType.GROUP_TYPE_GROUPBY)
+ .setInput(df.plan.getRoot)
+ .addAllGroupingExpressions(groupingExprs.asJava)
+ .addAllAggregateExpressions(aggExprs.asJava)
+ }
+ }
+
+ /**
+ * (Scala-specific) Compute aggregates by specifying the column names and aggregate methods. The
+ * resulting `DataFrame` will also contain the grouping columns.
+ *
+ * The available aggregate methods are `avg`, `max`, `min`, `sum`, `count`.
+ * {{{
+ * // Selects the age of the oldest employee and the aggregate expense for each department
+ * df.groupBy("department").agg(
+ * "age" -> "max",
+ * "expense" -> "sum"
+ * )
+ * }}}
+ *
+ * @since 3.4.0
+ */
+ def agg(aggExpr: (String, String), aggExprs: (String, String)*): DataFrame = {
+ toDF((aggExpr +: aggExprs).map { case (colName, expr) =>
+ strToExpr(expr)(df(colName).expr)
+ })
+ }
+
+ /**
+ * (Scala-specific) Compute aggregates by specifying a map from column name to aggregate
+ * methods. The resulting `DataFrame` will also contain the grouping columns.
+ *
+ * The available aggregate methods are `avg`, `max`, `min`, `sum`, `count`.
+ * {{{
+ * // Selects the age of the oldest employee and the aggregate expense for each department
+ * df.groupBy("department").agg(Map(
+ * "age" -> "max",
+ * "expense" -> "sum"
+ * ))
+ * }}}
+ *
+ * @since 3.4.0
+ */
+ def agg(exprs: Map[String, String]): DataFrame = {
+ toDF(exprs.map { case (colName, expr) =>
+ strToExpr(expr)(df(colName).expr)
+ }.toSeq)
+ }
+
+ /**
+ * (Java-specific) Compute aggregates by specifying a map from column name to aggregate methods.
+ * The resulting `DataFrame` will also contain the grouping columns.
+ *
+ * The available aggregate methods are `avg`, `max`, `min`, `sum`, `count`.
+ * {{{
+ * // Selects the age of the oldest employee and the aggregate expense for each department
+ * import com.google.common.collect.ImmutableMap;
+ * df.groupBy("department").agg(ImmutableMap.of("age", "max", "expense", "sum"));
+ * }}}
+ *
+ * @since 3.4.0
+ */
+ def agg(exprs: java.util.Map[String, String]): DataFrame = {
+ agg(exprs.asScala.toMap)
+ }
+
+ private[this] def strToExpr(expr: String): (proto.Expression => proto.Expression) = {
Review Comment:
Sure, removed this indirection.
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[GitHub] [spark] zhengruifeng commented on a diff in pull request #40057: [SPARK-42468][CONNECT] Implement agg by (String, String)*
Posted by "zhengruifeng (via GitHub)" <gi...@apache.org>.
zhengruifeng commented on code in PR #40057:
URL: https://github.com/apache/spark/pull/40057#discussion_r1109238172
##########
connector/connect/client/jvm/src/main/scala/org/apache/spark/sql/RelationalGroupedDataset.scala:
##########
@@ -0,0 +1,152 @@
+/*
+ * 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
+
+import java.util.Locale
+
+import scala.collection.JavaConverters._
+
+import org.apache.spark.connect.proto
+
+/**
+ * A set of methods for aggregations on a `DataFrame`, created by [[Dataset#groupBy groupBy]],
+ * [[Dataset#cube cube]] or [[Dataset#rollup rollup]] (and also `pivot`).
+ *
+ * The main method is the `agg` function, which has multiple variants. This class also contains
+ * some first-order statistics such as `mean`, `sum` for convenience.
+ *
+ * @note
+ * This class was named `GroupedData` in Spark 1.x.
+ *
+ * @since 3.4.0
+ */
+class RelationalGroupedDataset protected[sql] (
+ private[sql] val df: DataFrame,
+ private[sql] val groupingExprs: Seq[proto.Expression]) {
+
+ private[this] def toDF(aggExprs: Seq[proto.Expression]): DataFrame = {
+ // TODO: support other GroupByType such as Rollup, Cube, Pivot.
+ df.session.newDataset { builder =>
+ builder.getAggregateBuilder
+ .setGroupType(proto.Aggregate.GroupType.GROUP_TYPE_GROUPBY)
+ .setInput(df.plan.getRoot)
+ .addAllGroupingExpressions(groupingExprs.asJava)
+ .addAllAggregateExpressions(aggExprs.asJava)
+ }
+ }
+
+ /**
+ * (Scala-specific) Compute aggregates by specifying the column names and aggregate methods. The
+ * resulting `DataFrame` will also contain the grouping columns.
+ *
+ * The available aggregate methods are `avg`, `max`, `min`, `sum`, `count`.
+ * {{{
+ * // Selects the age of the oldest employee and the aggregate expense for each department
+ * df.groupBy("department").agg(
+ * "age" -> "max",
+ * "expense" -> "sum"
+ * )
+ * }}}
+ *
+ * @since 3.4.0
+ */
+ def agg(aggExpr: (String, String), aggExprs: (String, String)*): DataFrame = {
+ toDF((aggExpr +: aggExprs).map { case (colName, expr) =>
+ strToExpr(expr, df(colName).expr)
+ })
+ }
+
+ /**
+ * (Scala-specific) Compute aggregates by specifying a map from column name to aggregate
+ * methods. The resulting `DataFrame` will also contain the grouping columns.
+ *
+ * The available aggregate methods are `avg`, `max`, `min`, `sum`, `count`.
+ * {{{
+ * // Selects the age of the oldest employee and the aggregate expense for each department
+ * df.groupBy("department").agg(Map(
+ * "age" -> "max",
+ * "expense" -> "sum"
+ * ))
+ * }}}
+ *
+ * @since 3.4.0
+ */
+ def agg(exprs: Map[String, String]): DataFrame = {
+ toDF(exprs.map { case (colName, expr) =>
+ strToExpr(expr, df(colName).expr)
+ }.toSeq)
+ }
+
+ /**
+ * (Java-specific) Compute aggregates by specifying a map from column name to aggregate methods.
