In early 2006, Apache Hadoop was introduced as a framework for the distributed processing of large datasets stored across clusters of computers, using a programming model. Hadoop was developed as a solution to handle big data in a cost effective and easiest way possible. Hadoop consisted of a storage layer, that is, Hadoop Distributed File System (HDFS) and the MapReduce framework for managing resource utilization and job execution on a cluster. With the ability to deliver high performance parallel data analysis and to work with commodity hardware, Hadoop is used for big data analysis and batch processing of historical data through MapReduce programming.
With the exponential increase in the usage of social networking sites such as Facebook, Twitter, and LinkedIn and e-commerce sites such as Amazon, there was the need of a framework to support not only MapReduce batch processing, but real-time and interactive data analysis as well. Enterprises should be able to execute other applications over the cluster to ensure that cluster capabilities are utilized to the fullest. The data storage framework of Hadoop was able to counter the growing data size, but resource management became a bottleneck. The resource management framework for Hadoop needed a new design to solve the growing needs of big data.
YARN, an acronym for Yet Another Resource Negotiator, has been introduced as a second-generation resource management framework for Hadoop. YARN is added as a subproject of Apache Hadoop. With MapReduce focusing only on batch processing, YARN is designed to provide a generic processing platform for data stored across a cluster and a robust cluster resource management framework.
In this chapter, we will cover the following topics:
Introduction to MapReduce v1
Shortcomings of MapReduce v1
An overview of the YARN components
The YARN architecture
How YARN satisfies big data needs
Projects powered by YARN
MapReduce is a software framework used to write applications that simultaneously process vast amounts of data on large clusters of commodity hardware in a reliable, fault-tolerant manner. It is a batch-oriented model where a large amount of data is stored in Hadoop Distributed File System (HDFS), and the computation on data is performed as MapReduce phases. The basic principle for the MapReduce framework is to move computed data rather than move data over the network for computation. The MapReduce tasks are scheduled to run on the same physical nodes on which data resides. This significantly reduces the network traffic and keeps most of the I/O on the local disk or within the same rack.
Hadoop clients communicate with the JobTracker to submit or kill jobs and poll for jobs' progress
JobTracker validates the client request and if validated, then it allocates the TaskTracker nodes for map-reduce tasks execution
JobTracker monitors TaskTracker nodes and their resource utilization, that is, how many tasks are currently running, the count of map-reduce task slots available, decides whether the TaskTracker node needs to be marked as blacklisted node, and so on
JobTracker also keeps the history of the jobs executed on the cluster
TaskTracker is a per node daemon responsible for the execution of map-reduce tasks. A TaskTracker node is configured to accept a number of map-reduce tasks from the JobTracker, that is, the total map-reduce tasks a TaskTracker can execute simultaneously. The responsibilities are as follows:
TaskTracker monitors these JVM processes and updates the task progress to the JobTracker on regular intervals.
TaskTracker also sends a heartbeat signal and its current resource utilization metric (available task slots) to the JobTracker every few minutes.
Batch processing only: The resources across the cluster are tightly coupled with map-reduce programming. It does not support integration of other data processing frameworks and forces everything to look like a MapReduce job. The emerging customer requirements demand support for real-time and near real-time processing on the data stored on the distributed file systems.
Nonscalability and inefficiency: The MapReduce framework completely depends on the master daemon, that is, the JobTracker. It manages the cluster resources, execution of jobs, and fault tolerance as well.
It is observed that the Hadoop cluster performance degrades drastically when the cluster size increases above 4,000 nodes or the count of concurrent tasks crosses 40,000. The centralized handling of jobs control flow resulted in endless scalability concerns for the scheduler.
Unavailability and unreliability: The availability and reliability are considered to be critical aspects of a framework such as Hadoop. A single point of failure for the MapReduce framework is the failure of the JobTracker daemon. The JobTracker manages the jobs and resources across the cluster. If it goes down, information related to the running or queued jobs and the job history is lost. The queued and running jobs are killed if the JobTracker fails. The MapReduce v1 framework doesn't have any provision to recover the lost data or jobs.
Partitioning of resources: A MapReduce framework divides a job into multiple map and reduce tasks. The nodes with running the TaskTracker daemon are considered as resources. The capability of a resource to execute MapReduce jobs is expressed as the number of map-reduce tasks a resource can execute simultaneously. The framework forced the cluster resources to be partitioned into map and reduce task slots. Such partitioning of the resources resulted in less utilization of the cluster resources.
