Amazon Web Services, S3 and Elastic MapReduce

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1 Amazon Web Services, S3 and Elastic MapReduce Irene Finocchi and Emanuele Fusco Big Data Computing March 24, Master s Degree in Computer Science Academic Year , spring semester I.Finocchi and E.Fusco AWS 1/33

2 Amazon Web Services (AWS) With this lesson we will get you started to AWS. We will see how to deploy a Hadoop cluster and run some custom MapReduce task on it. In particular we will walk through the following steps: creation of a public/private key pair; creation of a bucket in the Simple Storage Service (S3); the handling of input/output data, logging, and program executable on S3; the creation of a cluster; the execution of a custom Hadoop program on the cluster; ssh login to the master and something more... I.Finocchi and E.Fusco AWS 2/33

3 Accessing the AWS Amazon Web Services are available at the following address: I.Finocchi and E.Fusco AWS 3/33

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5 The Console Once activated, an AWS account allows to access the Console: And in particular we will go to the Elastic Cloud 2 Dashboard: I our example we will create buckets and the Elastic MapReduce cluster on the European servers located in Ireland. I.Finocchi and E.Fusco AWS 5/33

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7 Creating a key pair and storing the secret key Why do we need the key? We will need to have a public/private key pair to be able to create an Elastic MapReduce Hadoop cluster. The key will be in particular needed to log into the Master node of the cluster (and from it to any worker node). I.Finocchi and E.Fusco AWS 7/33

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9 Storing the secret key (locally) The downloaded awsdemo.pem should be kept safe (it contains the private key to log into the Master node of the cluster). By default we probably have something like: 1 emanuele staff... awsdemo.pem whereas the permissions should allow only to the user to read and write. We can issue the following command: chmod 0600 awsdemo.pem so that 1 emanuele staff... awsdemo.pem I.Finocchi and E.Fusco AWS 9/33

10 Remote persistent storage The Elastic MapReduce Clusters have their own HDFS with working space that varies depending on the machine types and number. However, this filesystem is not persistent as it is destroyed at the time when the Elastic MapReduce Cluster is terminated. Since moving large files from our pc to the Amazon servers can be time- (and money-) consuming, it comes handy to have some disk space that does not get wiped as soon as we terminate our cluster. This space is provided by Simple Storage Service (S3). I.Finocchi and E.Fusco AWS 10/33

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13 Naming of the buckets The bucket name space is shared among all S3 users. This allows to share buckets with other users but also requires to pay some attention to our naming policies. For this example i used: it.uniroma1.di.bigdata2014demo (S3 does not allow me to use upper case characters in the bucket name) I.Finocchi and E.Fusco AWS 13/33

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19 Creating the cluster Now we are ready to create a cluster and run our code! Choosing the instance types You should choose the type of machines depending on your storage and computation needs: Keeping an eye on pricing! I.Finocchi and E.Fusco AWS 19/33

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23 The Master address Master public DNS: [someaddress].eu-west-1.compute.amazonaws.com Once obtained it allows to access the Master node via ssh. I.Finocchi and E.Fusco AWS 23/33

24 ssh -i awsdemo.pem The authenticity of host [name] ([ip]) can t be established. RSA key fingerprint is [fingerprint]. Are you sure you want to continue connecting (yes/no)? yes I.Finocchi and E.Fusco AWS 24/33

25 Warning: Permanently added ec eu-west-1.compute.amazonaws.com, (RSA) to the list of known hosts. _ ) _ ( / Amazon Linux AMI \ 31 package(s) needed for security, out of 251 available Run "sudo yum update" to apply all updates. Amazon Linux version is available Welcome to Amazon Elastic MapReduce running Hadoop and Amazon Linux. Hadoop is installed in /home/hadoop. Log files are in /mnt/var/log/hadoop. Check /mnt/var/log/hadoop/steps for diagnosing step failures. The Hadoop UI can be accessed via the following commands: ResourceManager NameNode lynx lynx bash: warning: setlocale: LC_CTYPE: cannot change locale (UTF-8): No such file or directory [hadoop@ip ~]$ I.Finocchi and E.Fusco AWS 25/33

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27 ssh into worker nodes You can obtain worker node addresses from dfsadmin report command: hdfs dfsadmin -report... Datanodes available: 2 (2 total, 0 dead) Live datanodes: Name: :9200 (ip eu-west-1.compute.internal) Hostname: ip eu-west-1.compute.internal... Name: :9200 (ip eu-west-1.compute.internal) Hostname: ip eu-west-1.compute.internal... I.Finocchi and E.Fusco AWS 27/33

28 ssh into worker nodes Once you obtained the addresses of the worker nodes you can ssh into them from the master node. Before you do that you need to copy your pem file to the master node. From your machine you can use scp: scp -i awsdemo.pem Then from the master node ssh -i awsdemo.pem I.Finocchi and E.Fusco AWS 28/33

29 tunneling to access the Hadoop web interface The following command will create a SOCKS5 proxy on port 8157: ssh -i awsdemo.pem -ND 8157 hadoop@[address] You can then set you computer to use the newly created proxy and access the hadoop web interface on port I.Finocchi and E.Fusco AWS 29/33

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32 Alter the default configuration yarn.site.xml You can lookup and alter the configuration parameters by editing the configuration files in the conf folder (in the hadoop user home folder). One parameter you may want to alter is the in the configuration file yarn.scheduler.minimum-allocation-mb yarn-site.xml You can apply the changes by killing the ResourceManager process (which you can discover from command jps). I.Finocchi and E.Fusco AWS 32/33

33 Don t leave the cluster on! Do not forget You pay your Elastic MapReduce cluster by the hour! Remember to terminate your cluster once you are done with it! I.Finocchi and E.Fusco AWS 33/33

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