Hi Richard,
Windowing/Analytics for stats are pretty simple. Example
import org.apache.spark.sql.expressions.Window
val wSpec =
Window.partitionBy('transactiontype).orderBy(desc("transactiondate"))
df.filter('transactiondescription.contains(HASHTAG)).select('transactiondate,'transactiondescription,
*rank().over(wSpec).as("rank")).*filter($"rank"===1).show(1)
val wSpec5 =
Window.partitionBy('hashtag).orderBy(substring('transactiondate,1,4))
val newDF = df.where('transactiontype === "DEB" &&
('transactiondescription).isNotNull).select(substring('transactiondate,1,4).as("Year"),
'hashtag.as("Retailer"),*round(sum('debitamount).over(wSpec5),2).as("Spent"*
))
newDF.distinct.orderBy('year,'Retailer).collect.foreach(println)
HTH
Dr Mich Talebzadeh
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On 25 August 2016 at 08:24, Richard Siebeling <rsiebel...@gmail.com> wrote:
> Hi Mich,
>
> thanks for the suggestion, I hadn't thought of that. We'll need to gather
> the statistics in two ways, incremental when new data arrives and over the
> complete set when aggregating or filtering (because I think it's difficult
> to gather statistics while aggregating or filtering).
> The analytic functions could help when gathering the statistics over the
> whole set,
>
> kind regards,
> Richard
>
>
>
> On Wed, Aug 24, 2016 at 10:54 PM, Mich Talebzadeh <
> mich.talebza...@gmail.com> wrote:
>
>> Hi Richard,
>>
>> can you use analytics functions for this purpose on DF
>>
>> HTH
>>
>> Dr Mich Talebzadeh
>>
>>
>>
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>>
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>>
>> *Disclaimer:* Use it at your own risk. Any and all responsibility for
>> any loss, damage or destruction of data or any other property which may
>> arise from relying on this email's technical content is explicitly
>> disclaimed. The author will in no case be liable for any monetary damages
>> arising from such loss, damage or destruction.
>>
>>
>>
>> On 24 August 2016 at 21:37, Richard Siebeling <rsiebel...@gmail.com>
>> wrote:
>>
>>> Hi Mich,
>>>
>>> I'd like to gather several statistics per column in order to make
>>> analysing data easier. These two statistics are some examples, other
>>> statistics I'd like to gather are the variance, the median, several
>>> percentiles, etc. We are building a data analysis platform based on Spark,
>>>
>>> kind regards,
>>> Richard
>>>
>>> On Wed, Aug 24, 2016 at 6:52 PM, Mich Talebzadeh <
>>> mich.talebza...@gmail.com> wrote:
>>>
>>>> Hi Richard,
>>>>
>>>> What is the business use case for such statistics?
>>>>
>>>> HTH
>>>>
>>>> Dr Mich Talebzadeh
>>>>
>>>>
>>>>
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>>>> any loss, damage or destruction of data or any other property which may
>>>> arise from relying on this email's technical content is explicitly
>>>> disclaimed. The author will in no case be liable for any monetary damages
>>>> arising from such loss, damage or destruction.
>>>>
>>>>
>>>>
>>>> On 24 August 2016 at 16:01, Bedrytski Aliaksandr <sp...@bedryt.ski>
>>>> wrote:
>>>>
>>>>> Hi Richard,
>>>>>
>>>>> these intermediate statistics should be calculated from the result of
>>>>> the calculation or during the aggregation?
>>>>> If they can be derived from the resulting dataframe, why not to cache
>>>>> (persist) that result just after the calculation?
>>>>> Then you may aggregate statistics from the cached dataframe.
>>>>> This way it won't hit performance too much.
>>>>>
>>>>> Regards
>>>>> --
>>>>> Bedrytski Aliaksandr
>>>>> sp...@bedryt.ski
>>>>>
>>>>>
>>>>>
>>>>> On Wed, Aug 24, 2016, at 16:42, Richard Siebeling wrote:
>>>>>
>>>>> Hi,
>>>>>
>>>>> what is the best way to calculate intermediate column statistics like
>>>>> the number of empty values and the number of distinct values each column
>>>>> in
>>>>> a dataset when aggregating of filtering data next to the actual result of
>>>>> the aggregate or the filtered data?
>>>>>
>>>>> We are developing an application in which the user can slice-and-dice
>>>>> through the data and we would like to, next to the actual resulting data,
>>>>> get column statistics of each column in the resulting dataset. We prefer
>>>>> to
>>>>> calculate the column statistics on the same pass over the data as the
>>>>> actual aggregation or filtering, is that possible?
>>>>>
>>>>> We could sacrifice a little bit of performance (but not too much),
>>>>> that's why we prefer one pass...
>>>>>
>>>>> Is this possible in the standard Spark or would this mean modifying
>>>>> the source a little bit and recompiling? Is that feasible / wise to do?
>>>>>
>>>>> thanks in advance,
>>>>> Richard
>>>>>
>>>>>
>>>>>
>>>>>
>>>>>
>>>>>
>>>>
>>>
>>
>
