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Apache Spark is a fast and general-purpose cluster computing system. It provides high-level APIs in Java, Scala and Python, and also an optimized engine which supports overall execution charts. It also supports a rich set of higher-level tools including Spark SQL for SQL and structured information processing, MLlib for machine learning, GraphX for graph processing, and Spark Streaming. Spark can be configured with multiple cluster managers. Along with that it can be configured in local mode and standalone mode. Standalone mode is good to go for a developing applications in spark. Spark processes runs in JVM. Java should be pre-installed on the machines on which we have to run Spark job.

nano /etc/apt/sources.list
deb http://ppa.launchpad.net/linuxuprising/java/ubuntu bionic main
apt install dirmngr
apt-key adv --keyserver hkp://keyserver.ubuntu.com:80 --recv-keys EA8CACC073C3DB2A

apt update
apt install oracle-java11-installer
apt install oracle-java11-set-default

wget http://mirrors.estointernet.in/apache/spark/spark-2.4.3/spark-2.4.3-bin-hadoop2.7.tgz
tar xvzf spark-2.4.3-bin-hadoop2.7.tgz
ln -s spark-2.4.3-bin-hadoop2.7 spark

nano ~/.bashrc
SPARK_HOME=/root/spark
export PATH=$SPARK_HOME/bin:$PATH
source ~/.bashrc
./spark/bin/spark-shell

Next we will write a basic Scala application to load a file and then see its content on the console. But before we start writing any java application let’s get familiar with few terms of spark application

Application jar
        User program and its dependencies are bundled into the application jar so that all of its dependencies are available to the application.
    Driver program
        It acts as the entry point of the application. This is the process which starts complete execution.
    Cluster Manager
        This is an external service which manages resources needed for the job execution.
        It can be standalone spark manager, Apache Mesos, YARN, etc.
    Deploy Mode
        Cluster – Here driver runs inside the cluster
        Client – Here driver is not part of the cluster. It is only responsible for job submission.
    Worker Node
        This is the node that runs the application program on the machine which contains the data.
    Executor
        Process launched on the worker node that runs tasks
        It uses worker node’s resources and memory
    Task
        Fundamental unit of the Job that is run by the executor
    Job
        It is combination of multiple tasks
    Stage
        Each job is divided into smaller set of tasks called stages. Each stage is sequential and depend on each other.

Learn Hadoop by working on interesting Big Data and Hadoop Projects

    SparkContext
        It gets the application program access to the distributed cluster.
        This acts as a handle to the resources of cluster.
        We can pass custom configuration using the sparkcontext object.
        It can be used to create RDD, accumulators and broadcast variable
    RDD(Resilient Distributed Dataset)
        RDD is the core of the spark’s API
        It distributes the source data into multiple chunks over which we can perform operation simultaneously
        Various transformation and actions can be applied over the RDD
        RDD is created through SparkContext
    Accumulator
        This is used to carry shared variable across all partitions.
        They can be used to implement counters (as in MapReduce) or sums
        Accumulator’s value  can only be read by the driver program
        It is set by the spark context
    Broadcast Variable
        Again a way of sharing variables across the partitions
        It is a read only variable
        Allows the programmer to distribute a read-only variable cached on each machine rather than shipping a copy of it with tasks thus avoiding wastage of disk space.
        Any common data that is needed by each stage is distributed across all the nodes

Spark provides different programming APIs to manipulate data like Java, R, Scala and Python. We have interactive shell for three programming languages i.e. R, Scala and Python among the four languages. Unfortunately Java doesn’t provide interactive shell as of now.

ETL Operation in Apache Spark

In this section we would learn a basic ETL (Extract, Load and Transform) operation in the interactive shell.

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4 thoughts on “How To Install Apache Spark on Debian Stretch”

  1. Hadoop (docker-search) on Ubuntu:

    nano /etc/apt/sources.list
    deb http://ppa.launchpad.net/linuxuprising/java/ubuntu bionic main
    apt install dirmngr
    apt-key adv –keyserver hkp://keyserver.ubuntu.com:80 –recv-keys EA8CACC073C3DB2A

    apt update
    apt install oracle-java11-installer
    apt install oracle-java11-set-default

    ssh-keygen -t rsa -P ” -f ~/.ssh/id_rsa
    cat ~/.ssh/id_rsa.pub >> ~/.ssh/authorized_keys
    chmod 0600 ~/.ssh/authorized_keys
    ssh localhost

    wget http://mirrors.estointernet.in/apache/hadoop/common/hadoop-3.2.0/hadoop-3.2.0.tar.gz
    tar xzf hadoop-3.2.0.tar.gz
    mv hadoop-3.2.0 hadoop

    nano ~/.bashrc
    export HADOOP_HOME=/root/hadoop
    export HADOOP_INSTALL=$HADOOP_HOME
    export HADOOP_MAPRED_HOME=$HADOOP_HOME
    export HADOOP_COMMON_HOME=$HADOOP_HOME
    export HADOOP_HDFS_HOME=$HADOOP_HOME
    export YARN_HOME=$HADOOP_HOME
    export HADOOP_COMMON_LIB_NATIVE_DIR=$HADOOP_HOME/lib/native
    export PATH=$PATH:$HADOOP_HOME/sbin:$HADOOP_HOME/bin

