Re: Rdd - zip with index

[ayan guha]

Hi

"I still dont understand how the spark will split the large file" -- This
is actually achieved by something called InputFormat, which in turn depends
on what type of file it is and what is the block size. Ex: If you have
block size of 64MB, then a 10GB file will roughly translate to 10240/64 =
160 partitions. (Roughly because line boundaries are taken into account).
Spark launches 1 task for each partitions, so you should see 160 tasks
created.

Because .gz is not splittable, Spark uses a different InputFormat, and
hence number of tasks are same as number of files, not per split (aka
partitions). Hence, a 10GB .gz file will incur only 1 task.

Now these tasks are unit of parallelism and they can be run in parallel.
You can roughly translate this to number of cores available to the cluster
during reading of the file. How many cores are available? Well that depends
how are you launching the job. Ex: If you are launching like local(*) that
means you want all of you local cores to be used.

In a distributed setting, you can ask Spark to group cores (and RAM) and
that is called an executor. Each executor can have 1 or more cores
(SparkConf driven). So each executor takes some of the tasks created above
and runs them in parallel.  Thats what you see in the Spark UI Executor
Page.

So depending on how you are launching the job, you should see
(a) How many executors are running and with how many cores
(b) How many tasks are scheduled to run
(c) Which executor is running those tasks

As a framework, Spark does all of these without you need to do anything, as
Sean said above. The question is why then you see no parallelism? Well,
hope these pointers leads you to atleast look at the right places. Please
share the format of the file, how are you launching the job and if possible
screenshots of Spark UI pages and I am sure good people of this forum will
help you out.

HTH

On Thu, Mar 25, 2021 at 3:54 AM Sean Owen  wrote:

> Right, that's all you do to tell it to treat the first line of the files
> as a header defining col names.
> Yes, .gz files still aren't splittable by nature. One huge CSV .csv file
> would be split into partitions, but one .gz file would not, which can be a
> problem.
> To be clear, you do not need to do anything to let Spark read parts of a
> large file in parallel (assuming compression isn't the issue).
>
> On Wed, Mar 24, 2021 at 11:00 AM Mich Talebzadeh <
> mich.talebza...@gmail.com> wrote:
>
>> How does Spark establish there is a csv header as a matter of interest?
>>
>> Example
>>
>> val df = spark.read.option("header", true).csv(location)
>>
>> I need to tell spark to ignore the header correct?
>>
>> From Spark Read CSV file into DataFrame — SparkByExamples
>> 
>>
>> If you have a header with column names on file, you need to explicitly
>> specify true for header option using option("header",true)
>> 
>>  not
>> mentioning this, the API treats header as a data record.
>>
>> Second point which may not be applicable to the newer versions of Spark. My
>> understanding is that the gz file is not splittable, therefore Spark needs
>> to read the whole file using a single core which will slow things down (CPU
>> intensive). After the read is done the data can be shuffled to increase
>> parallelism.
>>
>> HTH
>>
>>
>>view my Linkedin profile
>> 
>>
>>
>>
>> *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 Wed, 24 Mar 2021 at 12:40, Sean Owen  wrote:
>>
>>> No need to do that. Reading the header with Spark automatically is
>>> trivial.
>>>
>>> On Wed, Mar 24, 2021 at 5:25 AM Mich Talebzadeh <
>>> mich.talebza...@gmail.com> wrote:
>>>
 If it is a csv then it is a flat file somewhere in a directory I guess.

 Get the header out by doing

 */usr/bin/zcat csvfile.gz |head -n 1*
 Title Number,Tenure,Property
 Address,District,County,Region,Postcode,Multiple Address Indicator,Price
 Paid,Proprietor Name (1),Company Registration No. (1),Proprietorship
 Category (1),Country Incorporated (1),Proprietor (1) Address (1),Proprietor
 (1) Address (2),Proprietor (1) Address (3),Proprietor Name (2),Company
 Registration No. (2),Proprietorship Category (2),Country Incorporated
 (2),Proprietor (2) Address (1),Proprietor (2) Address (2),Proprietor (2)
 Address (3),Proprietor Name (3),Company Registration No. (3),Proprietorship
 Category (3),Country Incorporated (3),Proprietor (3) Address (1),Proprietor
 (3) Address (2),Proprietor (3) 

Re: Rdd - zip with index

[Sean Owen]

Right, that's all you do to tell it to treat the first line of the files as
a header defining col names.
Yes, .gz files still aren't splittable by nature. One huge CSV .csv file
would be split into partitions, but one .gz file would not, which can be a
problem.
To be clear, you do not need to do anything to let Spark read parts of a
large file in parallel (assuming compression isn't the issue).