+ * The resulting `DataFrame` will also contain the grouping columns.
+ *
+ * The available aggregate methods are `avg`, `max`, `min`, `sum`, `count`.
+ * {{{
+ * // Selects the age of the oldest employee and the aggregate expense for each department
+ * import com.google.common.collect.ImmutableMap;
+ * df.groupBy("department").agg(ImmutableMap.of("age", "max", "expense", "sum"));
+ * }}}
+ *
+ * @since 3.4.0
+ */
+ def agg(exprs: java.util.Map[String, String]): DataFrame = {
+ agg(exprs.asScala.toMap)
+ }
+
+ private[this] def strToExpr(expr: String, inputExpr: proto.Expression): proto.Expression = {
+ val builder = proto.Expression.newBuilder()
+
+ expr.toLowerCase(Locale.ROOT) match {
+ // We special handle a few cases that have alias that are not in function registry.
+ case "avg" | "average" | "mean" =>
+ builder.getUnresolvedFunctionBuilder
+ .setFunctionName("avg")
+ .addArguments(inputExpr)
+ .setIsDistinct(false)
+ case "stddev" | "std" =>
+ builder.getUnresolvedFunctionBuilder
+ .setFunctionName("stddev")
+ .addArguments(inputExpr)
+ .setIsDistinct(false)
+ // Also special handle count because we need to take care count(*).
+ case "count" | "size" =>
+ // Turn count(*) into count(1)
Review Comment:
> This is to match existing scala side Dataframe impl.
LGTM, we can update them later
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[GitHub] [spark] hvanhovell commented on a diff in pull request #40057: [SPARK-42468][CONNECT] Implement agg by (String, String)*
Posted by "hvanhovell (via GitHub)" <gi...@apache.org>.
hvanhovell commented on code in PR #40057:
URL: https://github.com/apache/spark/pull/40057#discussion_r1109133653
##########
connector/connect/client/jvm/src/main/scala/org/apache/spark/sql/RelationalGroupedDataset.scala:
##########
@@ -0,0 +1,162 @@
+/*
+ * 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
+
+import java.util.Locale
+
+import scala.collection.JavaConverters._
+
+import org.apache.spark.connect.proto
+
+/**
+ * A set of methods for aggregations on a `DataFrame`, created by [[Dataset#groupBy groupBy]],
+ * [[Dataset#cube cube]] or [[Dataset#rollup rollup]] (and also `pivot`).
+ *
+ * The main method is the `agg` function, which has multiple variants. This class also contains
+ * some first-order statistics such as `mean`, `sum` for convenience.
+ *
+ * @note
+ * This class was named `GroupedData` in Spark 1.x.
+ *
+ * @since 3.4.0
+ */
+class RelationalGroupedDataset protected[sql] (
+ private[sql] val df: DataFrame,
+ private[sql] val groupingExprs: Seq[proto.Expression]) {
+
+ private[this] def toDF(aggExprs: Seq[proto.Expression]): DataFrame = {
+ // TODO: support other GroupByType such as Rollup, Cube, Pivot.
+ df.session.newDataset { builder =>
+ builder.getAggregateBuilder
+ .setGroupType(proto.Aggregate.GroupType.GROUP_TYPE_GROUPBY)
+ .setInput(df.plan.getRoot)
+ .addAllGroupingExpressions(groupingExprs.asJava)
+ .addAllAggregateExpressions(aggExprs.asJava)
+ }
+ }
+
+ /**
+ * (Scala-specific) Compute aggregates by specifying the column names and aggregate methods. The
+ * resulting `DataFrame` will also contain the grouping columns.
+ *
+ * The available aggregate methods are `avg`, `max`, `min`, `sum`, `count`.
+ * {{{
+ * // Selects the age of the oldest employee and the aggregate expense for each department
+ * df.groupBy("department").agg(
+ * "age" -> "max",
+ * "expense" -> "sum"
+ * )
+ * }}}
+ *
+ * @since 3.4.0
+ */
+ def agg(aggExpr: (String, String), aggExprs: (String, String)*): DataFrame = {
+ toDF((aggExpr +: aggExprs).map { case (colName, expr) =>
+ strToExpr(expr)(df(colName).expr)
+ })
+ }
+
+ /**
+ * (Scala-specific) Compute aggregates by specifying a map from column name to aggregate
+ * methods. The resulting `DataFrame` will also contain the grouping columns.
+ *
+ * The available aggregate methods are `avg`, `max`, `min`, `sum`, `count`.
+ * {{{
+ * // Selects the age of the oldest employee and the aggregate expense for each department
+ * df.groupBy("department").agg(Map(
+ * "age" -> "max",
+ * "expense" -> "sum"
+ * ))
+ * }}}
+ *
+ * @since 3.4.0
+ */
+ def agg(exprs: Map[String, String]): DataFrame = {
+ toDF(exprs.map { case (colName, expr) =>
+ strToExpr(expr)(df(colName).expr)
+ }.toSeq)
+ }
+
+ /**
+ * (Java-specific) Compute aggregates by specifying a map from column name to aggregate methods.
+ * The resulting `DataFrame` will also contain the grouping columns.
+ *
+ * The available aggregate methods are `avg`, `max`, `min`, `sum`, `count`.
+ * {{{
+ * // Selects the age of the oldest employee and the aggregate expense for each department
+ * import com.google.common.collect.ImmutableMap;
+ * df.groupBy("department").agg(ImmutableMap.of("age", "max", "expense", "sum"));
+ * }}}
+ *
+ * @since 3.4.0
+ */
+ def agg(exprs: java.util.Map[String, String]): DataFrame = {
+ agg(exprs.asScala.toMap)
+ }
+
+ private[this] def strToExpr(expr: String): (proto.Expression => proto.Expression) = {
+ val exprToFunc: (proto.Expression => proto.Expression) = {
+ val builder = proto.Expression.newBuilder()
+
+ (inputExpr: proto.Expression) =>
+ expr.toLowerCase(Locale.ROOT) match {
+ // We special handle a few cases that have alias that are not in function registry.
