Can you file a JIRA please?
On Tue, Jun 23, 2015 at 1:42 AM, StanZhai m...@zhaishidan.cn wrote:
Hi all,
After upgraded the cluster from spark 1.3.1 to 1.4.0(rc4), I encountered
the
following exception when use concat with UDF in where clause:
This should give accurate count for each batch, though for getting the rate
you have to make sure that you streaming app is stable, that is, batches
are processed as fast as they are received (scheduling delay in the spark
streaming UI is approx 0).
TD
On Tue, Jun 23, 2015 at 2:49 AM, anshu
Every language has its own quirks / features -- so I don't think there
exists a document on how to go about doing this for a new language. The
most related write up I know of is the wiki page on PySpark internals
https://cwiki.apache.org/confluence/display/SPARK/PySpark+Internals written
by Josh
Just FYI, it would be easiest to follow SparkR's example and add the DataFrame
API first. Other APIs will be designed to work on DataFrames (most notably
machine learning pipelines), and the surface of this API is much smaller than
of the RDD API. This API will also give you great performance
Hello,
I want to add language support for another language(other than Scala,
Java et. al.). Where is documentation that explains to provide support for
a new language?
Thank you,
Vasili
// + punya
Thanks for your quick response!
I'm not sure that using an unbounded buffer is a good solution to the
locking problem. For example, in the situation where I had 500 columns, I
am in fact storing 499 extra columns on the java side, which might make me
OOM if I have to store many rows.
Please vote on releasing the following candidate as Apache Spark version 1.4.1!
This release fixes a handful of known issues in Spark 1.4.0, listed here:
http://s.apache.org/spark-1.4.1
The tag to be voted on is v1.4.1-rc1 (commit 60e08e5):
Hi,
is there a configuration setting to set a custom port number for the master
REST URL? can that be included in the spark-defaults.conf?
cheers
--
Niranda
@n1r44 https://twitter.com/N1R44
https://pythagoreanscript.wordpress.com/
Hi all,
After upgraded the cluster from spark 1.3.1 to 1.4.0(rc4), I encountered the
following exception when use concat with UDF in where clause:
===Exception
org.apache.spark.sql.catalyst.analysis.UnresolvedException: Invalid call to
dataType on unresolved
I am calculating input rate using the following logic.
And i think this foreachRDD is always running on driver (println are
seen on driver)
1- Is there any other way to do that in less cost .
2- Will this give me the correct count for rate .
//code -
inputStream.foreachRDD(new
Hey Spark devs
I've been looking at DF UDFs and UDAFs. The approx distinct is using
hyperloglog,
but there is only an option to return the count as a Long.
It can be useful to be able to return and store the actual data structure
(ie serialized HLL). This effectively allows one to do aggregation
There are some committers who are active on JIRA and sometimes need to
do things that require JIRA admin access -- in particular thinking of
adding a new person as Contributor in order to assign them a JIRA.
We can't change what roles can do what (think that INFRA ticket is
dead) but can add to
BLUF: BatchPythonEvaluation's implementation is unusable at large scale,
but I have a proof-of-concept implementation that avoids caching the entire
dataset.
Hi,
We have been running into performance problems using Python UDFs with
DataFrames at large scale.
From the implementation of
Thanks for looking into it, I'd like the idea of having
ForkingIterator. If we have unlimited buffer in it, then will not have
the problem of deadlock, I think. The writing thread will be blocked
by Python process, so there will be not much rows be buffered(still be
a reason to OOM). At least,
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