Question

我使用此SQL为数据集创建session_id。如果用户处于非活动状态超过30分钟（30 * 60秒），则会分配一个新的session_id我是Spark SQL新手并尝试使用Spark SQL Context复制相同的过程。但是我遇到了一些错误。

session_id遵循命名约定：
userid_1，
userid_2，
userid_3，...

SQL（日期以秒为单位）：

CREATE TABLE tablename_with_session_id AS 
    SELECT * , userid || '_' || SUM(new_session) OVER (PARTITION BY userid ORDER BY date asc, new_session desc rows unbounded preceding) AS session_id 
        FROM 
                (SELECT *, 
                    CASE 
                    WHEN (date - LAG(date) OVER (PARTITION BY userid ORDER BY date) >= 30 * 60) 
                    THEN 1 
                    WHEN row_number() over (partition by userid order by date) = 1 
                    THEN 1 
                    ELSE 0 
                    END as new_session 
                    FROM 
                        tablename 
                ) 
    order by date;

我尝试在Spark-Scala中使用相同的SQL：

    val sqlContext = new org.apache.spark.sql.SQLContext(sc) 

    val tableSessionID = sqlContext.sql("SELECT * , CONCAT(userid,'_',SUM(new_session)) OVER (PARTITION BY userid ORDER BY date asc, new_session desc rows unbounded preceding) AS new_session_id FROM
(SELECT *, CASE WHEN (date - LAG(date) OVER (PARTITION BY userid ORDER BY date) >= 30 * 60) THEN 1 WHEN row_number() over (partition by userid order by date) = 1 THEN 1 ELSE 0 END as new_session FROM clickstream) order by date")

有些错误建议在窗口函数中包装Spark SQL表达式..sum（new_session）..

我尝试使用多个数据框：

val temp1 = sqlContext.sql("SELECT *, CASE WHEN (date - LAG(date) OVER (PARTITION BY userid ORDER BY date) >= 30 * 60) THEN 1 WHEN row_number() over (partition by userid order by date) = 1 THEN 1 ELSE 0 END as new_session FROM clickstream")

temp1.registerTempTable("clickstream_temp1")

val temp2 = sqlContext.sql("SELECT * , SUM(new_session) OVER (PARTITION BY userid ORDER BY date asc, new_session desc rows unbounded preceding) AS s_id FROM clickstream_temp1")

temp2.registerTempTable("clickstream_temp2")

val temp3 = sqlContext.sql("SELECT * , CONCAT(userid,'_',s_id) OVER (PARTITION BY userid ORDER BY date asc, new_session desc rows unbounded preceding) AS new_session_id FROM clickstream_temp2")

仅在上述语句中返回错误。＆＃39; val temp3 = ...＆＃39; 那个CONCAT（用户ID，＆＃39; _＆＃39;，s_id）不能在窗口函数中使用。

解决方法是什么？还有其他选择吗？

由于

Answer 1

要使用带有火花窗口功能的concat，您需要使用用户定义的聚合函数（UDAF）。你不能直接使用带窗函数的concat函数。

//Extend UserDefinedAggregateFunction to write custom aggregate function
//You can also specify any constructor arguments. For instance you can have
//CustomConcat(arg1: Int, arg2: String)
class CustomConcat() extends org.apache.spark.sql.expressions.UserDefinedAggregateFunction {
import org.apache.spark.sql.types._
import org.apache.spark.sql.expressions.MutableAggregationBuffer
import org.apache.spark.sql.Row
// Input Data Type Schema
def inputSchema: StructType = StructType(Array(StructField("description", StringType)))
// Intermediate Schema
def bufferSchema = StructType(Array(StructField("groupConcat", StringType)))
// Returned Data Type.
def dataType: DataType = StringType
// Self-explaining
def deterministic = true
// This function is called whenever key changes
def initialize(buffer: MutableAggregationBuffer) = {buffer(0) = " ".toString}
// Iterate over each entry of a group
def update(buffer: MutableAggregationBuffer, input: Row) = { buffer(0) = buffer.getString(0) + input.getString(0) }
// Merge two partial aggregates
def merge(buffer1: MutableAggregationBuffer, buffer2: Row) = { buffer1(0) = buffer1.getString(0) + buffer2.getString(0) }
// Called after all the entries are exhausted.
def evaluate(buffer: Row) = {buffer.getString(0)}
}
val newdescription = new CustomConcat
val newdesc1=newdescription($"description").over(windowspec)

您可以使用newdesc1作为窗口函数中串联的聚合函数。有关更多信息，请查看： databricks udaf 我希望这会回答你的问题。

Spark SQL：无法在窗口函数

1 个答案: