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Some background to what we discussed:
Hortonworks Data Platform (HDP)
from their website: http://hortonworks.com/products/hortonworksdataplatform/
Hortonworks Data Platform (HDP) is a 100% open source data management platform based on Apache Hadoop. It allows you to load, store, process and manage data in virtually any format and at any scale. As the foundation for the next generation enterprise data architecture, HDP includes all of the necessary components to begin uncovering business insights from the quickly growing streams of data flowing into and throughout your business.
Hortonworks Data Platform is ideal for organizations that want to combine the power and cost-effectiveness of Apache Hadoop with the advanced services required for enterprise deployments. It is also ideal for solution providers that wish to integrate or extend their solutions with an open and extensible Apache Hadoop-based platform.
- Integrated and Tested Package – HDP includes stable versions of all the critical Apache Hadoop components in an integrated and tested package.
- Easy Installation – HDP includes an installation and provisioning tool with a modern, intuitive user interface.
- Management and Monitoring Services – HDP includes intuitive dashboards for monitoring your clusters and creating alerts.
- Data Integration Services – HDP includes Talend Open Studio for Big Data, the leading open source integration tool for easily connecting Hadoop to hundreds of data systems without having to write code.
- Metadata Services – HDP includes Apache HCatalog, which simplifies data sharing between Hadoop applications and between Hadoop and other data systems.
- High Availability – HDP has been extended to seamlessly integrate with proven high availability solutions.
Apache Hadoop 2.0
from their website: http://hadoop.apache.org/common/docs/current/
Apache Hadoop 2.x consists of significant improvements over the previous stable release (hadoop-1.x).
Here is a short overview of the improvments to both HDFS and MapReduce.
- HDFS FederationIn order to scale the name service horizontally, federation uses multiple independent Namenodes/Namespaces. The Namenodes are federated, that is, the Namenodes are independent and don’t require coordination with each other. The datanodes are used as common storage for blocks by all the Namenodes. Each datanode registers with all the Namenodes in the cluster. Datanodes send periodic heartbeats and block reports and handles commands from the Namenodes.More details are available in the HDFS Federation document.
- MapReduce NextGen
aka YARN aka MRv2The new architecture introduced in hadoop-0.23, divides
the two major functions of the JobTracker: resource management and job
life-cycle management into separate components.The new ResourceManager
manages the global assignment of compute resources to applications and
the per-application ApplicationMaster manages the application‚Äôs
scheduling and coordination.An application is either a single job in the
sense of classic MapReduce jobs or a DAG of such jobs.The
ResourceManager and per-machine NodeManager daemon, which manages the
user processes on that machine, form the computation fabric.The
per-application ApplicationMaster is, in effect, a framework specific
library and is tasked with negotiating resources from the
ResourceManager and working with the NodeManager(s) to execute and
monitor the tasks.
More details are available in the YARN document.
The Hadoop documentation includes the information you need to get started using Hadoop. Begin with the Single Node Setup which shows you how to set up a single-node Hadoop installation. Then move on to the Cluster Setup to learn how to set up a multi-node Hadoop installation.
MapReduce has undergone a complete overhaul in hadoop-0.23 and we now have, what we call, MapReduce 2.0 (MRv2) or YARN.
The fundamental idea of MRv2 is to split up the two major functionalities of the JobTracker, resource management and job scheduling/monitoring, into separate daemons. The idea is to have a global ResourceManager (RM) and per-application ApplicationMaster (AM). An application is either a single job in the classical sense of Map-Reduce jobs or a DAG of jobs.
The ResourceManager and per-node slave, the NodeManager (NM), form the data-computation framework. The ResourceManager is the ultimate authority that arbitrates resources among all the applications in the system.
The per-application ApplicationMaster is, in effect, a framework specific library and is tasked with negotiating resources from the ResourceManager and working with the NodeManager(s) to execute and monitor the tasks.
The ResourceManager has two main components: Scheduler and ApplicationsManager.
