FYI
The staging repository published as version 1.5.0 is at
https://repository.apache.org/content/repositories/orgapachespark-1136
while the staging repository published as version 1.5.0-rc1 is at
https://repository.apache.org/content/repositories/orgapachespark-1137
Thanks
Shivaram
On Thu, Aug
Please vote on releasing the following candidate as Apache Spark version
1.5.0!
The vote is open until Monday, Aug 17, 2015 at 20:00 UTC and passes if a
majority of at least 3 +1 PMC votes are cast.
[ ] +1 Release this package as Apache Spark 1.5.0
[ ] -1 Do not release this package because ...
Not sure what's going on or how you measure the time, but the difference
here is pretty big when I test on my laptop. Maybe you set the wrong config
variables? (spark.sql.* are sql variables that you set in
sqlContext.setConf -- and in 1.5, they are consolidated into a single
flag: spark.sql.tungst
Did git pull :)
Now I do get the difference in time between on/off Tungsten unsafe: it is 24-25
seconds (unsafe on) vs 32-26 seconds (unsafe off) for the example below.
Why I am not getting the improvement as advertised on Spark Summit (slide 23)?
http://www.slideshare.net/SparkSummit/deep-dive-
Please git pull :)
On Thu, Aug 20, 2015 at 5:35 PM, Ulanov, Alexander
wrote:
> I am using Spark 1.5 cloned from master on June 12. (The aggregate unsafe
> feature was added to Spark on April 29.)
>
>
>
> *From:* Reynold Xin [mailto:r...@databricks.com]
> *Sent:* Thursday, August 20, 2015 5:26 P
I am using Spark 1.5 cloned from master on June 12. (The aggregate unsafe
feature was added to Spark on April 29.)
From: Reynold Xin [mailto:r...@databricks.com]
Sent: Thursday, August 20, 2015 5:26 PM
To: Ulanov, Alexander
Cc: dev@spark.apache.org
Subject: Re: Dataframe aggregation with Tungsten
Yes - DataFrame and SQL are the same thing.
Which version are you running? Spark 1.4 doesn't run Janino --- but you
have a Janino exception?
On Thu, Aug 20, 2015 at 5:01 PM, Ulanov, Alexander
wrote:
> When I add the following option:
>
> spark.sql.codegen true
>
>
>
> Spark crashed on the
When I add the following option:
spark.sql.codegen true
Spark crashed on the “df.count” with concurrentException (below). Are you sure
that I need to set this flag to get unsafe? It looks like SQL flag, and I don’t
use sql.
java.util.concurrent.ExecutionException:
org.codehaus.commons.co
I think you might need to turn codegen on also in order for the unsafe
stuff to work.
On Thu, Aug 20, 2015 at 4:09 PM, Ulanov, Alexander
wrote:
> Hi Reynold,
>
> Thank you for suggestion. This code takes around 30 sec on my setup (5
> workers with 32GB). My issue is that I don't see the change
On Aug 20, 2015 4:57 PM, "Justin Uang [via Apache Spark Developers List]" <
ml-node+s1001551n13766...@n3.nabble.com> wrote:
>
> One other question: Do we have consensus on publishing the
pip-installable source distribution to PyPI? If so, is that something that
the maintainers need to add to the pr
Hi Reynold,
Thank you for suggestion. This code takes around 30 sec on my setup (5 workers
with 32GB). My issue is that I don't see the change in time if I unset the
unsafe flags. Could you explain why it might happen?
20 авг. 2015 г., в 15:32, Reynold Xin
mailto:r...@databricks.com>> написал(
BTW one other thing -- don't use the count() to do benchmark, since the
optimizer is smart enough to figure out that you don't actually need to run
the sum.
For the purpose of benchmarking, you can use
df.foreach(i => do nothing)
On Thu, Aug 20, 2015 at 3:31 PM, Reynold Xin wrote:
> I did
I didn't wait long enough earlier. Actually it did finish when I raised
memory to 8g.
In 1.5 with Tungsten (which should be the same as 1.4 with your unsafe
flags), the query took 40s with 4G of mem.
In 1.4, it took 195s with 8G of mem.
This is not a scientific benchmark and I only ran it once.
How did you run this? I couldn't run your query with 4G of RAM in 1.4, but
in 1.5 it ran.
Also I recommend just dumping the data to parquet on disk to evaluate,
rather than using the in-memory cache, which is super slow and we are
thinking of removing/replacing with something else.
val size = 10
Dear Spark developers,
I am trying to benchmark the new Dataframe aggregation implemented under the
project Tungsten and released with Spark 1.4 (I am using the latest Spark from
the repo, i.e. 1.5):
https://github.com/apache/spark/pull/5725
It tells that the aggregation should be faster due to
One other question: Do we have consensus on publishing the pip-installable
source distribution to PyPI? If so, is that something that the maintainers
need to add to the process that they use to publish releases?
On Thu, Aug 20, 2015 at 5:44 PM Justin Uang wrote:
> I would prefer to just do it wi
I would prefer to just do it without the jar first as well. My hunch is
that to run spark the way it is intended, we need the wrapper scripts, like
spark-submit. Does anyone know authoritatively if that is the case?
On Thu, Aug 20, 2015 at 4:54 PM Olivier Girardot <
o.girar...@lateral-thoughts.com
I would start with just the plain python package without the JAR and
then see if it makes sense to add the JAR over time.
On Thu, Aug 20, 2015 at 12:27 PM, Auberon Lopez wrote:
> Hi all,
>
> I wanted to bubble up a conversation from the PR to this discussion to see
> if there is support the idea
Thanks for reporting back, Mark.
I will soon post a release candidate.
On Thursday, August 20, 2015, mkhaitman wrote:
> Turns out it was a mix of user-error as well as a bug in the sbt/sbt build
> that has since been fixed in the current 1.5 branch (I built from this
> commit: b4f4e91c395cb69ce
Turns out it was a mix of user-error as well as a bug in the sbt/sbt build
that has since been fixed in the current 1.5 branch (I built from this
commit: b4f4e91c395cb69ced61d9ff1492d1b814f96828)
I've been testing out the dynamic allocation specifically and it's looking
pretty solid! Haven't come
I'm planning to close the survey to further responses early next week.
If you haven't chimed in yet, the link to the survey is here:
http://goo.gl/forms/erct2s6KRR
We already have some great responses, which you can view. I'll share a
summary after the survey is closed.
Cheers!
Nick
On Mon,
Auberon, can you also post this to the Jupyter Google Group?
On Wed, Aug 19, 2015 at 12:23 PM, Auberon Lopez wrote:
> Hi all,
>
> I've created an updated PR for this based off of the previous work of
> @prabinb:
> https://github.com/apache/spark/pull/8318
>
> I am not very familiar with python pa
2.2.0 is the default version spark uses if a specific version of hadoop is
not specified while building it.
spark-csv uses spark-packages to "link" with spark. ideally, it would not
care about any specific hadoop version. also ideally, spark-csv should not
have that hadoop import at all.
your worka
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