This document provides an overview of Ruby on Hadoop. It discusses the history and components of Hadoop including MapReduce, HDFS, and the Hadoop ecosystem. It then introduces Wukong as a Ruby framework for Hadoop and describes how to use processors to define mapping and reducing tasks to run jobs on Hadoop from Ruby.
Hadoop has a master/slave architecture. The master node runs the NameNode, JobTracker, and optionally SecondaryNameNode. The NameNode stores metadata about data locations. DataNodes on slave nodes store the actual data blocks. The JobTracker schedules jobs, assigning tasks to TaskTrackers on slaves which perform the work. The SecondaryNameNode assists the NameNode in the event of failures. MapReduce jobs split files into blocks, map tasks process the blocks in parallel on slaves, and reduce tasks consolidate the results.
This document provides an overview of the Hadoop framework. It describes the key components of Hadoop including the NameNode, DataNodes, JobTracker, TaskTracker, and SecondaryNameNode. The NameNode manages the file system metadata and data locations. DataNodes store the actual data blocks. The JobTracker manages jobs and schedules tasks on TaskTrackers running on slave nodes. The SecondaryNameNode assists the NameNode in the event of failure. Hadoop uses a master-slave architecture with the NameNode and JobTracker on the master and DataNodes and TaskTrackers on slave nodes.
This document provides an overview of the Hadoop framework. It describes the key components of Hadoop including the NameNode, DataNodes, JobTracker, TaskTracker, and SecondaryNameNode. The NameNode manages the file system metadata and data locations. DataNodes store the actual data blocks. The JobTracker manages jobs and schedules tasks on TaskTrackers running on slave nodes. The SecondaryNameNode assists the NameNode in the event of failure. Hadoop uses a master-slave architecture with the NameNode and JobTracker on the master and DataNodes and TaskTrackers on slave nodes.
The document summarizes key components of Hadoop including:
1) The NameNode, located on the master node, stores metadata for HDFS such as file locations and attributes.
2) DataNodes, located on slave nodes, store and retrieve data blocks.
3) The JobTracker, located on the master node, schedules jobs and assigns tasks to TaskTrackers on slave nodes.
Hadoop is a distributed processing framework for large data sets across clusters of commodity hardware. It has two main components: HDFS for reliable data storage, and MapReduce for distributed processing of large data sets. Hadoop can scale from single servers to thousands of machines, handling data measuring petabytes with very high throughput. It provides reliability even if individual machines fail, and is easy to set up and manage.
Soft-Shake 2013 : Enabling Realtime Queries to End UsersBenoit Perroud
Since it became an Apache Top Level Project in early 2008, Hadoop has established itself as the de-facto industry standard for batch processing. The two layers composing its core, HDFS and MapReduce, are strong building blocks for data processing. Running data analysis and crunching petabytes of data is no longer fiction. But the MapReduce framework does have two major drawbacks: query latency and data freshness.
At the same time, businesses have started to exchange more and more data through REST API, leveraging HTTP words (GET, POST, PUT, DELETE) and URI (for instance http://company/api/v2/domain/identifier), pushing the need to read data in a random access style – from simple key/value to complex queries.
Enhancing the BigData stack with real time search capabilities is the next natural step for the Hadoop ecosystem, because the MapReduce framework was not designed with synchronous processing in mind.
There is a lot of traction today in this area and this talk will try to answer the question of how to fill in this gap with specific open-source components, ultimately building a dedicated platform that will enable real-time queries on Internet-scale data sets. After discussing the evolution of the deployments of common Hadoop platform, a hybrid approach called lambda architecture will be proposed. It will be demonstrated with concrete examples, discussing which technology could be a good match, and how they would interact together.
This document provides an overview of the Hadoop framework and its key components. It describes the roles of the Master node, NameNode, DataNodes, JobTracker, SecondaryNameNode, and TaskTracker. The NameNode manages the file system namespace and regulates access to files. DataNodes store data blocks and serve read/write requests. The JobTracker coordinates jobs on Hadoop and assigns tasks to TaskTrackers on slave nodes. The SecondaryNameNode aids the NameNode in the event of failure.
This very short document does not contain enough substantive information to summarize meaningfully in 3 sentences or less. It consists of only the word "test" repeated twice without any additional context.
Hadoop has a master/slave architecture. The master node runs the NameNode, JobTracker, and optionally SecondaryNameNode. The NameNode stores metadata about data locations. DataNodes on slave nodes store the actual data blocks. The JobTracker schedules jobs, assigning tasks to TaskTrackers on slaves which perform the work. The SecondaryNameNode assists the NameNode in the event of failures. MapReduce jobs split files into blocks, map tasks process the blocks in parallel on slaves, and reduce tasks consolidate the results.
This document provides an overview of the Hadoop framework. It describes the key components of Hadoop including the NameNode, DataNodes, JobTracker, TaskTracker, and SecondaryNameNode. The NameNode manages the file system metadata and data locations. DataNodes store the actual data blocks. The JobTracker manages jobs and schedules tasks on TaskTrackers running on slave nodes. The SecondaryNameNode assists the NameNode in the event of failure. Hadoop uses a master-slave architecture with the NameNode and JobTracker on the master and DataNodes and TaskTrackers on slave nodes.
This document provides an overview of the Hadoop framework. It describes the key components of Hadoop including the NameNode, DataNodes, JobTracker, TaskTracker, and SecondaryNameNode. The NameNode manages the file system metadata and data locations. DataNodes store the actual data blocks. The JobTracker manages jobs and schedules tasks on TaskTrackers running on slave nodes. The SecondaryNameNode assists the NameNode in the event of failure. Hadoop uses a master-slave architecture with the NameNode and JobTracker on the master and DataNodes and TaskTrackers on slave nodes.
The document summarizes key components of Hadoop including:
1) The NameNode, located on the master node, stores metadata for HDFS such as file locations and attributes.
2) DataNodes, located on slave nodes, store and retrieve data blocks.
3) The JobTracker, located on the master node, schedules jobs and assigns tasks to TaskTrackers on slave nodes.
Hadoop is a distributed processing framework for large data sets across clusters of commodity hardware. It has two main components: HDFS for reliable data storage, and MapReduce for distributed processing of large data sets. Hadoop can scale from single servers to thousands of machines, handling data measuring petabytes with very high throughput. It provides reliability even if individual machines fail, and is easy to set up and manage.
Soft-Shake 2013 : Enabling Realtime Queries to End UsersBenoit Perroud
Since it became an Apache Top Level Project in early 2008, Hadoop has established itself as the de-facto industry standard for batch processing. The two layers composing its core, HDFS and MapReduce, are strong building blocks for data processing. Running data analysis and crunching petabytes of data is no longer fiction. But the MapReduce framework does have two major drawbacks: query latency and data freshness.
At the same time, businesses have started to exchange more and more data through REST API, leveraging HTTP words (GET, POST, PUT, DELETE) and URI (for instance http://company/api/v2/domain/identifier), pushing the need to read data in a random access style – from simple key/value to complex queries.
Enhancing the BigData stack with real time search capabilities is the next natural step for the Hadoop ecosystem, because the MapReduce framework was not designed with synchronous processing in mind.
There is a lot of traction today in this area and this talk will try to answer the question of how to fill in this gap with specific open-source components, ultimately building a dedicated platform that will enable real-time queries on Internet-scale data sets. After discussing the evolution of the deployments of common Hadoop platform, a hybrid approach called lambda architecture will be proposed. It will be demonstrated with concrete examples, discussing which technology could be a good match, and how they would interact together.
This document provides an overview of the Hadoop framework and its key components. It describes the roles of the Master node, NameNode, DataNodes, JobTracker, SecondaryNameNode, and TaskTracker. The NameNode manages the file system namespace and regulates access to files. DataNodes store data blocks and serve read/write requests. The JobTracker coordinates jobs on Hadoop and assigns tasks to TaskTrackers on slave nodes. The SecondaryNameNode aids the NameNode in the event of failure.
This very short document does not contain enough substantive information to summarize meaningfully in 3 sentences or less. It consists of only the word "test" repeated twice without any additional context.
This document provides an overview and introduction to big data concepts. It discusses what big data is, different data structures and sources, challenges of big data, and appropriate tools to use for big data analytics like Hadoop. It also provides step-by-step instructions for installing and configuring Hadoop, including installing Java, adding users, configuring SSH, installing Hadoop files and setting configuration parameters.
Hadoop is a distributed processing framework that operates on a master-slave architecture. The master node runs the NameNode, JobTracker, and SecondaryNameNode processes. The NameNode manages the file system metadata and location information for data stored across the DataNodes on slave nodes. The JobTracker manages job scheduling and coordination, assigning tasks to TaskTrackers on slaves. The SecondaryNameNode provides backup support in case the NameNode fails. Hadoop uses MapReduce as a programming model where user code is run as Maps on data subsets and the outputs are aggregated by Reduces.
Hadoop is a distributed processing framework that operates on a master-slave architecture. The master node runs the NameNode, JobTracker, and SecondaryNameNode processes. The NameNode manages the file system metadata and location information for data stored across the DataNodes on slave nodes. The JobTracker manages job scheduling and coordination, assigning tasks to TaskTrackers on slaves. The SecondaryNameNode provides backup support in case of NameNode failure.
Hadoop is a distributed processing framework that operates on a master-slave topology. The master node runs the NameNode, JobTracker, and SecondaryNameNode processes. The NameNode manages the file system metadata and location information. The JobTracker schedules and tracks jobs on the cluster. The SecondaryNameNode helps recover metadata if the NameNode fails. Slave nodes run the DataNode process to store file data blocks and the TaskTracker process to execute tasks assigned by the JobTracker. MapReduce is the programming model used in Hadoop for distributed processing of large datasets.
The document discusses the key components of Hadoop including the NameNode, DataNodes, JobTracker, TaskTracker, and SecondaryNameNode.
The NameNode stores metadata about data locations and attributes and is located on the master node. DataNodes store the actual data blocks across slave nodes. The JobTracker schedules jobs and is also located on the master node, while TaskTrackers execute tasks on slave nodes. The SecondaryNameNode assists if the NameNode fails.
Hadoop uses a master/slave architecture with the NameNode and JobTracker on the master and DataNodes and TaskTrackers on slave nodes. MapReduce is used for distributed processing where data is split into blocks and mapped and reduced across nodes.
The document discusses the key components of Hadoop including the NameNode, DataNodes, JobTracker, TaskTracker, and SecondaryNameNode.
The NameNode stores metadata for HDFS and tracks where data is stored across DataNodes on the slaves. The JobTracker schedules jobs on TaskTrackers across the slaves. The SecondaryNameNode assists if the NameNode fails. Hadoop uses a master-slave architecture with the NameNode and JobTracker on the master and DataNodes and TaskTrackers on the slaves.
The document discusses the key components of Hadoop including the NameNode, DataNodes, JobTracker, TaskTracker, and SecondaryNameNode.
The NameNode manages the file system metadata and keeps track of where data is stored across the DataNodes. The DataNodes store the actual data blocks.
The JobTracker manages jobs by determining which TaskTrackers on the slaves will execute tasks. The SecondaryNameNode helps if the NameNode fails by providing additional backup of metadata.
The document discusses the key components of Hadoop including the NameNode, DataNodes, JobTracker, TaskTracker, and SecondaryNameNode.
The NameNode manages the file system metadata and keeps track of where data is stored across the DataNodes. The DataNodes store the actual data blocks.
The JobTracker manages jobs by determining which TaskTrackers on the slaves will execute tasks. The SecondaryNameNode helps if the NameNode fails by providing additional backup of metadata.
The document discusses the key components of Hadoop including the NameNode, DataNodes, JobTracker, TaskTracker, and SecondaryNameNode.
