R 加入后筛选出错

我使用的代码如下所示：（只是一个简单的连接）

单个数据帧的内容并不重要。连接本身会起作用。为了节省计算能力，我想在加入后直接过滤（或其他什么）

但现在我得到了一条错误消息，Spark无法解析variable number.x

我不明白，因为变量是错误消息的一部分：

Error: org.apache.spark.sql.AnalysisException: cannot resolve '`number.x`' given input columns: [elemname.x, kind.y, timefrom, timetodeg, timeto, kind.x, elemuid, elemname.y, number.y, number.x]; line 7 pos 7;
'Project [*]
+- 'Filter ('number.x > 2500.0)
   +- SubqueryAlias yoxgbdyqlw
      +- Project [elemuid#7505 AS elemuid#7495, elemname#7506 AS elemname.x#7496, kind#7507 AS kind.x#7497, number#7508 AS number.x#7498, timefrom#7509 AS timefrom#7499, timeto#7510 AS timeto#7500, elemname#7512 AS elemname.y#7501, kind#7513 AS kind.y#7502, number#7514 AS number.y#7503, timetodeg#7516 AS timetodeg#7504]
         +- Join Inner, ((timefrom#7509 = timefromdeg#7515) && (elemuid#7505 = elemuid#7511))
            :- SubqueryAlias TBL_LEFT
            :  +- SubqueryAlias dez
            :     +- HiveTableRelation `default`.`dez`, org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe, [elemuid#7505, elemname#7506, kind#7507, number#7508, timefrom#7509, timeto#7510]
            +- SubqueryAlias TBL_RIGHT
               +- SubqueryAlias deg
                  +- HiveTableRelation `default`.`deg`, org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe, [elemuid#7511, elemname#7512, kind#7513, number#7514, timefromdeg#7515, timetodeg#7516]

加入后的

collect（）

不是选项，因为这样我的内存就用完了。有没有可能让事情发生

---

我会很高兴得到帮助的

TL；DR不要使用默认的

后缀
（
c（“.x”，“.y”）
）：

但据我所知，用
dplyr
API来表达这一点是不可能的（也许一些
rlang
技巧可以，但我现在想不出任何方法）

这个问题实际上并不特定于联接。您应该避免在名称中使用
。如果出于某种原因，您可以随时转到本机Spark API并解决问题：

df3 <- copy_to(sc, tibble(value.x = rnorm(42)))

df3 %>% 
  spark_dataframe() %>% 
  invoke("withColumnRenamed", "`value.x`", "value_x") %>%
  sdf_register()

# # Source:   table<sparklyr_tmp_61acdbbc592> [?? x 1]
# # Database: spark_connection
#    value_x
#      <dbl>
#  1 -0.0162
#  2  0.944 
#  3  0.821 
#  4  0.594 
#  5  0.919 
#  6  0.782 
#  7  0.0746
#  8 -1.99  
#  9  0.620 
# 10 -0.0561
# # ... with more r

df3%
spark_dataframe（）%>%
调用（“WithColumnRename”、“value.x`”、“value\ux”）%>%
sdf_寄存器（）
##来源：表[？x 1]
##数据库：spark_连接
#价值x
#      
#  1 -0.0162
#  2  0.944 
#  3  0.821 
#  4  0.594 
#  5  0.919 
#  6  0.782 
#  7  0.0746
#  8 -1.99  
#  9  0.620 
# 10 -0.0561
# # ... 带着更多的r

set.seed(1)

df1 <- copy_to(sc, tibble(id = 1:3, value = rnorm(3)))
df2 <- copy_to(sc, tibble(id = 1:3, value = rnorm(3)))

df1 %>% 
  inner_join(df2, by = c("id"), suffix=c("_x", "_y")) %>% 
  filter(value_y > -0.836)

# # Source:   lazy query [?? x 3]
# # Database: spark_connection
#      id value_x value_y
#   <dbl>   <dbl>   <dbl>
# 1    1.  -0.626   1.60 
# 2    2.   0.184   0.330
# 3    3.  -0.836  -0.820

`number.x`

df3 <- copy_to(sc, tibble(value.x = rnorm(42)))

df3 %>% 
  spark_dataframe() %>% 
  invoke("withColumnRenamed", "`value.x`", "value_x") %>%
  sdf_register()

# # Source:   table<sparklyr_tmp_61acdbbc592> [?? x 1]
# # Database: spark_connection
#    value_x
#      <dbl>
#  1 -0.0162
#  2  0.944 
#  3  0.821 
#  4  0.594 
#  5  0.919 
#  6  0.782 
#  7  0.0746
#  8 -1.99  
#  9  0.620 
# 10 -0.0561
# # ... with more r