Re: adding a column to a groupBy (dataframe)

[Marcelo Valle]

Hi Bruno, that's really interesting...

So, to use explode, I would have to do a group by on countries and a
collect_all on cities, then explode the cities, right? Am I understanding
the idea right?

I think this could produce the results I want. But what would be the
behaviour under the hood? Does collect_all return an iterator or does it
return a list? If I have a country with too many cities, would my server
have to store all cities of a country in memory?





On Thu, 6 Jun 2019 at 20:57, Bruno Nassivet 
wrote:

> Hi Marcelo,
>
> Maybe the spark.sql.functions.explode give what you need?
>
> // Bruno
>
>
> Le 6 juin 2019 à 16:02, Marcelo Valle  a écrit :
>
> Generating the city id (child) is easy, monotonically increasing id worked
> for me.
>
> The problem is the country (parent) which has to be in both countries and
> cities data frames.
>
>
>
> On Thu, 6 Jun 2019 at 14:57, Magnus Nilsson  wrote:
>
>> Well, you could do a repartition on cityname/nrOfCities and use the
>> spark_partition_id function or the mappartitionswithindex dataframe method
>> to add a city Id column. Then just split the dataframe into two subsets. Be
>> careful of hashcollisions on the reparition Key though, or more than one
>> city might end up in the same partition (you can use a custom partitioner).
>>
>> It all depends on what kind of Id you want/need for the city value. I.e.
>> will you later need to append new city Id:s or not. Do you always handle
>> the entire dataset when you make this change or not.
>>
>> On the other hand, getting a distinct list of citynames is a non
>> shuffling fast operation, add a row_number column and do a broadcast join
>> with the original dataset and then split into two subsets. Probably a bit
>> faster than reshuffling the entire dataframe. As always the proof is in the
>> pudding.
>>
>> //Magnus
>>
>> On Thu, Jun 6, 2019 at 2:53 PM Marcelo Valle 
>> wrote:
>>
>>> Akshay,
>>>
>>> First of all, thanks for the answer. I *am* using monotonically
>>> increasing id, but that's not my problem.
>>> My problem is I want to output 2 tables from 1 data frame, 1 parent
>>> table with ID for the group by and 1 child table with the parent id without
>>> the group by.
>>>
>>> I was able to solve this problem by grouping by, generating a parent
>>> data frame with an id, then joining the parent dataframe with the original
>>> one to get a child dataframe with a parent id.
>>>
>>> I would like to find a solution without this second join, though.
>>>
>>> Thanks,
>>> Marcelo.
>>>
>>>
>>> On Thu, 6 Jun 2019 at 10:49, Akshay Bhardwaj <
>>> akshay.bhardwaj1...@gmail.com> wrote:
>>>
 Hi Marcelo,

 If you are using spark 2.3+ and dataset API/SparkSQL,you can use this
 inbuilt function "monotonically_increasing_id" in Spark.
 A little tweaking using Spark sql inbuilt functions can enable you to
 achieve this without having to write code or define RDDs with map/reduce
 functions.

 Akshay Bhardwaj
 +91-97111-33849


 On Thu, May 30, 2019 at 4:05 AM Marcelo Valle 
 wrote:

> Hi all,
>
> I am new to spark and I am trying to write an application using
> dataframes that normalize data.
>
> So I have a dataframe `denormalized_cities` with 3 columns:  COUNTRY,
> CITY, CITY_NICKNAME
>
> Here is what I want to do:
>
>
>1. Map by country, then for each country generate a new ID and
>write to a new dataframe `countries`, which would have COUNTRY_ID, 
> COUNTRY
>- country ID would be generated, probably using
>`monotonically_increasing_id`.
>2. For each country, write several lines on a new dataframe
>`cities`, which would have COUNTRY_ID, ID, CITY, CITY_NICKNAME. 
> COUNTRY_ID
>would be the same generated on country table and ID would be another 
> ID I
>generate.
>
> What's the best way to do this, hopefully using only dataframes (no
> low level RDDs) unless it's not possible?
>
> I clearly see a MAP/Reduce process where for each KEY mapped I
> generate a row in countries table with COUNTRY_ID and for every value I
> write a row in cities table. But how to implement this in an easy and
> efficient way?
>
> I thought about using a `GroupBy Country` and then using `collect` to
> collect all values for that country, but then I don't know how to generate
> the country id and I am not sure about memory efficiency of `collect` for 
> a
> country with too many cities (bare in mind country/city is just an 
> example,
> my real entities are different).
>
> Could anyone point me to the direction of a good solution?
>
> Thanks,
> Marcelo.
>
> This email is confidential [and may be protected by legal privilege].
> If you are not the intended recipient, please do not copy or disclose its
> content but contact the sender immediately upon receipt.
>
> KTech 

