r - Sparklyr/Hive : how to use regex (regexp_replace) correctly?

Question

考虑下面的例子

dataframe_test<- data_frame(mydate = c('2011-03-01T00:00:04.226Z', '2011-03-01T00:00:04.226Z'))

# A tibble: 2 x 1
                    mydate
                     <chr>
1 2011-03-01T00:00:04.226Z
2 2011-03-01T00:00:04.226Z

sdf <- copy_to(sc, dataframe_test, overwrite = TRUE)

> sdf
# Source:   table<dataframe_test> [?? x 1]
# Database: spark_connection
                    mydate
                     <chr>
1 2011-03-01T00:00:04.226Z
2 2011-03-01T00:00:04.226Z

我想修改字符 timestamp 以使其具有更常规的格式。我尝试使用 regexp_replace 这样做，但失败了。

> sdf <- sdf %>% mutate(regex = regexp_replace(mydate, '(\\d{4})-(\\d{2})-(\\d{2})T(\\d{2}):(\\d{2}):(\\d{2}).(\\d{3})Z', '$1-$2-$3 $4:$5:$6.$7'))
> sdf
# Source:   lazy query [?? x 2]
# Database: spark_connection
                    mydate                    regex
                     <chr>                    <chr>
1 2011-03-01T00:00:04.226Z 2011-03-01T00:00:04.226Z
2 2011-03-01T00:00:04.226Z 2011-03-01T00:00:04.226Z

有任何想法吗？什么是正确的语法？

Answer 1

Spark SQL 和 Hive 提供两种不同的功能:

regexp_extract - 它需要字符串、模式和要提取的组的索引。

regexp_replace - 它接受一个字符串、模式和替换字符串。

前者可用于提取 单个组 ，索引语义为 being the same 与 java.util.regex.Matcher 相同

对于 regexp_replace 模式必须匹配整个字符串，如果没有匹配，则返回输入字符串:

sdf %>% mutate(
 regex = regexp_replace(mydate, '^([0-9]{4}).*', "$1"),
 regexp_bad = regexp_replace(mydate, '([0-9]{4})', "$1"))

## Source:   query [2 x 3]
## Database: spark connection master=local[8] app=sparklyr local=TRUE
## 
## # A tibble: 2 x 3
##                     mydate regex               regexp_bad
##                      <chr> <chr>                    <chr>
## 1 2011-03-01T00:00:04.226Z  2011 2011-03-01T00:00:04.226Z
## 2 2011-03-01T00:00:04.226Z  2011 2011-03-01T00:00:04.226Z

而使用 regexp_extract 则不需要:

sdf %>% mutate(regex = regexp_extract(mydate, '([0-9]{4})', 1))

## Source:   query [2 x 2]
## Database: spark connection master=local[8] app=sparklyr local=TRUE
## 
## # A tibble: 2 x 2
##                     mydate regex
##                      <chr> <chr>
## 1 2011-03-01T00:00:04.226Z  2011
## 2 2011-03-01T00:00:04.226Z  2011

此外，由于间接执行(R -> Java)，您必须转义两次:

sdf %>% mutate(
  regex = regexp_replace(
    mydate, 
    '^(\\\\d{4})-(\\\\d{2})-(\\\\d{2})T(\\\\d{2}):(\\\\d{2}):(\\\\d{2}).(\\\\d{3})Z$',
    '$1-$2-$3 $4:$5:$6.$7'))

通常人们会使用 Spark 日期时间函数:

spark_session(sc) %>%  
  invoke("sql",
    "SELECT *, DATE_FORMAT(CAST(mydate AS timestamp), 'yyyy-MM-dd HH:mm:ss.SSS') parsed from dataframe_test") %>% 
  sdf_register


## Source:   query [2 x 2]
## Database: spark connection master=local[8] app=sparklyr local=TRUE
## 
## # A tibble: 2 x 2
##                     mydate                  parsed
##                      <chr>                   <chr>
## 1 2011-03-01T00:00:04.226Z 2011-03-01 01:00:04.226
## 2 2011-03-01T00:00:04.226Z 2011-03-01 01:00:04.226

但遗憾的是 sparklyr 在这方面似乎极其有限，并将时间戳视为字符串。

另见 change string in DF using hive command and mutate with sparklyr 。