Oozie Hug May 2011
1 like758 views
The document discusses Oozie 3.0 features including the introduction of bundles to define and execute a set of related coordinators together. It describes how bundles improve scheduling and control of workflows. It also covers enhancements to scalability such as throttling active jobs and improved usability like redesigning coordinator job statuses. Future plans include increasing scalability through push-based data checks and improving adaptability, monitoring, load balancing, and usability.
1 of 14
Downloaded 20 times
Ad
Recommended
Nov 2011 HUG: Oozie
Nov 2011 HUG: Oozie Yahoo Developer Network
油
This document discusses Oozie, a workflow scheduler system for Hadoop. It presents on what Oozie is, how it fits into the Hadoop ecosystem, and some of its key features. The next release of Oozie will integrate with more recent versions of Hadoop and improve usability, reliability, and manageability. One challenge discussed is queue starvation, where high priority tasks can be delayed. This is addressed by checking for and prioritizing high priority tasks before normal queue processing. Future work may focus on easier adoption and prioritization of jobs and workflows.Gearman
GearmanBrian Moon
油
Gearman is a software framework that allows for the distribution of work across multiple machines. It consists of clients that submit jobs, workers that process jobs, and a central Gearmand daemon that coordinates jobs and workers. Some key features include asynchronous job handling, prioritization of jobs, and de-duplication of identical jobs. Gearman provides a way to distribute tasks like processing user requests, sending email, or logging data across a cluster.When Two Worlds Collide: Java and Ruby in the Enterprise
When Two Worlds Collide: Java and Ruby in the Enterprisebenbrowning
油
When Two Worlds Collide: Java and Ruby in the Enterprise is a presentation about using Ruby and Java together in an enterprise environment. It introduces JRuby, which allows Ruby code to run on the Java Virtual Machine and integrate with Java. It then discusses TorqueBox, which allows Ruby applications to run on the JBoss Application Server alongside Java applications. TorqueBox provides Ruby interfaces to enterprise services like messaging, background jobs, caching, and more commonly found in Java application servers. The presentation argues that TorqueBox allows Ruby to be a first-class citizen alongside Java in the enterprise.Armada - the way to ship microservices
Armada - the way to ship microservicesGameDesire Company
油
Armada is a comprehensive platform for managing microservices, enhancing Docker capabilities with features for development, deployment, and configuration management. Key functionalities include service discovery, load balancing, health checks, and disaster recovery, while also addressing issues like environmental configuration and service dependencies. The platform utilizes technologies such as Consul for service cataloging and HAProxy for load balancing, facilitating a streamlined development process with automated setup and multiple environment configurations.Oozie hugnov11
Oozie hugnov11mislam77
油
This document discusses Oozie, a workflow scheduler system for Hadoop. It describes Oozie's role in coordinating and scheduling Hadoop jobs such as Pig, Hive, and MapReduce. The next release of Oozie will integrate better with Hadoop 0.23 and HCatalog, and add new features like script and Distcp actions. Challenges include addressing queue starvation when suspending or killing jobs. The future of Oozie includes easier adoption, job notifications through JMS, and event-based data processing.Yarn at LinkedIn
Yarn at LinkedIn mislam77
油
The document discusses the Yarn Meetup from September 2013, highlighting LinkedIn's early adoption of Yarn for various internal projects, including Samza and Helix, which incorporate real-time processing capabilities. It details improvements in Giraph's architecture and outlines challenges with Yarn's deployment, focusing on memory management and system performance. Overall, it positions Yarn as a key technology for enhancing operational efficiency and supporting diverse execution platforms.Hive at LinkedIn
Hive at LinkedIn mislam77
油
The document outlines LinkedIn's data ecosystem, primarily focusing on performance improvements for Hive, a data query tool used by various teams at LinkedIn. It details the challenges faced with reading Avro records and presents improvements that reduced read overhead significantly. Additionally, it discusses Hive's user adoption among data scientists and engineers, highlighting concerns related to workflow complexity and performance.Oozie HUG May12
Oozie HUG May12mislam77
油
The document discusses Oozie, an open source workflow scheduling system for Hadoop, describing how it provides a scalable way to manage Hadoop jobs through a workflow definition, installation instructions, running examples, key features like security and scalability, and how it submits jobs to Hadoop clusters while maintaining isolation and horizontal scalability.Oozie Summit 2011
Oozie Summit 2011mislam77
油
The document provides an overview of Oozie, an open source workflow scheduler system for Hadoop. It discusses Oozie's features like workflows, coordinators, and bundles. It outlines enhancements in Oozie 3.x around bundle functionality, scalability, and usability. Challenges related to data availability checks, adaptability to Hadoop, and horizontal scalability are presented. Future plans for Oozie including automatic failover and improved monitoring and usability are briefly mentioned.October 2013 HUG: Oozie 4.x
October 2013 HUG: Oozie 4.xYahoo Developer Network
油
The document provides an overview of Oozie, a workflow scheduler for Hadoop that facilitates complex job management by defining workflows as directed acyclic graphs (DAGs) of jobs, including MapReduce, Pig, and Hive. It details Oozie's features such as job scheduling based on time and data dependencies, coordination of multiple workflows, and enhancements in version 4.0 like HCatalog integration, job notifications, and SLA monitoring. Additionally, it highlights the operational capabilities, metrics, and use cases for Oozie's application in managing large-scale data processing efficiently.Apache Oozie
Apache OozieShalish VJ
油
Apache Oozie is a system for running workflows of dependent Hadoop jobs. It has two main parts: a workflow engine that stores and runs workflows composed of different types of Hadoop jobs, and a coordinator engine that runs workflow jobs based on predefined schedules and data availability. Oozie workflows are composed of action nodes that perform tasks and control flow nodes that govern workflow execution. Workflows are defined using XML, packaged and deployed, then run on a schedule defined in the coordinator engine or based on data triggers.Learn Hadoop Administration
Learn Hadoop AdministrationEdureka!