Re: adding a column to a groupBy (dataframe)

[Bruno Nassivet]

Hi Marcelo,

Maybe the spark.sql.functions.explode give what you need?

// Bruno


> Le 6 juin 2019 à 16:02, Marcelo Valle  a écrit :
> 
> Generating the city id (child) is easy, monotonically increasing id worked 
> for me. 
> 
> The problem is the country (parent) which has to be in both countries and 
> cities data frames.
> 
> 
> 
> On Thu, 6 Jun 2019 at 14:57, Magnus Nilsson  > wrote:
> Well, you could do a repartition on cityname/nrOfCities and use the 
> spark_partition_id function or the mappartitionswithindex dataframe method to 
> add a city Id column. Then just split the dataframe into two subsets. Be 
> careful of hashcollisions on the reparition Key though, or more than one city 
> might end up in the same partition (you can use a custom partitioner).
> 
> It all depends on what kind of Id you want/need for the city value. I.e. will 
> you later need to append new city Id:s or not. Do you always handle the 
> entire dataset when you make this change or not.
> 
> On the other hand, getting a distinct list of citynames is a non shuffling 
> fast operation, add a row_number column and do a broadcast join with the 
> original dataset and then split into two subsets. Probably a bit faster than 
> reshuffling the entire dataframe. As always the proof is in the pudding.
> 
> //Magnus
> 
> On Thu, Jun 6, 2019 at 2:53 PM Marcelo Valle  > wrote:
> Akshay, 
> 
> First of all, thanks for the answer. I *am* using monotonically increasing 
> id, but that's not my problem. 
> My problem is I want to output 2 tables from 1 data frame, 1 parent table 
> with ID for the group by and 1 child table with the parent id without the 
> group by.
> 
> I was able to solve this problem by grouping by, generating a parent data 
> frame with an id, then joining the parent dataframe with the original one to 
> get a child dataframe with a parent id. 
> 
> I would like to find a solution without this second join, though.
> 
> Thanks,
> Marcelo.
> 
> 
> On Thu, 6 Jun 2019 at 10:49, Akshay Bhardwaj  > wrote:
> Hi Marcelo,
> 
> If you are using spark 2.3+ and dataset API/SparkSQL,you can use this inbuilt 
> function "monotonically_increasing_id" in Spark.
> A little tweaking using Spark sql inbuilt functions can enable you to achieve 
> this without having to write code or define RDDs with map/reduce functions.
> 
> Akshay Bhardwaj
> +91-97111-33849
> 
> 
> On Thu, May 30, 2019 at 4:05 AM Marcelo Valle  > wrote:
> Hi all, 
> 
> I am new to spark and I am trying to write an application using dataframes 
> that normalize data. 
> 
> So I have a dataframe `denormalized_cities` with 3 columns:  COUNTRY, CITY, 
> CITY_NICKNAME
> 
> Here is what I want to do: 
> 
> Map by country, then for each country generate a new ID and write to a new 
> dataframe `countries`, which would have COUNTRY_ID, COUNTRY - country ID 
> would be generated, probably using `monotonically_increasing_id`.
> For each country, write several lines on a new dataframe `cities`, which 
> would have COUNTRY_ID, ID, CITY, CITY_NICKNAME. COUNTRY_ID would be the same 
> generated on country table and ID would be another ID I generate. 
> What's the best way to do this, hopefully using only dataframes (no low level 
> RDDs) unless it's not possible?
> 
> I clearly see a MAP/Reduce process where for each KEY mapped I generate a row 
> in countries table with COUNTRY_ID and for every value I write a row in 
> cities table. But how to implement this in an easy and efficient way? 
> 
> I thought about using a `GroupBy Country` and then using `collect` to collect 
> all values for that country, but then I don't know how to generate the 
> country id and I am not sure about memory efficiency of `collect` for a 
> country with too many cities (bare in mind country/city is just an example, 
> my real entities are different).
> 
> Could anyone point me to the direction of a good solution?
> 
> Thanks,
> Marcelo.
> 
> This email is confidential [and may be protected by legal privilege]. If you 
> are not the intended recipient, please do not copy or disclose its content 
> but contact the sender immediately upon receipt.
> 
> KTech Services Ltd is registered in England as company number 10704940.
> 
> Registered Office: The River Building, 1 Cousin Lane, London EC4R 3TE, United 
> Kingdom
> 
> 
> This email is confidential [and may be protected by legal privilege]. If you 
> are not the intended recipient, please do not copy or disclose its content 
> but contact the sender immediately upon receipt.
> 
> KTech Services Ltd is registered in England as company number 10704940.
> 
> Registered Office: The River Building, 1 Cousin Lane, London EC4R 3TE, United 
> Kingdom
> 
> 
> This email is confidential [and may be protected by legal privilege]. If you 
> are not the intended recipient, please do not copy or disclose its content 
> but contact the sender 