The NameNode stores metadata about data locations and attributes and is located on the master node. DataNodes store the actual data blocks across slave nodes. The JobTracker schedules jobs and is also located on the master node, while TaskTrackers execute tasks on slave nodes. The SecondaryNameNode assists if the NameNode fails.
Hadoop uses a master/slave architecture with the NameNode and JobTracker on the master and DataNodes and TaskTrackers on slave nodes. It also utilizes MapReduce for parallel processing of large datasets split across blocks in HDFS.
The document discusses the key components of Hadoop including the NameNode, DataNodes, JobTracker, TaskTracker, and SecondaryNameNode.
The NameNode manages the file system metadata and keeps track of where data is stored across the DataNodes. The DataNodes store the actual data blocks.
The JobTracker manages jobs by determining which TaskTrackers on the slaves will execute tasks. The SecondaryNameNode helps if the NameNode fails by providing additional backup of metadata.
The document discusses the key components of Hadoop including the NameNode, DataNodes, JobTracker, and SecondaryNameNode.
The NameNode stores metadata about data locations and attributes and is located on the master node. DataNodes store the actual data blocks across slave nodes.
The JobTracker is responsible for scheduling jobs and is also located on the master node. When a job is submitted, the JobTracker determines which TaskTrackers on the slaves will execute the tasks.
The SecondaryNameNode acts as a backup to the NameNode in case of failure and is also located on the master node.
The document discusses the key components of Hadoop including the NameNode, DataNodes, JobTracker, TaskTracker, and SecondaryNameNode.
The NameNode stores metadata about data locations and attributes and is located on the master node. DataNodes store the actual data blocks across slave nodes. The JobTracker schedules jobs and is also located on the master node, while TaskTrackers execute tasks on slave nodes. The SecondaryNameNode assists if the NameNode fails.
Hadoop uses a master/slave architecture with the NameNode and JobTracker on the master and DataNodes and TaskTrackers on slave nodes. MapReduce is used for distributed processing where tasks are assigned to nodes and results are aggregated.
The document discusses the key components of Hadoop including the NameNode, DataNodes, JobTracker, TaskTracker, and SecondaryNameNode.
The NameNode stores metadata for HDFS and tracks where data is stored across DataNodes on the slaves. The JobTracker schedules jobs on TaskTrackers across the slaves. The SecondaryNameNode assists the NameNode in the event of failure. Hadoop uses a master-slave architecture with the NameNode and JobTracker on the master and DataNodes and TaskTrackers on the slaves.
The document discusses the key components of Hadoop including the NameNode, DataNodes, JobTracker, TaskTracker, and SecondaryNameNode.
The NameNode stores metadata for HDFS and tracks where data is stored across DataNodes on the slaves. The JobTracker schedules jobs on TaskTrackers across the slaves. The SecondaryNameNode assists the NameNode in the event of failures. Hadoop uses a master-slave architecture with the NameNode and JobTracker on the master and DataNodes and TaskTrackers on the slaves.
The document discusses the key components of Hadoop including the NameNode, DataNodes, JobTracker, TaskTracker, and SecondaryNameNode.
The NameNode stores metadata for HDFS and tracks where data is stored across DataNodes on the slaves. The JobTracker schedules jobs on TaskTrackers across the slaves. The SecondaryNameNode assists the NameNode in the event of failure. Hadoop uses a master-slave architecture with the NameNode and JobTracker on the master and DataNodes and TaskTrackers on the slaves.
The document discusses the key components of Hadoop including the NameNode, DataNodes, JobTracker, TaskTracker, and SecondaryNameNode.
The NameNode stores metadata about data locations and attributes and is located on the master node. DataNodes store the actual data blocks across slave nodes. The JobTracker schedules jobs and is also located on the master node, while TaskTrackers execute tasks on slave nodes. The SecondaryNameNode assists if the NameNode fails.
Hadoop uses a master/slave architecture with the NameNode and JobTracker on the master and DataNodes and TaskTrackers on slave nodes. MapReduce is used for distributed processing where data is split into blocks and mapped and reduced across nodes.
The document discusses the key components of Hadoop including the NameNode, DataNodes, JobTracker, TaskTracker, and SecondaryNameNode.
The NameNode stores metadata for HDFS and tracks where data is stored across DataNodes on the slaves. The JobTracker schedules jobs on TaskTrackers across the slaves. The SecondaryNameNode assists the NameNode in the event of failures. Hadoop uses a master-slave architecture with the NameNode and JobTracker on the master and DataNodes and TaskTrackers on the slaves.
The document discusses the key components of Hadoop including the NameNode, DataNodes, JobTracker, TaskTracker, and SecondaryNameNode.
The NameNode stores metadata about data locations and attributes and is located on the master node. DataNodes store the actual data blocks across slave nodes. The JobTracker schedules jobs and is also located on the master node, while TaskTrackers execute tasks on slave nodes. The SecondaryNameNode assists if the NameNode fails.
Hadoop uses a master/slave architecture with the NameNode and JobTracker on the master and DataNodes and TaskTrackers on slave nodes. MapReduce is used for distributed processing where data is split into blocks and mapped and reduced across nodes.
The document discusses the key components of Hadoop including the NameNode, DataNodes, JobTracker, TaskTracker, and SecondaryNameNode.
The NameNode stores metadata about data locations and attributes and is located on the master node. DataNodes store the actual data blocks across slave nodes. The JobTracker schedules jobs and is also located on the master node, while TaskTrackers execute tasks on slave nodes. The SecondaryNameNode assists if the NameNode fails.
Hadoop uses a master/slave architecture with the NameNode and JobTracker on the master and DataNodes and TaskTrackers on slave nodes. MapReduce is used for distributed processing where data is split into blocks and mapped and reduced across nodes.
The document discusses the key components of Hadoop including the NameNode, DataNodes, JobTracker, TaskTracker, and SecondaryNameNode.
The NameNode stores metadata for HDFS and tracks where data is stored across DataNodes on the slaves. The JobTracker schedules jobs on TaskTrackers across the slaves. The SecondaryNameNode assists if the NameNode fails. Hadoop uses a master-slave architecture with the NameNode and JobTracker on the master and DataNodes and TaskTrackers on the slaves.
Best 20 SEO Techniques To Improve Website Visibility In SERPPixlogix Infotech
Boost your website's visibility with proven SEO techniques! Our latest blog dives into essential strategies to enhance your online presence, increase traffic, and rank higher on search engines. From keyword optimization to quality content creation, learn how to make your site stand out in the crowded digital landscape. Discover actionable tips and expert insights to elevate your SEO game.
This document provides an overview and introduction to big data concepts. It discusses what big data is, different data structures and sources, challenges of big data, and appropriate tools to use for big data analytics like Hadoop. It also provides step-by-step instructions for installing and configuring Hadoop, including installing Java, adding users, configuring SSH, installing Hadoop files and setting configuration parameters.
Hadoop is a distributed processing framework that operates on a master-slave architecture. The master node runs the NameNode, JobTracker, and SecondaryNameNode processes. The NameNode manages the file system metadata and location information for data stored across the DataNodes on slave nodes. The JobTracker manages job scheduling and coordination, assigning tasks to TaskTrackers on slaves. The SecondaryNameNode provides backup support in case the NameNode fails. Hadoop uses MapReduce as a programming model where user code is run as Maps on data subsets and the outputs are aggregated by Reduces.
Hadoop is a distributed processing framework that operates on a master-slave architecture. The master node runs the NameNode, JobTracker, and SecondaryNameNode processes. The NameNode manages the file system metadata and location information for data stored across the DataNodes on slave nodes. The JobTracker manages job scheduling and coordination, assigning tasks to TaskTrackers on slaves. The SecondaryNameNode provides backup support in case of NameNode failure.
Hadoop is a distributed processing framework that operates on a master-slave topology. The master node runs the NameNode, JobTracker, and SecondaryNameNode processes. The NameNode manages the file system metadata and location information. The JobTracker schedules and tracks jobs on the cluster. The SecondaryNameNode helps recover metadata if the NameNode fails. Slave nodes run the DataNode process to store file data blocks and the TaskTracker process to execute tasks assigned by the JobTracker. MapReduce is the programming model used in Hadoop for distributed processing of large datasets.
The document discusses the key components of Hadoop including the NameNode, DataNodes, JobTracker, TaskTracker, and SecondaryNameNode.
The NameNode stores metadata about data locations and attributes and is located on the master node. DataNodes store the actual data blocks across slave nodes. The JobTracker schedules jobs and is also located on the master node, while TaskTrackers execute tasks on slave nodes. The SecondaryNameNode assists if the NameNode fails.
Hadoop uses a master/slave architecture with the NameNode and JobTracker on the master and DataNodes and TaskTrackers on slave nodes. MapReduce is used for distributed processing where data is split into blocks and mapped and reduced across nodes.
The document discusses the key components of Hadoop including the NameNode, DataNodes, JobTracker, TaskTracker, and SecondaryNameNode.
The NameNode stores metadata for HDFS and tracks where data is stored across DataNodes on the slaves. The JobTracker schedules jobs on TaskTrackers across the slaves. The SecondaryNameNode assists if the NameNode fails. Hadoop uses a master-slave architecture with the NameNode and JobTracker on the master and DataNodes and TaskTrackers on the slaves.
The document discusses the key components of Hadoop including the NameNode, DataNodes, JobTracker, TaskTracker, and SecondaryNameNode.
The NameNode manages the file system metadata and keeps track of where data is stored across the DataNodes. The DataNodes store the actual data blocks.
The JobTracker manages jobs by determining which TaskTrackers on the slaves will execute tasks. The SecondaryNameNode helps if the NameNode fails by providing additional backup of metadata.
The document discusses the key components of Hadoop including the NameNode, DataNodes, JobTracker, TaskTracker, and SecondaryNameNode.
The NameNode manages the file system metadata and keeps track of where data is stored across the DataNodes. The DataNodes store the actual data blocks.
The JobTracker manages jobs by determining which TaskTrackers on the slaves will execute tasks. The SecondaryNameNode helps if the NameNode fails by providing additional backup of metadata.
The document discusses the key components of Hadoop including the NameNode, DataNodes, JobTracker, TaskTracker, and SecondaryNameNode.
The NameNode stores metadata about data locations and attributes and is located on the master node. DataNodes store the actual data blocks across slave nodes. The JobTracker schedules jobs and is also located on the master node, while TaskTrackers execute tasks on slave nodes. The SecondaryNameNode assists if the NameNode fails.
Hadoop uses a master/slave architecture with the NameNode and JobTracker on the master and DataNodes and TaskTrackers on slave nodes. It also utilizes MapReduce for parallel processing of large datasets split across blocks in HDFS.
The document discusses the key components of Hadoop including the NameNode, DataNodes, JobTracker, TaskTracker, and SecondaryNameNode.
The NameNode manages the file system metadata and keeps track of where data is stored across the DataNodes. The DataNodes store the actual data blocks.
The JobTracker manages jobs by determining which TaskTrackers on the slaves will execute tasks. The SecondaryNameNode helps if the NameNode fails by providing additional backup of metadata.
The document discusses the key components of Hadoop including the NameNode, DataNodes, JobTracker, and SecondaryNameNode.
The NameNode stores metadata about data locations and attributes and is located on the master node. DataNodes store the actual data blocks across slave nodes.
The JobTracker is responsible for scheduling jobs and is also located on the master node. When a job is submitted, the JobTracker determines which TaskTrackers on the slaves will execute the tasks.
The SecondaryNameNode acts as a backup to the NameNode in case of failure and is also located on the master node.
The document discusses the key components of Hadoop including the NameNode, DataNodes, JobTracker, TaskTracker, and SecondaryNameNode.
The NameNode stores metadata about data locations and attributes and is located on the master node. DataNodes store the actual data blocks across slave nodes. The JobTracker schedules jobs and is also located on the master node, while TaskTrackers execute tasks on slave nodes. The SecondaryNameNode assists if the NameNode fails.
