10.6.2.6 rqRowEval2 Function
The function rqRowEval2 when used in Oracle Autonomous
Database,chunks data into sets of rows and then runs a user-defined R function on each
chunk.
The function rqRowEval2 passes the data specified by the
INP_NAM parameter to the user-defined R function. You can pass arguments to
the R function with the PAR_LST parameter. The ROW_NUM
parameter specifies the number of rows that should be passed to each invocation of the R
function. The last chunk may have fewer rows than the number specified.
The rqRowEval2 function supports data-parallel execution, in which one or more R engines perform the same R function, or task, on disjoint chunks of data. Oracle Database handles the management and control of the potentially multiple R engines that run on the database server machine, automatically chunking and passing data to the R engines executing in parallel. Oracle Database ensures that R function executions for all chunks of rows complete, or the rqRowEval2 function returns an error.
You define the form of the returned value with the OUT_FMT
parameter.
Syntax
rqRowEval2(
INP_NAM VARCHAR2,
PAR_LST VARCHAR2,
OUT_FMT VARCHAR2,
ROW_NUM NUMBER,
SCR_NAME VARCHAR2,
SCR_OWNER VARCHAR2 DEFAULT NULL,
ENV_NAME VARCHAR2 DEFAULT NULL
)Parameters
Table 10-20 Parameters of the rqRowEval2 Function
| Parameter | Description |
|---|---|
|
|
The name of a table or view that specifies the data to pass to the R
function specified by the
You must have read access to the specified table or view. |
|
|
A JSON string that contains additional parameters to pass to the user-defined R
function specified by the For example, to capture images rendered in the R function, use:
See also: Special Control Arguments. |
|
|
The format of the output returned by the function. It can be one of the following:
See also: Output Formats. |
|
|
The number of rows in a chunk. The R script is executed in each chunk. |
|
|
The name of a user-defined R function in the OML4R script repository. |
|
|
The owner of the registered R script. The default value is
NULL. If NULL, will search for the R script in
the user’s script repository.
|
|
|
The name of the conda environment that should be used when running the named user-defined R function. |
Return Value
Function rqRowEval2 returns a table that has the structure
specified by the OUT_FMT parameter value.
Examples
Example 10-42 Using an rqRowEval2 Function
This example creates a user-defined function and saves the function in the OML4R script repository.
The PL/SQL block, creates the script scoreLM and add it to the script
repository.
%script
BEGIN
sys.rqScriptCreate('scoreLM',
'function(dat, dsname){
ore.load(dsname)
dat$Petal.Length_pred <- predict(mod, newdata=dat)
dat[,c("Petal.Length_pred","Petal.Length","Species")]}',
v_global => FALSE,
v_overwrite => TRUE);
END;
/
The results is:
PL/SQL procedure successfully completed.
---------------------------Example 10-43 JSON Output
The PAR_LST argument specifies using MEDIUM service level with the special control argument ore_service_level and. In the OUT_FMT argument, the string 'JSON', specifies that the table returned contains a CLOB that is a JSON string. The SCR_NAME parameter specifies the scoreLM function in the script repository as the R function to call. The JSON output is a CLOB. You can call set long [length] to get more output.
%script
set long 1000
SELECT * FROM table(rqRowEval2(
inp_nam => 'IRIS',
par_lst => '{"dsname":"ds-1", "ore_parallel_flag":true, "ore_service_level":"MEDIUM"}',
out_fmt => 'JSON',
row_num => 5,
scr_name => 'scoreLM'));The result is:
---------------------------
NAME VALUE
[{"Petal.Length_pred":1.5295,"Species":"setosa","Petal.Length":1},{"Petal.Length_pred":1.3066,"Species":"setosa","Petal.Length":1.1},{"Petal.Length_pred":1.5295,"Species":"setosa","Petal.Length":1.2},{"Petal.Length_pred":1.5295,"Species":"setosa","Petal.Length":1.2},{"Petal.Length_pred":1.5295,"Species":"setosa","Petal.Length":1.3},{"Petal.Length_pred":1.5295,"Species":"setosa","Petal.Length":1.3},{"Petal.Length_pred":1.5295,"Species":"setosa","Petal.Length":1.3},{"Petal.Length_pred":1.5295,"Species":"setosa","Petal.Length":1.3},{"Petal.Length_pred":1.7525,"Species":"setosa","Petal.Length":1.3},{"Petal.Length_pred":1.7525,"Species":"setosa","Petal.Length":1.3},{"Petal.Length_pred":1.9755,"Species":"setosa","Petal.Length":1.3},{"Petal.Length_pred":1.3066,"Species":"setosa","Petal.Length":1.4},{"Petal.Length_pred":1.3066,"Species":"setosa","Petal.Length":1.4},{"Petal.Length_pred":1.5295,"Species":"setosa","Petal.Length":1.4},{"Petal.Length_pred":1.5295,"Species":"setosa","Petal.Length":1 Example 10-44 Relational
Run the Select statement to get an
Relational output.
