Hi Niranda,
Interesting results. Did you do any analysis to understand what was the
main contributor to the performance differences? Along these lines, did
you try joins on any real world datasets? Are you using Spark SQL for
comparisons? Also why not use parquet as a starting point?
Thanks,
I haven't used Gandiva, but it seems like you could potentially make a
dummy field node [1] for this purpose where you provide the value.
As a side note, I'm not sure varying parameters each time is the best way
to implement a group-by-operation.
-Micah
[1]
Hi,
Are there any guidelines for using the Gandiva C++ lib from multiple
threads? As I increase the number of threads in an application Gandiva
begins to raise memory faults (e.g. double free). Sometimes it is on
projector creation, sometimes evaluation, seems to happen in multiple
MSYS2 package is updated:
1. [done] rebase master
2. [done] upload source
3. [done] upload binaries
4. [done/PR-ready] update website
5. [done] upload ruby gems
6. [ ] upload js packages
8. [done] upload C# packages
9. [andygrove] upload rust crates
10. [xhochy] update conda recipes
11.
Apologies for the noise, but would it make sense if the functions returned the
same type as the parameter to improve composition with other functions &
operators? My examples might be a hassle to use as-is.
> On Jul 26, 2020, at 1:10 PM, Troy Zimmerman
> wrote:
>
> Hi,
>
> Would it be
Hi,
Would it be possible to support additional types in the following functions, or
are they purposefully limited to decimal128? Naively, I assume the following
make sense:
abs(float64) -> float64
abs(int64) -> int64
ceil(float64) -> int64
floor(float64) -> int64
trunc(float64) -> int64
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