Management of user logs and job resources: The user logs refer to the logs generated by a MapReduce job. Logs for MapReduce jobs. These logs can be used to validate the correctness of a job or to perform log analysis to tune up the job's performance. In MapReduce v1, the user logs are generated and stored on the local file system of the slave nodes. Accessing logs on the slaves is a pain as users might not have the permissions issued. Since logs were stored on the local file system of a slave, in case the disk goes down, the logs will be lost.
A MapReduce job might require some extra resources for job execution. In the MapReduce v1 framework, the client copies job resources to the HDFS with the replication of 10. Accessing resources remotely or through HDFS is not efficient. Thus, there's a need for localization of resources and a robust framework to manage job resources.
In January 2008, Arun C. Murthy logged a bug in JIRA against the MapReduce architecture, which resulted in a generic resource scheduler and a per job user-defined component that manages the application execution.
You can see this at https://issues.apache.org/jira/browse/MAPREDUCE-279
YARN divides the responsibilities of JobTracker into separate components, each having a specified task to perform. In Hadoop-1, the JobTracker takes care of resource management, job scheduling, and job monitoring. YARN divides these responsibilities of JobTracker into ResourceManager and ApplicationMaster. Instead of TaskTracker, it uses NodeManager as the worker daemon for execution of map-reduce tasks. The ResourceManager and the NodeManager form the computation framework for YARN, and ApplicationMaster is an application-specific framework for application management.
A ResourceManager is a per cluster service that manages the scheduling of compute resources to applications. It optimizes cluster utilization in terms of memory, CPU cores, fairness, and SLAs. To allow different policy constraints, it has algorithms in terms of pluggable schedulers such as capacity and fair that allows resource allocation in a particular way.
Scheduler: This is a pure pluggable component that is only responsible for allocating resources to applications submitted to the cluster, applying constraint of capacities and queues. Scheduler does not provide any guarantee for job completion or monitoring, it only allocates the cluster resources governed by the nature of job and resource requirement.
ApplicationsManager (AsM): This is a service used to manage application masters across the cluster that is responsible for accepting the application submission, providing the resources for application master to start, monitoring the application progress, and restart, in case of application failure.
The NodeManager is a per node worker service that is responsible for the execution of containers based on the node capacity. Node capacity is calculated based on the installed memory and the number of CPU cores. The NodeManager service sends a heartbeat signal to the ResourceManager to update its health status. The NodeManager service is similar to the TaskTracker service in MapReduce v1. NodeManager also sends the status to ResourceManager, which could be the status of the node on which it is running or the status of tasks executing on it.
An ApplicationMaster is a per application framework-specific library that manages each instance of an application that runs within YARN. YARN treats ApplicationMaster as a third-party library responsible for negotiating the resources from the ResourceManager scheduler and works with NodeManager to execute the tasks. The ResourceManager allocates containers to the ApplicationMaster and these containers are then used to run the application-specific processes. ApplicationMaster also tracks the status of the application and monitors the progress of the containers. When the execution of a container gets complete, the ApplicationMaster unregisters the containers with the ResourceManager and unregisters itself after the execution of the application is complete.
A container is a logical bundle of resources in terms of memory, CPU, disk, and so on that is bound to a particular node. In the first version of YARN, a container is equivalent to a block of memory. The ResourceManager scheduler service dynamically allocates resources as containers. A container grants rights to an ApplicationMaster to use a specific amount of resources of a specific host. An ApplicationMaster is considered as the first container of an application and it manages the execution of the application logic on allocated containers.
The ResourceManager service runs on the master node of the cluster. A YARN client submits an application to the ResourceManager. An application can be a single MapReduce job, a directed acyclic graph of jobs, a java application, or any shell script. The client also defines an ApplicationMaster and a command to start the ApplicationMaster on a node.
The ApplicationManager service of resource manager will validate and accept the application request from the client. The scheduler service of resource manager will allocate a container for the ApplicationMaster on a node and the NodeManager service on that node will use the command to start the ApplicationMaster service. Each YARN application has a special container called ApplicationMaster. The ApplicationMaster container is the first container of an application.
