Scala 将big spark sql查询分解为较小的查询并将其合并
我有一个大的sparksql语句,我试图将它分成更小的块,以提高代码的可读性。我不想加入它,只是合并结果 当前工作sql语句-Scala 将big spark sql查询分解为较小的查询并将其合并,scala,apache-spark,apache-spark-sql,spark-streaming,spark-dataframe,Scala,Apache Spark,Apache Spark Sql,Spark Streaming,Spark Dataframe,我有一个大的sparksql语句,我试图将它分成更小的块,以提高代码的可读性。我不想加入它,只是合并结果 当前工作sql语句- val dfs = x.map(field => spark.sql(s" select ‘test’ as Table_Name, '$field' as Column_Name, min($field) as Min_Value, max($field) as Max_Value,
val dfs = x.map(field => spark.sql(s"
select ‘test’ as Table_Name,
'$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
”))
我试图从上面的sql中删除下面的查询
(SELECT 100 * approx_count_distinct($field)/count(1) from tempdftable) as perc
按照这个逻辑-
val Perce = x.map(field => spark.sql(s"(SELECT 100 * approx_count_distinct($field)/count(1) from parquetDFTable)"))
然后将这个val Perce与第一个大SQL语句合并到下面的语句中,但它不起作用-
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
”))
我们怎么写呢?为什么不全力以赴,将整个表达式转换为Spark代码
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 _)
在这样的测试示例中:
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|
+----+----+----+
我们得到
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|
+----------+-----------+---------+---------+------------------+----+
为什么不全力以赴将整个表达式转换为Spark代码
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 _)
在这样的测试示例中:
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|
+----+----+----+
我们得到
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|
+----------+-----------+---------+---------+------------------+----+
谢谢你,格伦尼!这很有帮助,我接受了这个答案,但我擅长SQL,而且很少有表达式使用过分析函数,比如RANK,纯Spark,不知道如何实现这些结果。导入
org.apache.Spark.SQL.functions.\u
可以提供大部分的SQL函数(??)。包括排名
;)再次感谢你,格伦尼!我可以问一下,我可以从哪里获得这些信息,我可以参考的任何文件成为大师:P:)嗯,是的,嗯。。。我不得不说Spark文档不是我见过的最好的;)就我个人而言,我从阅读博客博文中学到了很多东西(从Databricks、Cloudera等网站),但大部分时间我刚刚与Spark合作了近3年。不过,我要说的是,学习Spark语法——即使您擅长sql——也是非常值得的,如果您熟悉LINQ或Java Streams API,那么您将不需要几周就能熟练使用Spark:)谢谢Glennie!这当然有帮助……!:)谢谢你,格伦尼!这很有帮助,我接受了这个答案,但我擅长SQL,而且很少有表达式使用过分析函数,比如RANK,纯Spark,不知道如何实现这些结果。导入org.apache.Spark.SQL.functions.\u
可以提供大部分的SQL函数(??)。包括排名
;)再次感谢你,格伦尼!我可以问一下,我可以从哪里获得这些信息,我可以参考的任何文件成为大师:P:)嗯,是的,嗯。。。我不得不说Spark文档不是我见过的最好的;)就我个人而言,我从阅读博客博文中学到了很多东西(从Databricks、Cloudera等网站),但大部分时间我刚刚与Spark合作了近3年。不过,我要说的是,学习Spark语法——即使您擅长sql——也是非常值得的,如果您熟悉LINQ或Java Streams API,那么您将不需要几周就能熟练使用Spark:)谢谢Glennie!这当然有帮助……!:)