Update README.md

README.md

-# 3-spark-batch
+# Big Data, batch con spark
+
+### Prerequisitos
+* eclipse (disponible en el laboratorio)
+
+### Repositorio
+En el repositorio dispones de los ficheros java así como texto de entrada (parte del quijote para probarlo).
+
+
+
+
+## WordCount con spark
+Spark, al igual que hadoop, funciona con varios nodos de cómputo y puede usar diferentes almacenamientos (como HDFS), aunque es posible usar spark localmente usando el almacenamiento de la máquina así como un único nodo. 
+
+Para las rutas de los ficheros de entrada, usaremos diferentes URLs del tipo:
+* `file:///home/user/mifichero_entrada.txt` para el caso local
+* `hdfs://namenode:port/path` para ficheros almacenados en HDFS
+* `path` para rutas relativas al directorio de instalación (sin esquema)
+
+Para usar el nodo local, cuando configuremos el contexto spark, usaremos `local[numeroNodos]`
+
+
+### Proyecto y dependencias
+
+* crea un proyecto Java en eclipse, convierte el proyecto en Maven Project.
+
+* Añade las siguientes dependencias
+
+```xml
+	<dependencies>
+		<dependency>
+			<groupId>org.apache.spark</groupId>
+			<artifactId>spark-core_2.11</artifactId>
+			<version>2.3.2</version>
+		</dependency>
+	</dependencies>
+``` 
+
+* Crea una clase llamada JavaWordCount con este código:
+
+```java
+package cdist;
+
+import java.util.Arrays;
+import java.util.Iterator;
+import scala.Tuple2;
+
+import org.apache.spark.api.java.JavaRDD;
+import org.apache.spark.api.java.JavaPairRDD;
+import org.apache.spark.api.java.JavaSparkContext;
+import org.apache.spark.api.java.function.FlatMapFunction;
+import org.apache.spark.api.java.function.Function2;
+import org.apache.spark.api.java.function.PairFunction;
+
+
+/*  This example has been taken from Oreally examples */
+
+public class JavaWordCount {
+
+	
+	public static void main(String[] args) throws Exception {
+		
+	
+		// Set the input and output default files
+		// it can be a file from hdfs -> hdfs://server:port/path
+		// or a local file file:///path
+		// or a relative local file "fileName" under the working directory (ie. out)
+		String inputFile = "file:///var/home/lab/asig/labgcd/workspace-cdist-spark-and-streaming/spark-aptel/in.txt";
+		String outputFile = "out";
+		
+		// let the user add params optionally to define input and output file
+		if(args.length > 2)
+		{
+			inputFile = args[0];
+			outputFile = args[1];
+		}
+		
+			
+		
+		// Create a Java Spark Context, for the application with name "wordCount", use a local cluster 
+		// (for using a existing cluster just substitute "local" with the name of the machine
+		JavaSparkContext sc = new JavaSparkContext(
+			      "local", "wordcount", System.getenv("SPARK_HOME"), System.getenv("JARS"));
+		
+		// Load our input data. 
+		// will create an inmutable (RDD) set of strings (one per line)
+		JavaRDD<String> input = sc.textFile(inputFile);
+		
+		
+		// Split up into words.
+		// make a map (line -> words in that line) and make it flat (so a sequence of words irrespectively of their line)
+		JavaRDD<String> words = input.flatMap(new FlatMapFunction<String, String>() {
+			public Iterator<String> call(String x) {
+				return Arrays.asList(x.split(" ")).iterator();
+			}
+		});
+		
+		// Transform into word and count.
+		// associate 1 per word
+		// and then reduce by adding all the numbers per word (key)
+		
+		JavaPairRDD<String, Integer> counts = words.mapToPair(new PairFunction<String, String, Integer>() {
+			public Tuple2<String, Integer> call(String x) {
+				return new Tuple2<String, Integer>(x, 1);
+			}
+		}).reduceByKey(new Function2<Integer, Integer, Integer>() {
+			public Integer call(Integer x, Integer y) {
+				return x + y;
+			}
+		});
+		// Save the word count back out to a text file, causing evaluation.
+		counts.saveAsTextFile(outputFile);
+	}
+}
+```
+
+Analiza el código, y pruébalo.
+
+### Notación delta
+
+Java 8 soporta notación delta (`->`) que facilita la programación y la lectura. Esta misma clase puede programarse con notación delta. Pruebalo.
+
+* Crea una clase JavaWordCountDelta con el siguiente código:
+
+```java
+package cdist;
+
+import java.util.Arrays;
+import java.util.Iterator;
+import scala.Tuple2;
+
+import org.apache.spark.api.java.JavaRDD;
+import org.apache.spark.api.java.JavaPairRDD;
+import org.apache.spark.api.java.JavaSparkContext;
+import org.apache.spark.api.java.function.FlatMapFunction;
+import org.apache.spark.api.java.function.Function2;
+import org.apache.spark.api.java.function.PairFunction;
+
+
+/*  This example has been taken from Oreally examples */
+
+public class JavaWordCountDelta {
+
+	
+	public static void main(String[] args) throws Exception {
+		
+	
+		// Set the input and output default files
+		// it can be a file from hdfs -> hdfs://server:port/path
+		// or a local file file:///path
+		// or a relative local file "fileName" under the working directory (ie. out)
+		String inputFile = "file:///var/home/lab/asig/labgcd/workspace-cdist-spark-and-streaming/spark-aptel/in.txt";
+		String outputFile = "out";
+		
+		// let the user add params optionally to define input and output file
+		if(args.length > 2)
+		{
+			inputFile = args[0];
+			outputFile = args[1];
+		}
+		
+			
+		
+		// Create a Java Spark Context, for the application with name "wordCount", use a local cluster 
+		// (for using a existing cluster just substitute "local" with the name of the machine
+		JavaSparkContext sc = new JavaSparkContext(
+			      "local", "wordcount", System.getenv("SPARK_HOME"), System.getenv("JARS"));
+		
+		// Load our input data. 
+		// will create an inmutable (RDD) set of strings (one per line)
+		JavaRDD<String> input = sc.textFile(inputFile);
+		
+		
+		// Split up into words.
+		// make a map (line -> words in that line) and make it flat (so a sequence of words irrespectively of their line)
+		JavaRDD<String> words = input.flatMap(new FlatMapFunction<String, String>() {
+			public Iterator<String> call(String x) {
+				return Arrays.asList(x.split(" ")).iterator();
+			}
+		});
+		
+		// Transform into word and count.
+		// associate 1 per word
+		// and then reduce by adding all the numbers per word (key)
+		
+		JavaPairRDD<String, Integer> counts = words.mapToPair(new PairFunction<String, String, Integer>() {
+			public Tuple2<String, Integer> call(String x) {
+				return new Tuple2<String, Integer>(x, 1);
+			}
+		}).reduceByKey(new Function2<Integer, Integer, Integer>() {
+			public Integer call(Integer x, Integer y) {
+				return x + y;
+			}
+		});
+		// Save the word count back out to a text file, causing evaluation.
+		counts.saveAsTextFile(outputFile);
+	}
+}
+
+```
+
+
+