%script
SELECT * FROM table(rqRowEval2(
inp_nam => 'IRIS',
par_lst => '{"dsname":"ds-1", "ore_parallel_flag":true, "ore_service_level":"MEDIUM"}',
out_fmt => '{"Petal.Length_pred":"NUMBER", "Petal.Length":"NUMBER", "Species":"VARCHAR2(10)"}',
row_num => 5,
scr_name => 'scoreLM'));The result is:
Petal.Length_pred Petal.Length Species
1.5295 1 setosa
1.3066 1.1 setosa
1.5295 1.2 setosa
1.5295 1.2 setosa
1.5295 1.3 setosa
1.5295 1.3 setosa
1.5295 1.3 setosa
1.5295 1.3 setosa
1.7525 1.3 setosa
1.7525 1.3 setosa
1.9755 1.3 setosa
1.3066 1.4 setosa
1.3066 1.4 setosa
1.5295 1.4 setosa
Petal.Length_pred Petal.Length Species
1.5295 1.4 setosa
1.5295 1.4 setosa
1.5295 1.4 setosa
1.5295 1.4 setosa
1.5295 1.4 setosa
1.5295 1.4 setosa
1.5295 1.4 setosa
1.7525 1.4 setosa
1.7525 1.4 setosa
1.7525 1.4 setosa
1.3066 1.5 setosa
1.3066 1.5 setosa
1.5295 1.5 setosa
1.5295 1.5 setosa
Petal.Length_pred Petal.Length Species
1.5295 1.5 setosa
1.5295 1.5 setosa
1.5295 1.5 setosa
1.5295 1.5 setosa
1.5295 1.5 setosa
1.7525 1.5 setosa
1.9755 1.5 setosa
1.9755 1.5 setosa
1.9755 1.5 setosa
1.5295 1.6 setosa
1.5295 1.6 setosa
1.5295 1.6 setosa
1.5295 1.6 setosa
1.5295 1.6 setosa
Petal.Length_pred Petal.Length Species
1.9755 1.6 setosa
2.4215 1.6 setosa
1.5295 1.7 setosa
1.7525 1.7 setosa
1.9755 1.7 setosa
2.1985 1.7 setosa
1.5295 1.9 setosa
1.9755 1.9 setosa
3.5365 3 versicolor
3.3135 3.3 versicolor
3.3135 3.3 versicolor
3.3135 3.5 versicolor
3.3135 3.5 versicolor
3.9825 3.6 versicolor
Petal.Length_pred Petal.Length Species
3.3135 3.7 versicolor
3.5365 3.8 versicolor
3.5365 3.9 versicolor
3.7595 3.9 versicolor
4.2055 3.9 versicolor
3.3135 4 versicolor
3.7595 4 versicolor
3.9825 4 versicolor
3.9825 4 versicolor
3.9825 4 versicolor
3.3135 4.1 versicolor
3.9825 4.1 versicolor
3.9825 4.1 versicolor
3.7595 4.2 versicolor
Petal.Length_pred Petal.Length Species
3.9825 4.2 versicolor
3.9825 4.2 versicolor
4.4285 4.2 versicolor
3.9825 4.3 versicolor
3.9825 4.3 versicolor
3.7595 4.4 versicolor
3.9825 4.4 versicolor
4.2055 4.4 versicolor
4.2055 4.4 versicolor
4.8745 4.5 virginica
3.9825 4.5 versicolor
4.4285 4.5 versicolor
4.4285 4.5 versicolor
4.4285 4.5 versicolor
Petal.Length_pred Petal.Length Species
4.4285 4.5 versicolor
4.4285 4.5 versicolor
4.6515 4.5 versicolor
3.9825 4.6 versicolor
4.2055 4.6 versicolor
4.4285 4.6 versicolor
3.7595 4.7 versicolor
4.2055 4.7 versicolor
4.2055 4.7 versicolor
4.4285 4.7 versicolor
4.6515 4.7 versicolor
5.0975 4.8 virginica
5.0975 4.8 virginica
4.2055 4.8 versicolor
