We finde book :

Hadoop application architectures designing real-world big data applications - Develop Java MapReduce programs for Hadoop on HDInsight



The core of Apache Hadoop consists of a storage part, known as Hadoop Distributed File System (HDFS), and a processing part which is a MapReduce programming model. Hadoop splits files into large blocks and distributes them across nodes in a cluster. It then transfers packaged code into nodes to process the data in parallel. This approach takes advantage of data locality , [6] where nodes manipulate the data they have access to. This allows the dataset to be processed faster and more efficiently than it would be in a more conventional supercomputer architecture that relies on a parallel file system where computation and data are distributed via high-speed networking. [7] [8]

The term Hadoop has come to refer not just to the aforementioned base modules and sub-modules, but also to the ecosystem , [11] or collection of additional software packages that can be installed on top of or alongside Hadoop, such as Apache Pig , Apache Hive , Apache HBase , Apache Phoenix , Apache Spark , Apache ZooKeeper , Cloudera Impala , Apache Flume , Apache Sqoop , Apache Oozie , and Apache Storm . [12]

Apache Hadoop's MapReduce and HDFS components were inspired by Google papers on their MapReduce and Google File System . [13]

This chapter describes how to use the knowledge modules in Oracle Data Integrator (ODI) Application Adapter for Hadoop. It contains the following sections:

Apache Hadoop is designed to handle and process data that is typically from data sources that are nonrelational and data volumes that are beyond what is handled by relational databases.

Oracle Data Integrator (ODI) Application Adapter for Hadoop enables data integration developers to integrate and transform data easily within Hadoop using Oracle Data Integrator. Employing familiar and easy-to-use tools and preconfigured knowledge modules (KMs), the application adapter provides the following capabilities:

The core of Apache Hadoop consists of a storage part, known as Hadoop Distributed File System (HDFS), and a processing part which is a MapReduce programming model. Hadoop splits files into large blocks and distributes them across nodes in a cluster. It then transfers packaged code into nodes to process the data in parallel. This approach takes advantage of data locality , [6] where nodes manipulate the data they have access to. This allows the dataset to be processed faster and more efficiently than it would be in a more conventional supercomputer architecture that relies on a parallel file system where computation and data are distributed via high-speed networking. [7] [8]

The term Hadoop has come to refer not just to the aforementioned base modules and sub-modules, but also to the ecosystem , [11] or collection of additional software packages that can be installed on top of or alongside Hadoop, such as Apache Pig , Apache Hive , Apache HBase , Apache Phoenix , Apache Spark , Apache ZooKeeper , Cloudera Impala , Apache Flume , Apache Sqoop , Apache Oozie , and Apache Storm . [12]

Apache Hadoop's MapReduce and HDFS components were inspired by Google papers on their MapReduce and Google File System . [13]

This chapter describes how to use the knowledge modules in Oracle Data Integrator (ODI) Application Adapter for Hadoop. It contains the following sections:

Apache Hadoop is designed to handle and process data that is typically from data sources that are nonrelational and data volumes that are beyond what is handled by relational databases.

Oracle Data Integrator (ODI) Application Adapter for Hadoop enables data integration developers to integrate and transform data easily within Hadoop using Oracle Data Integrator. Employing familiar and easy-to-use tools and preconfigured knowledge modules (KMs), the application adapter provides the following capabilities:

Other posts in this series:
Introducing Apache Hadoop YARN
Apache Hadoop YARN – Background and an Overview
Apache Hadoop YARN – Concepts and Applications
Apache Hadoop YARN – ResourceManager
Apache Hadoop YARN – NodeManager

As previously described, YARN is essentially a system for managing distributed applications. It consists of a central ResourceManager , which arbitrates all available cluster resources, and a per-node NodeManager , which takes direction from the ResourceManager and is responsible for managing resources available on a single node.

In YARN, the ResourceManager is, primarily, a pure scheduler . In essence, it’s strictly limited to arbitrating available resources in the system among the competing applications – a market maker if you will.  It optimizes for cluster utilization (keep all resources in use all the time) against various constraints such as capacity guarantees, fairness, and SLAs. To allow for different policy constraints the ResourceManager has a pluggable scheduler that allows for different algorithms such as capacity and fair scheduling to be used as necessary.