油
The document outlines an educational course on Hadoop administration, detailing the weekly topics covered, including big data understanding, Hadoop versions, cluster configurations, job scheduling, and securing the cluster. It discusses Hadoop 2.0 features, configuration files, and various popular distributions like Cloudera, Hortonworks, and Amazon EMR. The document also emphasizes the importance of planning hardware and software requirements for Hadoop clusters.Oracle migrations and upgrades
Oracle migrations and upgradesDurga Gadiraju
油
The document discusses approaches, challenges, and solutions for Oracle database migrations and upgrades, emphasizing the need for zero or minimal downtime during these processes. It outlines various upgrade types, including in-place and out-of-place upgrades, and details the tools and techniques utilized, such as Goldengate and custom-built solutions like DYPO for efficient data migration. Key challenges include managing volume and availability, ensuring data integrity, and developing effective fallback plans, particularly when dealing with large databases.Big Data Introduction
Big Data IntroductionDurga Gadiraju
油
The document discusses the evolution and current state of big data ecosystems, highlighting architectural trends, challenges, and use cases involving batch and operational systems. It elaborates on various technologies like Hadoop and NoSQL, explaining their characteristics, advantages, and specific applications in areas such as customer analytics and enterprise data warehousing. Additionally, it touches on the job roles within the big data domain and industry reactions to the technology's advancement.Introduction to Cloudera's Administrator Training for Apache Hadoop
Introduction to Cloudera's Administrator Training for Apache HadoopCloudera, Inc.
油
The document provides an overview of Cloudera's Administrator Training course for Apache Hadoop. The training covers topics such as planning and deploying Hadoop clusters, installing and configuring Hadoop components like HDFS, Hive and Impala, using Cloudera Manager for administration, configuring advanced cluster options and HDFS high availability, and Hadoop security. The hands-on course includes exercises for deploying Hadoop clusters, importing data, and troubleshooting issues.Hadoop Ecosystem | Big Data Analytics Tools | Hadoop Tutorial | Edureka
Hadoop Ecosystem | Big Data Analytics Tools | Hadoop Tutorial | Edureka Edureka!
油
The document provides an overview of the Hadoop ecosystem and its various components such as HDFS, YARN, MapReduce, Spark, Pig, Hive, and more, which are essential for storing, processing, and analyzing big data. Each component is described with its functionality, including data ingestion by Flume and Sqoop, machine learning capabilities through Mahout, and job scheduling with Oozie. Additional details on related tools like Zookeeper and Ambari for cluster management are also included.Oozie sweet
Oozie sweetmislam77
油
Oozie is a workflow scheduling system for Hadoop that allows users to manage workflows as directed acyclic graphs (DAGs) of Hadoop jobs such as MapReduce, Pig, Hive, and Sqoop. It executes workflows based on time and data dependencies and provides a web interface for monitoring jobs. Oozie was designed specifically for Hadoop to take advantage of its features while addressing its shortcomings for workflow management.Workflow on Hadoop Using Oozie__HadoopSummit2010
Workflow on Hadoop Using Oozie__HadoopSummit2010Yahoo Developer Network
油
Oozie is a workflow scheduler system for managing Hadoop jobs. It allows users to create workflows as directed acyclic graphs (DAGs) of Hadoop jobs such as MapReduce, Pig, and Hive. Oozie 1 focused on workflow scheduling, while Oozie 2 added coordinator capabilities to schedule workflows based on time and data availability. In its first year, Oozie saw significant usage at Yahoo and helped automate many complex workflows and data pipelines.Everything you wanted to know, but were afraid to ask about Oozie
Everything you wanted to know, but were afraid to ask about OozieChicago Hadoop Users Group
油
Oozie is a workflow and coordination system specifically designed to manage Apache Hadoop jobs, featuring components such as workflows, coordinators, and bundles. It allows for extensibility with custom actions and provides APIs for integration with enterprise applications. While it offers good integration and a simple UI, it lacks visual support and has limitations on loop constructs and dynamic forking.Oozie @ Riot Games
Oozie @ Riot GamesMatt Goeke
油
The document presents an overview of workflow management at Riot Games, focusing on the implementation of Oozie to improve data processing efficiency for League of Legends. It discusses the previous architecture's limitations and outlines the advantages of using Oozie, such as job scheduling, scalability, and integration with Apache Hadoop. Additionally, the document details the structure of Oozie workflows, coordinators, and bundles, including examples and best practices for deployment.Apache Oozie.pptx
Apache Oozie.pptxV.V.Vanniaperumal College for Women
油
Apache Oozie is a Java web application designed to schedule and manage Hadoop jobs, combining various job types into a cohesive workflow. It features a workflow engine for execution of Hadoop jobs and a coordinator engine for timing based job triggers, supporting both parallel and sequential execution. Oozie is flexible, scalable, and allows for job management through web service APIs, facilitating error handling and notification processes.Apache Oozie
Apache OozieNagajothiN1
油
Apache Oozie is a workflow scheduling system for Hadoop jobs. It allows users to string together Hadoop jobs into logical workflows with control nodes. Oozie workflows consist of action nodes representing jobs and control nodes that dictate workflow execution. Oozie supports different types of workflows including simple workflows, bundles of multiple workflows, and coordinator jobs that are triggered by schedules or data availability. It is integrated with Hadoop and scales to manage thousands of workflows.July 2012 HUG: Overview of Oozie Qualification Process
July 2012 HUG: Overview of Oozie Qualification ProcessYahoo Developer Network
油
The document outlines the Oozie QE qualification process, detailing its purpose as a scalable and secure workflow scheduling system for Hadoop. It describes qualification stages, including developing test plans, executing tests, and supporting production deployments, while also highlighting challenges and future tasks. Key aspects include various types of testing, such as functional, stress, and integration tests, along with lessons learned and ongoing work to improve test coverage and system compatibility.Building and managing complex dependencies pipeline using Apache Oozie
Building and managing complex dependencies pipeline using Apache OozieDataWorks Summit/Hadoop Summit
油