+ case "avg" | "average" | "mean" =>
+ builder.getUnresolvedFunctionBuilder
+ .setFunctionName("avg")
+ .addArguments(inputExpr)
+ .setIsDistinct(false)
+ case "stddev" | "std" =>
+ builder.getUnresolvedFunctionBuilder
+ .setFunctionName("stddev")
+ .addArguments(inputExpr)
+ .setIsDistinct(false)
+ // Also special handle count because we need to take care count(*).
+ case "count" | "size" =>
+ // Turn count(*) into count(1)
+ inputExpr match {
+ case s if s.hasUnresolvedStar =>
+ val exprBuilder = proto.Expression.newBuilder
+ exprBuilder.getLiteralBuilder.setInteger(1)
+ builder.getUnresolvedFunctionBuilder
+ .setFunctionName("count")
+ .addArguments(exprBuilder)
+ .setIsDistinct(false)
+ case _ =>
+ builder.getUnresolvedFunctionBuilder
+ .setFunctionName("count")
+ .addArguments(inputExpr)
+ .setIsDistinct(false)
+ }
+ case name =>
+ builder.getUnresolvedFunctionBuilder
+ .setFunctionName(name)
+ .addArguments(inputExpr)
+ .setIsDistinct(false)
+ }
+ builder.build()
+ }
+ (inputExpr: proto.Expression) => exprToFunc(inputExpr)
+ }
+}
+
+private[sql] object RelationalGroupedDataset {
+ def apply(df: DataFrame, groupingExprs: Seq[proto.Expression]): RelationalGroupedDataset = {
Review Comment:
Do we need this?
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[GitHub] [spark] hvanhovell commented on a diff in pull request #40057: [SPARK-42468][CONNECT] Implement agg by (String, String)*
Posted by "hvanhovell (via GitHub)" <gi...@apache.org>.
hvanhovell commented on code in PR #40057:
URL: https://github.com/apache/spark/pull/40057#discussion_r1109133214
##########
connector/connect/client/jvm/src/main/scala/org/apache/spark/sql/RelationalGroupedDataset.scala:
##########
@@ -0,0 +1,162 @@
+/*
+ * 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
+
+import java.util.Locale
+
+import scala.collection.JavaConverters._
+
+import org.apache.spark.connect.proto
+
+/**
+ * A set of methods for aggregations on a `DataFrame`, created by [[Dataset#groupBy groupBy]],
+ * [[Dataset#cube cube]] or [[Dataset#rollup rollup]] (and also `pivot`).
+ *
+ * The main method is the `agg` function, which has multiple variants. This class also contains
+ * some first-order statistics such as `mean`, `sum` for convenience.
+ *
+ * @note
+ * This class was named `GroupedData` in Spark 1.x.
+ *
+ * @since 3.4.0
+ */
+class RelationalGroupedDataset protected[sql] (
+ private[sql] val df: DataFrame,
+ private[sql] val groupingExprs: Seq[proto.Expression]) {
+
+ private[this] def toDF(aggExprs: Seq[proto.Expression]): DataFrame = {
+ // TODO: support other GroupByType such as Rollup, Cube, Pivot.
+ df.session.newDataset { builder =>
+ builder.getAggregateBuilder
+ .setGroupType(proto.Aggregate.GroupType.GROUP_TYPE_GROUPBY)
+ .setInput(df.plan.getRoot)
+ .addAllGroupingExpressions(groupingExprs.asJava)
+ .addAllAggregateExpressions(aggExprs.asJava)
+ }
+ }
+
+ /**
+ * (Scala-specific) Compute aggregates by specifying the column names and aggregate methods. The
+ * resulting `DataFrame` will also contain the grouping columns.
+ *
+ * The available aggregate methods are `avg`, `max`, `min`, `sum`, `count`.
+ * {{{
+ * // Selects the age of the oldest employee and the aggregate expense for each department
+ * df.groupBy("department").agg(
+ * "age" -> "max",
+ * "expense" -> "sum"
+ * )
+ * }}}
+ *
+ * @since 3.4.0
+ */
+ def agg(aggExpr: (String, String), aggExprs: (String, String)*): DataFrame = {
+ toDF((aggExpr +: aggExprs).map { case (colName, expr) =>
+ strToExpr(expr)(df(colName).expr)
+ })
+ }
+
+ /**
+ * (Scala-specific) Compute aggregates by specifying a map from column name to aggregate
+ * methods. The resulting `DataFrame` will also contain the grouping columns.
+ *
+ * The available aggregate methods are `avg`, `max`, `min`, `sum`, `count`.
+ * {{{
+ * // Selects the age of the oldest employee and the aggregate expense for each department
+ * df.groupBy("department").agg(Map(
+ * "age" -> "max",
+ * "expense" -> "sum"
+ * ))
+ * }}}
+ *
+ * @since 3.4.0
+ */
+ def agg(exprs: Map[String, String]): DataFrame = {
+ toDF(exprs.map { case (colName, expr) =>
+ strToExpr(expr)(df(colName).expr)
+ }.toSeq)
+ }
+
+ /**
+ * (Java-specific) Compute aggregates by specifying a map from column name to aggregate methods.
+ * The resulting `DataFrame` will also contain the grouping columns.
+ *
+ * The available aggregate methods are `avg`, `max`, `min`, `sum`, `count`.
+ * {{{
+ * // Selects the age of the oldest employee and the aggregate expense for each department
+ * import com.google.common.collect.ImmutableMap;
+ * df.groupBy("department").agg(ImmutableMap.of("age", "max", "expense", "sum"));
+ * }}}
+ *
+ * @since 3.4.0
+ */
+ def agg(exprs: java.util.Map[String, String]): DataFrame = {
+ agg(exprs.asScala.toMap)
+ }
+
+ private[this] def strToExpr(expr: String): (proto.Expression => proto.Expression) = {
Review Comment:
For my understanding. What is the upside of returning a function here instead of just transforming the input directly? This might have been needed in the old DataFrame, but here it feels a bit over the top.