Re: adding a column to a groupBy (dataframe)

[Marcelo Valle]

Hi Magnus, Thanks for replying.

I didn't get the partition solution, tbh, but indeed, I was trying to
figure a way of solving only with data frames without rejoining.

I can't have a global list of countries in my real scenario, as the real
scenario is not reference data, countries was just an example.

It would be nice if dataframe api could evolve in the future to handle this
use case. This doesn't seem to be an isolated case, at least in the company
I work for.

Anyway, the question is answered, I wanted to hear that there was no better
way of doing it using data frames only from someone more experienced in
Spark - I am still in the beginning of my journey with it - thanks for the
help!

Thanks,
Marcelo.


On Thu, 6 Jun 2019 at 15:18, Magnus Nilsson  wrote:

> Sorry, misread. Just change city to country in my previous answer. I still
> believe the most efficient way to do this is to create a small dataset with
> country name and id columns and broadcast to join in the city dataset. Other
> than that you have to bring in all the cities from the same country into
> the same partition and do a partition based operation.
>
> If you want to do neither you need to be able to determine the Country ID
> from the string representation of the name, make it deterministic, and make
> sure your algorithm doesn't collide.
>
> I don't see any other way to do it. I still vote for doing a distinct
> collect, add an Id, do a  broadcast join unless your dataset is
> pre-bucketed along non-colliding Country name lines, then the
> partition-based solution is probably faster. Or better yet, pre-create a
> list of all the worlds countries with an Id and do a broadcast join
> straight away.
>
>
> Regards,
>
> Magnus
> --
> *From:* Marcelo Valle 
> *Sent:* Thursday, June 6, 2019 16:02
> *To:* Magnus Nilsson
> *Cc:* user @spark
> *Subject:* Re: adding a column to a groupBy (dataframe)
>
> Generating the city id (child) is easy, monotonically increasing id worked
> for me.
>
> The problem is the country (parent) which has to be in both countries and
> cities data frames.
>
>
>
> On Thu, 6 Jun 2019 at 14:57, Magnus Nilsson  wrote:
>
> Well, you could do a repartition on cityname/nrOfCities and use the
> spark_partition_id function or the mappartitionswithindex dataframe method
> to add a city Id column. Then just split the dataframe into two subsets. Be
> careful of hashcollisions on the reparition Key though, or more than one
> city might end up in the same partition (you can use a custom partitioner).
>
> It all depends on what kind of Id you want/need for the city value. I.e.
> will you later need to append new city Id:s or not. Do you always handle
> the entire dataset when you make this change or not.
>
> On the other hand, getting a distinct list of citynames is a non shuffling
> fast operation, add a row_number column and do a broadcast join with the
> original dataset and then split into two subsets. Probably a bit faster
> than reshuffling the entire dataframe. As always the proof is in the
> pudding.
>
> //Magnus
>
> On Thu, Jun 6, 2019 at 2:53 PM Marcelo Valle 
> wrote:
>
> Akshay,
>
> First of all, thanks for the answer. I *am* using monotonically increasing
> id, but that's not my problem.
> My problem is I want to output 2 tables from 1 data frame, 1 parent table
> with ID for the group by and 1 child table with the parent id without the
> group by.
>
> I was able to solve this problem by grouping by, generating a parent data
> frame with an id, then joining the parent dataframe with the original one
> to get a child dataframe with a parent id.
>
> I would like to find a solution without this second join, though.
>
> Thanks,
> Marcelo.
>
>
> On Thu, 6 Jun 2019 at 10:49, Akshay Bhardwaj <
> akshay.bhardwaj1...@gmail.com> wrote:
>
> Hi Marcelo,
>
> If you are using spark 2.3+ and dataset API/SparkSQL,you can use this
> inbuilt function "monotonically_increasing_id" in Spark.
> A little tweaking using Spark sql inbuilt functions can enable you to
> achieve this without having to write code or define RDDs with map/reduce
> functions.
>
> Akshay Bhardwaj
> +91-97111-33849
>
>
> On Thu, May 30, 2019 at 4:05 AM Marcelo Valle 
> wrote:
>
> Hi all,
>
> I am new to spark and I am trying to write an application using dataframes
> that normalize data.
>
> So I have a dataframe `denormalized_cities` with 3 columns:  COUNTRY,
> CITY, CITY_NICKNAME
>
> Here is what I want to do:
>
>
>1. Map by country, then for each country generate a new ID and write
>to a new dataframe `countries`, which would have COUNTRY_ID, COUNTRY -
>country ID would be generat