This document discusses Apache Oozie usage at Yahoo for managing complex data pipelines. It describes how Oozie is deployed at a large scale with high availability. It outlines the types of data pipelines used for tasks like ad targeting and content management. Challenges for large pipelines like dependency management, SLA monitoring, and reprocessing are discussed. User-built monitoring systems are described that integrate with Oozie for tasks like alerting and long job detection. Future work areas like improved testing and coordination are proposed.May 2012 HUG: Oozie: Towards a scalable Workflow Management System for Hadoop
May 2012 HUG: Oozie: Towards a scalable Workflow Management System for HadoopYahoo Developer Network
油
Oozie is a scalable workflow management system for Hadoop designed for multi-tenancy, security, and efficient job scheduling. It allows users to submit and manage jobs while ensuring safety through built-in authentication, and can handle thousands of jobs simultaneously by launching jobs in isolation from the server. The paper details installation steps, key design decisions, and performance metrics derived from production use at Yahoo.August 2016 HUG: Recent development in Apache Oozie
August 2016 HUG: Recent development in Apache OozieYahoo Developer Network
油
The document discusses the implementation and usage of Oozie at Yahoo, focusing on its capabilities for managing complex data pipelines and job dependencies across various platforms like Spark and Hive. It outlines the efficient handling of workflow jobs and coordinator jobs with insights on current limitations, enhancements, and future work in unit testing and dependency management. Additionally, it highlights Oozie's native support for Spark jobs and recent improvements in managing Spark actions within Oozie workflows.Hadoop Oozie
Hadoop OozieMadhur Nawandar
油
Oozie is a workflow scheduler system for Apache Hadoop that allows scheduling Hadoop jobs such as MapReduce, Pig, Hive, Sqoop, and DistCp jobs. It consists of a workflow engine that defines and runs workflows as directed acyclic graphs (DAGs) of Hadoop jobs, and a coordinator engine that runs workflows based on schedules and data availability. Oozie features include scaling to manage thousands of workflows, rerunning failed workflows, and submitting workflow definitions for immediate or later execution from clients to the Oozie server.oozieee.pdf
oozieee.pdfwwww63
油
Oozie is a workflow scheduler system for Apache Hadoop that manages dependent jobs as directed acyclic graphs (DAGs). It is integrated with Hadoop and supports jobs like MapReduce, Pig, Hive, Sqoop, and DistCp. Oozie consists of a workflow engine that stores and runs workflows of Hadoop jobs and a coordinator engine that runs workflows based on schedules and data availability. It is designed to scale to thousands of workflows and makes rerunning failed workflows more manageable.Introduction to Oozie | Big Data Hadoop Spark Tutorial | CloudxLab
Introduction to Oozie | Big Data Hadoop Spark Tutorial | CloudxLabCloudxLab
油
Oozie is a Java web application used to schedule Apache Hadoop jobs, seamlessly integrated with the Hadoop ecosystem, supporting various job types like MapReduce, Pig, and Hive. It features workflow and coordinator jobs that allow the execution of directed acyclic graphs and recurring jobs based on time and data availability. The document also provides step-by-step examples for running jobs, including MapReduce, Sqoop, and Hive processes.More Related Content
Viewers also liked (8)
Oozie Summit 2011
Oozie Summit 2011mislam77
油
The document provides an overview of Oozie, an open source workflow scheduler system for Hadoop. It discusses Oozie's features like workflows, coordinators, and bundles. It outlines enhancements in Oozie 3.x around bundle functionality, scalability, and usability. Challenges related to data availability checks, adaptability to Hadoop, and horizontal scalability are presented. Future plans for Oozie including automatic failover and improved monitoring and usability are briefly mentioned.October 2013 HUG: Oozie 4.x
October 2013 HUG: Oozie 4.xYahoo Developer Network
油
The document provides an overview of Oozie, a workflow scheduler for Hadoop that facilitates complex job management by defining workflows as directed acyclic graphs (DAGs) of jobs, including MapReduce, Pig, and Hive. It details Oozie's features such as job scheduling based on time and data dependencies, coordination of multiple workflows, and enhancements in version 4.0 like HCatalog integration, job notifications, and SLA monitoring. Additionally, it highlights the operational capabilities, metrics, and use cases for Oozie's application in managing large-scale data processing efficiently.Apache Oozie
Apache OozieShalish VJ
油
Apache Oozie is a system for running workflows of dependent Hadoop jobs. It has two main parts: a workflow engine that stores and runs workflows composed of different types of Hadoop jobs, and a coordinator engine that runs workflow jobs based on predefined schedules and data availability. Oozie workflows are composed of action nodes that perform tasks and control flow nodes that govern workflow execution. Workflows are defined using XML, packaged and deployed, then run on a schedule defined in the coordinator engine or based on data triggers.Learn Hadoop Administration
Learn Hadoop AdministrationEdureka!
油
The document outlines an educational course on Hadoop administration, detailing the weekly topics covered, including big data understanding, Hadoop versions, cluster configurations, job scheduling, and securing the cluster. It discusses Hadoop 2.0 features, configuration files, and various popular distributions like Cloudera, Hortonworks, and Amazon EMR. The document also emphasizes the importance of planning hardware and software requirements for Hadoop clusters.Oracle migrations and upgrades
Oracle migrations and upgradesDurga Gadiraju
油
The document discusses approaches, challenges, and solutions for Oracle database migrations and upgrades, emphasizing the need for zero or minimal downtime during these processes. It outlines various upgrade types, including in-place and out-of-place upgrades, and details the tools and techniques utilized, such as Goldengate and custom-built solutions like DYPO for efficient data migration. Key challenges include managing volume and availability, ensuring data integrity, and developing effective fallback plans, particularly when dealing with large databases.Big Data Introduction
Big Data IntroductionDurga Gadiraju
油
The document discusses the evolution and current state of big data ecosystems, highlighting architectural trends, challenges, and use cases involving batch and operational systems. It elaborates on various technologies like Hadoop and NoSQL, explaining their characteristics, advantages, and specific applications in areas such as customer analytics and enterprise data warehousing. Additionally, it touches on the job roles within the big data domain and industry reactions to the technology's advancement.Introduction to Cloudera's Administrator Training for Apache Hadoop
Introduction to Cloudera's Administrator Training for Apache HadoopCloudera, Inc.