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[GitHub] [spark] hvanhovell commented on a diff in pull request #40057: [SPARK-42468][CONNECT] Implement agg by (String, String)*
Posted by "hvanhovell (via GitHub)" <gi...@apache.org>.
hvanhovell commented on code in PR #40057:
URL: https://github.com/apache/spark/pull/40057#discussion_r1109131122
##########
connector/connect/client/jvm/src/main/scala/org/apache/spark/sql/Dataset.scala:
##########
@@ -1035,6 +1035,29 @@ class Dataset[T] private[sql] (val session: SparkSession, private[sql] val plan:
}
}
+ /**
+ * Groups the Dataset using the specified columns, so we can run aggregation on them. See
+ * [[RelationalGroupedDataset]] for all the available aggregate functions.
+ *
+ * {{{
+ * // Compute the average for all numeric columns grouped by department.
+ * ds.groupBy($"department").avg()
+ *
+ * // Compute the max age and average salary, grouped by department and gender.
+ * ds.groupBy($"department", $"gender").agg(Map(
+ * "salary" -> "avg",
+ * "age" -> "max"
+ * ))
+ * }}}
+ *
+ * @group untypedrel
+ * @since 3.4.0
+ */
+ @scala.annotation.varargs
+ def groupBy(cols: Column*): RelationalGroupedDataset = {
+ RelationalGroupedDataset(toDF(), cols.map(_.expr))
Review Comment:
Nit: you don't have to convert to DataFrame here (as soon as we introduce encoders it might actually be a bit faster if we don't). You could also pass in the columns as is.
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[GitHub] [spark] hvanhovell commented on a diff in pull request #40057: [SPARK-42468][CONNECT] Implement agg by (String, String)*
Posted by "hvanhovell (via GitHub)" <gi...@apache.org>.
hvanhovell commented on code in PR #40057:
URL: https://github.com/apache/spark/pull/40057#discussion_r1109130509
##########
connector/connect/client/jvm/src/main/scala/org/apache/spark/sql/RelationalGroupedDataset.scala:
##########
@@ -0,0 +1,162 @@
+/*
+ * 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
+
+import java.util.Locale
+
+import scala.collection.JavaConverters._
+
+import org.apache.spark.connect.proto
+
+/**
+ * A set of methods for aggregations on a `DataFrame`, created by [[Dataset#groupBy groupBy]],
+ * [[Dataset#cube cube]] or [[Dataset#rollup rollup]] (and also `pivot`).
+ *
+ * The main method is the `agg` function, which has multiple variants. This class also contains
+ * some first-order statistics such as `mean`, `sum` for convenience.
+ *
+ * @note
+ * This class was named `GroupedData` in Spark 1.x.
+ *
+ * @since 3.4.0
+ */
+class RelationalGroupedDataset protected[sql] (
+ private[sql] val df: DataFrame,
+ private[sql] val groupingExprs: Seq[proto.Expression]) {
+
+ private[this] def toDF(aggExprs: Seq[proto.Expression]): DataFrame = {
+ // TODO: support other GroupByType such as Rollup, Cube, Pivot.
+ df.session.newDataset { builder =>
+ builder.getAggregateBuilder
+ .setGroupType(proto.Aggregate.GroupType.GROUP_TYPE_GROUPBY)
+ .setInput(df.plan.getRoot)
+ .addAllGroupingExpressions(groupingExprs.asJava)
+ .addAllAggregateExpressions(aggExprs.asJava)
+ }
+ }
+
+ /**
+ * (Scala-specific) Compute aggregates by specifying the column names and aggregate methods. The
+ * resulting `DataFrame` will also contain the grouping columns.
+ *
+ * The available aggregate methods are `avg`, `max`, `min`, `sum`, `count`.
+ * {{{
+ * // Selects the age of the oldest employee and the aggregate expense for each department
+ * df.groupBy("department").agg(
+ * "age" -> "max",
+ * "expense" -> "sum"
+ * )
+ * }}}
+ *
+ * @since 3.4.0
+ */
+ def agg(aggExpr: (String, String), aggExprs: (String, String)*): DataFrame = {
+ toDF((aggExpr +: aggExprs).map { case (colName, expr) =>
+ strToExpr(expr)(df(colName).expr)
+ })
+ }
+
+ /**
+ * (Scala-specific) Compute aggregates by specifying a map from column name to aggregate
+ * methods. The resulting `DataFrame` will also contain the grouping columns.
+ *
+ * The available aggregate methods are `avg`, `max`, `min`, `sum`, `count`.
+ * {{{
+ * // Selects the age of the oldest employee and the aggregate expense for each department
+ * df.groupBy("department").agg(Map(
+ * "age" -> "max",
+ * "expense" -> "sum"
+ * ))
+ * }}}
+ *
+ * @since 3.4.0
+ */
+ def agg(exprs: Map[String, String]): DataFrame = {
+ toDF(exprs.map { case (colName, expr) =>
+ strToExpr(expr)(df(colName).expr)
+ }.toSeq)
+ }
+
+ /**
+ * (Java-specific) Compute aggregates by specifying a map from column name to aggregate methods.
+ * The resulting `DataFrame` will also contain the grouping columns.
+ *
+ * The available aggregate methods are `avg`, `max`, `min`, `sum`, `count`.
+ * {{{
+ * // Selects the age of the oldest employee and the aggregate expense for each department
+ * import com.google.common.collect.ImmutableMap;
+ * df.groupBy("department").agg(ImmutableMap.of("age", "max", "expense", "sum"));
+ * }}}
+ *
+ * @since 3.4.0
+ */
+ def agg(exprs: java.util.Map[String, String]): DataFrame = {
+ agg(exprs.asScala.toMap)
+ }
+
+ private[this] def strToExpr(expr: String): (proto.Expression => proto.Expression) = {
+ val exprToFunc: (proto.Expression => proto.Expression) = {
+ val builder = proto.Expression.newBuilder()
+
+ (inputExpr: proto.Expression) =>
+ expr.toLowerCase(Locale.ROOT) match {
+ // We special handle a few cases that have alias that are not in function registry.
+ case "avg" | "average" | "mean" =>
Review Comment:
As soon as we merge https://github.com/apache/spark/pull/40050 we should just use the functions in there.