Hadoop uses a master/slave architecture with the NameNode and JobTracker on the master and DataNodes and TaskTrackers on slave nodes. MapReduce is used for distributed processing where tasks are assigned to nodes and results are aggregated.
The document discusses the key components of Hadoop including the NameNode, DataNodes, JobTracker, TaskTracker, and SecondaryNameNode.
The NameNode stores metadata for HDFS and tracks where data is stored across DataNodes on the slaves. The JobTracker schedules jobs on TaskTrackers across the slaves. The SecondaryNameNode assists the NameNode in the event of failure. Hadoop uses a master-slave architecture with the NameNode and JobTracker on the master and DataNodes and TaskTrackers on the slaves.
The document discusses the key components of Hadoop including the NameNode, DataNodes, JobTracker, TaskTracker, and SecondaryNameNode.
The NameNode stores metadata for HDFS and tracks where data is stored across DataNodes on the slaves. The JobTracker schedules jobs on TaskTrackers across the slaves. The SecondaryNameNode assists the NameNode in the event of failures. Hadoop uses a master-slave architecture with the NameNode and JobTracker on the master and DataNodes and TaskTrackers on the slaves.
The document discusses the key components of Hadoop including the NameNode, DataNodes, JobTracker, TaskTracker, and SecondaryNameNode.
The NameNode stores metadata for HDFS and tracks where data is stored across DataNodes on the slaves. The JobTracker schedules jobs on TaskTrackers across the slaves. The SecondaryNameNode assists the NameNode in the event of failure. Hadoop uses a master-slave architecture with the NameNode and JobTracker on the master and DataNodes and TaskTrackers on the slaves.
The document discusses the key components of Hadoop including the NameNode, DataNodes, JobTracker, TaskTracker, and SecondaryNameNode.
The NameNode stores metadata about data locations and attributes and is located on the master node. DataNodes store the actual data blocks across slave nodes. The JobTracker schedules jobs and is also located on the master node, while TaskTrackers execute tasks on slave nodes. The SecondaryNameNode assists if the NameNode fails.
Hadoop uses a master/slave architecture with the NameNode and JobTracker on the master and DataNodes and TaskTrackers on slave nodes. MapReduce is used for distributed processing where data is split into blocks and mapped and reduced across nodes.
The document discusses the key components of Hadoop including the NameNode, DataNodes, JobTracker, TaskTracker, and SecondaryNameNode.
The NameNode stores metadata for HDFS and tracks where data is stored across DataNodes on the slaves. The JobTracker schedules jobs on TaskTrackers across the slaves. The SecondaryNameNode assists the NameNode in the event of failures. Hadoop uses a master-slave architecture with the NameNode and JobTracker on the master and DataNodes and TaskTrackers on the slaves.
The document discusses the key components of Hadoop including the NameNode, DataNodes, JobTracker, TaskTracker, and SecondaryNameNode.
The NameNode stores metadata about data locations and attributes and is located on the master node. DataNodes store the actual data blocks across slave nodes. The JobTracker schedules jobs and is also located on the master node, while TaskTrackers execute tasks on slave nodes. The SecondaryNameNode assists if the NameNode fails.
Hadoop uses a master/slave architecture with the NameNode and JobTracker on the master and DataNodes and TaskTrackers on slave nodes. MapReduce is used for distributed processing where data is split into blocks and mapped and reduced across nodes.
The document discusses the key components of Hadoop including the NameNode, DataNodes, JobTracker, TaskTracker, and SecondaryNameNode.
The NameNode stores metadata about data locations and attributes and is located on the master node. DataNodes store the actual data blocks across slave nodes. The JobTracker schedules jobs and is also located on the master node, while TaskTrackers execute tasks on slave nodes. The SecondaryNameNode assists if the NameNode fails.
Hadoop uses a master/slave architecture with the NameNode and JobTracker on the master and DataNodes and TaskTrackers on slave nodes. MapReduce is used for distributed processing where data is split into blocks and mapped and reduced across nodes.
The document discusses the key components of Hadoop including the NameNode, DataNodes, JobTracker, TaskTracker, and SecondaryNameNode.
The NameNode stores metadata for HDFS and tracks where data is stored across DataNodes on the slaves. The JobTracker schedules jobs on TaskTrackers across the slaves. The SecondaryNameNode assists if the NameNode fails. Hadoop uses a master-slave architecture with the NameNode and JobTracker on the master and DataNodes and TaskTrackers on the slaves.
Best 20 SEO Techniques To Improve Website Visibility In SERPPixlogix Infotech
Boost your website's visibility with proven SEO techniques! Our latest blog dives into essential strategies to enhance your online presence, increase traffic, and rank higher on search engines. From keyword optimization to quality content creation, learn how to make your site stand out in the crowded digital landscape. Discover actionable tips and expert insights to elevate your SEO game.
[OReilly Superstream] Occupy the Space: A grassroots guide to engineering (an...Jason Yip
The typical problem in product engineering is not bad strategy, so much as “no strategy”. This leads to confusion, lack of motivation, and incoherent action. The next time you look for a strategy and find an empty space, instead of waiting for it to be filled, I will show you how to fill it in yourself. If you’re wrong, it forces a correction. If you’re right, it helps create focus. I’ll share how I’ve approached this in the past, both what works and lessons for what didn’t work so well.
Dandelion Hashtable: beyond billion requests per second on a commodity serverAntonios Katsarakis
This slide deck presents DLHT, a concurrent in-memory hashtable. Despite efforts to optimize hashtables, that go as far as sacrificing core functionality, state-of-the-art designs still incur multiple memory accesses per request and block request processing in three cases. First, most hashtables block while waiting for data to be retrieved from memory. Second, open-addressing designs, which represent the current state-of-the-art, either cannot free index slots on deletes or must block all requests to do so. Third, index resizes block every request until all objects are copied to the new index. Defying folklore wisdom, DLHT forgoes open-addressing and adopts a fully-featured and memory-aware closed-addressing design based on bounded cache-line-chaining. This design offers lock-free index operations and deletes that free slots instantly, (2) completes most requests with a single memory access, (3) utilizes software prefetching to hide memory latencies, and (4) employs a novel non-blocking and parallel resizing. In a commodity server and a memory-resident workload, DLHT surpasses 1.6B requests per second and provides 3.5x (12x) the throughput of the state-of-the-art closed-addressing (open-addressing) resizable hashtable on Gets (Deletes).
