Supporting persisted tables for Spark SQL backend

[zacdav-db]

Spark SQL (in this case, against Databricks) should be able to support non-temporary writes, currently this errors like so:

> results <- tbl(con, I("samples.nyctaxi.trips")) %>%
+   group_by(pickup_zip) %>%
+   summarise(avg_trip_dist = mean(trip_distance))

> compute(results, I("zacdav.default.avg_trip_dist"), temporary = FALSE)
Error in `db_compute()`:
! Spark SQL only support temporary tables
Run `rlang::last_trace()` to see where the error occurred.
Warning message:
Missing values are always removed in SQL aggregation functions.
Use `na.rm = TRUE` to silence this warning
This warning is displayed once every 8 hours. 

> rlang::last_trace(drop = FALSE)
<error/rlang_error>
Error in `db_compute()`:
! Spark SQL only support temporary tables
---
Backtrace:
    ▆
 1. ├─dplyr::compute(results, I("zacdav.default.avg_trip_dist"), temporary = FALSE)
 2. └─dbplyr:::compute.tbl_sql(...)
 3.   ├─dbplyr::db_compute(...)
 4.   └─dbplyr:::`db_compute.Spark SQL`(...)
 5.     └─cli::cli_abort("Spark SQL only support temporary tables")
 6.       └─rlang::abort(...)

It looks like the following functions likely need to be adjusted:

• Adjust db_copy_to.Spark SQL to not invoke NextMethod and directly invole db_compute (code)
• Adjust db_compute.Spark SQL to conditionally generate CTAS (code)

[zacdav-db]

This is now resolved via #1514