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[GitHub] [spark] hvanhovell commented on a diff in pull request #40057: [SPARK-42468][CONNECT] Implement agg by (String, String)*
Posted by "hvanhovell (via GitHub)" <gi...@apache.org>.
hvanhovell commented on code in PR #40057:
URL: https://github.com/apache/spark/pull/40057#discussion_r1109131122
##########
connector/connect/client/jvm/src/main/scala/org/apache/spark/sql/Dataset.scala:
##########
@@ -1035,6 +1035,29 @@ class Dataset[T] private[sql] (val session: SparkSession, private[sql] val plan:
}
}
+ /**
+ * Groups the Dataset using the specified columns, so we can run aggregation on them. See
+ * [[RelationalGroupedDataset]] for all the available aggregate functions.
+ *
+ * {{{
+ * // Compute the average for all numeric columns grouped by department.
+ * ds.groupBy($"department").avg()
+ *
+ * // Compute the max age and average salary, grouped by department and gender.
+ * ds.groupBy($"department", $"gender").agg(Map(
+ * "salary" -> "avg",
+ * "age" -> "max"
+ * ))
+ * }}}
+ *
+ * @group untypedrel
+ * @since 3.4.0
+ */
+ @scala.annotation.varargs
+ def groupBy(cols: Column*): RelationalGroupedDataset = {
+ RelationalGroupedDataset(toDF(), cols.map(_.expr))
Review Comment:
Nit: you don't have to convert to DataFrame here. You could also pass in the columns as is.
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[GitHub] [spark] hvanhovell commented on a diff in pull request #40057: [SPARK-42468][CONNECT] Implement agg by (String, String)*
Posted by "hvanhovell (via GitHub)" <gi...@apache.org>.
hvanhovell commented on code in PR #40057:
URL: https://github.com/apache/spark/pull/40057#discussion_r1109243236
##########
connector/connect/client/jvm/src/main/scala/org/apache/spark/sql/RelationalGroupedDataset.scala:
##########
@@ -0,0 +1,152 @@
+/*
+ * 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
+
+import java.util.Locale
+
+import scala.collection.JavaConverters._
+
+import org.apache.spark.connect.proto
+
+/**
+ * A set of methods for aggregations on a `DataFrame`, created by [[Dataset#groupBy groupBy]],
+ * [[Dataset#cube cube]] or [[Dataset#rollup rollup]] (and also `pivot`).
+ *
+ * The main method is the `agg` function, which has multiple variants. This class also contains
+ * some first-order statistics such as `mean`, `sum` for convenience.
+ *
+ * @note
+ * This class was named `GroupedData` in Spark 1.x.
+ *
+ * @since 3.4.0
+ */
+class RelationalGroupedDataset protected[sql] (
+ private[sql] val df: DataFrame,
+ private[sql] val groupingExprs: Seq[proto.Expression]) {
+
+ private[this] def toDF(aggExprs: Seq[proto.Expression]): DataFrame = {
+ // TODO: support other GroupByType such as Rollup, Cube, Pivot.
+ df.session.newDataset { builder =>
+ builder.getAggregateBuilder
+ .setGroupType(proto.Aggregate.GroupType.GROUP_TYPE_GROUPBY)
+ .setInput(df.plan.getRoot)
+ .addAllGroupingExpressions(groupingExprs.asJava)
+ .addAllAggregateExpressions(aggExprs.asJava)
+ }
+ }
+
+ /**
+ * (Scala-specific) Compute aggregates by specifying the column names and aggregate methods. The
+ * resulting `DataFrame` will also contain the grouping columns.
+ *
+ * The available aggregate methods are `avg`, `max`, `min`, `sum`, `count`.
+ * {{{
+ * // Selects the age of the oldest employee and the aggregate expense for each department
+ * df.groupBy("department").agg(
+ * "age" -> "max",
+ * "expense" -> "sum"
+ * )
+ * }}}
+ *
+ * @since 3.4.0
+ */
+ def agg(aggExpr: (String, String), aggExprs: (String, String)*): DataFrame = {
+ toDF((aggExpr +: aggExprs).map { case (colName, expr) =>
+ strToExpr(expr, df(colName).expr)
+ })
+ }
+
+ /**
+ * (Scala-specific) Compute aggregates by specifying a map from column name to aggregate
+ * methods. The resulting `DataFrame` will also contain the grouping columns.
+ *
+ * The available aggregate methods are `avg`, `max`, `min`, `sum`, `count`.
+ * {{{
+ * // Selects the age of the oldest employee and the aggregate expense for each department
+ * df.groupBy("department").agg(Map(
+ * "age" -> "max",
+ * "expense" -> "sum"
+ * ))
+ * }}}
+ *
+ * @since 3.4.0
+ */
+ def agg(exprs: Map[String, String]): DataFrame = {
+ toDF(exprs.map { case (colName, expr) =>
+ strToExpr(expr, df(colName).expr)
+ }.toSeq)
+ }
+
+ /**
+ * (Java-specific) Compute aggregates by specifying a map from column name to aggregate methods.
+ * The resulting `DataFrame` will also contain the grouping columns.
+ *
+ * The available aggregate methods are `avg`, `max`, `min`, `sum`, `count`.
+ * {{{
+ * // Selects the age of the oldest employee and the aggregate expense for each department
+ * import com.google.common.collect.ImmutableMap;
+ * df.groupBy("department").agg(ImmutableMap.of("age", "max", "expense", "sum"));
+ * }}}
+ *
+ * @since 3.4.0
+ */
+ def agg(exprs: java.util.Map[String, String]): DataFrame = {
+ agg(exprs.asScala.toMap)
+ }
+
+ private[this] def strToExpr(expr: String, inputExpr: proto.Expression): proto.Expression = {
+ val builder = proto.Expression.newBuilder()
+
+ expr.toLowerCase(Locale.ROOT) match {
+ // We special handle a few cases that have alias that are not in function registry.
+ case "avg" | "average" | "mean" =>
+ builder.getUnresolvedFunctionBuilder
+ .setFunctionName("avg")
+ .addArguments(inputExpr)
+ .setIsDistinct(false)
+ case "stddev" | "std" =>
+ builder.getUnresolvedFunctionBuilder
+ .setFunctionName("stddev")
+ .addArguments(inputExpr)
+ .setIsDistinct(false)
+ // Also special handle count because we need to take care count(*).
+ case "count" | "size" =>
+ // Turn count(*) into count(1)
Review Comment:
Yeah, we don't need it. We can address when we replace this stuff by the functions API.
