Hi everyone,
I am currently looking into how Flink can coexist and interoperate with other
frameworks in a cluster, such as plain single-machine processes or Spark?.
?Tachyon seems to be nice solution to exchange data between them.
However, I think it is a problem that Flink's taskmanagers al
r setup. In a YARN setup, you can flexibly start
and stop Flink sessions with different configurations (memory, TMs, slots) or
run a single job. When running a single job, Flink will allocate resources and
free them after the job is done.
Best, Fabian
2015-12-09 9:46 GMT+01:00 Kruse, Sebastian
mai
Hi everyone,
>From playing around a bit around with delta iterations, I saw that you can
>update elements from the solution set and add new elements. My question is: is
>it possible to remove elements from the solution set (apart from marking them
>as "deleted" somehow)?
My use case at hand fo
particular algorithm? I was thinking
about examples iterative algorithms with this property...
Regards,
A.
2015-02-10 14:18 GMT+01:00 Kruse, Sebastian
mailto:sebastian.kr...@hpi.de>>:
Hi everyone,
From playing around a bit around with delta iterations, I saw that you can
update elemen
:40 AM, Kruse, Sebastian
mailto:sebastian.kr...@hpi.de>> wrote:
Thanks for your answers.
I am trying to build an apriori-like algorithm to find key candidates in a
relational dataset. I was considering delta iterations, because the algorithm
should maintain two datasets: a set of
Hi everyone,
I think that during one of the meetups, it was mentioned that Flink can in some
cases operate on serialized data. Given I understood that correctly, which
cases that would be, i.e, which data types and operators support such a feature?
Cheers,
Sebastian
---
Sebastian Kruse
Doktor
Hello everyone,
we are having a seminar at HPI that is supposed to compare Flink and Spark. The
students should implement solutions for different data analytics problems on
both platforms.
Since Flink is still moving a lot, we would like to have the students work on
the latest version and mayb
Hello everyone,
I was just wondering, which class would be most efficient to store collections
of primitive elements, and which one to store objects, within POJOs and tuples
from a serialization point of view. And would it make any difference if such a
collection is not embedded within a POJO/t
Hi folks,
I would like to do some load balancing within one of my Flink jobs to achieve
good scalability. The rebalance() method is not applicable in my case, as the
runtime is dominated by the processing of very few larger elements in my
dataset. Hence, I need to distribute the processing work
Hi,
You might want to translate your SQL statement into an expression of the
relational algebra at first [1]. This expression can be expressed with Flink's
operators in a straight-forward manner.
In the end, it will look something like this:
Employees.filter(_.job_id = ...).join(departments.fil
...@gmail.com>> wrote:
Hi Sebastian,
I agree, shuffling only specific elements would be a very useful feature, but
unfortunately it's not supported (yet).
Would you like to open a JIRA for that?
Cheers, Fabian
2015-06-09 17:22 GMT+02:00 Kruse, Sebastian
mailto:sebastian.kr...@hpi.de&
That example was written in Scala, if you are using Java, then the join
function is applied with the function "with(..)". However, logically you do the
same in Scala and Java, there a just some minor differences in the API.
-Original Message-
From: hagersaleh [mailto:loveallah1...@yahoo.
u can process the subelements in parallel, and finally group by key to
aggregate the result.
Cheers,
--
Gianmarco
On 11 June 2015 at 19:16, Kruse, Sebastian
mailto:sebastian.kr...@hpi.de>> wrote:
Hi Gianmarco,
Thanks for the pointer!
I had a quick look at the paper, but unfortunately I
Hi everyone,
I did not reenact it, but I think the problem here is rather the anonymous
class. It looks like it is created within a class, not an object. Thus it is
not “static” in Java terms, which means that also its surrounding class (the
job class) will be serialized. And in this job class,
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