The Scheduler is responsible for allocating resources to the various running applications subject to familiar constraints of capacities, queues etc. The Scheduler is pure scheduler in the sense that it performs no monitoring or tracking of status for the application. Also, it offers no guarantees about restarting failed tasks either due to application failure or hardware failures. The Scheduler performs its scheduling function based the resource requirements of the applications; it does so based on the abstract notion of a resource Container which incorporates elements such as memory, cpu, disk, network etc. In the first version, only memory is supported.
The Scheduler has a pluggable policy plug-in, which is responsible for partitioning the cluster resources among the various queues, applications etc. The current Map-Reduce schedulers such as the CapacityScheduler and the FairScheduler would be some examples of the plug-in.
The CapacityScheduler supports hierarchical queues to allow for more predictable sharing of cluster resources
The ApplicationsManager is responsible for accepting job-submissions, negotiating the first container for executing the application specific ApplicationMaster and provides the service for restarting the ApplicationMaster container on failure.
The NodeManager is the per-machine framework agent who is responsible for containers, monitoring their resource usage (cpu, memory, disk, network) and reporting the same to the ResourceManager/Scheduler.
The per-application ApplicationMaster has the responsibility of negotiating appropriate resource containers from the Scheduler, tracking their status and monitoring for progress.
MRV2 maintains API compatibility with previous stable release (hadoop-0.20.205). This means that all Map-Reduce jobs should still run unchanged on top of MRv2 with just a recompile.
HDFS has two main layers:
- Consists of directories, files and blocks
- It supports all the namespace related file system operations such as create, delete, modify and list files and directories.
- Block Storage Service has two parts
- Block Management (which is done in Namenode)
- Provides datanode cluster membership by handling registrations, and periodic heart beats.
- Processes block reports and maintains location of blocks.
- Supports block related operations such as create, delete, modify and get block location.
- Manages replica placement and replication of a block for under replicated blocks and deletes blocks that are over replicated.
- Storage – is provided by datanodes by storing blocks on the local file system and allows read/write access.
The prior HDFS architecture allows only a single namespace for the entire cluster. A single Namenode manages this namespace. HDFS Federation addresses limitation of the prior architecture by adding support multiple Namenodes/namespaces to HDFS file system.
- Block Management (which is done in Namenode)
In order to scale the name service horizontally, federation uses multiple independent Namenodes/namespaces. The Namenodes are federated, that is, the Namenodes are independent and don’t require coordination with each other. The datanodes are used as common storage for blocks by all the Namenodes. Each datanode registers with all the Namenodes in the cluster. Datanodes send periodic heartbeats and block reports and handles commands from the Namenodes.
A Block Pool is a set of blocks that belong to a single namespace. Datanodes store blocks for all the block pools in the cluster. It is managed independently of other block pools. This allows a namespace to generate Block IDs for new blocks without the need for coordination with the other namespaces. The failure of a Namenode does not prevent the datanode from serving other Namenodes in the cluster.
A Namespace and its block pool together are called Namespace Volume. It is a self-contained unit of management. When a Namenode/namespace is deleted, the corresponding block pool at the datanodes is deleted. Each namespace volume is upgraded as a unit, during cluster upgrade.
A new identifier ClusterID is added to identify all the nodes in the cluster. When a Namenode is formatted, this identifier is provided or auto generated. This ID should be used for formatting the other Namenodes into the cluster.
- Namespace Scalability – HDFS cluster storage scales horizontally but the namespace does not. Large deployments or deployments using lot of small files benefit from scaling the namespace by adding more Namenodes to the cluster
- Performance – File system operation throughput is limited by a single Namenode in the prior architecture. Adding more Namenodes to the cluster scales the file system read/write operations throughput.
- Isolation – A single Namenode offers no isolation in multi user environment. An experimental application can overload the Namenode and slow down production critical applications. With multiple Namenodes, different categories of applications and users can be isolated to different namespaces.