This can be somewhat mitigated by AOT compilation. Here's a link to some experiments from using SubstrateVM to create a self-contained executable from sjsonnet: https://github.com/databricks/sjsonnet/issues/2
Netflix ICE has a few quirks but overall it's a pretty useful free tool.
Unfortunately, the project has been largely abandoned and the current version in the Netflix GitHub repository won't work out of the box for many companies due to lack of support for new AWS regions, instances, reservation types, and services, as well as showstopping bugs (such as https://github.com/Netflix/ice/issues/210). In order to effectively use ICE today most users will need to maintain their own patched fork.
I'm currently campaigning to create a community-maintained version of ICE, with committers from multiple organizations, in order to revive the project: https://github.com/Netflix/ice/issues/240#issuecomment-29960...
The blog is referring to source- and binary-API compatibility guarantees that Spark provides.
IIRC, in order for DataFrames and Datasets to share a common parent class certain common methods' return types would need to change in a binary-incompatible way that would break DataFrame code which was compiled against older Spark versions.
Due to Spark's API compatibility and versioning policies, this type of compatibility break can only occur in major releases, so it can only be done in Spark 2.0.0 and not Spark 1.6.
There's work in progress to support importing CSV data as SchemaRDDs:
https://issues.apache.org/jira/browse/SPARK-2360 https://github.com/apache/spark/pull/1351
The site is also accessible at chatrt.com.
Perhaps there will be an option to disable Google Buzz for the Google Apps version of Gmail.
I prefer The Unarchiver http://wakaba.c3.cx/s/apps/unarchiver.html
Gitx is a native OS X GUI for git. It comes with a nice command line tool, gitx, that works like gitk. http://gitx.frim.nl/
I'm a vim user and I've been using snipMate: