在SQLite中使用子查询更新表

Question

我想使用ALTER TABLE和UPDATE语句添加一个列到我的表,而不是重新创建完整的表.

在我的UPDATE语句中使用子查询时,我没有得到我期望的输出.

建立可重复的数据

library(dplyr)
library(dbplyr)
library(DBI)
con <- DBI::dbConnect(RSQLite::SQLite(), path = ":memory:")
copy_to(con, iris[c(1,2,51),],"iris")

tbl(con,"iris")
# # Source:   table<iris> [?? x 5]
# # Database: sqlite 3.19.3 []
#   Sepal.Length Sepal.Width Petal.Length Petal.Width    Species
#          <dbl>       <dbl>        <dbl>       <dbl>      <chr>
# 1          5.1         3.5          1.4         0.2     setosa
# 2          4.9         3.0          1.4         0.2     setosa
# 3          7.0         3.2          4.7         1.4 versicolor

在单独的表中创建新列

DBI::dbSendQuery(con, "CREATE TABLE new_table AS SELECT t2.new_col from
                 iris t1 inner join 
                 (SELECT Species, sum(`Sepal.Width`) as new_col FROM iris GROUP BY Species) t2
                 on t1.Species = t2.Species")

tbl(con,"new_table")
# # Source:   table<new_table> [?? x 1]
# # Database: sqlite 3.19.3 []
#   new_col
#     <dbl>
# 1     6.5
# 2     6.5
# 3     3.2

在旧表中创建新列

DBI::dbSendQuery(con, "ALTER TABLE iris ADD COLUMN new_col DOUBLE")

尝试从new_table那里插入新列

DBI::dbSendQuery(con, "UPDATE iris SET new_col = (SELECT new_col FROM new_table)")

tbl(con,"iris")
# # Source:   table<iris> [?? x 6]
# # Database: sqlite 3.19.3 []
#   Sepal.Length Sepal.Width Petal.Length Petal.Width    Species new_col
#          <dbl>       <dbl>        <dbl>       <dbl>      <chr>   <dbl>
# 1          5.1         3.5          1.4         0.2     setosa     6.5
# 2          4.9         3.0          1.4         0.2     setosa     6.5
# 3          7.0         3.2          4.7         1.4 versicolor     6.5

正如您所看到的,我new_col只包含了6.5我预期3.2在最后一行中所具有的值.我怎样才能解决这个问题 ？

Answer 1

SQL数据库中表中的行没有固有顺序.所以你不能像在R中那样分配值的"向量".但是,你可以稍微修改你的查询:

library(dplyr)
library(DBI)
con <- DBI::dbConnect(RSQLite::SQLite(), path = ":memory:")
copy_to(con, iris[c(1,2,51),],"iris")

使用聚合数据创建单独的表

DBI::dbSendQuery(con, "CREATE TABLE new_table AS 
                       SELECT Species, sum(`Sepal.Width`) as new_col FROM iris GROUP BY Species")

tbl(con,"new_table")
#> # Source:   table<new_table> [?? x 2]
#> # Database: sqlite 3.22.0 []
#>   Species    new_col
#>   <chr>        <dbl>
#> 1 setosa         6.5
#> 2 versicolor     3.2

在旧表中创建新列

DBI::dbSendQuery(con, "ALTER TABLE iris ADD COLUMN new_col DOUBLE")

使用相关子查询将数据移动到原始表

DBI::dbSendQuery(con, "UPDATE iris SET new_col = (SELECT new_col FROM new_table t2
                               WHERE iris.Species = t2.Species)")

tbl(con,"iris")
#> # Source:   table<iris> [?? x 6]
#> # Database: sqlite 3.22.0 []
#>   Sepal.Length Sepal.Width Petal.Length Petal.Width Species    new_col
#>          <dbl>       <dbl>        <dbl>       <dbl> <chr>        <dbl>
#> 1          5.1         3.5          1.4         0.2 setosa         6.5
#> 2          4.9         3            1.4         0.2 setosa         6.5
#> 3          7           3.2          4.7         1.4 versicolor     3.2

如果您有多个计算列,可以UPDATE ... SET (c1, c2, ...) = (...)像这样使用:

library(dplyr)
library(dbplyr)
library(DBI)
con <- DBI::dbConnect(RSQLite::SQLite(), path = ":memory:")
copy_to(con, iris[c(1,2,51),],"iris")

DBI::dbSendQuery(con, "CREATE TABLE aggs AS 
                       SELECT Species, 
                              SUM(`Sepal.Width`) AS sw_sum,
                              AVG(`Sepal.Width`) AS sw_avg 
                       FROM iris GROUP BY Species")
tbl(con,"aggs")
#> # Source:   table<aggs> [?? x 3]
#> # Database: sqlite 3.22.0 []
#>   Species    sw_sum sw_avg
#>   <chr>       <dbl>  <dbl>
#> 1 setosa        6.5   3.25
#> 2 versicolor    3.2   3.2

DBI::dbSendQuery(con, "ALTER TABLE iris ADD COLUMN sw_sum DOUBLE")
DBI::dbSendQuery(con, "ALTER TABLE iris ADD COLUMN sw_avg DOUBLE")

DBI::dbSendQuery(con, "UPDATE iris 
                       SET (sw_sum, sw_avg) = (SELECT sw_sum, sw_avg 
                           FROM aggs WHERE iris.Species = aggs.Species)")

tbl(con,"iris")
#> # Source:   table<iris> [?? x 7]
#> # Database: sqlite 3.22.0 []
#>   Sepal.Length Sepal.Width Petal.Length Petal.Width Species  sw_sum sw_avg
#>          <dbl>       <dbl>        <dbl>       <dbl> <chr>     <dbl>  <dbl>
#> 1          5.1         3.5          1.4         0.2 setosa      6.5   3.25
#> 2          4.9         3            1.4         0.2 setosa      6.5   3.25
#> 3          7           3.2          4.7         1.4 versico…    3.2   3.2

这也适用于Postgres,但可能不适用于SQL Server.

实际上,在这种情况下,不需要中间表:

library(dplyr)
library(dbplyr)
library(DBI)
con <- DBI::dbConnect(RSQLite::SQLite(), path = ":memory:")
copy_to(con, iris[c(1,2,51),],"iris")

DBI::dbSendQuery(con, "ALTER TABLE iris ADD COLUMN sw_sum DOUBLE")
DBI::dbSendQuery(con, "ALTER TABLE iris ADD COLUMN sw_avg DOUBLE")

DBI::dbSendQuery(con, "UPDATE iris 
                       SET (sw_sum, sw_avg) = 
                              (SELECT sw_sum, sw_avg FROM 
                                    (SELECT Species, 
                                            SUM(`Sepal.Width`) AS sw_sum, 
                                            AVG(`Sepal.Width`) AS sw_avg 
                                     FROM iris GROUP BY Species) aggs 
                               WHERE iris.Species = aggs.Species)")

tbl(con,"iris")
#> # Source:   table<iris> [?? x 7]
#> # Database: sqlite 3.22.0 []
#>   Sepal.Length Sepal.Width Petal.Length Petal.Width Species  sw_sum sw_avg
#>          <dbl>       <dbl>        <dbl>       <dbl> <chr>     <dbl>  <dbl>
#> 1          5.1         3.5          1.4         0.2 setosa      6.5   3.25
#> 2          4.9         3            1.4         0.2 setosa      6.5   3.25
#> 3          7           3.2          4.7         1.4 versico…    3.2   3.2

但是,中间表在其他情况下可能会有所帮助.例如,在链接问题中使用R创建它时.