    nano /root/hadoop/etc/hadoop/hadoop-env.sh
    export JAVA_HOME=/usr/lib/jvm/java-1.8.0-openjdk-amd64

    hdfs namenode -format
    cd /root/hadoop/sbin/
    ./start-dfs.sh

    cd /root/hadoop/etc/hadoop
    nano core-site.xml
    fs.default.name
    hdfs://localhost:9000

    nano hdfs-site.xml
    dfs.replication
    1
    dfs.name.dir
    file:///root/hadoop/hadoopdata/hdfs/namenode
    dfs.data.dir
    file:///root/hadoop/hadoopdata/hdfs/datanode

    nano mapred-site.xml
    mapreduce.framework.name
    yarn

    nano yarn-site.xml
    yarn.nodemanager.aux-services
    mapreduce_shuffle

    hdfs namenode -format
    cd $HADOOP_HOME/sbin/
    ./start-dfs.sh
    ./start-yarn.sh

    nano ~/.bashrc
    wget http://mirrors.estointernet.in/apache/spark/spark-2.4.3/spark-2.4.3-bin-hadoop2.7.tgz
    tar xvzf spark-2.4.3-bin-hadoop2.7.tgz
    ln -s spark-2.4.3-bin-hadoop2.7 spark

    apt-get install hadoop-2.7 hadoop-0.20-namenode hadoop-0.20-datanode hadoop-0.20-jobtracker hadoop-0.20-tasktracker

    nano ~/.bashrc
    SPARK_HOME=/root/spark
    export PATH=$SPARK_HOME/bin:$PATH
    source ~/.bashrc
    ./spark/bin/spark-shell

    apt-get install apt-transport-https ca-certificates curl gnupg2 software-properties-common
    curl -fsSL https://download.docker.com/linux/debian/gpg | apt-key add –
    add-apt-repository “deb [arch=amd64] https://download.docker.com/linux/debian stretch stable”

    apt-get update
    apt-get install docker-ce
    systemctl status docker

    docker search hadoop
    docker pull hadoop
    docker images
    docker run -i -t hadoop /bin/bash
    docker ps
    docker ps -a

    docker start
    docker stop

    docker attach

    Running your first crawl job in minutes
    wget https://raw.githubusercontent.com/USCDataScience/sparkler/master/bin/dockler.sh

    Starts docker container and forwards ports to host
    bash dockler.sh

    Inject seed urls
    /data/sparkler/bin/sparkler.sh inject -id 1 -su ‘http://www.bbc.com/news’

    Start the crawl job
    /data/sparkler/bin/sparkler.sh crawl -id 1 -tn 100 -i 2 # id=1, top 100 URLs, do -i=2 iterations

    Running Sparkler with seed urls file:
    nano sparkler/bin/seed-urls.txt
    copy paste your urls

    Inject seed urls using the following command,
    /sparkler/bin/sparkler.sh inject -id 1 -sf seed-urls.txt

    Start the crawl job.
    To crawl until the end of all new URLS, use -i -1, Example: /data/sparkler/bin/sparkler.sh crawl -id 1 -i -1

    Access the dashboard http://localhost:8983/banana/ (forwarded from docker image).

  2. Apache Solr is an open-source search platform written in Java. Solr provides full-text search, spell suggestions, custom document ordering and ranking, Snippet generation and highlighting. We will help you to install Apache Solr on Debian using Solution Point VPS Cloud.

    apt install default-java
    java -version

    wget http://www-eu.apache.org/dist/lucene/solr/8.2.0/solr-8.2.0.tgz
    tar xzf solr-8.2.0.tgz solr-8.2.0/bin/install_solr_service.sh –strip-components=2
    bash ./install_solr_service.sh solr-8.2.0.tgz

    systemctl stop solr
    systemctl start solr
    systemctl status solr

    su – solr -c “/opt/solr/bin/solr create -c spcloud1 -n data_driven_schema_configs”

    http://demo.osspl.com:8983

  3. Your own Rocketchat Server on Ubuntu:

    snap install rocketchat-server
    snap refresh rocketchat-server
    service snap.rocketchat-server.rocketchat-server status
    sudo service snap.rocketchat-server.rocketchat-mongo status
    sudo service snap.rocketchat-server.rocketchat-caddy status

    sudo journalctl -f -u snap.rocketchat-server.rocketchat-server
    sudo journalctl -f -u snap.rocketchat-server.rocketchat-mongo
    sudo journalctl -f -u snap.rocketchat-server.rocketchat-caddy

    How do I backup my snap data?
    sudo service snap.rocketchat-server.rocketchat-server stop
    sudo service snap.rocketchat-server.rocketchat-mongo status | grep Active
    Active: active (running) (…)
    sudo snap run rocketchat-server.backupdb

    sudo service snap.rocketchat-server.rocketchat-server start
    sudo service snap.rocketchat-server.rocketchat-server restart
    sudo service snap.rocketchat-server.rocketchat-mongo restart
    sudo service snap.rocketchat-server.rocketchat-caddy restart

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