mysql – 在“GROUP BY”子句中重用select表达式的结果？

在MySQL中,我可以有这样的查询：

select  
    cast(from_unixtime(t.time, '%Y-%m-%d %H:00') as datetime) as timeHour
    , ... 
from
    some_table t 
group by
    timeHour, ...
order by
    timeHour, ...

其中GROUP BY中的timeHour是select表达式的结果.

但我只是尝试了类似于Sqark sql的查询,我得到了一个错误

Error: org.apache.spark.sql.AnalysisException: 
cannot resolve '`timeHour`' given input columns: ...

我对Spark sql的查询是这样的：

select  
      cast(t.unixTime as timestamp) as timeHour
    , ...
from
    another_table as t
group by
    timeHour, ...
order by
    timeHour, ...

这个构造在Spark sql中是否可行？

解决方法:

Is this construct possible in Spark sql?

是的.您可以通过两种方式使它在Spark sql中工作,以在GROUP BY和ORDER BY子句中使用新列

方法1使用子查询：

SELECT timeHour, someThing FROM (SELECT  
      from_unixtime((starttime/1000)) AS timeHour
    , sum(...)                          AS someThing
    , starttime
FROM
    some_table) 
WHERE
    starttime >= 1000*unix_timestamp('2017-09-16 00:00:00')
      AND starttime <= 1000*unix_timestamp('2017-09-16 04:00:00')
GROUP BY
    timeHour
ORDER BY
    timeHour
LIMIT 10;

方法2使用WITH //优雅方式：

-- create alias 
WITH table_aliase AS(SELECT  
      from_unixtime((starttime/1000)) AS timeHour
    , sum(...)                          AS someThing
    , starttime
FROM
    some_table)

-- use the same alias as table
SELECT timeHour, someThing FROM table_aliase
WHERE
    starttime >= 1000*unix_timestamp('2017-09-16 00:00:00')
      AND starttime <= 1000*unix_timestamp('2017-09-16 04:00:00')
GROUP BY
    timeHour
ORDER BY
    timeHour
LIMIT 10;

替代使用Scala的Spark DataFrame(wo sql)API：

// This code may need additional import to work well

val df = .... //load the actual table as df

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

df.withColumn("timeHour", from_unixtime($"starttime"/1000))
  .groupBy($"timeHour")
  .agg(sum("...").as("someThing"))
  .orderBy($"timeHour")
  .show()

//another way - as per eliasah comment
df.groupBy(from_unixtime($"starttime"/1000).as("timeHour"))
  .agg(sum("...").as("someThing"))
  .orderBy($"timeHour")
  .show()