油
The document provides an overview of Cloudera's Administrator Training course for Apache Hadoop. The training covers topics such as planning and deploying Hadoop clusters, installing and configuring Hadoop components like HDFS, Hive and Impala, using Cloudera Manager for administration, configuring advanced cluster options and HDFS high availability, and Hadoop security. The hands-on course includes exercises for deploying Hadoop clusters, importing data, and troubleshooting issues.Hadoop Ecosystem | Big Data Analytics Tools | Hadoop Tutorial | Edureka
Hadoop Ecosystem | Big Data Analytics Tools | Hadoop Tutorial | Edureka Edureka!
油
The document provides an overview of the Hadoop ecosystem and its various components such as HDFS, YARN, MapReduce, Spark, Pig, Hive, and more, which are essential for storing, processing, and analyzing big data. Each component is described with its functionality, including data ingestion by Flume and Sqoop, machine learning capabilities through Mahout, and job scheduling with Oozie. Additional details on related tools like Zookeeper and Ambari for cluster management are also included.Similar to Oozie Hug May 2011 (20)
Oozie sweet
Oozie sweetmislam77
油
Oozie is a workflow scheduling system for Hadoop that allows users to manage workflows as directed acyclic graphs (DAGs) of Hadoop jobs such as MapReduce, Pig, Hive, and Sqoop. It executes workflows based on time and data dependencies and provides a web interface for monitoring jobs. Oozie was designed specifically for Hadoop to take advantage of its features while addressing its shortcomings for workflow management.Workflow on Hadoop Using Oozie__HadoopSummit2010
Workflow on Hadoop Using Oozie__HadoopSummit2010Yahoo Developer Network
油
Oozie is a workflow scheduler system for managing Hadoop jobs. It allows users to create workflows as directed acyclic graphs (DAGs) of Hadoop jobs such as MapReduce, Pig, and Hive. Oozie 1 focused on workflow scheduling, while Oozie 2 added coordinator capabilities to schedule workflows based on time and data availability. In its first year, Oozie saw significant usage at Yahoo and helped automate many complex workflows and data pipelines.Everything you wanted to know, but were afraid to ask about Oozie
Everything you wanted to know, but were afraid to ask about OozieChicago Hadoop Users Group
油
Oozie is a workflow and coordination system specifically designed to manage Apache Hadoop jobs, featuring components such as workflows, coordinators, and bundles. It allows for extensibility with custom actions and provides APIs for integration with enterprise applications. While it offers good integration and a simple UI, it lacks visual support and has limitations on loop constructs and dynamic forking.Oozie @ Riot Games
Oozie @ Riot GamesMatt Goeke
油
The document presents an overview of workflow management at Riot Games, focusing on the implementation of Oozie to improve data processing efficiency for League of Legends. It discusses the previous architecture's limitations and outlines the advantages of using Oozie, such as job scheduling, scalability, and integration with Apache Hadoop. Additionally, the document details the structure of Oozie workflows, coordinators, and bundles, including examples and best practices for deployment.Apache Oozie.pptx
Apache Oozie.pptxV.V.Vanniaperumal College for Women
油
Apache Oozie is a Java web application designed to schedule and manage Hadoop jobs, combining various job types into a cohesive workflow. It features a workflow engine for execution of Hadoop jobs and a coordinator engine for timing based job triggers, supporting both parallel and sequential execution. Oozie is flexible, scalable, and allows for job management through web service APIs, facilitating error handling and notification processes.Apache Oozie
Apache OozieNagajothiN1
油
Apache Oozie is a workflow scheduling system for Hadoop jobs. It allows users to string together Hadoop jobs into logical workflows with control nodes. Oozie workflows consist of action nodes representing jobs and control nodes that dictate workflow execution. Oozie supports different types of workflows including simple workflows, bundles of multiple workflows, and coordinator jobs that are triggered by schedules or data availability. It is integrated with Hadoop and scales to manage thousands of workflows.July 2012 HUG: Overview of Oozie Qualification Process
July 2012 HUG: Overview of Oozie Qualification ProcessYahoo Developer Network
油
The document outlines the Oozie QE qualification process, detailing its purpose as a scalable and secure workflow scheduling system for Hadoop. It describes qualification stages, including developing test plans, executing tests, and supporting production deployments, while also highlighting challenges and future tasks. Key aspects include various types of testing, such as functional, stress, and integration tests, along with lessons learned and ongoing work to improve test coverage and system compatibility.Building and managing complex dependencies pipeline using Apache Oozie
Building and managing complex dependencies pipeline using Apache OozieDataWorks Summit/Hadoop Summit
油
This document discusses Apache Oozie usage at Yahoo for managing complex data pipelines. It describes how Oozie is deployed at a large scale with high availability. It outlines the types of data pipelines used for tasks like ad targeting and content management. Challenges for large pipelines like dependency management, SLA monitoring, and reprocessing are discussed. User-built monitoring systems are described that integrate with Oozie for tasks like alerting and long job detection. Future work areas like improved testing and coordination are proposed.May 2012 HUG: Oozie: Towards a scalable Workflow Management System for Hadoop