On Wed, Mar 24, 2021 at 11:00 AM Mich Talebzadeh 
wrote:

> How does Spark establish there is a csv header as a matter of interest?
>
> Example
>
> val df = spark.read.option("header", true).csv(location)
>
> I need to tell spark to ignore the header correct?
>
> From Spark Read CSV file into DataFrame — SparkByExamples
> 
>
> If you have a header with column names on file, you need to explicitly
> specify true for header option using option("header",true)
> 
>  not
> mentioning this, the API treats header as a data record.
>
> Second point which may not be applicable to the newer versions of Spark. My
> understanding is that the gz file is not splittable, therefore Spark needs
> to read the whole file using a single core which will slow things down (CPU
> intensive). After the read is done the data can be shuffled to increase
> parallelism.
>
> HTH
>
>
>view my Linkedin profile
> 
>
>
>
> *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 Wed, 24 Mar 2021 at 12:40, Sean Owen  wrote:
>
>> No need to do that. Reading the header with Spark automatically is
>> trivial.
>>
>> On Wed, Mar 24, 2021 at 5:25 AM Mich Talebzadeh <
>> mich.talebza...@gmail.com> wrote:
>>
>>> If it is a csv then it is a flat file somewhere in a directory I guess.
>>>
>>> Get the header out by doing
>>>
>>> */usr/bin/zcat csvfile.gz |head -n 1*
>>> Title Number,Tenure,Property
>>> Address,District,County,Region,Postcode,Multiple Address Indicator,Price
>>> Paid,Proprietor Name (1),Company Registration No. (1),Proprietorship
>>> Category (1),Country Incorporated (1),Proprietor (1) Address (1),Proprietor
>>> (1) Address (2),Proprietor (1) Address (3),Proprietor Name (2),Company
>>> Registration No. (2),Proprietorship Category (2),Country Incorporated
>>> (2),Proprietor (2) Address (1),Proprietor (2) Address (2),Proprietor (2)
>>> Address (3),Proprietor Name (3),Company Registration No. (3),Proprietorship
>>> Category (3),Country Incorporated (3),Proprietor (3) Address (1),Proprietor
>>> (3) Address (2),Proprietor (3) Address (3),Proprietor Name (4),Company
>>> Registration No. (4),Proprietorship Category (4),Country Incorporated
>>> (4),Proprietor (4) Address (1),Proprietor (4) Address (2),Proprietor (4)
>>> Address (3),Date Proprietor Added,Additional Proprietor Indicator
>>>
>>>
>>> 10GB is not much of a big CSV file
>>>
>>> that will resolve the header anyway.
>>>
>>>
>>> Also how are you running the spark, in a local mode (single jvm) or
>>> other distributed modes (yarn, standalone) ?
>>>
>>>
>>> HTH
>>>
>>

Re: Rdd - zip with index

[KhajaAsmath Mohammed]

Thanks Mich. I understood what I am supposed to do now, will try these
options.

I still dont understand how the spark will split the large file. I have a
10 GB file which I want to split automatically after reading. I can split
and load the file before reading but it is a very big requirement change
for all our data pipeline.

Is there a way to split the file once it is read to achieve parallelism ?
I will group groupby on one column to see if that improves my job.

On Wed, Mar 24, 2021 at 10:56 AM Mich Talebzadeh 
wrote:

> How does Spark establish there is a csv header as a matter of interest?
>
> Example
>
> val df = spark.read.option("header", true).csv(location)
>
> I need to tell spark to ignore the header correct?
>
> From Spark Read CSV file into DataFrame — SparkByExamples
> 
>
> If you have a header with column names on file, you need to explicitly
> specify true for header option using option("header",true)
> 
>  not
> mentioning this, the API treats header as a data record.
>
> Second point which may not be applicable to the newer versions of Spark. My
> understanding is that the gz file is not splittable, therefore Spark needs
> to read the whole file using a single core which will slow things down (CPU
> intensive). After the read is done the data can be shuffled to increase
> parallelism.
>
> HTH
>
>
>view my Linkedin profile
> 
>
>
>
> *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 Wed, 24 Mar 2021 at 12:40, Sean Owen  wrote:
>
>> No need to do that. Reading the header with Spark automatically is
>> trivial.
>>
>> On Wed, Mar 24, 2021 at 5:25 AM Mich Talebzadeh <
>> mich.talebza...@gmail.com> wrote:
>>
>>> If it is a csv then it is a flat file somewhere in a directory I guess.
>>>
>>> Get the header out by doing
>>>
>>> */usr/bin/zcat csvfile.gz |head -n 1*
>>> Title Number,Tenure,Property
>>> Address,District,County,Region,Postcode,Multiple Address Indicator,Price
>>> Paid,Proprietor Name (1),Company Registration No. (1),Proprietorship
>>> Category (1),Country Incorporated (1),Proprietor (1) Address (1),Proprietor
>>> (1) Address (2),Proprietor (1) Address (3),Proprietor Name (2),Company
>>> Registration No. (2),Proprietorship Category (2),Country Incorporated
>>> (2),Proprietor (2) Address (1),Proprietor (2) Address (2),Proprietor (2)
>>> Address (3),Proprietor Name (3),Company Registration No. (3),Proprietorship
>>> Category (3),Country Incorporated (3),Proprietor (3) Address (1),Proprietor
>>> (3) Address (2),Proprietor (3) Address (3),Proprietor Name (4),Company
>>> Registration No. (4),Proprietorship Category (4),Country Incorporated
>>> (4),Proprietor (4) Address (1),Proprietor (4) Address (2),Proprietor (4)
>>> Address (3),Date Proprietor Added,Additional Proprietor Indicator
>>>
>>>
>>> 10GB is not much of a big CSV file
>>>
>>> that will resolve the header anyway.
>>>
>>>
>>> Also how are you running the spark, in a local mode (single jvm) or
>>> other distributed modes (yarn, standalone) ?
>>>
>>>
>>> HTH
>>>
>>

Re: Rdd - zip with index

[Mich Talebzadeh]

How does Spark establish there is a csv header as a matter of interest?

Example

val df = spark.read.option("header", true).csv(location)

I need to tell spark to ignore the header correct?

>From Spark Read CSV file into DataFrame — SparkByExamples


If you have a header with column names on file, you need to explicitly
specify true for header option using option("header",true)

not
mentioning this, the API treats header as a data record.

Second point which may not be applicable to the newer versions of Spark. My
understanding is that the gz file is not splittable, therefore Spark needs
to read the whole file using a single core which will slow things down (CPU
intensive). After the read is done the data can be shuffled to increase
parallelism.

HTH


   view my Linkedin profile




*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 Wed, 24 Mar 2021 at 12:40, Sean Owen  wrote:

> No need to do that. Reading the header with Spark automatically is trivial.
>
> On Wed, Mar 24, 2021 at 5:25 AM Mich Talebzadeh 
> wrote:
>
>> If it is a csv then it is a flat file somewhere in a directory I guess.
>>
>> Get the header out by doing
>>
>> */usr/bin/zcat csvfile.gz |head -n 1*
>> Title Number,Tenure,Property
>> Address,District,County,Region,Postcode,Multiple Address Indicator,Price
>> Paid,Proprietor Name (1),Company Registration No. (1),Proprietorship
>> Category (1),Country Incorporated (1),Proprietor (1) Address (1),Proprietor
>> (1) Address (2),Proprietor (1) Address (3),Proprietor Name (2),Company
>> Registration No. (2),Proprietorship Category (2),Country Incorporated
>> (2),Proprietor (2) Address (1),Proprietor (2) Address (2),Proprietor (2)
>> Address (3),Proprietor Name (3),Company Registration No. (3),Proprietorship
>> Category (3),Country Incorporated (3),Proprietor (3) Address (1),Proprietor
>> (3) Address (2),Proprietor (3) Address (3),Proprietor Name (4),Company
>> Registration No. (4),Proprietorship Category (4),Country Incorporated
>> (4),Proprietor (4) Address (1),Proprietor (4) Address (2),Proprietor (4)
>> Address (3),Date Proprietor Added,Additional Proprietor Indicator
>>
>>
>> 10GB is not much of a big CSV file
>>
>> that will resolve the header anyway.
>>
>>
>> Also how are you running the spark, in a local mode (single jvm) or
>> other distributed modes (yarn, standalone) ?
>>
>>
>> HTH
>>
>

Re: Rdd - zip with index

[Sean Owen]

No need to do that. Reading the header with Spark automatically is trivial.

