each record and then
> filter out certain files.
>
> Regards,
> Gourav
>
> On Wed, Jul 10, 2019 at 6:47 AM Wei Chen wrote:
>
>> Hello All,
>>
>> I am using spark to process some files parallelly.
>> While most files are able to be processed within 3 s
Hello All,
I am using spark to process some files parallelly.
While most files are able to be processed within 3 seconds,
it is possible that we stuck on 1 or 2 files as they will never finish (or
will take more than 48 hours).
Since it is a 3rd party file conversion tool, we are not able to debug
Found it. In case someone else if looking for this:
cvModel.bestModel.asInstanceOf[org.apache.spark.ml.classification.LogisticRegressionModel].weights
On Tue, Apr 19, 2016 at 1:12 PM, Wei Chen wrote:
> Hi All,
>
> I am using the example of model selection via cross-validation
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>
--
Wei Chen, Ph.D.
Astronomer and Data Scientist
Phone: (832)646-7124
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Forgot to mention, I am using 1.5.2 Scala version.
On Tue, Apr 19, 2016 at 1:12 PM, Wei Chen wrote:
> Hi All,
>
> I am using the example of model selection via cross-validation from the
> documentation here: http://spark.apache.org/docs/latest/ml-guide.html.
> After I get the &qu
Hi All,
I am using the example of model selection via cross-validation from the
documentation here: http://spark.apache.org/docs/latest/ml-guide.html.
After I get the "cvModel", I would like to see the weights for each feature
for the best logistic regression model. I've been looking at the method
Hi All,
I have data partitioned by year=/month=mm/day=dd, what is the best way
to get two months of data from a given year (let's say June and July)?
Two ways I can think of:
1. use unionAll
df1 = sqc.read.parquet('xxx/year=2015/month=6')
df2 = sqc.read.parquet('xxx/year=2015/month=7')
df = d
,b,c, rank() over (partition by a order by b) r from df)
> x
> where r = 1
>
> You can register your DF as a temp table and use the sql form. Or, (>Spark
> 1.4) you can use window methods and their variants in Spark SQL module.
>
> HTH
>
> On Wed, Jan 6, 2016 at 11:
Hi,
I am trying to retrieve the rows with a minimum value of a column for each
group. For example: the following dataframe:
a | b | c
--
1 | 1 | 1
1 | 2 | 2
1 | 3 | 3
2 | 1 | 4
2 | 2 | 5
2 | 3 | 6
3 | 1 | 7
3 | 2 | 8
3 | 3 | 9
--
I group by 'a', and want the rows with the smalles
Hi,
I am wondering if there is UDAF support in PySpark with Spark 1.5. If not,
is Spark 1.6 going to incorporate that?
Thanks,
Wei
Hi,
I am wondering if there is UDAF support in PySpark with Spark 1.5. If not,
is Spark 1.6 going to incorporate that?
Thanks,
Wei
Hey Friends,
I am trying to use sqlContext.write.parquet() to write dataframe to parquet
files. I have the following questions.
1. number of partitions
The default number of partition seems to be 200. Is there any way other
than using df.repartition(n) to change this number? I was told repartitio
Hey Friends,
I've been using partition discovery with folder structures that have
"field=" in folder names. However, I've also encountered a lot of folders
structures without "field=" in folder names, especially when it is year,
month, day. Is there anyway that we can assign a field to each level
Hey Friends,
Recently I have been using Spark 1.3.1, mainly pyspark.sql. I noticed that
the Row object collected directly from a DataFrame is different from the
Row object we directly defined from Row(*arg, **kwarg).
>>>from pyspark.sql.types import Row
>>>aaa = Row(a=1, b=2, c=Row(a=1, b=2))
>>>
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