识别在 Spark 中还原的值

Question

我有一个客户的 Spark DataFrame，如下所示。

#SparkR code
customers <- data.frame(custID = c("001", "001", "001", "002", "002", "002", "002"),
  date = c("2017-02-01", "2017-03-01", "2017-04-01", "2017-01-01", "2017-02-01", "2017-03-01", "2017-04-01"),
  value = c('new', 'good', 'good', 'new', 'good', 'new', 'bad'))
customers <- createDataFrame(customers)
display(customers)

custID|  date     | value
--------------------------
001   | 2017-02-01| new
001   | 2017-03-01| good
001   | 2017-04-01| good
002   | 2017-01-01| new
002   | 2017-02-01| good
002   | 2017-03-01| new
002   | 2017-04-01| bad

在 custID 的第一个月观察中，客户获得 value 的 'new'。此后，它们被分类为 'good' 或 'bad'。但是，客户可以在开设第二个帐户的情况下从 'good' 或 'bad' 恢复到 'new'。发生这种情况时，我想用“2”而不是“1”来标记客户，以表明他们开设了第二个帐户，如下所示。我如何在 Spark 中执行此操作？ SparkR 或 PySpark 命令都有效。

#What I want to get 
custID|  date     | value | tag
--------------------------------
001   | 2017-02-01| new   | 1
001   | 2017-03-01| good  | 1
001   | 2017-04-01| good  | 1
002   | 2017-01-01| new   | 1
002   | 2017-02-01| good  | 1
002   | 2017-03-01| new   | 2
002   | 2017-04-01| bad   | 2

Answer 1

在 pyspark 中：

from pyspark.sql import functions as f

spark = SparkSession.builder.getOrCreate()

# df is equal to your customers dataframe
df = spark.read.load('file:///home/zht/PycharmProjects/test/text_file.txt', format='csv', header=True, sep='|').cache()

df_new = df.filter(df['value'] == 'new').withColumn('tag', f.rank().over(Window.partitionBy('custID').orderBy('date')))
df = df_new.union(df.filter(df['value'] != 'new').withColumn('tag', f.lit(None)))
df = df.withColumn('tag', f.collect_list('tag').over(Window.partitionBy('custID').orderBy('date'))) \
    .withColumn('tag', f.UserDefinedFunction(lambda x: x.pop(), IntegerType())('tag'))

df.show()

并输出：

+------+----------+-----+---+                                                   
|custID|      date|value|tag|
+------+----------+-----+---+
|   001|2017-02-01|  new|  1|
|   001|2017-03-01| good|  1|
|   001|2017-04-01| good|  1|
|   002|2017-01-01|  new|  1|
|   002|2017-02-01| good|  1|
|   002|2017-03-01|  new|  2|
|   002|2017-04-01|  bad|  2|
+------+----------+-----+---+

顺便说一句，pandas可以轻松做到。

Answer 2

这可以使用以下代码完成：

过滤掉所有符合"new"

的记录

df_new<-sql("select * from df where value="new")
createOrReplaceTempView(df_new,"df_new")

df_new<-sql("select *,row_number() over(partiting by custID order by date) 
tag from df_new")
createOrReplaceTempView(df_new,"df_new")

df<-sql("select custID,date,value,min(tag) as tag from 
(select t1.*,t2.tag from df t1 left outer join df_new t2 on 
t1.custID=t2.custID and t1.date>=t2.date) group by 1,2,3")

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