Explain in detail the functions and underlying implementation logic of the groups sets statement in SQL

Abstract ： This article first briefly introduces Grouping Sets Usage of , And then to Spark SQL As an entry point , In depth analysis of Grouping Sets Implementation mechanism .

This article is shared from Huawei cloud community 《 In depth understanding of SQL Medium Grouping Sets sentence 》, author ： Yuan Runzi .

Preface

SQL in  Group By  Everyone is familiar with the sentence , Group data according to specified rules , Often and Aggregate functions Use it together .

such as , Consider having a table  dealer, The data in the table is as follows ：

If you execute SQL sentence  SELECT id, sum(quantity) FROM dealer GROUP BY id ORDER BY id, We will get the following results ：

 +---+-------------+

 | id|sum(quantity)|

 +---+-------------+

 |100|           32|

 |200|           33|

 |300|           13|

 +---+-------------+

Above SQL Statement means to press  id  Columns to group , Then pair  quantity  To sum .

Group By  In addition to the simple usage above , There are more advanced uses , Common is  Grouping Sets、RollUp  and  Cube, They are OLAP More commonly used when . among ,RollUp  and  Cube  It's all about  Grouping Sets  Based on , therefore , I understand  Grouping Sets, I understand  RollUp  and  Cube .

This article first briefly introduces  Grouping Sets  Usage of , And then to Spark SQL As an entry point , In depth analysis of  Grouping Sets  Implementation mechanism .

Spark SQL yes Apache Spark A sub module of big data processing framework , Used to process structured information . It can be SQL Sentence translation multiple tasks in Spark Execute on cluster , Allow users to pass directly through SQL To process data , Greatly improved usability .

Grouping Sets brief introduction

Spark SQL In official documents  SQL Syntax  One section right  Grouping Sets  The statement is described below ：

Groups the rows for each grouping set specified after GROUPING SETS. （... Some examples ） This clause is a shorthand for a UNION ALL where each leg of the UNION ALL operator performs aggregation of each grouping set specified in the GROUPING SETS clause. （... Some examples ）

That is to say ,Grouping Sets  The function of the statement is to specify several  grouping set  As  Group By  Grouping rules , Then combine the results . Its effect and , First of all, separate these grouping set Conduct  Group By  After grouping , Re pass Union All Combine the results , It's the same .

such as , about  dealer  surface ,Group By Grouping Sets ((city, car_model), (city), (car_model), ())  and  Union All((Group By city, car_model), (Group By city), (Group By car_model), Global aggregation )  The effect is the same ：

First look at Grouping Sets Implementation results of version ：

 spark-sql> SELECT city, car_model, sum(quantity) AS sum FROM dealer

          > GROUP BY GROUPING SETS ((city, car_model), (city), (car_model), ())

          > ORDER BY city, car_model;

 +--------+------------+---+

 |   city|   car_model|sum|

 +--------+------------+---+

 |    null|        null| 78|

 |    null|Honda Accord| 33|

 |    null|   Honda CRV| 10|

 |    null| Honda Civic| 35|

 | Dublin|        null| 33|

 | Dublin|Honda Accord| 10|

 | Dublin|   Honda CRV|  3|

 | Dublin| Honda Civic| 20|

 | Fremont|        null| 32|

 | Fremont|Honda Accord| 15|

 | Fremont|   Honda CRV|  7|

 | Fremont| Honda Civic| 10|

 |San Jose|        null| 13|

 |San Jose|Honda Accord|  8|

 |San Jose| Honda Civic|  5|

 +--------+------------+---+

Look again Union All Implementation results of version ：

 spark-sql> (SELECT city, car_model, sum(quantity) AS sum FROM dealer GROUP BY city, car_model) UNION ALL

          > (SELECT city, NULL as car_model, sum(quantity) AS sum FROM dealer GROUP BY city) UNION ALL

          > (SELECT NULL as city, car_model, sum(quantity) AS sum FROM dealer GROUP BY car_model) UNION ALL

          > (SELECT NULL as city, NULL as car_model, sum(quantity) AS sum FROM dealer)

          > ORDER BY city, car_model;

 +--------+------------+---+

 |   city|   car_model|sum|

 +--------+------------+---+

 |    null|        null| 78|

 |    null|Honda Accord| 33|

 |    null|   Honda CRV| 10|

 |    null| Honda Civic| 35|

 | Dublin|        null| 33|

 | Dublin|Honda Accord| 10|

 | Dublin|   Honda CRV|  3|

 | Dublin| Honda Civic| 20|

 | Fremont|        null| 32|

 | Fremont|Honda Accord| 15|

 | Fremont|   Honda CRV|  7|

 | Fremont| Honda Civic| 10|

 |San Jose|        null| 13|

 |San Jose|Honda Accord|  8|

 |San Jose| Honda Civic|  5|

 +--------+------------+---+

The query results of the two versions are exactly the same .

Grouping Sets Implementation plan of

In terms of execution results ,Grouping Sets Version and Union All Version of SQL It is equivalent. , but Grouping Sets The version is more concise .

that ,Grouping Sets  It's just  Union All  An abbreviation of , Or grammar sugar ？

In order to further explore  Grouping Sets  Whether the underlying implementation of is consistent with  Union All  It's consistent , We can take a look at the implementation plan of the two .

First , We go through  explain extended  Check it out. Union All Version of  Optimized Logical Plan:

 spark-sql> explain extended (SELECT city, car_model, sum(quantity) AS sum FROM dealer GROUP BY city, car_model) UNION ALL(SELECT city, NULL as car_model, sum(quantity) AS sum FROM dealer GROUP BY city) UNION ALL (SELECT NULL as city, car_model, sum(quantity) AS sum FROM dealer GROUP BY car_model) UNION ALL (SELECT NULL as city, NULL as car_model, sum(quantity) AS sum FROM dealer) ORDER BY city, car_model;

 == Parsed Logical Plan ==

 ...

 == Analyzed Logical Plan ==

 ...

 == Optimized Logical Plan ==

 Sort [city#93 ASC NULLS FIRST, car_model#94 ASC NULLS FIRST], true

 +- Union false, false

    :- Aggregate [city#93, car_model#94], [city#93, car_model#94, sum(quantity#95) AS sum#79L]

    :  +- Project [city#93, car_model#94, quantity#95]

    :     +- HiveTableRelation [`default`.`dealer`, ..., Data Cols: [id#92, city#93, car_model#94, quantity#95], Partition Cols: []]

    :- Aggregate [city#97], [city#97, null AS car_model#112, sum(quantity#99) AS sum#81L]

    :  +- Project [city#97, quantity#99]

    :     +- HiveTableRelation [`default`.`dealer`, ..., Data Cols: [id#96, city#97, car_model#98, quantity#99], Partition Cols: []]

    :- Aggregate [car_model#102], [null AS city#113, car_model#102, sum(quantity#103) AS sum#83L]

    :  +- Project [car_model#102, quantity#103]

    :     +- HiveTableRelation [`default`.`dealer`, ..., Data Cols: [id#100, city#101, car_model#102, quantity#103], Partition Cols: []]

    +- Aggregate [null AS city#114, null AS car_model#115, sum(quantity#107) AS sum#86L]

       +- Project [quantity#107]

          +- HiveTableRelation [`default`.`dealer`, ..., Data Cols: [id#104, city#105, car_model#106, quantity#107], Partition Cols: []]

 == Physical Plan ==

 ...

From the above Optimized Logical Plan It can be seen clearly Union All Implementation logic of version ：

1. Execute each subquery statement , Calculate the query results . among , The logic of each query statement is like this ：

   • stay  HiveTableRelation  Node pair  dealer  Table scanning .

   • stay  Project  The node selects the columns related to the query statement results , For example, for sub query statements  SELECT NULL as city, NULL as car_model, sum(quantity) AS sum FROM dealer, Just keep  quantity  Just column .

   • stay  Aggregate  Node to complete  quantity  Column pair aggregation . In the above Plan in ,Aggregate Immediately following is the column used for grouping , such as  Aggregate [city#902]  It means according to  city  Column to group .

2. stay  Union  The node completes the union of each sub query result .

3. Last , stay  Sort  The node finishes sorting the data , Above Plan in  Sort [city#93 ASC NULLS FIRST, car_model#94 ASC NULLS FIRST]  It means according to  city  and  car_model  Sort columns in ascending order .

Next , We go through  explain extended  Check it out. Grouping Sets Version of Optimized Logical Plan：

 spark-sql> explain extended SELECT city, car_model, sum(quantity) AS sum FROM dealer GROUP BY GROUPING SETS ((city, car_model), (city), (car_model), ()) ORDER BY city, car_model;

 == Parsed Logical Plan ==

 ...

 == Analyzed Logical Plan ==

 ...