The ApplicationMaster requests resources from the ResourceManager. The RequestRequest will have the location of the node, memory, and CPU cores required. The ResourceManager will allocate the resources as containers on a set of nodes. The ApplicationMaster will connect to the NodeManager services and request NodeManager to start containers. The ApplicationMaster manages the execution of the containers and will notify the ResourceManager once the application execution is over. Application execution and progress monitoring is the responsibility of ApplicationMaster rather than ResourceManager.
The NodeManager service runs on each slave of the YARN cluster. It is responsible for running application's containers. The resources specified for a container are taken from the NodeManager resources. Each NodeManager periodically updates ResourceManager for the set of available resources. The ResourceManager scheduler service uses this resource matrix to allocate new containers to ApplicationMaster or to start execution of a new application.
Scalability and higher cluster utilization: Scalability is the ability of a software or product to implement well under an expanding workload. In YARN, the responsibility of resource management and job scheduling / monitoring is divided into separate daemons, allowing YARN daemons to scale the cluster without degrading the performance of the cluster.
With a flexible and generic resource model in YARN, the scheduler handles an overall resource profile for each type of application. This structure makes the communication and storage of resource requests efficient for the scheduler resulting in higher cluster utilization.
High availability for components: Fault tolerance is a core design principle for any multitenancy platform such as YARN. This responsibility is delegated to ResourceManager and ApplicationMaster. The application specific framework, ApplicationMaster, handles the failure of a container. The ResourceManager handles the failure of NodeManager and ApplicationMaster.
Flexible resource model: In MapReduce v1, resources are defined as the number of map and reduce task slots available for the execution of a job. Every resource request cannot be mapped as map/reduce slots. In YARN, a resource-request is defined in terms of memory, CPU, locality, and so on. It results in a generic definition for a resource request by an application. The NodeManager node is the worker node and its capability is calculated based on the installed memory and cores of the CPU.
Multiple data processing algorithms: The MapReduce framework is bounded to batch processing only. YARN is developed with a need to perform a wide variety of data processing over the data stored over Hadoop HDFS. YARN is a framework for generic resource management and allows users to execute multiple data processing algorithms over the data.
Log aggregation and resource localization: As discussed earlier, accessing and managing user logs is difficult in the Hadoop 1.x framework. To manage user logs, YARN introduced a concept of log aggregation. In YARN, once the application is finished, the NodeManager service aggregates the user logs related to an application and these aggregated logs are written out to a single log file in HDFS. To access the logs, users can use either the YARN command-line options, YARN web interface, or can fetch directly from HDFS.
A container might require external resources such as jars, files, or scripts on a local file system. These are made available to containers before they are started. An ApplicationMaster defines a list of resources that are required to run the containers. For efficient disk utilization and access security, the NodeManager ensures the availability of specified resources and their deletion after use.
Efficient and reliable resource management is a basic need of a distributed application framework. YARN provides a generic resource management framework to support data analysis through multiple data processing algorithms. There are a lot of projects that have started using YARN for resource management. We've listed a few of these projects here and discussed how YARN integration solves their business requirements:
Apache Giraph: Giraph is a framework for offline batch processing of semistructured graph data stored using Hadoop. With the Hadoop 1.x version, Giraph had no control over the scheduling policies, heap memory of the mappers, and locality awareness for the running job. Also, defining a Giraph job on the basis of mappers / reducers slots was a bottleneck. YARN's flexible resource allocation model, locality awareness principle, and application master framework ease the Giraph's job management and resource allocation to tasks.
Apache Spark: Spark enables iterative data processing and machine learning algorithms to perform analysis over data available through HDFS, HBase, or other storage systems. Spark uses YARN's resource management capabilities and framework to submit the DAG of a job. The spark user can focus more on data analytics' use cases rather than how spark is integrated with Hadoop or how jobs are executed.
A page on Hadoop wiki lists a number of projects/applications that are migrating to or using YARN as their resource management tool.
You can see this at http://wiki.apache.org/hadoop/PoweredByYarn.
It is time to summarize the learning from this chapter and let you know what's to come in the next chapter. In this chapter, you learnt about the MapReduce v1 framework and its shortcomings. The chapter also covered an introduction to YARN, its components, architecture, and different projects powered by YARN. It also explained how YARN solves big data needs. In the next chapter, you will create single as well as multiple node Hadoop-YARN clusters and begin with your first step to YARN.