Re: adding a column to a groupBy (dataframe)

[Marcelo Valle]

Generating the city id (child) is easy, monotonically increasing id worked
for me.

The problem is the country (parent) which has to be in both countries and
cities data frames.



On Thu, 6 Jun 2019 at 14:57, Magnus Nilsson  wrote:

> Well, you could do a repartition on cityname/nrOfCities and use the
> spark_partition_id function or the mappartitionswithindex dataframe method
> to add a city Id column. Then just split the dataframe into two subsets. Be
> careful of hashcollisions on the reparition Key though, or more than one
> city might end up in the same partition (you can use a custom partitioner).
>
> It all depends on what kind of Id you want/need for the city value. I.e.
> will you later need to append new city Id:s or not. Do you always handle
> the entire dataset when you make this change or not.
>
> On the other hand, getting a distinct list of citynames is a non shuffling
> fast operation, add a row_number column and do a broadcast join with the
> original dataset and then split into two subsets. Probably a bit faster
> than reshuffling the entire dataframe. As always the proof is in the
> pudding.
>
> //Magnus
>
> On Thu, Jun 6, 2019 at 2:53 PM Marcelo Valle 
> wrote:
>
>> Akshay,
>>
>> First of all, thanks for the answer. I *am* using monotonically
>> increasing id, but that's not my problem.
>> My problem is I want to output 2 tables from 1 data frame, 1 parent table
>> with ID for the group by and 1 child table with the parent id without the
>> group by.
>>
>> I was able to solve this problem by grouping by, generating a parent data
>> frame with an id, then joining the parent dataframe with the original one
>> to get a child dataframe with a parent id.
>>
>> I would like to find a solution without this second join, though.
>>
>> Thanks,
>> Marcelo.
>>
>>
>> On Thu, 6 Jun 2019 at 10:49, Akshay Bhardwaj <
>> akshay.bhardwaj1...@gmail.com> wrote:
>>
>>> Hi Marcelo,
>>>
>>> If you are using spark 2.3+ and dataset API/SparkSQL,you can use this
>>> inbuilt function "monotonically_increasing_id" in Spark.
>>> A little tweaking using Spark sql inbuilt functions can enable you to
>>> achieve this without having to write code or define RDDs with map/reduce
>>> functions.
>>>
>>> Akshay Bhardwaj
>>> +91-97111-33849
>>>
>>>
>>> On Thu, May 30, 2019 at 4:05 AM Marcelo Valle 
>>> wrote:
>>>
 Hi all,

 I am new to spark and I am trying to write an application using
 dataframes that normalize data.