 == Optimized Logical Plan ==

 Sort [city#138 ASC NULLS FIRST, car_model#139 ASC NULLS FIRST], true

 +- Aggregate [city#138, car_model#139, spark_grouping_id#137L], [city#138, car_model#139, sum(quantity#133) AS sum#124L]

    +- Expand [[quantity#133, city#131, car_model#132, 0], [quantity#133, city#131, null, 1], [quantity#133, null, car_model#132, 2], [quantity#133, null, null, 3]], [quantity#133, city#138, car_model#139, spark_grouping_id#137L]

       +- Project [quantity#133, city#131, car_model#132]

          +- HiveTableRelation [`default`.`dealer`, org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe, Data Cols: [id#130, city#131, car_model#132, quantity#133], Partition Cols: []]

 == Physical Plan ==

 ...

from Optimized Logical Plan Look at ,Grouping Sets The version should be much simpler ！ The specific execution logic is as follows ：

1. stay  HiveTableRelation  Node pair  dealer  Table scanning .

2. stay  Project  The node selects the columns related to the query statement results .

3. Next  Expand  Nodes are the key , After the data passes through this node , There's more  spark_grouping_id  Column . from Plan You can see that ,Expand The node contains  Grouping Sets  Every one of them grouping set Information , such as  [quantity#133, city#131, null, 1]  The corresponding is  (city)  this grouping set. and , Every grouping set Corresponding  spark_grouping_id  The values of the columns are fixed , such as  (city)  Corresponding  spark_grouping_id  by  1.

4. stay  Aggregate  Node to complete  quantity  Column pair aggregation , The grouping rule is  city, car_model, spark_grouping_id. Be careful , Data after Aggregate After node ,spark_grouping_id  The column was deleted ！

5. Last , stay  Sort  The node finishes sorting the data .

from Optimized Logical Plan Look at , although Union All Version and Grouping Sets The effect of the version is consistent , But there are huge differences in their underlying implementations .

among ,Grouping Sets Version of Plan The most important thing is Expand node , at present , We only know that after the data passes through it , There's more  spark_grouping_id  Column . And from the end result ,spark_grouping_id  It's just Spark SQL Internal implementation details of , Not for users . that ：

1. Expand What is the implementation logic of , Why can we achieve  Union All  The effect of ？

2. Expand What is the output data of the node ？

3. spark_grouping_id  What is the function of columns ？

adopt Physical Plan, We found that Expand The operator name corresponding to the node is also  Expand:

 == Physical Plan ==

 AdaptiveSparkPlan isFinalPlan=false

 +- Sort [city#138 ASC NULLS FIRST, car_model#139 ASC NULLS FIRST], true, 0

    +- Exchange rangepartitioning(city#138 ASC NULLS FIRST, car_model#139 ASC NULLS FIRST, 200), ENSURE_REQUIREMENTS, [plan_id=422]

       +- HashAggregate(keys=[city#138, car_model#139, spark_grouping_id#137L], functions=[sum(quantity#133)], output=[city#138, car_model#139, sum#124L])

          +- Exchange hashpartitioning(city#138, car_model#139, spark_grouping_id#137L, 200), ENSURE_REQUIREMENTS, [plan_id=419]

             +- HashAggregate(keys=[city#138, car_model#139, spark_grouping_id#137L], functions=[partial_sum(quantity#133)], output=[city#138, car_model#139, spark_grouping_id#137L, sum#141L])

                +- Expand [[quantity#133, city#131, car_model#132, 0], [quantity#133, city#131, null, 1], [quantity#133, null, car_model#132, 2], [quantity#133, null, null, 3]], [quantity#133, city#138, car_model#139, spark_grouping_id#137L]

                   +- Scan hive default.dealer [quantity#133, city#131, car_model#132], HiveTableRelation [`default`.`dealer`, ..., Data Cols: [id#130, city#131, car_model#132, quantity#133], Partition Cols: []]

With the previous questions , Next, let's go deep into Spark SQL Of  Expand  Operator source code to find the answer .

Expand The realization of operators

Expand The operator is in Spark SQL The implementation in the source code is  ExpandExec  class （Spark SQL The names of operator implementation classes in are  XxxExec  The format of , among  Xxx  Is the specific operator name , such as Project The implementation class of the operator is  ProjectExec）, The core code is as follows ：

 /**

  * Apply all of the GroupExpressions to every input row, hence we will get

  * multiple output rows for an input row.

