tdplyr internally creates database objects (tables and views) as required while executing several functions. The temp.table.database.name and temp.view.database.name configurable options allow users to specify database names where tables and views are created, respectively. These two options give users flexibility to specify database names other than the default database to create views and tables.
The td_create_context function provides temp.database and database arguments that also allow user to specify the database name, where database objects can be created. If none of them are specified then the objects are created in users' default database.
All the database objects created by tdplyr are garbage-collected at the end of the session.
While using these options,
- If the option temp.table.database.name is NULL, then tables are created in the database specified at the time of context creation.
- If the option temp.view.database.name is NULL, then views are created in the database specified at the time of context creation.
- Context need not be created when these two configurable options are used.
- Teradata recommends using these options when following conditions are true, otherwise, use the arguments in the td_create_context() function:
- When a user has permissions to create tables in one database while views in other database.
- In case a user wishes to create these temporary objects in a database different than the one specified at the time of context creation. This allows the user to change the database name without re-creating the context.
- In case a user forgets to use the td_create_context() arguments to set the temp database name, then use these options.
Example
> options("temp.table.database.name" = "tempdb")
> td_glm_out1 <- td_glm_mle(formula = (admitted ~ stats + masters + gpa + programming),
family = "LOGISTIC",
linkfunction = "LOGIT",
data = admissions_train,
weights = "1",
threshold = 0.01,
maxit = 25,
step = FALSE,
intercept = TRUE
)
SQL-MR Query :
SELECT * FROM GLM (
ON "ALICE"."admissions_train" as InputTable
OUT TABLE OutputTable("tempdb".r__t__td_glm_mle0_1623374476821412)
USING
WeightColumn('1')
Family('LOGISTIC')
LinkFunction('LOGIT')
StopThreshold('0.01')
MaxIterNum('25')
Intercept('TRUE')
Step('FALSE')
InputColumns('admitted','stats','masters','gpa','programming')
CategoricalColumns('stats','masters','programming')
) as sqlmr
> td_glm_out1
############ coefficients ############
# Source: table<"tempdb"."r__t__td_glm_mle0_1623374476821412"> [?? x 9]
# Database: Teradata
attribute predictor category estimate std_err z_score p_value significance
<int> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <chr>
1 4 masters yes NA NA NA NA NA
2 8 programm~ Beginner -1.77 1.07 -1.66 0.0978 "."
3 -1 Loglik NA -19.5 40 6 0 NA
4 1 stats Advanced NA NA NA NA NA
5 5 masters no 2.22 1.02 2.17 0.0298 "*"
6 7 programm~ Advanced NA NA NA NA NA
7 9 programm~ Novice -0.980 1.14 -0.860 0.390 " "
8 3 stats Beginner 0.527 1.22 0.431 0.667 " "
9 6 gpa NA -0.114 0.803 -0.142 0.887 " "
10 2 stats Novice 0.0407 1.12 0.0365 0.971 " "
# ... with more rows, and 1 more variable: family <chr>
############ output ############
# Source: table<"tempdb"."r__t__sqlmr_stdout_1623374477351814"> [?? x 6]
# Database: Teradata
predictor estimate std_error z_score p_value significance
<chr> <dbl> <dbl> <dbl> <dbl> <chr>
1 stats.Beginner 0.527 1.22 0.431 0.667 " "
2 gpa -0.114 0.803 -0.142 0.887 " "
3 programming.Begi~ -1.77 1.07 -1.66 0.0978 "."
4 programming.Novi~ -0.980 1.14 -0.860 0.390 " "
5 ROWS # 40 0 0 0 "Number of rows"
6 Residual deviance 38.9 0 0 0 "on 33 degrees of freedom"
7 Pearson goodness~ 37.8 0 0 0 "on 33 degrees of freedom"
8 ITERATIONS # 4 0 0 0 "Number of Fisher Scoring~
9 masters.no 2.22 1.02 2.17 0.0298 "*"
10 stats.Novice 0.0407 1.12 0.0365 0.971 " "
# ... with more rows