Agreed. For the same reason dataframes / dataset which is another DSL used
in Spark
On Wed, Jul 26, 2017 at 1:00 AM Georg Heiler <georg.kf.hei...@gmail.com>
wrote:
> Because sparks dsl partially supports compile time type safety. E.g. the
> compiler will notify you that a sql function was misspelled when using the
> dsl opposed to the plain sql string which is only parsed at runtime.
> Sathish Kumaran Vairavelu <vsathishkuma...@gmail.com> schrieb am Di. 25.
> Juli 2017 um 23:42:
>
>> Just a thought. SQL itself is a DSL. Why DSL on top of another DSL?
>> On Tue, Jul 25, 2017 at 4:47 AM kant kodali <kanth...@gmail.com> wrote:
>>
>>> Hi All,
>>>
>>> I am thinking to express Spark SQL using JSON in the following the way.
>>>
>>> For Example:
>>>
>>> *Query using Spark DSL*
>>>
>>> DS.filter(col("name").equalTo("john"))
>>> .groupBy(functions.window(df1.col("TIMESTAMP"), "24 hours", "24
>>> hours"), df1.col("hourlyPay"))
>>> .agg(sum("hourlyPay").as("total"));
>>>
>>>
>>> *Query using JSON*
>>>
>>>
>>>
>>>
>>>
>>> The Goal is to design a DSL in JSON such that users can and express
>>> SPARK SQL queries in JSON so users can send Spark SQL queries over rest and
>>> get the results out. Now, I am sure there are BI tools and notebooks like
>>> Zeppelin that can accomplish the desired behavior however I believe there
>>> maybe group of users who don't want to use those BI tools or notebooks
>>> instead they want all the communication from front end to back end using
>>> API's.
>>>
>>> Also another goal would be the DSL design in JSON should closely mimic
>>> the underlying Spark SQL DSL.
>>>
>>> Please feel free to provide some feedback or criticize to whatever
>>> extent you like!
>>>
>>> Thanks!
>>>
>>>
>>>