SAS Malaysia
Suite 6-3A, Level 6, Menara CIMB
Jalan Stesen Sentral 2
Kuala Lumpur Sentral
50490 Kuala Lumpur
Malaysia
Tel:  (+603) 2725 2288
Fax: (+603) 2725 2222

As the World Wide Web grew in the late 1900s and early 2000s, search engines and indexes were created to help locate relevant information amid the text-based content. In the early years, search results were returned by humans. But as the web grew from dozens to millions of pages, automation was needed. Web crawlers were created, many as university-led research projects, and search engine start-ups took off (Yahoo, AltaVista, etc.).

One such project was an open-source web search engine called Nutch – the brainchild of Doug Cutting and Mike Cafarella. They wanted to return web search results faster by distributing data and calculations across different computers so multiple tasks could be accomplished simultaneously. During this time, another search engine project called Google was in progress. It was based on the same concept – storing and processing data in a distributed, automated way so that relevant web search results could be returned faster.

During the execution of an application, the ApplicationMaster communicates NodeManagers through NMClientAsync object. All container events are handled by NMClientAsync.CallbackHandler , associated with NMClientAsync . A typical callback handler handles client start, stop, status update and error. ApplicationMaster also reports execution progress to ResourceManager by handling the getProgress() method of AMRMClientAsync.CallbackHandler .

Other than asynchronous clients, there are synchronous versions for certain workflows ( AMRMClient and NMClient ). The asynchronous clients are recommended because of (subjectively) simpler usages, and this article will mainly cover the asynchronous clients. Please refer to AMRMClient and NMClient for more information on synchronous clients.

Launch containers. Communicate with NodeManagers by using NMClientAsync objects, handling container events by NMClientAsync.CallbackHandler

The core of Apache Hadoop consists of a storage part, known as Hadoop Distributed File System (HDFS), and a processing part which is a MapReduce programming model. Hadoop splits files into large blocks and distributes them across nodes in a cluster. It then transfers packaged code into nodes to process the data in parallel. This approach takes advantage of data locality , [6] where nodes manipulate the data they have access to. This allows the dataset to be processed faster and more efficiently than it would be in a more conventional supercomputer architecture that relies on a parallel file system where computation and data are distributed via high-speed networking. [7] [8]

The term Hadoop has come to refer not just to the aforementioned base modules and sub-modules, but also to the ecosystem , [11] or collection of additional software packages that can be installed on top of or alongside Hadoop, such as Apache Pig , Apache Hive , Apache HBase , Apache Phoenix , Apache Spark , Apache ZooKeeper , Cloudera Impala , Apache Flume , Apache Sqoop , Apache Oozie , and Apache Storm . [12]

Apache Hadoop's MapReduce and HDFS components were inspired by Google papers on their MapReduce and Google File System . [13]

This chapter describes how to use the knowledge modules in Oracle Data Integrator (ODI) Application Adapter for Hadoop. It contains the following sections:

Apache Hadoop is designed to handle and process data that is typically from data sources that are nonrelational and data volumes that are beyond what is handled by relational databases.

Oracle Data Integrator (ODI) Application Adapter for Hadoop enables data integration developers to integrate and transform data easily within Hadoop using Oracle Data Integrator. Employing familiar and easy-to-use tools and preconfigured knowledge modules (KMs), the application adapter provides the following capabilities:

Other posts in this series:
Introducing Apache Hadoop YARN
Apache Hadoop YARN – Background and an Overview
Apache Hadoop YARN – Concepts and Applications
Apache Hadoop YARN – ResourceManager
Apache Hadoop YARN – NodeManager

As previously described, YARN is essentially a system for managing distributed applications. It consists of a central ResourceManager , which arbitrates all available cluster resources, and a per-node NodeManager , which takes direction from the ResourceManager and is responsible for managing resources available on a single node.

In YARN, the ResourceManager is, primarily, a pure scheduler . In essence, it’s strictly limited to arbitrating available resources in the system among the competing applications – a market maker if you will.  It optimizes for cluster utilization (keep all resources in use all the time) against various constraints such as capacity guarantees, fairness, and SLAs. To allow for different policy constraints the ResourceManager has a pluggable scheduler that allows for different algorithms such as capacity and fair scheduling to be used as necessary.

