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Part 2 : Spark using Scala

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Oussama Ben Khiroun edited this page Nov 14, 2021 · 7 revisions

A. Installation et Configuration de Spark

1- Mise à jour de la base des téléchargements sous Ubuntu

sudo apt update

2- Vérification si Java est installé et bien configuré (si la commande donne une version de Java ignorer l'étape suivante)

java -version

3- Installation de Java

sudo apt install default-jdk

4- Installation de Scala

sudo apt install scala

5- Téléchargement de Spark (vérifier le lien depuis le site de Spark)

wget https://dlcdn.apache.org/spark/spark-3.0.3/spark-3.0.3-bin-hadoop2.7.tgz

6- Décompression de l'archive Spark

sudo tar -xvzf spark-3.0.3-bin-hadoop2.7.tgz

7- Déplacement du dossier décompressé vers /opt/spark/

sudo mv spark-3.0.3-bin-hadoop2.7 /opt/spark

cd /opt/spark

8- Exportation du chemin de Spark dans la variable d'environnement PATH

export SPARK_HOME=/opt/spark

export PATH=$PATH:SPARK_HOME/bin:SPARK_HOME/sbin

echo $PATH

/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/usr/games:/usr/local/games:/snap/bin:SPARK_HOME/bin:SPARK_HOME/sbin

Deux nouveaux chemins sont ajoutés à PATH -> /opt/spark/bin/ ET /opt/spark/sbin/

./spark-shell

Tester avec Spark

Exemple 3

val rdd1 = sc.setFile("/home/nom_user/hello.txt")
rdd1.count

Exemple 2

val list1 = List("apple","banana","orange")
val rdd2 = sc.parallelize(list1)
rdd2.collect
scala> val rdd4 = sc.textFile("/home/oussama/hello.txt")
rdd4: org.apache.spark.rdd.RDD[String] = /home/oussama/hello.txt MapPartitionsRDD[20] at textFile at <console>:23

scala> rdd4.collect res9: Array[String] = Array(Bonjour tout le monde Big Data, Formation Big Data Jour Jour, Jour 2)

scala> val count1 = rdd4.flatMap(line => line.split(" ")) count1: org.apache.spark.rdd.RDD[String] = MapPartitionsRDD[21] at flatMap at :23

scala> count1.collect res10: Array[String] = Array(Bonjour, tout, le, monde, Big, Data, Formation, Big, Data, Jour, Jour, Jour, 2)

scala> val count2 = count1.map(word => (word,1)) count2: org.apache.spark.rdd.RDD[(String, Int)] = MapPartitionsRDD[22] at map at :23

scala> count2.collect res11: Array[(String, Int)] = Array((Bonjour,1), (tout,1), (le,1), (monde,1), (Big,1), (Data,1), (Formation,1), (Big,1), (Data,1), (Jour,1), (Jour,1), (Jour,1), (2,1))

scala> val count3 = count2.reduceByKey(+) count3: org.apache.spark.rdd.RDD[(String, Int)] = ShuffledRDD[23] at reduceByKey at :23

scala> count3.collect res12: Array[(String, Int)] = Array((monde,1), (Big,2), (le,1), (2,1), (tout,1), (Bonjour,1), (Jour,3), (Formation,1), (Data,2))

scala> count3.saveAsTextFile("/home/oussama/resultat-spark")

    /home/oussama/resultat-spark/
    └── part-00000

scala> val rddmap = sc.parallelize(List("test1 test2", "test3 test4", "test5 test6")) rddmap: org.apache.spark.rdd.RDD[String] = ParallelCollectionRDD[28] at parallelize at :23

scala> rddmap.map(x => x.split(" ")).collect res16: Array[Array[String]] = Array(Array(test1, test2), Array(test3, test4), Array(test5, test6))

scala> rddmap.flatMap(x => x.split(" ")).collect res17: Array[String] = Array(test1, test2, test3, test4, test5, test6)

Data Frames

scala> val rdd = sc.parallelize(List((1, "toto", "yoyo"),(2,"titi","jiji"),(3,"tata","nono"))) rdd: org.apache.spark.rdd.RDD[(Int, String, String)] = ParallelCollectionRDD[33] at parallelize at :23

scala> val dataframe = rdd.toDF("id","nom","prénom") dataframe: org.apache.spark.sql.DataFrame = [id: int, nom: string ... 1 more field]

scala> dataframe.createOrReplaceTempView("personnes")

scala> val dataframeSQL = spark.sql("select * from personnes") dataframeSQL: org.apache.spark.sql.DataFrame = [id: int, nom: string ... 1 more field]

scala> dataframeSQL.show() +---+----+------+ | id| nom|prénom| +---+----+------+ | 1|toto| yoyo| | 2|titi| jiji| | 3|tata| nono| +---+----+------+

    scala> spark.sql("SELECT count(*) FROM personnes").show
+--------+
|count(1)|
+--------+
|       3|
+--------+
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