Thanks.
But there is a problem that the classes referenced in the code need to be
modified. I want to try not to change the existing code.
Gabor Somogyi 于2020年12月25日周五 上午12:16写道：
> One can wrap the JDBC driver and such a way eveything can be sniffed.
>
> On Thu, 24 Dec 2020, 03:51 lec ssmi,
One can wrap the JDBC driver and such a way eveything can be sniffed.
On Thu, 24 Dec 2020, 03:51 lec ssmi, wrote:
> Hi:
>guys, I have some spark programs that have database connection
> operations. I want to acquire the connection information, such as jdbc
> connection properties , but
Why not just use STDDEV_SAMP? it's probably more accurate than the
differences-of-squares calculation.
You can write an aggregate UDF that calls numpy and register it for SQL,
but, it is already a built-in.
On Thu, Dec 24, 2020 at 8:12 AM Mich Talebzadeh
wrote:
> Thanks for the feedback.
>
> I
Thanks for the feedback.
I have a question here. I want to use numpy STD as well but just using sql
in pyspark. Like below
sqltext = f"""
SELECT
rs.Customer_ID
, rs.Number_of_orders
, rs.Total_customer_amount
, rs.Average_order
,
I don't know which one is 'correct' (it's not standard SQL?) or whether
it's the sample stdev for a good reason or just historical now. But you can
always call STDDEV_SAMP (in any DB) if needed. It's equivalent to numpy.std
with ddof=1, the Bessel-corrected standard deviation.
On Thu, Dec 24,
Well the truth is that we had this discussion in 2016 :(. what Hive calls
Standard Deviation Function STDDEV is a pointer to STDDEV_POP. This is
incorrect and has not been rectified yet!
Spark-sql, Oracle and Sybase point STDDEV to STDDEV_SAMP and not
STDDEV_POP. Run a test on *Hive*
SELECT