On Wed, Mar 24, 2021 at 5:25 AM Mich Talebzadeh 
wrote:

> If it is a csv then it is a flat file somewhere in a directory I guess.
>
> Get the header out by doing
>
> */usr/bin/zcat csvfile.gz |head -n 1*
> Title Number,Tenure,Property
> Address,District,County,Region,Postcode,Multiple Address Indicator,Price
> Paid,Proprietor Name (1),Company Registration No. (1),Proprietorship
> Category (1),Country Incorporated (1),Proprietor (1) Address (1),Proprietor
> (1) Address (2),Proprietor (1) Address (3),Proprietor Name (2),Company
> Registration No. (2),Proprietorship Category (2),Country Incorporated
> (2),Proprietor (2) Address (1),Proprietor (2) Address (2),Proprietor (2)
> Address (3),Proprietor Name (3),Company Registration No. (3),Proprietorship
> Category (3),Country Incorporated (3),Proprietor (3) Address (1),Proprietor
> (3) Address (2),Proprietor (3) Address (3),Proprietor Name (4),Company
> Registration No. (4),Proprietorship Category (4),Country Incorporated
> (4),Proprietor (4) Address (1),Proprietor (4) Address (2),Proprietor (4)
> Address (3),Date Proprietor Added,Additional Proprietor Indicator
>
>
> 10GB is not much of a big CSV file
>
> that will resolve the header anyway.
>
>
> Also how are you running the spark, in a local mode (single jvm) or
> other distributed modes (yarn, standalone) ?
>
>
> HTH
>

Re: Rdd - zip with index

[Mich Talebzadeh]

If it is a csv then it is a flat file somewhere in a directory I guess.

Get the header out by doing

*/usr/bin/zcat csvfile.gz |head -n 1*
Title Number,Tenure,Property
Address,District,County,Region,Postcode,Multiple Address Indicator,Price
Paid,Proprietor Name (1),Company Registration No. (1),Proprietorship
Category (1),Country Incorporated (1),Proprietor (1) Address (1),Proprietor
(1) Address (2),Proprietor (1) Address (3),Proprietor Name (2),Company
Registration No. (2),Proprietorship Category (2),Country Incorporated
(2),Proprietor (2) Address (1),Proprietor (2) Address (2),Proprietor (2)
Address (3),Proprietor Name (3),Company Registration No. (3),Proprietorship
Category (3),Country Incorporated (3),Proprietor (3) Address (1),Proprietor
(3) Address (2),Proprietor (3) Address (3),Proprietor Name (4),Company
Registration No. (4),Proprietorship Category (4),Country Incorporated
(4),Proprietor (4) Address (1),Proprietor (4) Address (2),Proprietor (4)
Address (3),Date Proprietor Added,Additional Proprietor Indicator


10GB is not much of a big CSV file

that will resolve the header anyway.


Also how are you running the spark, in a local mode (single jvm) or
other distributed modes (yarn, standalone) ?


HTH


   view my Linkedin profile




*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 Wed, 24 Mar 2021 at 03:24, ayan guha  wrote:

> Best case is use dataframe and df.columns will automatically give you
> column names. Are you sure your file is indeed in csv? maybe it is easier
> if you share the code?
>
> On Wed, 24 Mar 2021 at 2:12 pm, Sean Owen  wrote:
>
>> It would split 10GB of CSV into multiple partitions by default, unless
>> it's gzipped. Something else is going on here.
>>
>> ‪On Tue, Mar 23, 2021 at 10:04 PM ‫"Yuri Oleynikov (‫יורי
>> אולייניקוב‬‎)"‬‎  wrote:‬
>>
>>> I’m not Spark core developer and do not want to confuse you but it seems
>>> logical to me that just reading from single file (no matter what format of
>>> the file is used) gives no parallelism unless you do repartition by some
>>> column just after csv load, but the if you’re telling you’ve already tried
>>> repartition with no luck...
>>>
>>>
>>> > On 24 Mar 2021, at 03:47, KhajaAsmath Mohammed <
>>> mdkhajaasm...@gmail.com> wrote:
>>> >
>>> > So spark by default doesn’t split the large 10gb file when loaded?
>>> >
>>> > Sent from my iPhone
>>> >
>>> >> On Mar 23, 2021, at 8:44 PM, Yuri Oleynikov (‫יורי אולייניקוב‬‎) <
>>> yur...@gmail.com> wrote:
>>> >>
>>> >> Hi, Mohammed
>>> >> I think that the reason that only one executor is running and have
>>> single partition is because you have single file that might be read/loaded
>>> into memory.
>>> >>
>>> >> In order to achieve better parallelism I’d suggest to split the csv
>>> file.
>>> >>
>>>
>>> --
> Best Regards,
> Ayan Guha
>