1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
|
#' Backend: Databricks Spark SQL
#'
#' @description
#' See `vignette("translation-function")` and `vignette("translation-verb")` for
#' details of overall translation technology. Key differences for this backend
#' are better translation of statistical aggregate functions
#' (e.g. `var()`, `median()`) and use of temporary views instead of temporary
#' tables when copying data.
#'
#' Use `simulate_spark_sql()` with `lazy_frame()` to see simulated SQL without
#' converting to live access database.
#'
#' @name backend-spark-sql
#' @aliases NULL
#' @examples
#' library(dplyr, warn.conflicts = FALSE)
#'
#' lf <- lazy_frame(a = TRUE, b = 1, d = 2, c = "z", con = simulate_spark_sql())
#'
#' lf %>% summarise(x = median(d, na.rm = TRUE))
#' lf %>% summarise(x = var(c, na.rm = TRUE), .by = d)
#'
#' lf %>% mutate(x = first(c))
#' lf %>% mutate(x = first(c), .by = d)
NULL
#' @export
#' @rdname backend-spark-sql
simulate_spark_sql <- function() simulate_dbi("Spark SQL")
#' @export
`dbplyr_edition.Spark SQL` <- function(con) {
2L
}
#' @export
`sql_translation.Spark SQL` <- function(con) {
sql_variant(
sql_translator(.parent = base_odbc_scalar,
# clock ---------------------------------------------------------------
add_days = function(x, n, ...) {
check_dots_empty()
sql_expr(date_add(!!x, !!n))
},
add_years = function(x, n, ...) {
check_dots_empty()
sql_expr(add_months(!!!x, !!n*12))
},
date_build = function(year, month = 1L, day = 1L, ..., invalid = NULL) {
sql_expr(make_date(!!year, !!month, !!day))
},
get_year = function(x) {
sql_expr(date_part('YEAR', !!x))
},
get_month = function(x) {
sql_expr(date_part('MONTH', !!x))
},
get_day = function(x) {
sql_expr(date_part('DAY', !!x))
},
difftime = function(time1, time2, tz, units = "days") {
if (!missing(tz)) {
cli::cli_abort("The {.arg tz} argument is not supported for SQL backends.")
}
if (units[1] != "days") {
cli::cli_abort('The only supported value for {.arg units} on SQL backends is "days"')
}
sql_expr(datediff(!!time2, !!time1))
}
),
sql_translator(.parent = base_odbc_agg,
var = sql_aggregate("VARIANCE", "var"),
quantile = sql_quantile("PERCENTILE"),
median = sql_aggregate("MEDIAN"),
first = function(x, na_rm = FALSE) {
check_na_rm(na_rm)
glue_sql2(sql_current_con(), "FIRST({.val x})")
},
last = function(x, na_rm = FALSE) {
check_na_rm(na_rm)
glue_sql2(sql_current_con(), "LAST({.val x})")
},
),
sql_translator(.parent = base_odbc_win,
var = win_aggregate("VARIANCE"),
quantile = sql_quantile("PERCENTILE", window = TRUE),
median = win_aggregate("MEDIAN"),
first = function(x, order_by = NULL, na_rm = FALSE) {
sql_nth(x, 1L, order_by = order_by, na_rm = na_rm, ignore_nulls = "bool")
},
last = function(x, order_by = NULL, na_rm = FALSE) {
sql_nth(x, Inf, order_by = order_by, na_rm = na_rm, ignore_nulls = "bool")
},
nth = function(x, n, order_by = NULL, na_rm = FALSE) {
sql_nth(x, n, order_by = order_by, na_rm = na_rm, ignore_nulls = "bool")
},
)
)
}
#' @export
`sql_table_analyze.Spark SQL` <- function(con, table, ...) {
# https://docs.databricks.com/en/sql/language-manual/sql-ref-syntax-aux-analyze-table.html
glue_sql2(con, "ANALYZE TABLE {.tbl table} COMPUTE STATISTICS")
}
#' @export
`supports_window_clause.Spark SQL` <- function(con) {
TRUE
}
#' @export
`db_copy_to.Spark SQL` <- function(con,
table,
values,
...,
overwrite = FALSE,
types = NULL,
temporary = TRUE,
unique_indexes = NULL,
indexes = NULL,
analyze = TRUE,
in_transaction = FALSE) {
if (temporary) {
sql <- sql_values_subquery(con, values, types = types, lvl = 1)
db_compute(con, table, sql, overwrite = overwrite)
} else {
NextMethod()
}
}
#' @export
`db_compute.Spark SQL` <- function(con,
table,
sql,
...,
overwrite = FALSE,
temporary = TRUE,
unique_indexes = list(),
indexes = list(),
analyze = TRUE,
in_transaction = FALSE) {
if (!temporary) {
cli::cli_abort("Spark SQL only support temporary tables")
}
sql <- glue_sql2(
con,
"CREATE ", if (overwrite) "OR REPLACE ",
"TEMPORARY VIEW {.tbl {table}} AS \n",
"{.from {sql}}"
)
DBI::dbExecute(con, sql)
table
}
utils::globalVariables(c("regexp_replace", "date_add", "add_months", "datediff"))
|
