40. Amazon EMR

40. Amazon EMR Overview

• Amazon EMR (previously called Amazon Elastic MapReduce) is a managed cluster platform that simplifies running big data frameworks, such as Apache Hadoop and Apache Spark, on AWS to process and analyze vast amounts of data.
• Amazon EMR also lets you transform and move large amounts of data into and out of other AWS data stores and databases, such as Amazon Simple Storage Service (Amazon S3) and Amazon DynamoDB.

Understanding clusters and nodes

• The central component of Amazon EMR is the cluster.
• A cluster is a collection of Amazon Elastic Compute Cloud (Amazon EC2) instances.
• Each instance in the cluster is called a node.
• Each node has a role within the cluster, referred to as the node type.
  • Master node: A node that manages the cluster by running software components to coordinate the distribution of data and tasks among other nodes for processing. The master node tracks the status of tasks and monitors the health of the cluster. Every cluster has a master node, and it's possible to create a single-node cluster with only the master node.
  • Core node: A node with software components that run tasks and store data in the Hadoop Distributed File System (HDFS) on your cluster. Multi-node clusters have at least one core node.
  • Task node: A node with software components that only runs tasks and does not store data in HDFS. Task nodes are optional.
• cluster lifecycle
  • Amazon EMR first provisions EC2 instances in the cluster for each instance according to your specifications. During this phase, the cluster state is STARTING.
  • Amazon EMR runs bootstrap actions that you specify on each instance. You can use bootstrap actions to install custom applications and perform customizations that you require. During this phase, the cluster state is BOOTSTRAPPING.
  • Amazon EMR installs the native applications that you specify when you create the cluster, such as Hive, Hadoop, Spark, and so on.
  • After bootstrap actions are successfully completed and native applications are installed, the cluster state is RUNNING. At this point, you can connect to cluster instances, and the cluster sequentially runs any steps that you specified when you created the cluster.
  • After steps run successfully, the cluster goes into a WAITING state.
    • If a cluster is configured to auto-terminate after the last step is complete, it goes into a TERMINATING state and then into the TERMINATED state.
    • If the cluster is configured to wait, you must manually shut it down when you no longer need it. 

Amazon EMR architecture Storage

• Hadoop Distributed File System (HDFS) is a distributed, scalable file system for Hadoop.
  • HDFS distributes the data it stores across instances in the cluster, storing multiple copies of data on different instances to ensure that no data is lost if an individual instance fails.
  • HDFS is ephemeral storage that is reclaimed when you terminate a cluster.
  • HDFS is useful for caching intermediate results during MapReduce processing or for workloads that have significant random I/O. 
• EMR File System (EMRFS), Amazon EMR extends Hadoop to add the ability to directly access data stored in Amazon S3 as if it were a file system like HDFS. You can use either HDFS or Amazon S3 as the file system in your cluster. Most often, Amazon S3 is used to store input and output data and intermediate results are stored in HDFS.
• The local file system refers to a locally connected disk.
  • When you create a Hadoop cluster, each node is created from an Amazon EC2 instance that comes with a preconfigured block of pre-attached disk storage called an instance store.
  • Data on instance store volumes persists only during the lifecycle of its Amazon EC2 instance.

Cluster resource management

• The resource management layer is responsible for managing cluster resources and scheduling the jobs for processing data.
• By default, Amazon EMR uses YARN (Yet Another Resource Negotiator), which is a component introduced in Apache Hadoop 2.0 to centrally manage cluster resources for multiple data-processing frameworks.
• However, there are other frameworks and applications that are offered in Amazon EMR that do not use YARN as a resource manager. 

Data processing frameworks

• The data processing framework layer is the engine used to process and analyze data.
• Hadoop MapReduce is an open-source programming model for distributed computing.
• Spark is a cluster framework and programming model for processing big data workloads.

Applications and programs

• Amazon EMR supports many applications, such as Hive, Pig, and the Spark Streaming library to provide capabilities such as using higher-level languages to create processing workloads, leveraging machine learning algorithms, making stream processing applications, and building data warehouses.

Workflow

Amazon EMR Studio

• Amazon EMR Studio is a web-based integrated development environment (IDE) for fully managed Jupyter notebooks that run on Amazon EMR clusters.
• You can set up an Amazon EMR Studio for your team to develop, visualize, and debug applications written in R, Python, Scala, and PySpark.
• Amazon EMR Studio is integrated with AWS Identity and Access Management (IAM) and AWS SSO so users can log in using their corporate credentials.
• You can create an EMR Studio at no cost. Applicable charges for Amazon S3 storage and for Amazon EMR clusters apply when you use EMR Studio. 

Amazon EMR on EKS

• Amazon EMR on EKS provides a deployment option for Amazon EMR that allows you to run open-source big data frameworks on Amazon Elastic Kubernetes Service (Amazon EKS). With this deployment option, you can focus on running analytics workloads while Amazon EMR on EKS builds, configures, and manages containers for open-source applications. 

Reference

What is Amazon EMR? - Amazon EMR

What is Amazon EMR on EKS - Amazon EMR