[SPARK-3112][MLLIB] Add documentation and example for StreamingLR

docs/mllib-linear-methods.md

@@ -518,6 +518,81 @@ print("Mean Squared Error = " + str(MSE))
 </div>
 </div>
 
+## Streaming linear regression
+
+When data arrive in a streaming fashion, it is useful to fit regression models online, 
+updating the parameters of the model as new data arrives. MLlib currently supports 
+streaming linear regression using ordinary least squares. The fitting is similar
+to that performed offline, except fitting occurs on each batch of data, so that
+the model continually updates to reflect the data from the stream.
+
+### Examples
+
+The following example demonstrates how to load training and testing data from two different
+input streams of text files, parse the streams as labeled points, fit a linear regression model
+online to the first stream, and make predictions on the second stream.
+
+<div class="codetabs">
+
+<div data-lang="scala" markdown="1">
+
+First, we import the necessary classes for parsing our input data and creating the model. 
+
+{% highlight scala %}
+
+import org.apache.spark.mllib.linalg.Vectors
+import org.apache.spark.mllib.regression.LabeledPoint
+import org.apache.spark.mllib.regression.StreamingLinearRegressionWithSGD
+
+{% endhighlight %}
+
+Then we make input streams for training and testing data. We assume a StreamingContext `ssc`
+has already been created, see [Spark Streaming Programming Guide](streaming-programming-guide.html#initializing)
+for more info. For this example, we use labeled points in training and testing streams, 
+but in practice you will likely want to use unlabeled vectors for test data.
+
+{% highlight scala %}
+
+val trainingData = ssc.textFileStream('/training/data/dir').map(LabeledPoint.parse)
+val testData = ssc.textFileStream('/testing/data/dir').map(LabeledPoint.parse)
+
+{% endhighlight %}
+
+We create our model by initializing the weights to 0
+
+{% highlight scala %}
+
+val numFeatures = 3
+val model = new StreamingLinearRegressionWithSGD()
+    .setInitialWeights(Vectors.zeros(numFeatures))
+
+{% endhighlight %}
+
+Now we register the streams for training and testing and start the job. 
+Printing predictions alongside true labels lets us easily see the result.
+
+{% highlight scala %}
+
+model.trainOn(trainingData)
+model.predictOnValues(testData.map(lp => (lp.label, lp.features))).print()
+
+ssc.start()
+ssc.awaitTermination()
+
+{% endhighlight %}
+
+We can now save text files with data to the training or testing folders.
+Each line should be a data point formatted as `(y,[x1,x2,x3])` where `y` is the label 
+and `x1,x2,x3` are the features. Anytime a text file is placed in `/training/data/dir` 
+the model will update. Anytime a text file is placed in `/testing/data/dir` you will see predictions. 
+As you feed more data to the training directory, the predictions 
+will get better!
+
+</div>
+
+</div>
+
+
 ## Implementation (developer)
 
 Behind the scene, MLlib implements a simple distributed version of stochastic gradient descent