May 2012 HUG: Oozie: Towards a scalable Workflow Management System for HadoopYahoo Developer Network
油
Oozie is a scalable workflow management system for Hadoop designed for multi-tenancy, security, and efficient job scheduling. It allows users to submit and manage jobs while ensuring safety through built-in authentication, and can handle thousands of jobs simultaneously by launching jobs in isolation from the server. The paper details installation steps, key design decisions, and performance metrics derived from production use at Yahoo.August 2016 HUG: Recent development in Apache Oozie
August 2016 HUG: Recent development in Apache OozieYahoo Developer Network
油
The document discusses the implementation and usage of Oozie at Yahoo, focusing on its capabilities for managing complex data pipelines and job dependencies across various platforms like Spark and Hive. It outlines the efficient handling of workflow jobs and coordinator jobs with insights on current limitations, enhancements, and future work in unit testing and dependency management. Additionally, it highlights Oozie's native support for Spark jobs and recent improvements in managing Spark actions within Oozie workflows.Hadoop Oozie
Hadoop OozieMadhur Nawandar
油
Oozie is a workflow scheduler system for Apache Hadoop that allows scheduling Hadoop jobs such as MapReduce, Pig, Hive, Sqoop, and DistCp jobs. It consists of a workflow engine that defines and runs workflows as directed acyclic graphs (DAGs) of Hadoop jobs, and a coordinator engine that runs workflows based on schedules and data availability. Oozie features include scaling to manage thousands of workflows, rerunning failed workflows, and submitting workflow definitions for immediate or later execution from clients to the Oozie server.oozieee.pdf
oozieee.pdfwwww63
油
Oozie is a workflow scheduler system for Apache Hadoop that manages dependent jobs as directed acyclic graphs (DAGs). It is integrated with Hadoop and supports jobs like MapReduce, Pig, Hive, Sqoop, and DistCp. Oozie consists of a workflow engine that stores and runs workflows of Hadoop jobs and a coordinator engine that runs workflows based on schedules and data availability. It is designed to scale to thousands of workflows and makes rerunning failed workflows more manageable.Introduction to Oozie | Big Data Hadoop Spark Tutorial | CloudxLab
Introduction to Oozie | Big Data Hadoop Spark Tutorial | CloudxLabCloudxLab
油
Oozie is a Java web application used to schedule Apache Hadoop jobs, seamlessly integrated with the Hadoop ecosystem, supporting various job types like MapReduce, Pig, and Hive. It features workflow and coordinator jobs that allow the execution of directed acyclic graphs and recurring jobs based on time and data availability. The document also provides step-by-step examples for running jobs, including MapReduce, Sqoop, and Hive processes.Apache Hadoop India Summit 2011 talk "Oozie - Workflow for Hadoop" by Andreas N
Apache Hadoop India Summit 2011 talk "Oozie - Workflow for Hadoop" by Andreas NYahoo Developer Network
油
Oozie is a workflow scheduler system for Hadoop that allows users to create and manage workflows that execute Hadoop jobs. It allows workflows to be defined as a directed acyclic graph (DAG) of actions like MapReduce, Pig, Hive, Sqoop and sub-workflows. Oozie also supports periodic scheduling of workflows as well as data-driven workflows that are triggered based on availability of input data.Apache Oozie The Workflow Scheduler for Hadoop 1st Edition Mohammad Kamrul Islam
Apache Oozie The Workflow Scheduler for Hadoop 1st Edition Mohammad Kamrul Islamosamafewelyo
油
Apache Oozie The Workflow Scheduler for Hadoop 1st Edition Mohammad Kamrul Islam
Apache Oozie The Workflow Scheduler for Hadoop 1st Edition Mohammad Kamrul Islam
Apache Oozie The Workflow Scheduler for Hadoop 1st Edition Mohammad Kamrul IslamApache Oozie Workflow Scheduler - Module 10
Apache Oozie Workflow Scheduler - Module 10Rohit Agrawal
油
Apache Oozie is an open-source workflow scheduler for Hadoop that enables the coordination of various job types, including MapReduce, Pig, and Hive. It comprises two main components: workflows, which define a series of actions, and coordinators, which schedule job execution based on time or data availability. Workflows are structured with XML files stored in HDFS and can include job-specific properties, while coordinators automate the triggering of these workflows.Oozie at Yahoo
Oozie at YahooMona Chitnis
油
Yahoo runs one of the largest Oozie clusters in the world, processing over 1 million workflows per month. To handle this scale, Yahoo made several performance optimizations including compressing database fields, reducing launcher overhead, and synchronously executing actions. Yahoo also improved usability with features like active job filters, updating coordinators on the fly, and sharelib enhancements. For high availability, Yahoo integrated HCat and implemented server-server authentication and distributed sequencing. Customers requested further improvements to dependency management, error handling, log viewing and zero downtime upgrades.AI&BigData Lab. 仍亠从舒仆亟 仂仆仂仗从仂 "Celos: 仂从亠亳仂于舒仆亳亠 亳 亠亳仂于舒仆亳亠 亰舒亟舒...
