After looking at the ExecPlan output of some queries, it jumped out at me how we translate int_field == 5 in R as cast(int_field, float64) == 5 because 5 is a double in R.
This extra work has a noticeable performance impact. Here's a simple query on the taxi dataset, filtering down to 54 out of 1.5 billion rows and selecting a single column. My idea was to make a query that does not much other than evaluate the filter.
> system.time(ds |> select(passenger_count) |> filter(passenger_count > 10) |> compute())
usersystemelapsed0.3910.0240.362 > system.time(ds |> select(passenger_count) |> filter(passenger_count > Scalar$create(10, type = int8())) |> compute())
usersystemelapsed0.2060.0250.179
You can see the difference in the query plans too:
> ds |> select(passenger_count) |> filter(passenger_count > 10) |> explain()
ExecPlanwith4nodes:
3:SinkNode{}
2:ProjectNode{projection=[passenger_count]}
1:FilterNode{filter=(cast(passenger_count, {to_type=double, allow_int_overflow=false, allow_time_truncate=false, allow_time_overflow=false, allow_decimal_truncate=false, allow_float_truncate=false, allow_invalid_utf8=false}) > 10)}
0:SourceNode{}
> ds |> select(passenger_count) |> filter(passenger_count > Scalar$create(10, type = int8())) |> explain()
ExecPlanwith4nodes:
3:SinkNode{}
2:ProjectNode{projection=[passenger_count]}
1:FilterNode{filter=(passenger_count > 10)}
0:SourceNode{}Ideally Acero would do this more intelligently (cf. ARROW-11402), but we should also be able to do smarter things when assembling the Expression in R.
Reporter: Neal Richardson / @nealrichardson
Assignee: Neal Richardson / @nealrichardson
Related issues:
PRs and other links:
Note: This issue was originally created as ARROW-17462. Please see the migration documentation for further details.
After looking at the ExecPlan output of some queries, it jumped out at me how we translate
int_field == 5in R ascast(int_field, float64) == 5because 5 is a double in R.This extra work has a noticeable performance impact. Here's a simple query on the taxi dataset, filtering down to 54 out of 1.5 billion rows and selecting a single column. My idea was to make a query that does not much other than evaluate the filter.
You can see the difference in the query plans too:
Ideally Acero would do this more intelligently (cf. ARROW-11402), but we should also be able to do smarter things when assembling the Expression in R.
Reporter: Neal Richardson / @nealrichardson
Assignee: Neal Richardson / @nealrichardson
Related issues:
mutate(x2=ifelse(x=='',NA,x))Error: Function 'if_else' has no kernel matching input types (fixes)PRs and other links:
Note: This issue was originally created as ARROW-17462. Please see the migration documentation for further details.