What is an RPA CoE? Session 1 – CoE VisionDianaGray10
In the first session, we will review the organization's vision and how this has an impact on the COE Structure.
Topics covered:
• The role of a steering committee
• How do the organization’s priorities determine CoE Structure?
Speaker:
Chris Bolin, Senior Intelligent Automation Architect Anika Systems
Freshworks Rethinks NoSQL for Rapid Scaling & Cost-EfficiencyScyllaDB
Freshworks creates AI-boosted business software that helps employees work more efficiently and effectively. Managing data across multiple RDBMS and NoSQL databases was already a challenge at their current scale. To prepare for 10X growth, they knew it was time to rethink their database strategy. Learn how they architected a solution that would simplify scaling while keeping costs under control.
FREE A4 Cyber Security Awareness Posters-Social Engineering part 3Data Hops
Free A4 downloadable and printable Cyber Security, Social Engineering Safety and security Training Posters . Promote security awareness in the home or workplace. Lock them Out From training providers datahops.com
Ivanti’s Patch Tuesday breakdown goes beyond patching your applications and brings you the intelligence and guidance needed to prioritize where to focus your attention first. Catch early analysis on our Ivanti blog, then join industry expert Chris Goettl for the Patch Tuesday Webinar Event. There we’ll do a deep dive into each of the bulletins and give guidance on the risks associated with the newly-identified vulnerabilities.
Fueling AI with Great Data with Airbyte WebinarZilliz
This talk will focus on how to collect data from a variety of sources, leveraging this data for RAG and other GenAI use cases, and finally charting your course to productionalization.
HCL Notes and Domino License Cost Reduction in the World of DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-and-domino-license-cost-reduction-in-the-world-of-dlau/
The introduction of DLAU and the CCB & CCX licensing model caused quite a stir in the HCL community. As a Notes and Domino customer, you may have faced challenges with unexpected user counts and license costs. You probably have questions on how this new licensing approach works and how to benefit from it. Most importantly, you likely have budget constraints and want to save money where possible. Don’t worry, we can help with all of this!
We’ll show you how to fix common misconfigurations that cause higher-than-expected user counts, and how to identify accounts which you can deactivate to save money. There are also frequent patterns that can cause unnecessary cost, like using a person document instead of a mail-in for shared mailboxes. We’ll provide examples and solutions for those as well. And naturally we’ll explain the new licensing model.
Join HCL Ambassador Marc Thomas in this webinar with a special guest appearance from Franz Walder. It will give you the tools and know-how to stay on top of what is going on with Domino licensing. You will be able lower your cost through an optimized configuration and keep it low going forward.
These topics will be covered
- Reducing license cost by finding and fixing misconfigurations and superfluous accounts
- How do CCB and CCX licenses really work?
- Understanding the DLAU tool and how to best utilize it
- Tips for common problem areas, like team mailboxes, functional/test users, etc
- Practical examples and best practices to implement right away
Programming Foundation Models with DSPy - Meetup SlidesZilliz
Prompting language models is hard, while programming language models is easy. In this talk, I will discuss the state-of-the-art framework DSPy for programming foundation models with its powerful optimizers and runtime constraint system.
GraphRAG for Life Science to increase LLM accuracyTomaz Bratanic
GraphRAG for life science domain, where you retriever information from biomedical knowledge graphs using LLMs to increase the accuracy and performance of generated answers
Driving Business Innovation: Latest Generative AI Advancements & Success StorySafe Software
Are you ready to revolutionize how you handle data? Join us for a webinar where we’ll bring you up to speed with the latest advancements in Generative AI technology and discover how leveraging FME with tools from giants like Google Gemini, Amazon, and Microsoft OpenAI can supercharge your workflow efficiency.
During the hour, we’ll take you through:
Guest Speaker Segment with Hannah Barrington: Dive into the world of dynamic real estate marketing with Hannah, the Marketing Manager at Workspace Group. Hear firsthand how their team generates engaging descriptions for thousands of office units by integrating diverse data sources—from PDF floorplans to web pages—using FME transformers, like OpenAIVisionConnector and AnthropicVisionConnector. This use case will show you how GenAI can streamline content creation for marketing across the board.
Ollama Use Case: Learn how Scenario Specialist Dmitri Bagh has utilized Ollama within FME to input data, create custom models, and enhance security protocols. This segment will include demos to illustrate the full capabilities of FME in AI-driven processes.
Custom AI Models: Discover how to leverage FME to build personalized AI models using your data. Whether it’s populating a model with local data for added security or integrating public AI tools, find out how FME facilitates a versatile and secure approach to AI.
We’ll wrap up with a live Q&A session where you can engage with our experts on your specific use cases, and learn more about optimizing your data workflows with AI.
This webinar is ideal for professionals seeking to harness the power of AI within their data management systems while ensuring high levels of customization and security. Whether you're a novice or an expert, gain actionable insights and strategies to elevate your data processes. Join us to see how FME and AI can revolutionize how you work with data!
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-und-domino-lizenzkostenreduzierung-in-der-welt-von-dlau/
DLAU und die Lizenzen nach dem CCB- und CCX-Modell sind für viele in der HCL-Community seit letztem Jahr ein heißes Thema. Als Notes- oder Domino-Kunde haben Sie vielleicht mit unerwartet hohen Benutzerzahlen und Lizenzgebühren zu kämpfen. Sie fragen sich vielleicht, wie diese neue Art der Lizenzierung funktioniert und welchen Nutzen sie Ihnen bringt. Vor allem wollen Sie sicherlich Ihr Budget einhalten und Kosten sparen, wo immer möglich. Das verstehen wir und wir möchten Ihnen dabei helfen!