  * @param projections The group of expressions, all of the group expressions should

  *                   output the same schema specified bye the parameter `output`

  * @param output     The output Schema

  * @param child       Child operator

  */

 case class ExpandExec(

     projections: Seq[Seq[Expression]],

     output: Seq[Attribute],

     child: SparkPlan)

   extends UnaryExecNode with CodegenSupport {

 ​

  ...

   //  Key points 1, take child.output, That is, the output data of the upstream operator schema,

   //  Bound to an expression array exprs, To calculate the output data 

   private[this] val projection =

    (exprs: Seq[Expression]) => UnsafeProjection.create(exprs, child.output)

 ​

   // doExecute() Method is Expand Where the operator executes logic 

   protected override def doExecute(): RDD[InternalRow] = {

     val numOutputRows = longMetric("numOutputRows")

 ​

     //  Processing the output data of upstream operators ,Expand The input data of the operator starts from iter Iterator gets 

     child.execute().mapPartitions { iter =>

       //  Key points 2,projections Corresponding Grouping Sets Each of them grouping set The expression of ,

       //  Expression output data schema by this.output,  such as  (quantity, city, car_model, spark_grouping_id)

       //  The logic here is to generate a UnsafeProjection object , Through the apply The method will lead to Expand The output data of the operator 

       val groups = projections.map(projection).toArray

       new Iterator[InternalRow] {

         private[this] var result: InternalRow = _

         private[this] var idx = -1  // -1 means the initial state

         private[this] var input: InternalRow = _

 ​

         override final def hasNext: Boolean = (-1 < idx && idx < groups.length) || iter.hasNext

 ​

         override final def next(): InternalRow = {

           //  Key points 3, For each record of input data , Are reused N Time , among N The size of corresponds to projections Size of array ,

           //  That is to say Grouping Sets In the specified grouping set The number of 

           if (idx <= 0) {

             // in the initial (-1) or beginning(0) of a new input row, fetch the next input tuple

             input = iter.next()

             idx = 0

          }

           //  Key points 4, For each record of input data , adopt UnsafeProjection Calculate the output data ,

           //  Every grouping set Corresponding UnsafeProjection Will be the same input Do a calculation 

           result = groups(idx)(input)

           idx += 1

 ​

           if (idx == groups.length && iter.hasNext) {

             idx = 0

          }

 ​

           numOutputRows += 1

           result

        }

      }

    }

  }

  ...

 }

ExpandExec  The implementation of is not complicated , Want to understand how it works , The key is to understand what is mentioned in the above source code 4 A key point .

Key points 1  and   Key points 2  It's the foundation , Key points 2  Medium  groups  It's a  UnsafeProjection[N]  An array type , Each of them  UnsafeProjection  On behalf of  Grouping Sets  Specified in the statement grouping set, It's defined like this ：

 // A projection that returns UnsafeRow.

 abstract class UnsafeProjection extends Projection {

   override def apply(row: InternalRow): UnsafeRow

 }

 ​

 // The factory object for `UnsafeProjection`.

 object UnsafeProjection

     extends CodeGeneratorWithInterpretedFallback[Seq[Expression], UnsafeProjection] {

   // Returns an UnsafeProjection for given sequence of Expressions, which will be bound to

   // `inputSchema`.

   def create(exprs: Seq[Expression], inputSchema: Seq[Attribute]): UnsafeProjection = {

     create(bindReferences(exprs, inputSchema))

  }

  ...

 }

UnsafeProjection  It is similar to the function of column projection , among , apply  Method is based on the parameters passed during creation  exprs  and  inputSchema, Column projection of input records , Get the output record .

such as , Ahead  GROUPING SETS ((city, car_model), (city), (car_model), ())  Example , Its corresponding  groups  That's true ：

among ,AttributeReference  Expression of type , At the time of calculation , It will directly reference the value of the corresponding column of the input data ;Iteral  Expression of type , At the time of calculation , The value is fixed .

Key points 3  and   Key points 4  yes Expand The essence of operators ,ExpandExec  Through these two paragraphs of logic , Record every input , Expand （Expand） become N Output records .

Key points 4  in  groups(idx)(input)  Equate to  groups(idx).apply(input) .

Or the front  GROUPING SETS ((city, car_model), (city), (car_model), ())  As an example , The effect is this ：

Come here , We have made it clear Expand How the operator works , Look back at the previous mentioned 3 A question , It's not difficult to answer ：

1. Expand What is the implementation logic of , Why can we achieve  Union All  The effect of ？

   if  Union All  It is to aggregate first and then unite , that Expand Is to unite first and then aggregate .Expand utilize  groups  Inside N An expression evaluates each input record , Expanded into N Output records . When we aggregate later , Can achieve and  Union All  The same effect .