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[GitHub] [spark] hvanhovell closed pull request #40057: [SPARK-42468][CONNECT] Implement agg by (String, String)*
Posted by "hvanhovell (via GitHub)" <gi...@apache.org>.
hvanhovell closed pull request #40057: [SPARK-42468][CONNECT] Implement agg by (String, String)*
URL: https://github.com/apache/spark/pull/40057
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[GitHub] [spark] amaliujia commented on a diff in pull request #40057: [SPARK-42468][CONNECT] Implement agg by (String, String)*
Posted by "amaliujia (via GitHub)" <gi...@apache.org>.
amaliujia commented on code in PR #40057:
URL: https://github.com/apache/spark/pull/40057#discussion_r1109157262
##########
connector/connect/client/jvm/src/main/scala/org/apache/spark/sql/RelationalGroupedDataset.scala:
##########
@@ -0,0 +1,162 @@
+/*
+ * 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
+
+import java.util.Locale
+
+import scala.collection.JavaConverters._
+
+import org.apache.spark.connect.proto
+
+/**
+ * A set of methods for aggregations on a `DataFrame`, created by [[Dataset#groupBy groupBy]],
+ * [[Dataset#cube cube]] or [[Dataset#rollup rollup]] (and also `pivot`).
+ *
+ * The main method is the `agg` function, which has multiple variants. This class also contains
+ * some first-order statistics such as `mean`, `sum` for convenience.
+ *
+ * @note
+ * This class was named `GroupedData` in Spark 1.x.
+ *
+ * @since 3.4.0
+ */
+class RelationalGroupedDataset protected[sql] (
+ private[sql] val df: DataFrame,
+ private[sql] val groupingExprs: Seq[proto.Expression]) {
+
+ private[this] def toDF(aggExprs: Seq[proto.Expression]): DataFrame = {
+ // TODO: support other GroupByType such as Rollup, Cube, Pivot.
+ df.session.newDataset { builder =>
+ builder.getAggregateBuilder
+ .setGroupType(proto.Aggregate.GroupType.GROUP_TYPE_GROUPBY)
+ .setInput(df.plan.getRoot)
+ .addAllGroupingExpressions(groupingExprs.asJava)
+ .addAllAggregateExpressions(aggExprs.asJava)
+ }
+ }
+
+ /**
+ * (Scala-specific) Compute aggregates by specifying the column names and aggregate methods. The
+ * resulting `DataFrame` will also contain the grouping columns.
+ *
+ * The available aggregate methods are `avg`, `max`, `min`, `sum`, `count`.
+ * {{{
+ * // Selects the age of the oldest employee and the aggregate expense for each department
+ * df.groupBy("department").agg(
+ * "age" -> "max",
+ * "expense" -> "sum"
+ * )
+ * }}}
+ *
+ * @since 3.4.0
+ */
+ def agg(aggExpr: (String, String), aggExprs: (String, String)*): DataFrame = {
+ toDF((aggExpr +: aggExprs).map { case (colName, expr) =>
+ strToExpr(expr)(df(colName).expr)
+ })
+ }
+
+ /**
+ * (Scala-specific) Compute aggregates by specifying a map from column name to aggregate
+ * methods. The resulting `DataFrame` will also contain the grouping columns.
+ *
+ * The available aggregate methods are `avg`, `max`, `min`, `sum`, `count`.
+ * {{{
+ * // Selects the age of the oldest employee and the aggregate expense for each department
+ * df.groupBy("department").agg(Map(
+ * "age" -> "max",
+ * "expense" -> "sum"
+ * ))
+ * }}}
+ *
+ * @since 3.4.0
+ */
+ def agg(exprs: Map[String, String]): DataFrame = {
+ toDF(exprs.map { case (colName, expr) =>
+ strToExpr(expr)(df(colName).expr)
+ }.toSeq)
+ }
+
+ /**
+ * (Java-specific) Compute aggregates by specifying a map from column name to aggregate methods.
+ * The resulting `DataFrame` will also contain the grouping columns.
+ *
+ * The available aggregate methods are `avg`, `max`, `min`, `sum`, `count`.
+ * {{{
+ * // Selects the age of the oldest employee and the aggregate expense for each department
+ * import com.google.common.collect.ImmutableMap;
+ * df.groupBy("department").agg(ImmutableMap.of("age", "max", "expense", "sum"));
+ * }}}
+ *
+ * @since 3.4.0
+ */
+ def agg(exprs: java.util.Map[String, String]): DataFrame = {
+ agg(exprs.asScala.toMap)
+ }
+
+ private[this] def strToExpr(expr: String): (proto.Expression => proto.Expression) = {
+ val exprToFunc: (proto.Expression => proto.Expression) = {
+ val builder = proto.Expression.newBuilder()
+
+ (inputExpr: proto.Expression) =>
+ expr.toLowerCase(Locale.ROOT) match {
+ // We special handle a few cases that have alias that are not in function registry.
+ case "avg" | "average" | "mean" =>
+ builder.getUnresolvedFunctionBuilder
+ .setFunctionName("avg")
+ .addArguments(inputExpr)
+ .setIsDistinct(false)
+ case "stddev" | "std" =>
+ builder.getUnresolvedFunctionBuilder
+ .setFunctionName("stddev")
+ .addArguments(inputExpr)
+ .setIsDistinct(false)
+ // Also special handle count because we need to take care count(*).
+ case "count" | "size" =>
+ // Turn count(*) into count(1)
+ inputExpr match {
+ case s if s.hasUnresolvedStar =>
+ val exprBuilder = proto.Expression.newBuilder
+ exprBuilder.getLiteralBuilder.setInteger(1)
+ builder.getUnresolvedFunctionBuilder
+ .setFunctionName("count")
+ .addArguments(exprBuilder)
+ .setIsDistinct(false)
+ case _ =>
+ builder.getUnresolvedFunctionBuilder
+ .setFunctionName("count")
+ .addArguments(inputExpr)
+ .setIsDistinct(false)
+ }
+ case name =>
+ builder.getUnresolvedFunctionBuilder
+ .setFunctionName(name)
+ .addArguments(inputExpr)
+ .setIsDistinct(false)
+ }
+ builder.build()
+ }
+ (inputExpr: proto.Expression) => exprToFunc(inputExpr)
+ }
+}
+
+private[sql] object RelationalGroupedDataset {
+ def apply(df: DataFrame, groupingExprs: Seq[proto.Expression]): RelationalGroupedDataset = {
Review Comment:
I think without this, I will need to `new class()` to invoke the class constructor. But existing codebase does not do that but offering the object with an apply implementation. So I am following that.