AI&BigData Lab. 仍亠从舒仆亟 仂仆仂仗从仂 "Celos: 仂从亠亳仂于舒仆亳亠 亳 亠亳仂于舒仆亳亠 亰舒亟舒...GeeksLab Odessa
油
The document provides an overview of Hadoop workflow management with Celos. It discusses some limitations of existing workflow engines like Cron, Oozie, Luigi, and Chronos. It then describes key features of Celos like flexible triggers, schedules, and an easy way to create and manage workflows using JavaScript. The document also covers continuous deployment, testing, and examples of using Celos.Oozie meetup - HA + Cron Scheduling
Oozie meetup - HA + Cron SchedulingMona Chitnis
油
The document summarizes an Oozie meetup agenda discussing log HA, cron scheduling, and the prospective 4.1 release. For log HA, it proposes copying logs to HDFS during rotation for easy implementation with minimal performance impact. It discusses cron scheduling use cases and common mistakes. The 4.1 release will focus on HA improvements and new coordinator functionality.Building and managing complex dependencies pipeline using Apache Oozie
Building and managing complex dependencies pipeline using Apache OozieDataWorks Summit/Hadoop Summit
油
May 2012 HUG: Oozie: Towards a scalable Workflow Management System for Hadoop
May 2012 HUG: Oozie: Towards a scalable Workflow Management System for HadoopYahoo Developer Network
油
Apache Hadoop India Summit 2011 talk "Oozie - Workflow for Hadoop" by Andreas N
Apache Hadoop India Summit 2011 talk "Oozie - Workflow for Hadoop" by Andreas NYahoo Developer Network
油
Ad
Recently uploaded (20)
Crypto Super 500 - 14th Report - June2025.pdf
Crypto Super 500 - 14th Report - June2025.pdfStephen Perrenod
油
This OrionX's 14th semi-annual report on the state of the cryptocurrency mining market. The report focuses on Proof-of-Work cryptocurrencies since those use substantial supercomputer power to mint new coins and encode transactions on their blockchains. Only two make the cut this time, Bitcoin with $18 billion of annual economic value produced and Dogecoin with $1 billion. Bitcoin has now reached the Zettascale with typical hash rates of 0.9 Zettahashes per second. Bitcoin is powered by the world's largest decentralized supercomputer in a continuous winner take all lottery incentive network.Connecting Data and Intelligence: The Role of FME in Machine Learning
Connecting Data and Intelligence: The Role of FME in Machine LearningSafe Software
油
In this presentation, we want to explore powerful data integration and preparation for Machine Learning. FME is known for its ability to manipulate and transform geospatial data, connecting diverse data sources into efficient and automated workflows. By integrating FME with Machine Learning techniques, it is possible to transform raw data into valuable insights faster and more accurately, enabling intelligent analysis and data-driven decision making.Improving Data Integrity: Synchronization between EAM and ArcGIS Utility Netw...
Improving Data Integrity: Synchronization between EAM and ArcGIS Utility Netw...Safe Software
油
Utilities and water companies play a key role in the creation of clean drinking water. The creation and maintenance of clean drinking water is becoming a critical problem due to pollution and pressure on the environment. A lot of data is necessary to create clean drinking water. For fieldworkers, two types of data are key: Asset data in an asset management system (EAM for example) and Geographic data in a GIS (ArcGIS Utility Network ). Keeping this type of data up to date and in sync is a challenge for many organizations, leading to duplicating data and creating a bulk of extra attributes and data to keep everything in sync. Using FME, it is possible to synchronize Enterprise Asset Management (EAM) data with the ArcGIS Utility Network in real time. Changes (creation, modification, deletion) in ArcGIS Pro are relayed to EAM via FME, and vice versa. This ensures continuous synchronization of both systems without daily bulk updates, minimizes risks, and seamlessly integrates with ArcGIS Utility Network services. This presentation focuses on the use of FME at a Dutch water company, to create a sync between the asset management and GIS.OWASP Barcelona 2025 Threat Model Library
OWASP Barcelona 2025 Threat Model LibraryPetraVukmirovic
油
Threat Model Library Launch at OWASP Barcelona 2025
https://owasp.org/www-project-threat-model-library/FIDO Seminar: Authentication for a Billion Consumers - Amazon.pptx
FIDO Seminar: Authentication for a Billion Consumers - Amazon.pptxFIDO Alliance
油
FIDO Seminar: Authentication for a Billion Consumers - AmazonCan We Use Rust to Develop Extensions for PostgreSQL? (POSETTE: An Event for ...
Can We Use Rust to Develop Extensions for PostgreSQL? (POSETTE: An Event for ...NTT DATA Technology & Innovation
油
Can We Use Rust to Develop Extensions for PostgreSQL?
(POSETTE: An Event for Postgres 2025)
June 11, 2025
Shinya Kato
NTT DATA Japan Corporation"Database isolation: how we deal with hundreds of direct connections to the d...
"Database isolation: how we deal with hundreds of direct connections to the d...Fwdays
油
What can go wrong if you allow each service to access the database directly? In a startup, this seems like a quick and easy solution, but as the system scales, problems appear that no one could have guessed.
In my talk, I'll share Solidgate's experience in transforming its architecture: from the chaos of direct connections to a service-based data access model. I will talk about the transition stages, bottlenecks, and how isolation affected infrastructure support. I will honestly show what worked and what didn't. In short, we will analyze the controversy of this talk.FIDO Seminar: New Data: Passkey Adoption in the Workforce.pptx
FIDO Seminar: New Data: Passkey Adoption in the Workforce.pptxFIDO Alliance
油
FIDO Seminar: New Data: Passkey Adoption in the WorkforceOpenACC and Open Hackathons Monthly Highlights June 2025
OpenACC and Open Hackathons Monthly Highlights June 2025OpenACC
油
The OpenACC organization focuses on enhancing parallel computing skills and advancing interoperability in scientific applications through hackathons and training. The upcoming 2025 Open Accelerated Computing Summit (OACS) aims to explore the convergence of AI and HPC in scientific computing and foster knowledge sharing. This year's OACS welcomes talk submissions from a variety of topics, from Using Standard Language Parallelism to Computer Vision Applications. The document also highlights several open hackathons, a call to apply for NVIDIA Academic Grant Program and resources for optimizing scientific applications using OpenACC directives.FME for Distribution & Transmission Integrity Management Program (DIMP & TIMP)
FME for Distribution & Transmission Integrity Management Program (DIMP & TIMP)Safe Software
油
Peoples Gas in Chicago, IL has changed to a new Distribution & Transmission Integrity Management Program (DIMP & TIMP) software provider in recent years. In order to successfully deploy the new software we have created a series of ETL processes using FME Form to transform our gas facility data to meet the required DIMP & TIMP data specifications. This presentation will provide an overview of how we used FME to transform data from ESRIs Utility Network and several other internal and external sources to meet the strict data specifications for the DIMP and TIMP software solutions.War_And_Cyber_3_Years_Of_Struggle_And_Lessons_For_Global_Security.pdf
War_And_Cyber_3_Years_Of_Struggle_And_Lessons_For_Global_Security.pdfbiswajitbanerjee38
油
Russia is one of the most aggressive nations when it comes to state coordinated cyberattacksand Ukraine has been at the center of their crosshairs for 3 years. This report, provided the State Service of Special Communications and Information Protection of Ukraine contains an incredible amount of cybersecurity insights, showcasing the coordinated aggressive cyberwarfare campaigns of Russia against Ukraine.
