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
> <https://sparkbyexamples.com/spark/spark-read-csv-file-into-dataframe/>
>
> If you have a header with column names on file, you need to explicitly
> specify true for header option using option("header",true)
> <https://sparkbyexamples.com/spark/spark-read-csv-file-into-dataframe/#header>
> 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
> <https://www.linkedin.com/in/mich-talebzadeh-ph-d-5205b2/>
>
>
>
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>
>
> On Wed, 24 Mar 2021 at 12:40, Sean Owen <sro...@gmail.com> 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
>>>
>>