2. Expand What is the output data of the node ？

   stay schema On ,Expand The output data will be more than the input data  spark_grouping_id  Column ; On the record number , Is the number of input data records N times .

3. spark_grouping_id  What is the function of columns ？

   spark_grouping_id  For each grouping set Number , such , Even in Expand The stage combines the data first , stay Aggregate Stage （ hold  spark_grouping_id  Add to the grouping rule ） It can also ensure that the data can be in accordance with each grouping set Aggregate separately , Ensure the correctness of the results .

Query performance comparison

From the above, we can see that ,Grouping Sets and Union All Two versions of SQL Statements have the same effect , But there are huge differences in their implementation plans . below , We will compare the performance differences between the two versions .

spark-sql After execution SQL The time-consuming information will be printed after the statement , We have two versions of SQL Separately 10 Time , Get the following information ：

 // Grouping Sets  Version execution 10 Time consuming information 

 // SELECT city, car_model, sum(quantity) AS sum FROM dealer GROUP BY GROUPING SETS ((city, car_model), (city), (car_model), ()) ORDER BY city, car_model;

 Time taken: 0.289 seconds, Fetched 15 row(s)

 Time taken: 0.251 seconds, Fetched 15 row(s)

 Time taken: 0.259 seconds, Fetched 15 row(s)

 Time taken: 0.258 seconds, Fetched 15 row(s)

 Time taken: 0.296 seconds, Fetched 15 row(s)

 Time taken: 0.247 seconds, Fetched 15 row(s)

 Time taken: 0.298 seconds, Fetched 15 row(s)

 Time taken: 0.286 seconds, Fetched 15 row(s)

 Time taken: 0.292 seconds, Fetched 15 row(s)

 Time taken: 0.282 seconds, Fetched 15 row(s)

 ​

 // Union All  Version execution 10 Time consuming information 

 // (SELECT city, car_model, sum(quantity) AS sum FROM dealer GROUP BY city, car_model) UNION ALL (SELECT city, NULL as car_model, sum(quantity) AS sum FROM dealer GROUP BY city) UNION ALL (SELECT NULL as city, car_model, sum(quantity) AS sum FROM dealer GROUP BY car_model) UNION ALL (SELECT NULL as city, NULL as car_model, sum(quantity) AS sum FROM dealer) ORDER BY city, car_model;

 Time taken: 0.628 seconds, Fetched 15 row(s)

 Time taken: 0.594 seconds, Fetched 15 row(s)

 Time taken: 0.591 seconds, Fetched 15 row(s)

 Time taken: 0.607 seconds, Fetched 15 row(s)

 Time taken: 0.616 seconds, Fetched 15 row(s)

 Time taken: 0.64 seconds, Fetched 15 row(s)

 Time taken: 0.623 seconds, Fetched 15 row(s)

 Time taken: 0.625 seconds, Fetched 15 row(s)

 Time taken: 0.62 seconds, Fetched 15 row(s)

 Time taken: 0.62 seconds, Fetched 15 row(s)

You can work out ,Grouping Sets Version of SQL The average time taken is  0.276s;Union All Version of SQL The average time taken is  0.616s, It's the former  2.2 times ！

therefore ,Grouping Sets Version of SQL It is not only more concise in expression , It is also more efficient in performance .

RollUp and Cube

Group By  In advanced usage , also  RollUp  and  Cube  Two are commonly used .

First , Let's take a look at  RollUp  sentence .

Spark SQL In official documents  SQL Syntax  One section right  RollUp  The statement is described below ：

Specifies multiple levels of aggregations in a single statement. This clause is used to compute aggregations based on multiple grouping sets. ROLLUP is a shorthand for GROUPING SETS. （... Some examples ）

In official documents , hold  RollUp  Described as  Grouping Sets  Abbreviation , The equivalence rule is ：RollUp(A, B, C) == Grouping Sets((A, B, C), (A, B), (A), ()).

such as ,Group By RollUp(city, car_model)  Is equivalent to  Group By Grouping Sets((city, car_model), (city), ()).

below , We go through  expand extended  look down RollUp edition SQL Of Optimized Logical Plan：

 spark-sql> explain extended SELECT city, car_model, sum(quantity) AS sum FROM dealer GROUP BY ROLLUP(city, car_model) ORDER BY city, car_model;

 == Parsed Logical Plan ==

 ...

