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From "Devi P.V" <devip2...@gmail.com>
Subject Re: How to convert a unix timestamp column into date format(yyyy-MM-dd) ?
Date Mon, 05 Dec 2016 08:47:50 GMT
Thanks.It works.

On Mon, Dec 5, 2016 at 2:03 PM, Michal Šenkýř <mike.senkyr@gmail.com> wrote:

> Yet another approach:
> scala> val df1 = df.selectExpr("client_id", "from_unixtime(ts/1000,'yyyy-MM-dd')
> as ts")
>
> Mgr. Michal Šenkýřmike.senkyr@gmail.com
> +420 605 071 818
>
> On 5.12.2016 09:22, Deepak Sharma wrote:
>
> Another simpler approach will be:
> scala> val findf = sqlContext.sql("select client_id,from_unixtime(ts/1000,'yyyy-MM-dd')
> ts from ts")
> findf: org.apache.spark.sql.DataFrame = [client_id: string, ts: string]
>
> scala> findf.show
> +--------------------+----------+
> |           client_id|        ts|
> +--------------------+----------+
> |cd646551-fceb-416...|2016-11-01|
> |3bc61951-0f49-43b...|2016-11-01|
> |688acc61-753f-4a3...|2016-11-23|
> |5ff1eb6c-14ec-471...|2016-11-23|
> +--------------------+----------+
>
> I registered temp table out of the original DF
> Thanks
> Deepak
>
> On Mon, Dec 5, 2016 at 1:49 PM, Deepak Sharma <deepakmca05@gmail.com>
> wrote:
>
>> This is the correct way to do it.The timestamp that you mentioned was not
>> correct:
>>
>> scala> val ts1 = from_unixtime($"ts"/1000, "yyyy-MM-dd")
>> ts1: org.apache.spark.sql.Column = fromunixtime((ts / 1000),yyyy-MM-dd)
>>
>> scala> val finaldf = df.withColumn("ts1",ts1)
>> finaldf: org.apache.spark.sql.DataFrame = [client_id: string, ts: string,
>> ts1: string]
>>
>> scala> finaldf.show
>> +--------------------+-------------+----------+
>> |           client_id|           ts|       ts1|
>> +--------------------+-------------+----------+
>> |cd646551-fceb-416...|1477989416803|2016-11-01|
>> |3bc61951-0f49-43b...|1477983725292|2016-11-01|
>> |688acc61-753f-4a3...|1479899459947|2016-11-23|
>> |5ff1eb6c-14ec-471...|1479901374026|2016-11-23|
>> +--------------------+-------------+----------+
>>
>>
>> Thanks
>> Deepak
>>
>> On Mon, Dec 5, 2016 at 1:46 PM, Deepak Sharma <deepakmca05@gmail.com>
>> wrote:
>>
>>> This is how you can do it in scala:
>>> scala> val ts1 = from_unixtime($"ts", "yyyy-MM-dd")
>>> ts1: org.apache.spark.sql.Column = fromunixtime(ts,yyyy-MM-dd)
>>>
>>> scala> val finaldf = df.withColumn("ts1",ts1)
>>> finaldf: org.apache.spark.sql.DataFrame = [client_id: string, ts:
>>> string, ts1: string]
>>>
>>> scala> finaldf.show
>>> +--------------------+-------------+-----------+
>>> |           client_id|           ts|        ts1|
>>> +--------------------+-------------+-----------+
>>> |cd646551-fceb-416...|1477989416803|48805-08-14|
>>> |3bc61951-0f49-43b...|1477983725292|48805-06-09|
>>> |688acc61-753f-4a3...|1479899459947|48866-02-22|
>>> |5ff1eb6c-14ec-471...|1479901374026|48866-03-16|
>>> +--------------------+-------------+-----------+
>>>
>>> The year is returning wrong here.May be the input timestamp is not
>>> correct .Not sure.
>>>
>>> Thanks
>>> Deepak
>>>
>>> On Mon, Dec 5, 2016 at 1:34 PM, Devi P.V <devip2136@gmail.com> wrote:
>>>
>>>> Hi,
>>>>
>>>> Thanks for replying to my question.
>>>> I am using scala
>>>>
>>>> On Mon, Dec 5, 2016 at 1:20 PM, Marco Mistroni <mmistroni@gmail.com>
>>>> wrote:
>>>>
>>>>> Hi
>>>>>  In python you can use date time.fromtimestamp(......).str
>>>>> ftime('%Y%m%d')........
>>>>> Which spark API are you using?
>>>>> Kr
>>>>>
>>>>> On 5 Dec 2016 7:38 am, "Devi P.V" <devip2136@gmail.com> wrote:
>>>>>
>>>>>> Hi all,
>>>>>>
>>>>>> I have a dataframe like following,
>>>>>>
>>>>>> +------------------------------------+---------------+
>>>>>> |client_id                           |timestamp|
>>>>>> +------------------------------------+---------------+
>>>>>> |cd646551-fceb-4166-acbc-b9|1477989416803  |
>>>>>> |3bc61951-0f49-43bf-9848-b2|1477983725292  |
>>>>>> |688acc61-753f-4a33-a034-bc|1479899459947  |
>>>>>> |5ff1eb6c-14ec-4716-9798-00|1479901374026  |
>>>>>> +------------------------------------+---------------+
>>>>>>
>>>>>>  I want to convert timestamp column into yyyy-MM-dd format.
>>>>>> How to do this?
>>>>>>
>>>>>>
>>>>>> Thanks
>>>>>>
>>>>>
>>>>
>>>
>>>
>>> --
>>> Thanks
>>> Deepak
>>> www.bigdatabig.com
>>> www.keosha.net
>>>
>>
>>
>>
>> --
>> Thanks
>> Deepak
>> www.bigdatabig.com
>> www.keosha.net
>>
>
>
>
> --
> Thanks
> Deepak
> www.bigdatabig.com
> www.keosha.net
>
>
>

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