 So I have a dataframe `denormalized_cities` with 3 columns:  COUNTRY,
 CITY, CITY_NICKNAME

 Here is what I want to do:


1. Map by country, then for each country generate a new ID and
write to a new dataframe `countries`, which would have COUNTRY_ID, 
 COUNTRY
- country ID would be generated, probably using
`monotonically_increasing_id`.
2. For each country, write several lines on a new dataframe
`cities`, which would have COUNTRY_ID, ID, CITY, CITY_NICKNAME. 
 COUNTRY_ID
would be the same generated on country table and ID would be another ID 
 I
generate.

 What's the best way to do this, hopefully using only dataframes (no low
 level RDDs) unless it's not possible?

 I clearly see a MAP/Reduce process where for each KEY mapped I generate
 a row in countries table with COUNTRY_ID and for every value I write a row
 in cities table. But how to implement this in an easy and efficient way?

 I thought about using a `GroupBy Country` and then using `collect` to
 collect all values for that country, but then I don't know how to generate
 the country id and I am not sure about memory efficiency of `collect` for a
 country with too many cities (bare in mind country/city is just an example,
 my real entities are different).

 Could anyone point me to the direction of a good solution?

 Thanks,
 Marcelo.

 This email is confidential [and may be protected by legal privilege].
 If you are not the intended recipient, please do not copy or disclose its
 content but contact the sender immediately upon receipt.

 KTech Services Ltd is registered in England as company number 10704940.

 Registered Office: The River Building, 1 Cousin Lane, London EC4R 3TE,
 United Kingdom

>>>
>> This email is confidential [and may be protected by legal privilege]. If
>> you are not the intended recipient, please do not copy or disclose its
>> content but contact the sender immediately upon receipt.
>>
>> KTech Services Ltd is registered in England as company number 10704940.
>>
>> Registered Office: The River Building, 1 Cousin Lane, London EC4R 3TE,
>> United Kingdom
>>
>

This email is confidential [and may be protected by legal privilege]. If you 
are not the intended recipient, please do not copy or disclose its content but 
contact the sender immediately upon receipt.

KTech Services Ltd is registered in 

Re: adding a column to a groupBy (dataframe)

[Magnus Nilsson]

Well, you could do a repartition on cityname/nrOfCities and use the
spark_partition_id function or the mappartitionswithindex dataframe method
to add a city Id column. Then just split the dataframe into two subsets. Be
careful of hashcollisions on the reparition Key though, or more than one
city might end up in the same partition (you can use a custom partitioner).

It all depends on what kind of Id you want/need for the city value. I.e.
will you later need to append new city Id:s or not. Do you always handle
the entire dataset when you make this change or not.

On the other hand, getting a distinct list of citynames is a non shuffling
fast operation, add a row_number column and do a broadcast join with the
original dataset and then split into two subsets. Probably a bit faster
than reshuffling the entire dataframe. As always the proof is in the
pudding.

//Magnus

On Thu, Jun 6, 2019 at 2:53 PM Marcelo Valle 
wrote:

