do not reuse feature vector
Merge pull request #5 from mengxr/mllib-fix

do not reuse feature vector
Updated streaming-test code.
Added new streaming perf classes.
Updated config and run scripts to run new streaming tests.
Fixed config and run scripts for streaming tests
Removed unnecessary change in testsuites
Refactored MLlib tests to run different Spark builds for the same algorithm
Merge remote-tracking branch 'db/master' into refactoringMerge
"Fixed merge conflicts"
Conflicts:
	lib/sparkperf/main.py
More updated to config and run scripts for streaming tests
Minor change based on PR comments.
Merge pull request #7 from tdas/master

Updated streaming tests - updateStateByKey, groupByKeyAndWindow, and ReduceByKeyAndWindow
Merge pull request #6 from brkyvz/refactoringMerge

Refactored MLlib tests to run different Spark builds for the same algorithm
Move disk warmup code into Cluster class.
Move code to build Spark into its own file.
Introduce SparkBuildManager to maintain build cache:

SparkBuildManager maintains a cache of binary Spark distributions,
indexed by their SHAs. This makes it much faster to test against
multiple Spark versions because we don’t have to completely re-build
Spark for each test run.

I removed the SPARK_SKIP_PREP configuration option, since it’s not very
useful anymore: if we’re running multiple tests against the same Spark
version, we’ll just use the cached build.

This commit part of the groundwork for automated testing against
multiple Spark versions.