Sparklyr中R的list()函数的等效功能是什么? [英] What is the equivalent of R's list() function in sparklyr?

问题描述

下面是一个示例R代码.我想在sparklyr中做同样的事情.

Below is a sample R code. I would like to do the same in sparklyr.

custTrans1 <- Pdt_table %>% 
  group_by(Main_CustomerID) %>% 
  summarise(Invoice = as.vector(list(Invoice_ID)),Industry = as.vector(list(Industry)))

其中Pdt_table是spark数据帧，而Main_CustomerID，Invoice_ID和Industry是变量.

where Pdt_table is spark data frame and Main_CustomerID, Invoice_ID and Industry are variables.

我想创建上述变量的列表并将其转换为向量.如何在sparklyr中做到这一点?

I would like to create list of the above variables and convert it to vector. How can I do it in sparklyr?

推荐答案

您可以使用

You can use collect_list or collect_set:

set.seed(1)
df <- copy_to(
  sc, tibble(group = rep(c("a", "b"), 3), value = runif(6)),
  name = "df"
)

result <- df %>% group_by(group) %>% summarise(values = collect_list(value))
result

# Source:   lazy query [?? x 2]
# Database: spark_connection
  group values    
  <chr> <list>    
1 b     <list [3]>
2 a     <list [3]>

将转换为以下查询:

result %>% show_query()

<SQL>
SELECT `group`, COLLECT_LIST(`value`) AS `values`
FROM `df`
GROUP BY `group`

具有相应的执行计划:

result %>% optimizedPlan()

<jobj[213]>
  org.apache.spark.sql.catalyst.plans.logical.Aggregate
  Aggregate [group#259], [group#259, collect_list(value#260, 0, 0) AS values#345]
+- InMemoryRelation [group#259, value#260], true, 10000, StorageLevel(disk, memory, deserialized, 1 replicas), `df`
      +- Scan ExistingRDD[group#259,value#260]

和架构(带有array<...>列):

root
 |-- group: string (nullable = true)
 |-- values: array (nullable = true)
 |    |-- element: double (containsNull = true)

请记住:

• 这种操作在分布式系统中非常昂贵.
• 依靠数据分布可能不可行.
一般而言，
• 复杂类型在Spark中很难处理，而sparklyr具有整洁的数据焦点，这并不会使事情变得容易.为了有效地处理结果，您可能需要Scala扩展.

• Operation like this one is very expensive in a distributed system.
• Depending on the data distribution might not be feasible.
• Complex types are somewhat hard to handle in Spark in general, and sparklyr with it's tidy data focus, doesn't make things easier. To process the result efficiently you may require a Scala extension.

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