It brings to the forefront that understanding your adversary, especially an aggressive nation state, is important for cyber defense. Knowing their motivations, capabilities, and tactics becomes an advantage when allocating resources for maximum impact.
Intelligence shows Russia is on a cyber rampage, leveraging FSB, SVR, and GRU resources to professionally target Ukraines critical infrastructures, military, and international diplomacy support efforts.
The number of total incidents against Ukraine, originating from Russia, has steadily increased from 1350 in 2021 to 4315 in 2024, but the number of actual critical incidents has been managed down from a high of 1048 in 2022 to a mere 59 in 2024showcasing how the rapid detection and response to cyberattacks has been impacted by Ukraines improved cyber resilience.
Even against a much larger adversary, Ukraine is showcasing outstanding cybersecurity, enabled by strong strategies and sound tactics. There are lessons to learn for any enterprise that could potentially be targeted by aggressive nation states.
Definitely worth the read!Information Security Response Team Nepal_npCERT_Vice_President_Sudan_Jha.pdf
Information Security Response Team Nepal_npCERT_Vice_President_Sudan_Jha.pdfICT Frame Magazine Pvt. Ltd.
油
Artificial Intelligence (AI) is rapidly changing the face of cybersecurity across the globe. In Nepal, the shift is already underway. Vice President of the Information Security Response Team Nepal (npCERT) and Information Security Consultant at One Cover Pvt. Ltd., Sudan Jha, recently presented an in-depth workshop on how AI can strengthen national security and digital defenses.Edge-banding-machines-edgeteq-s-200-en-.pdf
Edge-banding-machines-edgeteq-s-200-en-.pdfAmirStern2
油
廬 廡 廬 廨廬 廡廬 廬 (廬 ).
廡 廡 廚廖, 廣 廣 廡 3 " 廣 廨 廣 40 ". 廡廨 廩 廬廨廣 廣 廬廡廬, 廣 廖 廬廣廩廬 廬 廬.FIDO Seminar: Perspectives on Passkeys & Consumer Adoption.pptx
FIDO Seminar: Perspectives on Passkeys & Consumer Adoption.pptxFIDO Alliance
油
FIDO Seminar: Perspectives on Passkeys & Consumer AdoptionViral>Wondershare Filmora 14.5.18.12900 Crack Free Download
Viral>Wondershare Filmora 14.5.18.12900 Crack Free DownloadPuppy jhon
油
艶COPY & PASTE LINK も https://drfiles.net/
Wondershare Filmora Crack is a user-friendly video editing software designed for both beginners and experienced users.
Smarter Aviation Data Management: Lessons from Swedavia Airports and Sweco
Smarter Aviation Data Management: Lessons from Swedavia Airports and SwecoSafe Software
油
Managing airport and airspace data is no small task, especially when youre expected to deliver it in AIXM format without spending a fortune on specialized tools. But what if there was a smarter, more affordable way?
Join us for a behind-the-scenes look at how Sweco partnered with Swedavia, the Swedish airport operator, to solve this challenge using FME and Esri.
Learn how they built automated workflows to manage periodic updates, merge airspace data, and support data extracts all while meeting strict government reporting requirements to the Civil Aviation Administration of Sweden.
Even better? Swedavia built custom services and applications that use the FME Flow REST API to trigger jobs and retrieve results streamlining tasks like securing the quality of new surveyor data, creating permdelta and baseline representations in the AIS schema, and generating AIXM extracts from their AIS data.
To conclude, FME expert Dean Hintz will walk through a GeoBorders reading workflow and highlight recent enhancements to FMEs AIXM (Aeronautical Information Exchange Model) processing and interpretation capabilities.
Discover how airports like Swedavia are harnessing the power of FME to simplify aviation data management, and how you can too.OpenPOWER Foundation & Open-Source Core Innovations
OpenPOWER Foundation & Open-Source Core InnovationsIBM
油
penPOWER offers a fully open, royalty-free CPU architecture for custom chip design.
It enables both lightweight FPGA cores (like Microwatt) and high-performance processors (like POWER10).
Developers have full access to source code, specs, and tools for end-to-end chip creation.
It supports AI, HPC, cloud, and embedded workloads with proven performance.
Backed by a global community, it fosters innovation, education, and collaboration.Enabling BIM / GIS integrations with Other Systems with FME
Enabling BIM / GIS integrations with Other Systems with FMESafe Software
油
Jacobs has successfully utilized FME to tackle the complexities of integrating diverse data sources in a confidential $1 billion campus improvement project. The project aimed to create a comprehensive digital twin by merging Building Information Modeling (BIM) data, Construction Operations Building Information Exchange (COBie) data, and various other data sources into a unified Geographic Information System (GIS) platform. The challenge lay in the disparate nature of these data sources, which were siloed and incompatible with each other, hindering efficient data management and decision-making processes.
To address this, Jacobs leveraged FME to automate the extraction, transformation, and loading (ETL) of data between ArcGIS Indoors and IBM Maximo. This process ensured accurate transfer of maintainable asset and work order data, creating a comprehensive 2D and 3D representation of the campus for Facility Management. FME's server capabilities enabled real-time updates and synchronization between ArcGIS Indoors and Maximo, facilitating automatic updates of asset information and work orders. Additionally, Survey123 forms allowed field personnel to capture and submit data directly from their mobile devices, triggering FME workflows via webhooks for real-time data updates. This seamless integration has significantly enhanced data management, improved decision-making processes, and ensured data consistency across the project lifecycle.AI vs Human Writing: Can You Tell the Difference?