Wir erklären Ihnen, wie Sie häufige Konfigurationsprobleme lösen können, die dazu führen können, dass mehr Benutzer gezählt werden als nötig, und wie Sie überflüssige oder ungenutzte Konten identifizieren und entfernen können, um Geld zu sparen. Es gibt auch einige Ansätze, die zu unnötigen Ausgaben führen können, z. B. wenn ein Personendokument anstelle eines Mail-Ins für geteilte Mailboxen verwendet wird. Wir zeigen Ihnen solche Fälle und deren Lösungen. Und natürlich erklären wir Ihnen das neue Lizenzmodell.
Nehmen Sie an diesem Webinar teil, bei dem HCL-Ambassador Marc Thomas und Gastredner Franz Walder Ihnen diese neue Welt näherbringen. Es vermittelt Ihnen die Tools und das Know-how, um den Überblick zu bewahren. Sie werden in der Lage sein, Ihre Kosten durch eine optimierte Domino-Konfiguration zu reduzieren und auch in Zukunft gering zu halten.
Diese Themen werden behandelt
- Reduzierung der Lizenzkosten durch Auffinden und Beheben von Fehlkonfigurationen und überflüssigen Konten
- Wie funktionieren CCB- und CCX-Lizenzen wirklich?
- Verstehen des DLAU-Tools und wie man es am besten nutzt
- Tipps für häufige Problembereiche, wie z. B. Team-Postfächer, Funktions-/Testbenutzer usw.
- Praxisbeispiele und Best Practices zum sofortigen Umsetzen
Introduction of Cybersecurity with OSS at Code Europe 2024Hiroshi SHIBATA
I develop the Ruby programming language, RubyGems, and Bundler, which are package managers for Ruby. Today, I will introduce how to enhance the security of your application using open-source software (OSS) examples from Ruby and RubyGems.
The first topic is CVE (Common Vulnerabilities and Exposures). I have published CVEs many times. But what exactly is a CVE? I'll provide a basic understanding of CVEs and explain how to detect and handle vulnerabilities in OSS.
Next, let's discuss package managers. Package managers play a critical role in the OSS ecosystem. I'll explain how to manage library dependencies in your application.
I'll share insights into how the Ruby and RubyGems core team works to keep our ecosystem safe. By the end of this talk, you'll have a better understanding of how to safeguard your code.
Monitoring and Managing Anomaly Detection on OpenShift.pdfTosin Akinosho
Monitoring and Managing Anomaly Detection on OpenShift
Overview
Dive into the world of anomaly detection on edge devices with our comprehensive hands-on tutorial. This SlideShare presentation will guide you through the entire process, from data collection and model training to edge deployment and real-time monitoring. Perfect for those looking to implement robust anomaly detection systems on resource-constrained IoT/edge devices.
Key Topics Covered
1. Introduction to Anomaly Detection
- Understand the fundamentals of anomaly detection and its importance in identifying unusual behavior or failures in systems.
2. Understanding Edge (IoT)
- Learn about edge computing and IoT, and how they enable real-time data processing and decision-making at the source.
3. What is ArgoCD?
- Discover ArgoCD, a declarative, GitOps continuous delivery tool for Kubernetes, and its role in deploying applications on edge devices.
4. Deployment Using ArgoCD for Edge Devices
- Step-by-step guide on deploying anomaly detection models on edge devices using ArgoCD.
5. Introduction to Apache Kafka and S3
- Explore Apache Kafka for real-time data streaming and Amazon S3 for scalable storage solutions.
6. Viewing Kafka Messages in the Data Lake
- Learn how to view and analyze Kafka messages stored in a data lake for better insights.
7. What is Prometheus?
- Get to know Prometheus, an open-source monitoring and alerting toolkit, and its application in monitoring edge devices.
8. Monitoring Application Metrics with Prometheus
- Detailed instructions on setting up Prometheus to monitor the performance and health of your anomaly detection system.
9. What is Camel K?
- Introduction to Camel K, a lightweight integration framework built on Apache Camel, designed for Kubernetes.
10. Configuring Camel K Integrations for Data Pipelines
- Learn how to configure Camel K for seamless data pipeline integrations in your anomaly detection workflow.
11. What is a Jupyter Notebook?
- Overview of Jupyter Notebooks, an open-source web application for creating and sharing documents with live code, equations, visualizations, and narrative text.
12. Jupyter Notebooks with Code Examples
- Hands-on examples and code snippets in Jupyter Notebooks to help you implement and test anomaly detection models.
Main news related to the CCS TSI 2023 (2023/1695)Jakub Marek
An English 🇬🇧 translation of a presentation to the speech I gave about the main changes brought by CCS TSI 2023 at the biggest Czech conference on Communications and signalling systems on Railways, which was held in Clarion Hotel Olomouc from 7th to 9th November 2023 (konferenceszt.cz). Attended by around 500 participants and 200 on-line followers.
The original Czech 🇨🇿 version of the presentation can be found here: https://www.slideshare.net/slideshow/hlavni-novinky-souvisejici-s-ccs-tsi-2023-2023-1695/269688092 .
The videorecording (in Czech) from the presentation is available here: https://youtu.be/WzjJWm4IyPk?si=SImb06tuXGb30BEH .
Connector Corner: Seamlessly power UiPath Apps, GenAI with prebuilt connectorsDianaGray10
Join us to learn how UiPath Apps can directly and easily interact with prebuilt connectors via Integration Service--including Salesforce, ServiceNow, Open GenAI, and more.
The best part is you can achieve this without building a custom workflow! Say goodbye to the hassle of using separate automations to call APIs. By seamlessly integrating within App Studio, you can now easily streamline your workflow, while gaining direct access to our Connector Catalog of popular applications.
We’ll discuss and demo the benefits of UiPath Apps and connectors including:
Creating a compelling user experience for any software, without the limitations of APIs.
Accelerating the app creation process, saving time and effort
Enjoying high-performance CRUD (create, read, update, delete) operations, for
seamless data management.