> Akshay,
>
> First of all, thanks for the answer. I *am* using monotonically increasing
> id, but that's not my problem.
> My problem is I want to output 2 tables from 1 data frame, 1 parent table
> with ID for the group by and 1 child table with the parent id without the
> group by.
>
> I was able to solve this problem by grouping by, generating a parent data
> frame with an id, then joining the parent dataframe with the original one
> to get a child dataframe with a parent id.
>
> I would like to find a solution without this second join, though.
>
> Thanks,
> Marcelo.
>
>
> On Thu, 6 Jun 2019 at 10:49, Akshay Bhardwaj <
> akshay.bhardwaj1...@gmail.com> wrote:
>
>> Hi Marcelo,
>>
>> If you are using spark 2.3+ and dataset API/SparkSQL,you can use this
>> inbuilt function "monotonically_increasing_id" in Spark.
>> A little tweaking using Spark sql inbuilt functions can enable you to
>> achieve this without having to write code or define RDDs with map/reduce
>> functions.
>>
>> Akshay Bhardwaj
>> +91-97111-33849
>>
>>
>> On Thu, May 30, 2019 at 4:05 AM Marcelo Valle 
>> wrote:
>>
>>> Hi all,
>>>
>>> I am new to spark and I am trying to write an application using
>>> dataframes that normalize data.
>>>
>>> So I have a dataframe `denormalized_cities` with 3 columns:  COUNTRY,
>>> CITY, CITY_NICKNAME
>>>
>>> Here is what I want to do:
>>>
>>>
>>>1. Map by country, then for each country generate a new ID and write
>>>to a new dataframe `countries`, which would have COUNTRY_ID, COUNTRY -
>>>country ID would be generated, probably using 
>>> `monotonically_increasing_id`.
>>>2. For each country, write several lines on a new dataframe
>>>`cities`, which would have COUNTRY_ID, ID, CITY, CITY_NICKNAME. 
>>> COUNTRY_ID
>>>would be the same generated on country table and ID would be another ID I
>>>generate.
>>>
>>> What's the best way to do this, hopefully using only dataframes (no low
>>> level RDDs) unless it's not possible?
>>>
>>> I clearly see a MAP/Reduce process where for each KEY mapped I generate
>>> a row in countries table with COUNTRY_ID and for every value I write a row
>>> in cities table. But how to implement this in an easy and efficient way?
>>>
>>> I thought about using a `GroupBy Country` and then using `collect` to
>>> collect all values for that country, but then I don't know how to generate
>>> the country id and I am not sure about memory efficiency of `collect` for a
>>> country with too many cities (bare in mind country/city is just an example,
>>> my real entities are different).
>>>
>>> Could anyone point me to the direction of a good solution?
>>>
>>> Thanks,
>>> Marcelo.
>>>
>>> This email is confidential [and may be protected by legal privilege]. If
>>> you are not the intended recipient, please do not copy or disclose its
>>> content but contact the sender immediately upon receipt.
>>>
>>> KTech Services Ltd is registered in England as company number 10704940.
>>>
>>> Registered Office: The River Building, 1 Cousin Lane, London EC4R 3TE,
>>> United Kingdom
>>>
>>
> This email is confidential [and may be protected by legal privilege]. If
> you are not the intended recipient, please do not copy or disclose its
> content but contact the sender immediately upon receipt.
>
> KTech Services Ltd is registered in England as company number 10704940.
>
> Registered Office: The River Building, 1 Cousin Lane, London EC4R 3TE,
> United Kingdom
>

Re: adding a column to a groupBy (dataframe)

[Marcelo Valle]

Akshay,

First of all, thanks for the answer. I *am* using monotonically increasing
id, but that's not my problem.
My problem is I want to output 2 tables from 1 data frame, 1 parent table
with ID for the group by and 1 child table with the parent id without the
group by.

I was able to solve this problem by grouping by, generating a parent data
frame with an id, then joining the parent dataframe with the original one
to get a child dataframe with a parent id.

I would like to find a solution without this second join, though.

Thanks,
Marcelo.


On Thu, 6 Jun 2019 at 10:49, Akshay Bhardwaj 
wrote:

