Spark MLLib Kmeans from dataframe, and back again

Question

I aim to apply a kmeans clustering algorithm to a very large data set using Spark (1.3.1) MLLib. I have called the data from an HDFS using a hiveContext from Spark, and woul

Answer 1

I understand that you want to get DataFrame at the end. I see two possible solutions. I'd say that choosing between them is matter of taste.

Create column from RDD

It's very easy to obtain pairs of ids and clusters in form of RDD:

val idPointRDD = data.rdd.map(s => (s.getInt(0), Vectors.dense(s.getDouble(1),s.getDouble(2)))).cache()
val clusters = KMeans.train(idPointRDD.map(_._2), 3, 20)
val clustersRDD = clusters.predict(idPointRDD.map(_._2))
val idClusterRDD = idPointRDD.map(_._1).zip(clustersRDD)

Then you create DataFrame from that

val idCluster = idClusterRDD.toDF("id", "cluster")

It works because map doesn't change order of the data in RDD, which is why you can just zip ids with results of prediction.

Use UDF (User Defined Function)

Second method involves using clusters.predict method as UDF:

val bcClusters = sc.broadcast(clusters)
def predict(x: Double, y: Double): Int = {
    bcClusters.value.predict(Vectors.dense(x, y))
}
sqlContext.udf.register("predict", predict _)

Now we can use it to add predictions to data:

val idCluster = data.selectExpr("id", "predict(x, y) as cluster")

Keep in mind that Spark API doesn't allow UDF deregistration. This means that closure data will be kept in the memory.

Wrong / unoptimal solutions

• Using clusters.predict without broadcasting

It won't work in the distributed setup. Edit: actually it will work, I was confused by implementation of predict for RDD, which uses broadcast.

• sc.makeRDD(clusters.predict(parsedData).toArray()).toDF()

toArray collects all data in the driver. This means that in distributed mode you will be copying cluster ids into one node.