t; sys.exit(1)
>
> Thanks again.
>
>
> Mich
>
>
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Hi Mich,
I'm a bit confused by what you mean when you say that you cannot call a
fixture in another fixture. The fixtures resolve dependencies among
themselves by means of their named parameters. So that means that if I have
a fixture
@pytest.fixture
def fixture1():
return SomeObj()
and
oyee_name
> now the http GET call has to be made for each employee_id and DataFrame is
> dynamic for each spark job run.
>
> Does it make sense?
>
> Thanks
>
>
> On Thu, May 14, 2020 at 5:12 PM Jerry Vinokurov
> wrote:
>
>> Hi Chetan,
>>
>> You
Hi Chetan,
You can pretty much use any client to do this. When I was using Spark at a
previous job, we used OkHttp, but I'm sure there are plenty of others. In
our case, we had a startup phase in which we gathered metadata via a REST
API and then broadcast it to the workers. I think if you need
This seems like a suboptimal situation for a join. How can Spark know in
advance that all the fields are present and the tables have the same number
of rows? I suppose you could just sort the two frames by id and concatenate
them, but I'm not sure what join optimization is available here.
On Fri,
our tolerance for error you could also use
>> percentile_approx().
>>
>> On Mon, Nov 11, 2019 at 10:14 AM Jerry Vinokurov
>> wrote:
>>
>>> Do you mean that you are trying to compute the percent rank of some
>>> data? You can use the SparkSQL percent_r
Do you mean that you are trying to compute the percent rank of some data?
You can use the SparkSQL percent_rank function for that, but I don't think
that's going to give you any improvement over calling the percentRank
function on the data frame. Are you currently using a user-defined function
for
t; ```
>>
>> then run with
>>
>> 'spark.kryo.referenceTracking': 'false',
>> 'spark.kryo.registrationRequired': 'false',
>> 'spark.kryo.registrator': 'com.datadog.spark.MyKryoRegistrator',
>> 'spark.kryo.unsafe': 'false',
>> 'spark.kryoserializer.buffer.max'
>
> 'spark.kryo.referenceTracking': 'false',
> 'spark.kryo.registrationRequired': 'false',
> 'spark.kryo.registrator': 'com.datadog.spark.MyKryoRegistrator',
> 'spark.kryo.unsafe': 'false',
> 'spark.kryoserializer.buffer.max': '256m',
>
> On Tue, Sep 17, 2019 at 10:38 AM Jerry Vin
isc.Launcher$AppClassLoader.loadClass(Launcher.java:349)
> at java.lang.ClassLoader.loadClass(ClassLoader.java:357)
> at java.lang.Class.forName0(Native Method)
> at java.lang.Class.forName(Class.java:348)
> at
> org.apache.spark.serializer.KryoSerializer$$anonfun$newKryo$4.apply(KryoSerializer.scal
Maybe I'm not understanding something about this use case, but why is
precomputation not an option? Is it because the matrices themselves change?
Because if the matrices are constant, then I think precomputation would
work for you even if the users request random correlations. You can just
store
Hi Ajay,
When a Spark SQL statement references a table, that table has to be
"registered" first. Usually the way this is done is by reading in a
DataFrame, then calling the createOrReplaceTempView (or one of a few other
functions) on that data frame, with the argument being the name under which
Hi all,
I am experiencing a strange intermittent failure of my Spark job that
results from serialization issues in Kryo. Here is the stack trace:
Caused by: java.lang.ClassNotFoundException: com.mycompany.models.MyModel
> at java.net.URLClassLoader.findClass(URLClassLoader.java:382)
>
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