> Hi Marcelo,
>
> If you are using spark 2.3+ and dataset API/SparkSQL,you can use this
> inbuilt function "monotonically_increasing_id" in Spark.
> A little tweaking using Spark sql inbuilt functions can enable you to
> achieve this without having to write code or define RDDs with map/reduce
> functions.
>
> Akshay Bhardwaj
> +91-97111-33849
>
>
> On Thu, May 30, 2019 at 4:05 AM Marcelo Valle 
> wrote:
>
>> Hi all,
>>
>> I am new to spark and I am trying to write an application using
>> dataframes that normalize data.
>>
>> So I have a dataframe `denormalized_cities` with 3 columns:  COUNTRY,
>> CITY, CITY_NICKNAME
>>
>> Here is what I want to do:
>>
>>
>>1. Map by country, then for each country generate a new ID and write
>>to a new dataframe `countries`, which would have COUNTRY_ID, COUNTRY -
>>country ID would be generated, probably using 
>> `monotonically_increasing_id`.
>>2. For each country, write several lines on a new dataframe `cities`,
>>which would have COUNTRY_ID, ID, CITY, CITY_NICKNAME. COUNTRY_ID would be
>>the same generated on country table and ID would be another ID I generate.
>>
>> What's the best way to do this, hopefully using only dataframes (no low
>> level RDDs) unless it's not possible?
>>
>> I clearly see a MAP/Reduce process where for each KEY mapped I generate a
>> row in countries table with COUNTRY_ID and for every value I write a row in
>> cities table. But how to implement this in an easy and efficient way?
>>
>> I thought about using a `GroupBy Country` and then using `collect` to
>> collect all values for that country, but then I don't know how to generate
>> the country id and I am not sure about memory efficiency of `collect` for a
>> country with too many cities (bare in mind country/city is just an example,
>> my real entities are different).
>>
>> Could anyone point me to the direction of a good solution?
>>
>> Thanks,
>> Marcelo.
>>
>> This email is confidential [and may be protected by legal privilege]. If
>> you are not the intended recipient, please do not copy or disclose its
>> content but contact the sender immediately upon receipt.
>>
>> KTech Services Ltd is registered in England as company number 10704940.
>>
>> Registered Office: The River Building, 1 Cousin Lane, London EC4R 3TE,
>> United Kingdom
>>
>

This email is confidential [and may be protected by legal privilege]. If you 
are not the intended recipient, please do not copy or disclose its content but 
contact the sender immediately upon receipt.

KTech Services Ltd is registered in England as company number 10704940.

Registered Office: The River Building, 1 Cousin Lane, London EC4R 3TE, United 
Kingdom

Re: adding a column to a groupBy (dataframe)

[Akshay Bhardwaj]

Additionally there is "uuid" function available as well if that helps your
use case.


Akshay Bhardwaj
+91-97111-33849


On Thu, Jun 6, 2019 at 3:18 PM Akshay Bhardwaj <
akshay.bhardwaj1...@gmail.com> wrote:

> Hi Marcelo,
>
> If you are using spark 2.3+ and dataset API/SparkSQL,you can use this
> inbuilt function "monotonically_increasing_id" in Spark.
> A little tweaking using Spark sql inbuilt functions can enable you to
> achieve this without having to write code or define RDDs with map/reduce
> functions.
>
> Akshay Bhardwaj
> +91-97111-33849
>
>
> On Thu, May 30, 2019 at 4:05 AM Marcelo Valle 
> wrote:
>
>> Hi all,
>>
>> I am new to spark and I am trying to write an application using
>> dataframes that normalize data.
>>
>> So I have a dataframe `denormalized_cities` with 3 columns:  COUNTRY,
>> CITY, CITY_NICKNAME
>>
>> Here is what I want to do:
>>
>>
>>1. Map by country, then for each country generate a new ID and write
>>to a new dataframe `countries`, which would have COUNTRY_ID, COUNTRY -
>>country ID would be generated, probably using 
>> `monotonically_increasing_id`.
>>2. For each country, write several lines on a new dataframe `cities`,
>>which would have COUNTRY_ID, ID, CITY, CITY_NICKNAME. COUNTRY_ID would be
>>the same generated on country table and ID would be another ID I generate.
>>
>> What's the best way to do this, hopefully using only dataframes (no low
>> level RDDs) unless it's not possible?
>>
>> I clearly see a MAP/Reduce process where for each KEY mapped I generate a
>> row in countries table with COUNTRY_ID and for every value I write a row in
>> cities table. But how to implement this in an easy and efficient way?
>>
>> I thought about using a `GroupBy Country` and then using `collect` to
>> collect all values for that country, but then I don't know how to generate
>> the country id and I am not sure about memory efficiency of `collect` for a
>> country with too many cities (bare in mind country/city is just an example,
>> my real entities are different).
>>
>> Could anyone point me to the direction of a good solution?
>>
>> Thanks,
>> Marcelo.
>>
>> This email is confidential [and may be protected by legal privilege]. If
>> you are not the intended recipient, please do not copy or disclose its
>> content but contact the sender immediately upon receipt.
>>
>> KTech Services Ltd is registered in England as company number 10704940.
>>
>> Registered Office: The River Building, 1 Cousin Lane, London EC4R 3TE,
>> United Kingdom
>>
>

