Lantao Jin shared an issue with you
> Spark-sql do not support for void column datatype of view
> -
>
> Key: SPARK-20680
> URL:
Hi, All.
Apache Spark always has been a fast and general engine, and
since SPARK-2883, Spark supports Apache ORC inside `sql/hive` module with Hive
dependency.
With Apache ORC 1.4.0 (released yesterday), we can make Spark on ORC faster and
get some benefits.
- Speed: Use both Spark
Thank you for creating the JIRA. I am working towards making it
configurable very soon.
On Tue, May 9, 2017 at 4:12 PM, Yogesh Mahajan
wrote:
> Hi Team,
>
> Any plans to make the StateStoreProvider/StateStore in structured
> streaming pluggable ?
> Currently
Hi Team,
Any plans to make the StateStoreProvider/StateStore in structured streaming
pluggable ?
Currently StateStore#loadedProviders has only
one HDFSBackedStateStoreProvider and it's not configurable.
If we make this configurable, users can bring in their own implementation
of StateStore.
Closely related to the PyPi upload thread (https://s.apache.org/WLtM), I
just wanted to give a heads up that we are working on submitting SparkR
from Spark 2.1.1 as a package to CRAN. The package submission is under
review with CRAN right now and I will post updates to this thread.
The main
+1 (non-binding)
I tested it on Ubuntu 16.04 and OpenJDK8 on ppc64le. All of the tests for
core have passed.
$ java -version
openjdk version "1.8.0_111"
OpenJDK Runtime Environment (build
1.8.0_111-8u111-b14-2ubuntu0.16.04.2-b14)
OpenJDK 64-Bit Server VM (build 25.111-b14, mixed mode)
$
Hi,
I have a scenario in which I am caching my RDDs for future use. But I observed
that when I use my RDD, complete DAG is re-executed and RDD gets created again.
How can I avoid this scenario and make sure that RDD.cacheDataSet() caches RDD
every time.
Regards,
Jasbir Singh
So I have a PR to add this to the release process documentation - I'm
waiting on the necessary approvals from PyPi folks before I merge that
incase anything changes as a result of the discussion (like uploading to
the legacy host or something). As for conda-forge, it's not something we
need to do,