AI vs Human Writing: Can You Tell the Difference?Shashi Sathyanarayana, Ph.D
油
This slide illustrates a side-by-side comparison between human-written, AI-written, and ambiguous content. It highlights subtle cues that help readers assess authenticity, raising essential questions about the future of communication, trust, and thought leadership in the age of generative AI.Can We Use Rust to Develop Extensions for PostgreSQL? (POSETTE: An Event for ...
Can We Use Rust to Develop Extensions for PostgreSQL? (POSETTE: An Event for ...NTT DATA Technology & Innovation
油
Information Security Response Team Nepal_npCERT_Vice_President_Sudan_Jha.pdf
Information Security Response Team Nepal_npCERT_Vice_President_Sudan_Jha.pdfICT Frame Magazine Pvt. Ltd.
油
Ad
Oozie Hug May 2011
- 1. Oozie3:ImprovedScheduling andControlOfWork鍖ows MohammadKIslam kamrul@yahooinc.com
- 2. Introduc?ons ≒ WhoIam ≒ TechnicalLeadatYahoo! ≒ OozieTeam ≒ Architecture,Development,Management MayankBansal AngeloHuang MohammadIslam AmolKekre AndreasNewman LeiZhang ≒ Externalcontributors. ≒ QE MarcyChang MichelleChiang
- 3. Agenda ≒ OozieOverview ≒ Oozie3.0features: Bundle Scalability Usability ≒ FuturePlan ≒ Q&A
- 4. Overview:Work鍖ow ≒ Oozieexecuteswork鍖owde鍖nedasDAGofjobs. ≒ Thejobtypeincludes:MapReduce/Pipes/Streaming/ Pig/CustomJavaCodeetc. ≒ IntroducedinOozie1.x. M/R streaming job M/R start fork join job Pig MORE decision job M/R ENOUGH job FS end Java job
- 5. Overview:Coordinator ≒ Oozieexecuteswork鍖owbasedon: TimeDependency(Frequency) DataDependency ≒ IntroducedinOozie2.x. OozieServer Check WSAPI Oozie DataAvailability Coordinator Oozie Oozie Work鍖ow Client Hadoop
- 6. Bundle ≒ WhatisBundle? AnewabstracconlayerontopofCoordinator. Userscande鍖neandexecutea油bunch油of油 coordinatorapplicacons. IntroducedinOozie3.x. ≒ Whyitisrequired? Datapipeline:Asetofinterrelatedcoordinators applicaconrequiredforlargedataprocessing. Operaconalnightmare:Hardtomaintainand controlthesepipelinesforServiceEngineering team.
- 7. BundleCont. ≒ Userde鍖nesthebundlethroughanew油XML. ≒ Usercouldstart/stop/suspend/resume/rerun油 inthebundlelevel. ≒ Bundleisop3onal.油 OozieServer Check WSAPI DataAvailability Bundle Coordinator Oozie Work鍖ow Client Hadoop
- 8. 或看噛庄艶A恢壊岳姻温界?看稼L温霞艶姻壊 Bundle油 Layer1 Coord油Job油1油 Coord油Job油2油 Layer2 Coord油 Coord油 Coord油 Coord油 Action油1油 Action油2油 Action1油油 Action油2油 WF油Job油1油 WF油Job油1油 WF油Job油2油 WF油Job油2油 PIG油 Layer3 Job油 M/R油 M/R油 PIG油 Job油 Job油 Job油 FS油 Job油
- 9. EnhancedStabilityandScalability ≒ Issue:Atveryhighload,Ooziebecomesslow. ≒ Impact:90%ofthetotalOoziesupportincidence. ≒ Reason: Lotofaccvebutnonprogressingjobs. Nonprogressingjobsareconsumingalotof resources. Oozieinternalqueueisfull. ≒ Resolucon: Throhlethenumberofaccvejobs/coordinator Putthejobintocmeoutstate. Enforcetheuniquenessforooziequeueelement.
- 10. ImprovedUsability ≒ Issue:Coordinatorjobsstatusisnotintuicveand causesconfusiontotheOozieuser. ≒ Impact:UserconfusionandrelatedOozie support. ≒ Reason: StatusSUCCEEDEDdoesntmeanjobissuccessful!! StatusPREMATERisforoozieinternaluseonly.Butit wasexposedtouser. ≒ Resolucon: RedesignCoordinatorstatus
- 11. CoordinatorStatusRedesign Current SUSPENDED KILLED PREP PREMATER Running SUCCEEDED FAILED New SUSPENDED KILLED SUCCEEDED PREP Running DONE_WITH_ERROR PAUSED FAILED
- 12. FuturePlan ≒ HigherScalability:Changepollingbaseddata dependencychecktopushmodelthroughHCatalog andNoc鍖caconsystem. ≒ Adaptability:GracefulhandlingHadoopdowncme: IfHadoopisdown,blocksubmission. WhenHadoopbecomesavailable ≒ Submittheblockedjob ≒ Autoresubmittheuntracedjob. ≒ Monitoring:RichWSAPIforapplicaconMonitoring/ Alercng.
- 13. FuturePlanCont. ≒ Automa?cFailover:UsingZooKeeper. ≒ LoadBalancing:Throughserverreplicacon ≒ ImprovedUsability: Distcpaccon HiveAccon ≒ Asynchronousdataprocessing. ≒ Incrementaldataprocessing. ≒ ApacheMigra?on:Worksinicated.
- 14. Q&A ≒ Githublink:hhp://yahoo.github.com/oozie ≒ Mailinglist:Oozie-users@yahoogroups.com MohammadKIslam kamrul@yahooinc.com