Very subtle but someone might take
“We will drop Python 2 support in a future release in 2020”
To mean any / first release in 2020. Whereas the next statement indicates patch
release is not included in above. Might help reorder the items or clarify the
wording.
Bucketing will only help you with joins. And these usually happen on a key.
You mentioned that there is no such key in your data. If just want to
search through large quantities of data sorting an partitioning by time
is left.
Rishi Shah schrieb am Sa. 1. Juni 2019 um 05:57:
> Thanks much for
Thanks much for your input Gourav, Silvio.
I have about 10TB of data, which gets stored daily. There's no qualifying
column for partitioning, which makes querying this table super slow. So I
wanted to sort the results before storing them daily. This is why I was
thinking to use bucketing and
+1000 ;)
On Sat, Jun 1, 2019 at 6:53 AM Denny Lee wrote:
> +1
>
> On Fri, May 31, 2019 at 17:58 Holden Karau wrote:
>
>> +1
>>
>> On Fri, May 31, 2019 at 5:41 PM Bryan Cutler wrote:
>>
>>> +1 and the draft sounds good
>>>
>>> On Thu, May 30, 2019, 11:32 AM Xiangrui Meng wrote:
>>>
Here
+1
On Fri, May 31, 2019 at 17:58 Holden Karau wrote:
> +1
>
> On Fri, May 31, 2019 at 5:41 PM Bryan Cutler wrote:
>
>> +1 and the draft sounds good
>>
>> On Thu, May 30, 2019, 11:32 AM Xiangrui Meng wrote:
>>
>>> Here is the draft announcement:
>>>
>>> ===
>>> Plan for dropping Python 2
+1
On Fri, May 31, 2019 at 5:41 PM Bryan Cutler wrote:
> +1 and the draft sounds good
>
> On Thu, May 30, 2019, 11:32 AM Xiangrui Meng wrote:
>
>> Here is the draft announcement:
>>
>> ===
>> Plan for dropping Python 2 support
>>
>> As many of you already knew, Python core development team and
+1 and the draft sounds good
On Thu, May 30, 2019, 11:32 AM Xiangrui Meng wrote:
> Here is the draft announcement:
>
> ===
> Plan for dropping Python 2 support
>
> As many of you already knew, Python core development team and many
> utilized Python packages like Pandas and NumPy will drop
Spark does allow appending new files to bucketed tables. When the data is read
in, Spark will combine the multiple files belonging to the same buckets into
the same partitions.
Having said that, you need to be very careful with bucketing especially as
you’re appending to avoid generating lots
Trying to save a sample data into C* table
I am getting below error :
*java.util.NoSuchElementException: Columns not found in table
abc.company_vals: companyId, companyName*
Though I have all the columns and re checked them again and again.
I dont see any issue with columns.
I am using
You can start spark-shell with these properties:
--conf spark.dynamicAllocation.enabled=true --conf
spark.dynamicAllocation.initialExecutors=2 --conf
spark.dynamicAllocation.minExecutors=2 --conf
spark.dynamicAllocation.maxExecutors=5
On Fri, May 31, 2019 at 5:30 AM Qian He wrote:
> Sometimes
Hi Rishi,
I think that if you are using sorting and then appending data locally there
will no need to bucket data and you are good with external tables that way.
Regards,
Gourav
On Fri, May 31, 2019 at 3:43 AM Rishi Shah wrote:
> Hi All,
>
> Can we use bucketing with sorting functionality to
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