Re: Facing issue with floor function in spark SQL query

[Mich Talebzadeh]

Spark sql has both FLOOR and CEILING functions

spark-sql> select FLOOR(11.95),CEILING(11.95);
11.012.0



Dr Mich Talebzadeh



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On 4 March 2016 at 12:35, Ajay Chander  wrote:

> Hi Ashok,
>
> Try using hivecontext instead of sqlcontext. I suspect sqlcontext doesnot
> have that functionality. Let me know if it works.
>
> Thanks,
> Ajay
>
>
> On Friday, March 4, 2016, ashokkumar rajendran <
> ashokkumar.rajend...@gmail.com> wrote:
>
>> Hi Ayan,
>>
>> Thanks for the response. I am using SQL query (not Dataframe). Could you
>> please explain how I should import this sql function to it? Simply
>> importing this class to my driver code does not help here.
>>
>> Many functions that I need are already there in the sql.functions so I do
>> not want to rewrite them.
>>
>> Regards
>> Ashok
>>
>> On Fri, Mar 4, 2016 at 3:52 PM, ayan guha  wrote:
>>
>>> Most likely you are missing import of  org.apache.spark.sql.functions.
>>>
>>> In any case, you can write your own function for floor and use it as UDF.
>>>
>>> On Fri, Mar 4, 2016 at 7:34 PM, ashokkumar rajendran <
>>> ashokkumar.rajend...@gmail.com> wrote:
>>>
 Hi,

 I load json file that has timestamp (as long in milliseconds) and
 several other attributes. I would like to group them by 5 minutes and store
 them as separate file.

 I am facing couple of problems here..
 1. Using Floor function at select clause (to bucket by 5mins) gives me
 error saying "java.util.NoSuchElementException: key not found: floor". How
 do I use floor function in select clause? I see that floor method is
 available in org.apache.spark.sql.functions clause but not sure why its not
 working here.
 2. Can I use the same in Group by clause?
 3. How do I store them as separate file after grouping them?

 String logPath = "my-json.gz";
 DataFrame logdf = sqlContext.read().json(logPath);
 logdf.registerTempTable("logs");
 DataFrame bucketLogs = sqlContext.sql("Select `user.timestamp`
 as rawTimeStamp, `user.requestId` as requestId,
 *floor(`user.timestamp`/72000*) as timeBucket FROM logs");
 bucketLogs.toJSON().saveAsTextFile("target_file");

 Regards
 Ashok

>>>
>>>
>>>
>>> --
>>> Best Regards,
>>> Ayan Guha
>>>
>>
>>

Re: Facing issue with floor function in spark SQL query

[Ajay Chander]

Hi Ashok,

Try using hivecontext instead of sqlcontext. I suspect sqlcontext doesnot
have that functionality. Let me know if it works.

Thanks,
Ajay

On Friday, March 4, 2016, ashokkumar rajendran <
ashokkumar.rajend...@gmail.com> wrote:

> Hi Ayan,
>
> Thanks for the response. I am using SQL query (not Dataframe). Could you
> please explain how I should import this sql function to it? Simply
> importing this class to my driver code does not help here.
>
> Many functions that I need are already there in the sql.functions so I do
> not want to rewrite them.
>
> Regards
> Ashok
>
> On Fri, Mar 4, 2016 at 3:52 PM, ayan guha  > wrote:
>
>> Most likely you are missing import of  org.apache.spark.sql.functions.
>>
>> In any case, you can write your own function for floor and use it as UDF.
>>
>> On Fri, Mar 4, 2016 at 7:34 PM, ashokkumar rajendran <
>> ashokkumar.rajend...@gmail.com
>> > wrote:
>>
>>> Hi,
>>>
>>> I load json file that has timestamp (as long in milliseconds) and
>>> several other attributes. I would like to group them by 5 minutes and store
>>> them as separate file.
>>>
>>> I am facing couple of problems here..
>>> 1. Using Floor function at select clause (to bucket by 5mins) gives me
>>> error saying "java.util.NoSuchElementException: key not found: floor". How
>>> do I use floor function in select clause? I see that floor method is
>>> available in org.apache.spark.sql.functions clause but not sure why its not
>>> working here.
>>> 2. Can I use the same in Group by clause?
>>> 3. How do I store them as separate file after grouping them?
>>>
>>> String logPath = "my-json.gz";
>>> DataFrame logdf = sqlContext.read().json(logPath);
>>> logdf.registerTempTable("logs");
>>> DataFrame bucketLogs = sqlContext.sql("Select `user.timestamp`
>>> as rawTimeStamp, `user.requestId` as requestId,
>>> *floor(`user.timestamp`/72000*) as timeBucket FROM logs");
>>> bucketLogs.toJSON().saveAsTextFile("target_file");
>>>
>>> Regards
>>> Ashok
>>>
>>
>>
>>
>> --
>> Best Regards,
>> Ayan Guha
>>
>
>

