Github user SparkQA commented on the pull request:
https://github.com/apache/spark/pull/4956#issuecomment-78415932
[Test build #28490 has
finished](https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/28490/consoleFull)
for PR 4956 at commit
[`819408c`](https://github.com/apache/spark/commit/819408c09d86d75a9e82d0adb51ec1b6da0d2745).
* 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](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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