Hi Folks!
I am trying to implement a spark job to calculate the similarity of my database
products, using only name and descriptions.
I would like to use TF-IDF to represent my text data and cosine similarity to
calculate all similarities.
My goal is, after job completes, get all similarities a
Thanks Nick :)
Abid, you may also want to check out
http://conferences.oreilly.com/strata/big-data-conference-ny-2015/public/schedule/detail/43484,
which describes our work on a combination of Spark and Tachyon for Deep
Learning. We found significant gains in using Tachyon (with co-processing)
fo
Spark ML offers a multi-layer perceptron and has some machinery in place that
will support development of further deep-learning models.
There is also deeplearning4j and some work on distributed tensorflow on Spark
(https://spark-summit.org/east-2016/events/distributed-tensor-flow-on-spark-scalin
Thanks Ted. In my application jar there was no spark 1.3.1 artifacts.
Anyhow I got it working via Oozie spark action.
On Thu, Jan 28, 2016 at 7:42 PM, Ted Yu wrote:
> Looks like '--properties-file' is no longer supported.
>
> Was it possible that Spark 1.3.1 artifact / dependency leaked into you
Hi Tim,
Yes we are running Spark on Mesos in cluster mode with supervise flag.
Submit script looks like this:
spark-submit \
--conf "spark.executor.extraJavaOptions=-XX:+UseG1GC -XX:+UseCompressedOops
-XX:-UseGCOverheadLimit" \
--supervise \
--deploy-mode cluster \
--class \
--master mesos://:70
Dear all;
Is there any work in this area?
Thanks
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Hi Duc,
Are you running Spark on Mesos with cluster mode? And what's your cluster
mode submission, and version of Spark are you running?
Tim
On Sat, Jan 30, 2016 at 8:19 AM, PhuDuc Nguyen
wrote:
> I have a spark job running on Mesos in multi-master and supervise mode. If
> I kill it, it is res
I have a spark job running on Mesos in multi-master and supervise mode. If
I kill it, it is resilient as expected and respawns on another node.
However, I cannot kill it when I need to. I have tried 2 methods:
1) ./bin/spark-class org.apache.spark.deploy.Client kill
2) ./bin/spark-submit --mast
The following should work as long as your tables are created using Spark SQL
event_wk.repartition(2).write.partitionBy("eventDate").format("parquet"
).insertInto("event)
If you want to stick to using "insert overwrite" for Hive compatibility,
then you can repartition twice, instead of setting the