Re: adding a column to a groupBy (dataframe)

[Akshay Bhardwaj]

Hi Marcelo,

If you are using spark 2.3+ and dataset API/SparkSQL,you can use this
inbuilt function "monotonically_increasing_id" in Spark.
A little tweaking using Spark sql inbuilt functions can enable you to
achieve this without having to write code or define RDDs with map/reduce
functions.

Akshay Bhardwaj
+91-97111-33849


On Thu, May 30, 2019 at 4:05 AM Marcelo Valle 
wrote:

> Hi all,
>
> I am new to spark and I am trying to write an application using dataframes
> that normalize data.
>
> So I have a dataframe `denormalized_cities` with 3 columns:  COUNTRY,
> CITY, CITY_NICKNAME
>
> Here is what I want to do:
>
>
>1. Map by country, then for each country generate a new ID and write
>to a new dataframe `countries`, which would have COUNTRY_ID, COUNTRY -
>country ID would be generated, probably using 
> `monotonically_increasing_id`.
>2. For each country, write several lines on a new dataframe `cities`,
>which would have COUNTRY_ID, ID, CITY, CITY_NICKNAME. COUNTRY_ID would be
>the same generated on country table and ID would be another ID I generate.
>
> What's the best way to do this, hopefully using only dataframes (no low
> level RDDs) unless it's not possible?
>
> I clearly see a MAP/Reduce process where for each KEY mapped I generate a
> row in countries table with COUNTRY_ID and for every value I write a row in
> cities table. But how to implement this in an easy and efficient way?
>
> I thought about using a `GroupBy Country` and then using `collect` to
> collect all values for that country, but then I don't know how to generate
> the country id and I am not sure about memory efficiency of `collect` for a
> country with too many cities (bare in mind country/city is just an example,
> my real entities are different).
>
> Could anyone point me to the direction of a good solution?
>
> Thanks,
> Marcelo.
>
> This email is confidential [and may be protected by legal privilege]. If
> you are not the intended recipient, please do not copy or disclose its
> content but contact the sender immediately upon receipt.
>
> KTech Services Ltd is registered in England as company number 10704940.
>
> Registered Office: The River Building, 1 Cousin Lane, London EC4R 3TE,
> United Kingdom
>

adding a column to a groupBy (dataframe)

[Marcelo Valle]

Hi all,

I am new to spark and I am trying to write an application using dataframes
that normalize data.

So I have a dataframe `denormalized_cities` with 3 columns:  COUNTRY, CITY,
CITY_NICKNAME

Here is what I want to do:


   1. Map by country, then for each country generate a new ID and write to
   a new dataframe `countries`, which would have COUNTRY_ID, COUNTRY - country
   ID would be generated, probably using `monotonically_increasing_id`.
   2. For each country, write several lines on a new dataframe `cities`,
   which would have COUNTRY_ID, ID, CITY, CITY_NICKNAME. COUNTRY_ID would be
   the same generated on country table and ID would be another ID I generate.

What's the best way to do this, hopefully using only dataframes (no low
level RDDs) unless it's not possible?

I clearly see a MAP/Reduce process where for each KEY mapped I generate a
row in countries table with COUNTRY_ID and for every value I write a row in
cities table. But how to implement this in an easy and efficient way?

I thought about using a `GroupBy Country` and then using `collect` to
collect all values for that country, but then I don't know how to generate
the country id and I am not sure about memory efficiency of `collect` for a
country with too many cities (bare in mind country/city is just an example,
my real entities are different).

Could anyone point me to the direction of a good solution?

Thanks,
Marcelo.

This email is confidential [and may be protected by legal privilege]. If you 
are not the intended recipient, please do not copy or disclose its content but 
contact the sender immediately upon receipt.

KTech Services Ltd is registered in England as company number 10704940.

Registered Office: The River Building, 1 Cousin Lane, London EC4R 3TE, United 
Kingdom