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[GitHub] [spark] amaliujia commented on a diff in pull request #40057: [SPARK-42468][CONNECT] Implement agg by (String, String)*
Posted by "amaliujia (via GitHub)" <gi...@apache.org>.
amaliujia commented on code in PR #40057:
URL: https://github.com/apache/spark/pull/40057#discussion_r1109158644
##########
connector/connect/client/jvm/src/main/scala/org/apache/spark/sql/RelationalGroupedDataset.scala:
##########
@@ -0,0 +1,162 @@
+/*
+ * 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
+
+import java.util.Locale
+
+import scala.collection.JavaConverters._
+
+import org.apache.spark.connect.proto
+
+/**
+ * A set of methods for aggregations on a `DataFrame`, created by [[Dataset#groupBy groupBy]],
+ * [[Dataset#cube cube]] or [[Dataset#rollup rollup]] (and also `pivot`).
+ *
+ * The main method is the `agg` function, which has multiple variants. This class also contains
+ * some first-order statistics such as `mean`, `sum` for convenience.
+ *
+ * @note
+ * This class was named `GroupedData` in Spark 1.x.
+ *
+ * @since 3.4.0
+ */
+class RelationalGroupedDataset protected[sql] (
+ private[sql] val df: DataFrame,
+ private[sql] val groupingExprs: Seq[proto.Expression]) {
+
+ private[this] def toDF(aggExprs: Seq[proto.Expression]): DataFrame = {
+ // TODO: support other GroupByType such as Rollup, Cube, Pivot.
+ df.session.newDataset { builder =>
+ builder.getAggregateBuilder
+ .setGroupType(proto.Aggregate.GroupType.GROUP_TYPE_GROUPBY)
+ .setInput(df.plan.getRoot)
+ .addAllGroupingExpressions(groupingExprs.asJava)
+ .addAllAggregateExpressions(aggExprs.asJava)
+ }
+ }
+
+ /**
+ * (Scala-specific) Compute aggregates by specifying the column names and aggregate methods. The
+ * resulting `DataFrame` will also contain the grouping columns.
+ *
+ * The available aggregate methods are `avg`, `max`, `min`, `sum`, `count`.
+ * {{{
+ * // Selects the age of the oldest employee and the aggregate expense for each department
+ * df.groupBy("department").agg(
+ * "age" -> "max",
+ * "expense" -> "sum"
+ * )
+ * }}}
+ *
+ * @since 3.4.0
+ */
+ def agg(aggExpr: (String, String), aggExprs: (String, String)*): DataFrame = {
+ toDF((aggExpr +: aggExprs).map { case (colName, expr) =>
+ strToExpr(expr)(df(colName).expr)
+ })
+ }
+
+ /**
+ * (Scala-specific) Compute aggregates by specifying a map from column name to aggregate
+ * methods. The resulting `DataFrame` will also contain the grouping columns.
+ *
+ * The available aggregate methods are `avg`, `max`, `min`, `sum`, `count`.
+ * {{{
+ * // Selects the age of the oldest employee and the aggregate expense for each department
+ * df.groupBy("department").agg(Map(
+ * "age" -> "max",
+ * "expense" -> "sum"
+ * ))
+ * }}}
+ *
+ * @since 3.4.0
+ */
+ def agg(exprs: Map[String, String]): DataFrame = {
+ toDF(exprs.map { case (colName, expr) =>
+ strToExpr(expr)(df(colName).expr)
+ }.toSeq)
+ }
+
+ /**
+ * (Java-specific) Compute aggregates by specifying a map from column name to aggregate methods.
+ * The resulting `DataFrame` will also contain the grouping columns.
+ *
+ * The available aggregate methods are `avg`, `max`, `min`, `sum`, `count`.
+ * {{{
+ * // Selects the age of the oldest employee and the aggregate expense for each department
+ * import com.google.common.collect.ImmutableMap;
+ * df.groupBy("department").agg(ImmutableMap.of("age", "max", "expense", "sum"));
+ * }}}
+ *
+ * @since 3.4.0
+ */
+ def agg(exprs: java.util.Map[String, String]): DataFrame = {
+ agg(exprs.asScala.toMap)
+ }
+
+ private[this] def strToExpr(expr: String): (proto.Expression => proto.Expression) = {
Review Comment:
I am not seeing difference on if return a function or just return the expression directly.
Unless there is a downside, otherwise either way is fine to me?
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[GitHub] [spark] amaliujia commented on a diff in pull request #40057: [SPARK-42468][CONNECT] Implement agg by (String, String)*
Posted by "amaliujia (via GitHub)" <gi...@apache.org>.
amaliujia commented on code in PR #40057:
URL: https://github.com/apache/spark/pull/40057#discussion_r1109235158
##########
connector/connect/client/jvm/src/main/scala/org/apache/spark/sql/RelationalGroupedDataset.scala:
##########
@@ -0,0 +1,152 @@
+/*
+ * 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
+
+import java.util.Locale
+
+import scala.collection.JavaConverters._
+
+import org.apache.spark.connect.proto
+
+/**
+ * A set of methods for aggregations on a `DataFrame`, created by [[Dataset#groupBy groupBy]],
+ * [[Dataset#cube cube]] or [[Dataset#rollup rollup]] (and also `pivot`).
+ *
+ * The main method is the `agg` function, which has multiple variants. This class also contains
+ * some first-order statistics such as `mean`, `sum` for convenience.
+ *
+ * @note
+ * This class was named `GroupedData` in Spark 1.x.
+ *
+ * @since 3.4.0
+ */
+class RelationalGroupedDataset protected[sql] (
+ private[sql] val df: DataFrame,
+ private[sql] val groupingExprs: Seq[proto.Expression]) {
+
+ private[this] def toDF(aggExprs: Seq[proto.Expression]): DataFrame = {
+ // TODO: support other GroupByType such as Rollup, Cube, Pivot.
