Github user SparkQA commented on the pull request:
https://github.com/apache/spark/pull/4956#issuecomment-78356347
[Test build #28477 has
finished](https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/28477/consoleFull)
for PR 4956 at commit
[`debe484`](https://github.com/apache/spark/commit/debe4847074395c7fc6f4d4e8f0acc1e0b32599b).
* This patch **passes all tests**.
* This patch merges cleanly.
* This patch adds the following public classes _(experimental)_:
* `case class Row(word: String)`
* `class JavaSQLContextSingleton `
* `public class JavaRow implements java.io.Serializable `
* `You can also easily use machine learning algorithms provided by
[MLlib](mllib-guide.html). First of all, there are streaming machine learning
algorithms (e.g. (Streaming Linear
Regression](mllib-linear-methods.html#streaming-linear-regression), [Streaming
KMeans](file:///Users/tdas/Projects/Spark/spark/docs/_site/mllib-clustering.html#streaming-k-means),
etc.) which can simultaneously learn from the streaming data as well as apply
the model on the streaming data. Beyond these, for a much larger class of
machine learning algorithms, you can learn a learning model offline (i.e. using
historical data) and then apply the model online on streaming data. See the
[MLlib](mllib-guide.html) guide for more details.`
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