Re: Facing issue with floor function in spark SQL query

[ashokkumar rajendran]

Hi Ayan,

Thanks for the response. I am using SQL query (not Dataframe). Could you
please explain how I should import this sql function to it? Simply
importing this class to my driver code does not help here.

Many functions that I need are already there in the sql.functions so I do
not want to rewrite them.

Regards
Ashok

On Fri, Mar 4, 2016 at 3:52 PM, ayan guha  wrote:

> Most likely you are missing import of  org.apache.spark.sql.functions.
>
> In any case, you can write your own function for floor and use it as UDF.
>
> On Fri, Mar 4, 2016 at 7:34 PM, ashokkumar rajendran <
> ashokkumar.rajend...@gmail.com> wrote:
>
>> Hi,
>>
>> I load json file that has timestamp (as long in milliseconds) and several
>> other attributes. I would like to group them by 5 minutes and store them as
>> separate file.
>>
>> I am facing couple of problems here..
>> 1. Using Floor function at select clause (to bucket by 5mins) gives me
>> error saying "java.util.NoSuchElementException: key not found: floor". How
>> do I use floor function in select clause? I see that floor method is
>> available in org.apache.spark.sql.functions clause but not sure why its not
>> working here.
>> 2. Can I use the same in Group by clause?
>> 3. How do I store them as separate file after grouping them?
>>
>> String logPath = "my-json.gz";
>> DataFrame logdf = sqlContext.read().json(logPath);
>> logdf.registerTempTable("logs");
>> DataFrame bucketLogs = sqlContext.sql("Select `user.timestamp` as
>> rawTimeStamp, `user.requestId` as requestId,
>> *floor(`user.timestamp`/72000*) as timeBucket FROM logs");
>> bucketLogs.toJSON().saveAsTextFile("target_file");
>>
>> Regards
>> Ashok
>>
>
>
>
> --
> Best Regards,
> Ayan Guha
>

Re: Facing issue with floor function in spark SQL query

[ayan guha]

Most likely you are missing import of  org.apache.spark.sql.functions.

In any case, you can write your own function for floor and use it as UDF.

On Fri, Mar 4, 2016 at 7:34 PM, ashokkumar rajendran <
ashokkumar.rajend...@gmail.com> wrote:

> Hi,
>
> I load json file that has timestamp (as long in milliseconds) and several
> other attributes. I would like to group them by 5 minutes and store them as
> separate file.
>
> I am facing couple of problems here..
> 1. Using Floor function at select clause (to bucket by 5mins) gives me
> error saying "java.util.NoSuchElementException: key not found: floor". How
> do I use floor function in select clause? I see that floor method is
> available in org.apache.spark.sql.functions clause but not sure why its not
> working here.
> 2. Can I use the same in Group by clause?
> 3. How do I store them as separate file after grouping them?
>
> String logPath = "my-json.gz";
> DataFrame logdf = sqlContext.read().json(logPath);
> logdf.registerTempTable("logs");
> DataFrame bucketLogs = sqlContext.sql("Select `user.timestamp` as
> rawTimeStamp, `user.requestId` as requestId,
> *floor(`user.timestamp`/72000*) as timeBucket FROM logs");
> bucketLogs.toJSON().saveAsTextFile("target_file");
>
> Regards
> Ashok
>



-- 
Best Regards,
Ayan Guha