Speakers:
Russell Alfeche, Technology Leader, RPA at qBotic and UiPath MVP
Charlie Greenberg, host
4. History of MapReduce
• First implemented
by Google
• Used in CouchDB,
Hadoop, etc.
• Helps to “distill” data into
a concentrated result set
Tuesday, January 8, 13
6. What is MapReduce?
sum = 0
input = ["deer", "bear",
input.each do |x|
"river", "car", "car", "river", input.map! { |x| [x, 1] }
sum += x[1]
"deer", "car", "bear"]
end
Tuesday, January 8, 13
8. History of Hadoop
•Doug Cutting @ Yahoo!
•It is a Toy Elephant
•It is also a framework for
distributed computing
•It is a distributed filesystem
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11. Hadoop Cluster
NameNode
•Keeps track of the DataNodes
•Uses “heartbeat” to determine a node’s health
•The most resources should be spent here
555.555.1.* 555.555.2.* 444.444.1.*
JobTracker NameNode TaskTracker/DataNode
TaskTracker/DataNode TaskTracker/DataNode
♥ TaskTracker/DataNode
TaskTracker/DataNode TaskTracker/DataNode TaskTracker/DataNode
TaskTracker/DataNode TaskTracker/DataNode TaskTracker/DataNode
TaskTracker/DataNode TaskTracker/DataNode TaskTracker/DataNode
TaskTracker/DataNode TaskTracker/DataNode TaskTracker/DataNode
Tuesday, January 8, 13
12. Hadoop Cluster
DataNode
•Stores filesystem blocks
•Can be scaled. Spun up/down.
•Replicate based on a set replication factor
555.555.1.* 555.555.2.* 444.444.1.*
JobTracker NameNode TaskTracker/DataNode
TaskTracker/DataNode TaskTracker/DataNode TaskTracker/DataNode
TaskTracker/DataNode TaskTracker/DataNode TaskTracker/DataNode
TaskTracker/DataNode TaskTracker/DataNode TaskTracker/DataNode
TaskTracker/DataNode TaskTracker/DataNode TaskTracker/DataNode
TaskTracker/DataNode TaskTracker/DataNode TaskTracker/DataNode
Tuesday, January 8, 13
13. Hadoop Cluster
JobTracker
•Delegates which TaskTrackers should handle a
MapReduce job
•Communicates with the NameNode to assign a TaskTracker
close to the DataNode where the source exists
555.555.1.* 555.555.2.* 444.444.1.*
JobTracker NameNode TaskTracker/DataNode
TaskTracker/DataNode
♥ TaskTracker/DataNode TaskTracker/DataNode
TaskTracker/DataNode TaskTracker/DataNode TaskTracker/DataNode
TaskTracker/DataNode TaskTracker/DataNode TaskTracker/DataNode
TaskTracker/DataNode TaskTracker/DataNode TaskTracker/DataNode
TaskTracker/DataNode TaskTracker/DataNode TaskTracker/DataNode
Tuesday, January 8, 13
14. Hadoop Cluster
TaskTracker
•Worker for MapReduce jobs
•The closer to the DataNode with the data, the better
555.555.1.* 555.555.2.* 444.444.1.*
JobTracker NameNode TaskTracker/DataNode
TaskTracker/DataNode TaskTracker/DataNode TaskTracker/DataNode
TaskTracker/DataNode TaskTracker/DataNode TaskTracker/DataNode
TaskTracker/DataNode TaskTracker/DataNode TaskTracker/DataNode
TaskTracker/DataNode TaskTracker/DataNode TaskTracker/DataNode
TaskTracker/DataNode TaskTracker/DataNode TaskTracker/DataNode
Tuesday, January 8, 13
19. Hadoop Streaming
Pig Hive Wukong
Pig Latin SQL-ish Ruby!
Hadoop Ecosystem
Tuesday, January 8, 13
20. Wukong
•Infochimps
•Currently going through
heavy development
•Use the 3.0.0.pre3 gem
https://github.com/infochimps-labs/wukong/tree/3.0.0
•Model your jobs with
wukong-hadoop
https://github.com/infochimps-labs/wukong-hadoop
Tuesday, January 8, 13
21. Wukong
Wukong wukong-hadoop
•Write mappers and reducers •A CLI to use with Hadoop
using Ruby •Created around building tasks
•As of 3.0.0, Wukong uses with Wukong
“Processors”, which are Ruby •Better than piping in the shell
classes that define map, reduce,
(you can see this with --dry_run)
and other tasks
Tuesday, January 8, 13
22. Wukong Processors
Wukong.processor(:mapper) do
field :min_length, Integer, :default => 1
field :max_length, Integer, :default => 256
field :split_on, Regexp, :default => /s+/
field :remove, Regexp, :default => /[^a-zA-Z0-9']+/
field :fold_case, :boolean, :default => false
def process string
•Fields are accessible tokenize(string).each do |token|
yield token if acceptable?(token)
end
through switches in shell end
private
•Local hand-off is made at def tokenize string
string.split(split_on).map do |token|
STDOUT to STDIN stripped = token.gsub(remove, '')
fold_case ? stripped.downcase : stripped
end
end
def acceptable? token
(min_length..max_length).include?(token.length)
end
end
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23. Wukong Processors
Wukong.processor(:reducer, Wukong::Processor::Accumulator) do
attr_accessor :count
def start record
self.count = 0
end
def accumulate record
self.count += 1
end
def finalize
yield [key, count].join("t")
end
end
Tuesday, January 8, 13
24. Wukong Processors
wu-hadoop /home/hduser/wukong-hadoop/examples/word_count.rb
--mode=local
--input=/home/hduser/simpsons/simpsonssubs/Simpsons [1.08].sub
Simpsons - Ep 8
do 7
Doctor 1
Does 2
doesn't 1
dog 2
D'oh 1
doif 1
doing 2
done 1
doneYou 1
don't 10
Don't 1
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25. The End
Thank you!
@tomeara
ted@tedomeara.com
Tuesday, January 8, 13