CODEWITHSUNDEEP
scalaapache-sparkspark-streamingapache-spark-sql
learner
learner

Reputation: 155

Break big spark sql query into smaller queries and merge it

I have a big spark sql statement which I'm trying to break into smaller chunks for better code readability. I do not want to join it but just merge the result.

Current working sql statement- 

val dfs = x.map(field => spark.sql(s"
   select ‘test’ as Table_Name,
          '$field' as Column_Name, 
          min($field) as Min_Value, 
          max($field) as Max_Value, 
          approx_count_distinct($field) as Unique_Value_Count,
          (
            SELECT 100 * approx_count_distinct($field)/count(1) 
            from tempdftable
          ) as perc 
   from tempdftable
”))

I'm trying to take the below query out of the above sql 

(SELECT 100 * approx_count_distinct($field)/count(1) from tempdftable) as perc

with this logic -

 val Perce = x.map(field => spark.sql(s"(SELECT 100 * approx_count_distinct($field)/count(1) from parquetDFTable)"))

and later merge this val Perce with the very first big SQL statement with below statement but it is not working - 

val dfs = x.map(field => spark.sql(s"
  select ‘test’ as Table_Name,
         '$field' as Column_Name, 
         min($field) as Min_Value, 
         max($field) as Max_Value, 
         approx_count_distinct($field) as Unique_Value_Count,
         '"+Perce+ "'
  from tempdftable
”))

How do we write this ?

Upvotes: 0

Views: 1370

Answers (1)

Glennie Helles Sindholt
Glennie Helles Sindholt

Reputation: 13154

Why not go all in and convert your entire expression to Spark code?

import spark.implicits._
import org.apache.spark.sql.functions._

val fraction = udf((approxCount: Double, totalCount: Double) => 100 * approxCount/totalCount)

val fields = Seq("colA", "colB", "colC")

val dfs = fields.map(field => {
  tempdftable
    .select(min(field) as "Min_Value", max(field) as "Max_Value", approx_count_distinct(field) as "Unique_Value_Count", count(field) as "Total_Count")
    .withColumn("Table_Name", lit("test"))
    .withColumn("Column_Name", lit(field))
    .withColumn("Perc", fraction('Unique_Value_Count, 'Total_Count))
    .select('Table_Name, 'Column_Name, 'Min_Value, 'Max_Value, 'Unique_Value_Count, 'Perc)
})

val df = dfs.reduce(_ union _)

On a test example like this:

val tempdftable = spark.sparkContext.parallelize(List((3.0, 7.0, 2.0), (1.0, 4.0, 10.0), (3.0, 7.0, 2.0), (5.0, 0.0, 2.0))).toDF("colA", "colB", "colC")

tempdftable.show

+----+----+----+
|colA|colB|colC|
+----+----+----+
| 3.0| 7.0| 2.0|
| 1.0| 4.0|10.0|
| 3.0| 7.0| 2.0|
| 5.0| 0.0| 2.0|
+----+----+----+

We get

df.show

+----------+-----------+---------+---------+------------------+----+
|Table_Name|Column_Name|Min_Value|Max_Value|Unique_Value_Count|Perc|
+----------+-----------+---------+---------+------------------+----+
|      test|       colA|      1.0|      5.0|                 3|75.0|
|      test|       colB|      0.0|      7.0|                 3|75.0|
|      test|       colC|      2.0|     10.0|                 2|50.0|
+----------+-----------+---------+---------+------------------+----+

Upvotes: 2

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