+ df.session.newDataset { builder =>
+ builder.getAggregateBuilder
+ .setGroupType(proto.Aggregate.GroupType.GROUP_TYPE_GROUPBY)
+ .setInput(df.plan.getRoot)
+ .addAllGroupingExpressions(groupingExprs.asJava)
+ .addAllAggregateExpressions(aggExprs.asJava)
+ }
+ }
+
+ /**
+ * (Scala-specific) Compute aggregates by specifying the column names and aggregate methods. The
+ * resulting `DataFrame` will also contain the grouping columns.
+ *
+ * The available aggregate methods are `avg`, `max`, `min`, `sum`, `count`.
+ * {{{
+ * // Selects the age of the oldest employee and the aggregate expense for each department
+ * df.groupBy("department").agg(
+ * "age" -> "max",
+ * "expense" -> "sum"
+ * )
+ * }}}
+ *
+ * @since 3.4.0
+ */
+ def agg(aggExpr: (String, String), aggExprs: (String, String)*): DataFrame = {
+ toDF((aggExpr +: aggExprs).map { case (colName, expr) =>
+ strToExpr(expr, df(colName).expr)
+ })
+ }
+
+ /**
+ * (Scala-specific) Compute aggregates by specifying a map from column name to aggregate
+ * methods. The resulting `DataFrame` will also contain the grouping columns.
+ *
+ * The available aggregate methods are `avg`, `max`, `min`, `sum`, `count`.
+ * {{{
+ * // Selects the age of the oldest employee and the aggregate expense for each department
+ * df.groupBy("department").agg(Map(
+ * "age" -> "max",
+ * "expense" -> "sum"
+ * ))
+ * }}}
+ *
+ * @since 3.4.0
+ */
+ def agg(exprs: Map[String, String]): DataFrame = {
+ toDF(exprs.map { case (colName, expr) =>
+ strToExpr(expr, df(colName).expr)
+ }.toSeq)
+ }
+
+ /**
+ * (Java-specific) Compute aggregates by specifying a map from column name to aggregate methods.
+ * The resulting `DataFrame` will also contain the grouping columns.
+ *
+ * The available aggregate methods are `avg`, `max`, `min`, `sum`, `count`.
+ * {{{
+ * // Selects the age of the oldest employee and the aggregate expense for each department
+ * import com.google.common.collect.ImmutableMap;
+ * df.groupBy("department").agg(ImmutableMap.of("age", "max", "expense", "sum"));
+ * }}}
+ *
+ * @since 3.4.0
+ */
+ def agg(exprs: java.util.Map[String, String]): DataFrame = {
+ agg(exprs.asScala.toMap)
+ }
+
+ private[this] def strToExpr(expr: String, inputExpr: proto.Expression): proto.Expression = {
+ val builder = proto.Expression.newBuilder()
+
+ expr.toLowerCase(Locale.ROOT) match {
+ // We special handle a few cases that have alias that are not in function registry.
+ case "avg" | "average" | "mean" =>
+ builder.getUnresolvedFunctionBuilder
+ .setFunctionName("avg")
+ .addArguments(inputExpr)
+ .setIsDistinct(false)
+ case "stddev" | "std" =>
+ builder.getUnresolvedFunctionBuilder
+ .setFunctionName("stddev")
+ .addArguments(inputExpr)
+ .setIsDistinct(false)
+ // Also special handle count because we need to take care count(*).
+ case "count" | "size" =>
+ // Turn count(*) into count(1)
Review Comment:
This is to match existing scala side Dataframe impl. @cloud-fan do you know if we need count(*) to count(1)? If not we can both change here and exuding DF.
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[GitHub] [spark] hvanhovell commented on a diff in pull request #40057: [SPARK-42468][CONNECT] Implement agg by (String, String)*
Posted by "hvanhovell (via GitHub)" <gi...@apache.org>.
hvanhovell commented on code in PR #40057:
URL: https://github.com/apache/spark/pull/40057#discussion_r1109164851
##########
connector/connect/client/jvm/src/main/scala/org/apache/spark/sql/Dataset.scala:
##########
@@ -1035,6 +1035,29 @@ class Dataset[T] private[sql] (val session: SparkSession, private[sql] val plan:
}
}
+ /**
+ * Groups the Dataset using the specified columns, so we can run aggregation on them. See
+ * [[RelationalGroupedDataset]] for all the available aggregate functions.
+ *
+ * {{{
+ * // Compute the average for all numeric columns grouped by department.
+ * ds.groupBy($"department").avg()
+ *
+ * // Compute the max age and average salary, grouped by department and gender.
+ * ds.groupBy($"department", $"gender").agg(Map(
+ * "salary" -> "avg",
+ * "age" -> "max"
+ * ))
+ * }}}
+ *
+ * @group untypedrel
+ * @since 3.4.0
+ */
+ @scala.annotation.varargs
+ def groupBy(cols: Column*): RelationalGroupedDataset = {
+ RelationalGroupedDataset(toDF(), cols.map(_.expr))
Review Comment:
The constructor does not need to have the same signature since an end-user is not supposed to instantiate this thing. BTW you are already breaking the signature because we use proto.Expression instead catalyst.Expression.
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[GitHub] [spark] hvanhovell commented on a diff in pull request #40057: [SPARK-42468][CONNECT] Implement agg by (String, String)*
Posted by "hvanhovell (via GitHub)" <gi...@apache.org>.
hvanhovell commented on code in PR #40057:
URL: https://github.com/apache/spark/pull/40057#discussion_r1109134749
##########
connector/connect/common/src/test/resources/query-tests/explain-results/groupby_agg.explain:
##########
@@ -0,0 +1,2 @@
+'Aggregate ['id], ['id, unresolvedalias('max('a), None), unresolvedalias('stddev('b), None), unresolvedalias('stddev('b), None), unresolvedalias('avg('b), None), unresolvedalias('avg('b), None), unresolvedalias('avg('b), None), unresolvedalias('count(1), None), unresolvedalias('count('a), None)]
Review Comment:
We need to regenerate this file.
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