Hi,
Do we support distinct count in the over clause in spark sql?
I ran a sql like this:
select a, count(distinct b) over ( order by a rows between unbounded preceding
and current row) from table limit 10
Currently, it return an error says: expression ‘a' is neither present in the
group by,
I think it cannot be right.
> 在 2016年1月22日，下午4:53，汪洋 写道：
>
> Hi,
>
> Do we support distinct count in the over clause in spark sql?
>
> I ran a sql like this:
>
> select a, count(distinct b) over ( order by a rows between unbounded
> preceding and current row) from table limit 10
>
> Curren
Hi Allen,
Thank you for your feedback.
An API to launch GPU kernels with JCuda is the our first step. A purpose
to release our prototype is to get feedback. In the future, we may use
other wrappers instead of JCuda.
We are very appreciate it if you would suggest or propose APIs to
effectively e
Hi Alexander,
The goal of our columnar to effectively drive GPUs in Spark. One of
important items is to effectively and easily enable highly-tuned libraries
for GPU such as BIDMach.
We will enable BIDMach with our columnar storage. On the other hand, it is
not easy task to scaling BIDMach with
My bad, thanks.
On Fri, Jan 22, 2016 at 4:34 PM, Reynold Xin wrote:
> The original email was asking about data partitioning (Hive style) for
> files, not in memory caching.
>
>
> On Thursday, January 21, 2016, Takeshi Yamamuro
> wrote:
>
>> You mean RDD#partitions are possibly split into multip
I'd like more complete Postgres JDBC support for ArrayType before the next
release. Some of them are still broken in 1.6.0. It would save me much time.
Please see SPARK-12747 @ https://issues.apache.org/jira/browse/SPARK-12747
Cheers!
Brandon Bradley
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Hi all,
(I'm author of netlib-java)
Interesting to see this discussion come to life again.
JNI is quite limiting: pinning (or critical array access) essentially
disables the GC for the whole JVM for the duration of the native call. I
can justify this for CPU heavy tasks because frankly there are
Hi Rajesh,
FYI, we are developing our own version of BIDMach integration with
Spark, and achieving large gains over Spark MLLib for both CPU and GPU
computation. You can find the project here:
https://github.com/BIDData/BIDMach_Spark
I'm not sure I follow your comment "However, I think compari