Petal.Length_pred Petal.Length Species
5.0975 4.8 versicolor
5.0975 4.9 virginica
5.0975 4.9 virginica
5.5434 4.9 virginica
4.4285 4.9 versicolor
4.4285 4.9 versicolor
4.4285 5 virginica
5.3204 5 virginica
5.5434 5 virginica
4.8745 5 versicolor
4.4285 5.1 virginica
5.0975 5.1 virginica
5.3204 5.1 virginica
5.3204 5.1 virginica
Petal.Length_pred Petal.Length Species
5.5434 5.1 virginica
6.2124 5.1 virginica
6.4354 5.1 virginica
4.6515 5.1 versicolor
5.5434 5.2 virginica
6.2124 5.2 virginica
5.3204 5.3 virginica
6.2124 5.3 virginica
5.7664 5.4 virginica
6.2124 5.4 virginica
5.0975 5.5 virginica
5.0975 5.5 virginica
5.7664 5.5 virginica
4.2055 5.6 virginica
Petal.Length_pred Petal.Length Species
5.0975 5.6 virginica
5.7664 5.6 virginica
5.9894 5.6 virginica
6.4354 5.6 virginica
6.4354 5.6 virginica
5.7664 5.7 virginica
6.2124 5.7 virginica
6.6584 5.7 virginica
4.6515 5.8 virginica
5.0975 5.8 virginica
5.9894 5.8 virginica
5.7664 5.9 virginica
6.2124 5.9 virginica
5.0975 6 virginica
Petal.Length_pred Petal.Length Species
6.6584 6 virginica
5.3204 6.1 virginica
6.2124 6.1 virginica
6.6584 6.1 virginica
5.0975 6.3 virginica
5.5434 6.4 virginica
5.7664 6.6 virginica
5.5434 6.7 virginica
5.9894 6.7 virginica
6.2124 6.9 virginica
150 rows selected.
---------------------------The following code runs a SQL query to retrieve all columns from the result of an rqRowEval2 function call, which generates a PNG output based on a specified script and environment.
select *
from table(rqRowEval2(
inp_nam => 'IRIS',
par_lst => '{"ore_graphics_flag":true}',
out_fmt => 'PNG',
row_num => 50,
scr_name => 'test_ggplot2_inp',
scr_owner => NULL,
env_name => 'myrenv'));The output appears as follows:
NAME ID VALUE IMAGE
---------- ---------- -------------------- ------------------------------
CHUNK_1 1 "hello world" 89504E470D0A1A0A0000000D494844
52000001E0000001E008060000007D
D4BE950000200049444154789CECDD
777C53D5FFC7F1579AA46D3A81963D
CAA60C0505BE381805510115411410
5CA8881B45
CHUNK_2 1 "hello world" 89504E470D0A1A0A0000000D494844
52000001E0000001E008060000007D
D4BE950000200049444154789CEDDD
777814F5DAC6F17B5B7AA5465A420F
NAME ID VALUE IMAGE
---------- ---------- -------------------- ------------------------------
4D22458ED251041BC502088258100B
0A2AA2A2E7
CHUNK_3 1 "hello world" 89504E470D0A1A0A0000000D494844
52000001E0000001E008060000007D
D4BE950000200049444154789CEDDD
777814F5C2C5F1B3BB61D30381D04B
420F4D90F2220AA1280AA84893AA08
2A6247011BParent topic: Embedded R Execution Functions (Autonomous Database)