Hi all,
I am trying to bring bucketing functionality and realize it is not allowed on
DataFrame write. Any work around for this or any update on when this
functionality will be made available in Spark?
Thanks
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I have been looking at Spark-Blast which calls Blast - a well known C++
program in parallel -
In my case I have tried to translate the C++ code to Java but am not
getting the same results - it is convoluted -
I have code that will call the program and read its results - the only real
issue is the
Hello,
I was reading Spark 2.4.0 release docs and I'd like to find out more
about barrier execution mode.
In particular I'd like to know what happens when number of partitions
exceeds number of nodes (which I think is allowed, Spark tuning doc
mentions that)?
Does Spark guarantee that all
I've never tried to run a stand-alone cluster alongside hadoop, but why not
run Spark as a yarn application? That way it can absolutely (in fact
preferably) use the distributed file system.
On Fri, Nov 9, 2018 at 5:04 PM, Arijit Tarafdar wrote:
> Hello All,
>
>
>
> We have a requirement to run
In order for the Spark to see Hive metastore you need to build Spark
Session accordingly:
val spark = SparkSession.builder()
.master("local[2]")
.appName("myApp")
.config("hive.metastore.uris","thrift://localhost:9083")
.enableHiveSupport()
.getOrCreate()
On Mon, Nov 12, 2018 at 11:49
Forgot to add the link
https://jira.apache.org/jira/browse/KAFKA-5649
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