Running Hadoop and HDFS on unsupported JVM runtime sounds a little adventurous. But as long as Spark can run in a separate Java 8 runtime it's all good. I think having lambdas and type inference is huge when writing these jobs and using Scala (paying the price of complexity, poor tooling etc etc) for this tiny feature is often not justified.

On Wed, May 7, 2014 at 2:03 AM, Dean Wampler <> wrote:
Cloudera customers will need to put pressure on them to support Java 8. They only officially supported Java 7 when Oracle stopped supporting Java 6.


On Wed, May 7, 2014 at 5:05 AM, Matei Zaharia <> wrote:
Java 8 support is a feature in Spark, but vendors need to decide for themselves when they’d like support Java 8 commercially. You can still run Spark on Java 7 or 6 without taking advantage of the new features (indeed our builds are always against Java 6).


On May 6, 2014, at 8:59 AM, Ian O'Connell <> wrote:

I think the distinction there might be they never said they ran that code under CDH5, just that spark supports it and spark runs under CDH5. Not that you can use these features while running under CDH5.

They could use mesos or the standalone scheduler to run them

On Tue, May 6, 2014 at 6:16 AM, Kristoffer Sjögren <> wrote:

I just read an article [1] about Spark, CDH5 and Java 8 but did not get exactly how Spark can run Java 8 on a YARN cluster at runtime. Is Spark using a separate JVM that run on data nodes or is it reusing the YARN JVM runtime somehow, like hadoop1?

CDH5 only supports Java 7 [2] as far as I know?


Dean Wampler, Ph.D.