 == Analyzed Logical Plan ==

 ...

 == Optimized Logical Plan ==

 Sort [city#2164 ASC NULLS FIRST, car_model#2165 ASC NULLS FIRST], true

 +- Aggregate [city#2164, car_model#2165, spark_grouping_id#2163L], [city#2164, car_model#2165, sum(quantity#2159) ASsum#2150L]

    +- Expand [[quantity#2159, city#2157, car_model#2158, 0], [quantity#2159, city#2157, null, 1], [quantity#2159, null, null, 3]], [quantity#2159, city#2164, car_model#2165, spark_grouping_id#2163L]

       +- Project [quantity#2159, city#2157, car_model#2158]

          +- HiveTableRelation [`default`.`dealer`, ..., Data Cols: [id#2156, city#2157, car_model#2158, quantity#2159], Partition Cols: []]

 == Physical Plan ==

 ...

From the above Plan It can be seen that ,RollUp  The underlying implementation is also used Expand operator , explain  RollUp  It's really based on  Grouping Sets  Realized . and  Expand [[quantity#2159, city#2157, car_model#2158, 0], [quantity#2159, city#2157, null, 1], [quantity#2159, null, null, 3]]  Also shows  RollUp  Conform to the equivalence rule .

below , We follow the same line of thinking , look down  Cube  sentence .

Spark SQL In official documents  SQL Syntax  One section right  Cube  The statement is described below ：

CUBE clause is used to perform aggregations based on combination of grouping columns specified in the GROUP BYclause. CUBE is a shorthand for GROUPING SETS. (... Some examples )

Again , Official documents put  Cube  Described as  Grouping Sets  Abbreviation , The equivalence rule is ：Cube(A, B, C) == Grouping Sets((A, B, C), (A, B), (A, C), (B, C), (A), (B), (C), ()).

such as ,Group By Cube(city, car_model)  Is equivalent to  Group By Grouping Sets((city, car_model), (city), (car_model), ()).

below , We go through  expand extended  look down Cube edition SQL Of Optimized Logical Plan：

 spark-sql> explain extended SELECT city, car_model, sum(quantity) AS sum FROM dealer GROUP BY CUBE(city, car_model) ORDER BY city, car_model;

 == Parsed Logical Plan ==

 ...

 == Analyzed Logical Plan ==

 ...

 == Optimized Logical Plan ==

 Sort [city#2202 ASC NULLS FIRST, car_model#2203 ASC NULLS FIRST], true

 +- Aggregate [city#2202, car_model#2203, spark_grouping_id#2201L], [city#2202, car_model#2203, sum(quantity#2197) ASsum#2188L]

    +- Expand [[quantity#2197, city#2195, car_model#2196, 0], [quantity#2197, city#2195, null, 1], [quantity#2197, null, car_model#2196, 2], [quantity#2197, null, null, 3]], [quantity#2197, city#2202, car_model#2203, spark_grouping_id#2201L]

       +- Project [quantity#2197, city#2195, car_model#2196]

          +- HiveTableRelation [`default`.`dealer`, ..., Data Cols: [id#2194, city#2195, car_model#2196, quantity#2197], Partition Cols: []]

 == Physical Plan ==

 ...

From the above Plan It can be seen that ,Cube  The bottom layer is also used Expand operator , explain  Cube  It's based on  Grouping Sets  Realization , And it also conforms to the equivalence rule .

therefore ,RollUp  and  Cube  It can be seen as  Grouping Sets  The grammar sugar of , The underlying implementation and performance are the same .

Last

This paper focuses on  Group By  Advanced usage  Groupings Sets  The function and underlying implementation of the statement .

although  Groupings Sets  The function of , adopt  Union All  Can also be realized , But the former is not the grammatical sugar of the latter , Their underlying implementation is completely different .Grouping Sets  It adopts the idea of combining first and then aggregating , adopt  spark_grouping_id  Columns to ensure the correctness of data ;Union All  Then we adopt the idea of first aggregation and then combination .Grouping Sets  stay SQL Statement expression and performance have greater advantages .

Group By  The other two advanced uses of  RollUp  and  Cube  It can be seen as  Grouping Sets  The grammar sugar of , Their bottom layer is based on Expand Operator implementation , In terms of performance and direct use  Grouping Sets  It's the same , But in SQL More concise expression .

Reference resources

[1] Spark SQL Guide, Apache Spark

[2] apache spark 3.3 Version source code , Apache Spark, GitHub

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