SAS Malaysia
Suite 6-3A, Level 6, Menara CIMB
Jalan Stesen Sentral 2
Kuala Lumpur Sentral
50490 Kuala Lumpur
Malaysia
Tel:  (+603) 2725 2288
Fax: (+603) 2725 2222

As the World Wide Web grew in the late 1900s and early 2000s, search engines and indexes were created to help locate relevant information amid the text-based content. In the early years, search results were returned by humans. But as the web grew from dozens to millions of pages, automation was needed. Web crawlers were created, many as university-led research projects, and search engine start-ups took off (Yahoo, AltaVista, etc.).

One such project was an open-source web search engine called Nutch – the brainchild of Doug Cutting and Mike Cafarella. They wanted to return web search results faster by distributing data and calculations across different computers so multiple tasks could be accomplished simultaneously. During this time, another search engine project called Google was in progress. It was based on the same concept – storing and processing data in a distributed, automated way so that relevant web search results could be returned faster.

The core of Apache Hadoop consists of a storage part, known as Hadoop Distributed File System (HDFS), and a processing part which is a MapReduce programming model. Hadoop splits files into large blocks and distributes them across nodes in a cluster. It then transfers packaged code into nodes to process the data in parallel. This approach takes advantage of data locality , [6] where nodes manipulate the data they have access to. This allows the dataset to be processed faster and more efficiently than it would be in a more conventional supercomputer architecture that relies on a parallel file system where computation and data are distributed via high-speed networking. [7] [8]

The term Hadoop has come to refer not just to the aforementioned base modules and sub-modules, but also to the ecosystem , [11] or collection of additional software packages that can be installed on top of or alongside Hadoop, such as Apache Pig , Apache Hive , Apache HBase , Apache Phoenix , Apache Spark , Apache ZooKeeper , Cloudera Impala , Apache Flume , Apache Sqoop , Apache Oozie , and Apache Storm . [12]

Apache Hadoop's MapReduce and HDFS components were inspired by Google papers on their MapReduce and Google File System . [13]

This chapter describes how to use the knowledge modules in Oracle Data Integrator (ODI) Application Adapter for Hadoop. It contains the following sections:

Apache Hadoop is designed to handle and process data that is typically from data sources that are nonrelational and data volumes that are beyond what is handled by relational databases.

Oracle Data Integrator (ODI) Application Adapter for Hadoop enables data integration developers to integrate and transform data easily within Hadoop using Oracle Data Integrator. Employing familiar and easy-to-use tools and preconfigured knowledge modules (KMs), the application adapter provides the following capabilities:

Other posts in this series:
Introducing Apache Hadoop YARN
Apache Hadoop YARN – Background and an Overview
Apache Hadoop YARN – Concepts and Applications
Apache Hadoop YARN – ResourceManager
Apache Hadoop YARN – NodeManager

As previously described, YARN is essentially a system for managing distributed applications. It consists of a central ResourceManager , which arbitrates all available cluster resources, and a per-node NodeManager , which takes direction from the ResourceManager and is responsible for managing resources available on a single node.

In YARN, the ResourceManager is, primarily, a pure scheduler . In essence, it’s strictly limited to arbitrating available resources in the system among the competing applications – a market maker if you will.  It optimizes for cluster utilization (keep all resources in use all the time) against various constraints such as capacity guarantees, fairness, and SLAs. To allow for different policy constraints the ResourceManager has a pluggable scheduler that allows for different algorithms such as capacity and fair scheduling to be used as necessary.

The core of Apache Hadoop consists of a storage part, known as Hadoop Distributed File System (HDFS), and a processing part which is a MapReduce programming model. Hadoop splits files into large blocks and distributes them across nodes in a cluster. It then transfers packaged code into nodes to process the data in parallel. This approach takes advantage of data locality , [6] where nodes manipulate the data they have access to. This allows the dataset to be processed faster and more efficiently than it would be in a more conventional supercomputer architecture that relies on a parallel file system where computation and data are distributed via high-speed networking. [7] [8]

The term Hadoop has come to refer not just to the aforementioned base modules and sub-modules, but also to the ecosystem , [11] or collection of additional software packages that can be installed on top of or alongside Hadoop, such as Apache Pig , Apache Hive , Apache HBase , Apache Phoenix , Apache Spark , Apache ZooKeeper , Cloudera Impala , Apache Flume , Apache Sqoop , Apache Oozie , and Apache Storm . [12]

Apache Hadoop's MapReduce and HDFS components were inspired by Google papers on their MapReduce and Google File System . [13]


5181mZjIcYL