What is AWS Map/Reduce?
Amazon Elastic MapReduce (EMR) is a web service that makes it easy to process large amounts of data quickly and cost-effectively. It is based on the popular open-source Apache Hadoop and Apache Spark frameworks for data processing and analysis.
With Amazon EMR, you can set up and scale a data processing cluster in the cloud with just a few clicks. You can then use EMR to run a variety of big data processing workloads, including batch processing, streaming data analysis, machine learning, and more.
MapReduce is a programming model that was developed to allow distributed processing of large data sets across a cluster of computers. It consists of two main phases: the “map” phase and the “reduce” phase. In the map phase, the input data is divided into smaller chunks and processed in parallel by the cluster nodes. In the reduce phase, the results from the map phase are combined and aggregated to produce the final output.
Amazon EMR makes it easy to run MapReduce jobs on large amounts of data stored in Amazon S3 or other data stores. You can use EMR to process and analyze data using a variety of tools and frameworks, including Hadoop, Spark, and others.
Example AWS Map/Reduce commandline
Here is an example of how you can use the AWS Command Line Interface (CLI) to submit a MapReduce job to Amazon EMR:
1. First, make sure that you have the AWS CLI installed and configured with your AWS credentials.
2. Next, create an Amazon S3 bucket to store your input data and output results.
3. Upload your input data to the S3 bucket.
4. Use the aws emr create-cluster command to create a new Amazon EMR cluster. For example:
aws emr create-cluster \ --name "My Cluster" \ --release-label emr-6.2.0 \ --applications Name=Hadoop Name=Spark \ --ec2-attributes KeyName=my-key-pair,InstanceProfile=EMR_EC2_DefaultRole \ --instance-type m4.large \ --instance-count 3 \ --use-default-roles
5. Use the aws emr add-steps command to add a MapReduce job to the cluster. For example:
aws emr add-steps \ --cluster-id j-1234567890ABCDEF \ --steps Type=CUSTOM_JAR,Name="My MapReduce Job",ActionOnFailure=CONTINUE,Jar=s3://my-bucket/my-mapreduce-job.jar,Args=["s3://input-bucket/input.txt","s3://output-bucket/output"]
6. Use the aws emr list-steps command to check the status of the job. When the job is complete, the output will be available in the S3 bucket that you specified.
Keep in mind that this is just a basic example, and you can customize the MapReduce job and cluster configuration as needed to fit your specific requirements. You can find more information and examples in the AWS EMR documentation.
Step by step to setup Amazon Elastic Map Reduce EMR
Here is a step-by-step guide to setting up Amazon Elastic MapReduce (EMR) in your AWS account:
1. Sign up for an AWS account if you don’t already have one.
2. Go to the Amazon EMR dashboard in the AWS Management Console.
3. Click the “Create cluster” button.
4. Select the EMR release that you want to use.
5. Choose the instance types and number of instances that you want to use for your cluster.
6. Select the applications that you want to install on the cluster, such as Hadoop, Spark, or others.
7. Choose a name for your cluster and specify the IAM role that will be used to create and manage the cluster.
8. Configure the network and security settings for your cluster.
9. Review the cluster configuration and click the “Create cluster” button to create the cluster.
It may take a few minutes for the cluster to be created and configured. Once the cluster is up and running, you can use it to run MapReduce jobs or other big data processing tasks.
Keep in mind that this is just a basic guide, and you can customize the cluster configuration as needed to fit your specific requirements. You can find more information and examples in the AWS EMR documentation.
Springboot Amazon Map/Reduce Example Code
Sure, here is an example of how you can use Amazon Elastic MapReduce (EMR) with Spring Boot to perform a word count on a set of documents:
1. First, you will need to set up an Amazon Web Services (AWS) account and create an Amazon EMR cluster.
2. Next, you will need to create a Spring Boot application and add the following dependencies to your pom.xml file:
org.springframework.cloud spring-cloud-aws-context 2.3.0.RELEASE org.springframework.cloud spring-cloud-aws-elasticmapreduce 2.3.0.RELEASE
3. In your Spring Boot application, create a configuration class that enables AWS resource injection:
@Configuration
@EnableRdsInstance
@EnableElasticMapReduce
public class AWSConfiguration {
}
4. Create a service class that will submit the MapReduce job to Amazon EMR and handle the results:
@Service
public class WordCountService {
@Autowired
private AmazonElasticMapReduce amazonElasticMapReduce;
public void countWords(List inputLocations, String outputLocation) {
// Create a new Hadoop Jar step to run the word count example
HadoopJarStepConfig hadoopJarStep = new HadoopJarStepConfig()
.withJar("command-runner.jar")
.withArgs("spark-submit", "--class", "org.apache.spark.examples.JavaWordCount",
"--master", "yarn", "lib/spark-examples.jar", "s3://input-bucket/input.txt", "s3://output-bucket/output");
// Create a step to submit the Hadoop Jar step to the EMR cluster
StepConfig stepConfig = new StepConfig()
.withName("Word Count")
.withHadoopJarStep(hadoopJarStep)
.withActionOnFailure("TERMINATE_JOB_FLOW");
// Create a new EMR cluster with 1 master and 2 core nodes
JobFlowInstancesConfig instances = new JobFlowInstancesConfig()
.withInstanceCount(3)
.withMasterInstanceType(InstanceType.M4Large.toString())
.withSlaveInstanceType(InstanceType.M4Large.toString())
.withHadoopVersion("2.8.5");
// Create a new EMR cluster with the step and instance configuration
RunJobFlowRequest request = new RunJobFlowRequest()
.withName("Word Count")
.withInstances(instances)
.withSteps(stepConfig)
.withLogUri("s3://log-bucket/");
// Submit the job to the EMR cluster and wait for it to complete
RunJobFlowResult result = amazonElasticMapReduce.runJobFlow(request);
