Playing with Hadoop 2013-10-31
•Download as ODP, PDF•
1 like•715 views
This document provides instructions for installing and running Hadoop on a single machine in pseudo-distributed mode. It outlines the necessary software, including Java and Linux, and steps to configure Hadoop including editing configuration files and starting the HDFS and MapReduce services. Examples are given to calculate Pi using Hadoop and run a word count job to demonstrate core Hadoop functionality in pseudo-distributed mode on a single machine.
Report
Share
Report
Share
Recommended
Playing with Hadoop (NPW2013)
This document is a presentation on Hadoop given by Søren Lund. It begins with disclaimers that the speaker has no production experience with Hadoop. It then provides an overview of Hadoop, how it addresses the problem of scaling to large amounts of data, and its core components. The presentation demonstrates how to install and run Hadoop on a single machine, provides examples of running word count jobs locally and on Hadoop, and discusses related tools like Hive and Pig. It concludes with notes on the Hadoop user interface, joins, running Hadoop in the cloud, and other Hadoop distributions.
Implementing Hadoop on a single cluster
This document provides instructions for setting up and running Hadoop on a single node cluster. It describes how to install Ubuntu, Java, Python and configure SSH. It then explains how to install and configure Hadoop, including editing configuration files and setting permissions. Instructions are provided for formatting the namenode, starting the cluster, running MapReduce jobs, and accessing the Hadoop web interfaces. The document also discusses writing MapReduce programs in Python and different Python implementation strategies.
SaltConf14 - Eric johnson, Google - Orchestrating Google Compute Engine with ...
Google is making the power of its datacenter, network, and technology innovations available to the world through its Cloud services. This presentation will provide an overview of the Google Cloud Platform and a deeper dive on Google Compute Engine. Google recently made an open source contribution to SaltStack and now you can now use Salt Cloud to manage your Compute Engine resources (IaaS virtual machine services). Come find out more about Google's Cloud Platform and how you can leverage Google scale with SaltStack.
Hadoop single node setup
This document provides instructions for setting up Hadoop in single node mode on Ubuntu. It describes adding a Hadoop user, installing Java and SSH, downloading and extracting Hadoop, configuring environment variables and Hadoop configuration files, and formatting the NameNode.
Spark on Kubernetes - Advanced Spark and Tensorflow Meetup - Jan 19 2017 - An...
https://www.meetup.com/Advanced-Spark-and-TensorFlow-Meetup/events/227622666/
Title: Spark on Kubernetes
Abstract: Engineers across several organizations are working on support for Kubernetes as a cluster scheduler backend within Spark. While designing this, we have encountered several challenges in translating Spark to use idiomatic Kubernetes constructs natively. This talk is about our high level design decisions and the current state of our work.
Speaker:
Anirudh Ramanathan is a software engineer on the Kubernetes team at Google. His focus is on running stateful and batch workloads. Previously, he worked on GGC (Google Global Cache) and prior to that, on the infrastructure team at NVIDIA."
Apache Hadoop & Hive installation with movie rating exercise
This document provides instructions for installing Hadoop and Hive. It outlines pre-requisites like Java, downloading Hadoop and Hive tarballs. It describes setting environment variables, configuring Hadoop for pseudo-distributed mode, formatting HDFS and starting services. Instructions are given for starting the Hive metastore and using sample rating data in Hive queries to learn the basics.
Hadoop on ec2
The document discusses configuring and running a Hadoop cluster on Amazon EC2 instances using the Cloudera distribution. It provides steps for launching EC2 instances, editing configuration files, starting Hadoop services, and verifying the HDFS and MapReduce functionality. It also demonstrates how to start and stop an HBase cluster on the same EC2 nodes.
Scripting Embulk Plugins
Scripting Embulk plugins makes plugin development easier drastically. You can develop, test, and productionize data integrations using any scripting languages. It's most suitable way to integrate data with SaaS using vendor-provided SDKs.
https://techplay.jp/event/781988
Recommended
Playing with Hadoop (NPW2013)
This document is a presentation on Hadoop given by Søren Lund. It begins with disclaimers that the speaker has no production experience with Hadoop. It then provides an overview of Hadoop, how it addresses the problem of scaling to large amounts of data, and its core components. The presentation demonstrates how to install and run Hadoop on a single machine, provides examples of running word count jobs locally and on Hadoop, and discusses related tools like Hive and Pig. It concludes with notes on the Hadoop user interface, joins, running Hadoop in the cloud, and other Hadoop distributions.
Implementing Hadoop on a single cluster
This document provides instructions for setting up and running Hadoop on a single node cluster. It describes how to install Ubuntu, Java, Python and configure SSH. It then explains how to install and configure Hadoop, including editing configuration files and setting permissions. Instructions are provided for formatting the namenode, starting the cluster, running MapReduce jobs, and accessing the Hadoop web interfaces. The document also discusses writing MapReduce programs in Python and different Python implementation strategies.
SaltConf14 - Eric johnson, Google - Orchestrating Google Compute Engine with ...
Google is making the power of its datacenter, network, and technology innovations available to the world through its Cloud services. This presentation will provide an overview of the Google Cloud Platform and a deeper dive on Google Compute Engine. Google recently made an open source contribution to SaltStack and now you can now use Salt Cloud to manage your Compute Engine resources (IaaS virtual machine services). Come find out more about Google's Cloud Platform and how you can leverage Google scale with SaltStack.
Hadoop single node setup
This document provides instructions for setting up Hadoop in single node mode on Ubuntu. It describes adding a Hadoop user, installing Java and SSH, downloading and extracting Hadoop, configuring environment variables and Hadoop configuration files, and formatting the NameNode.
Spark on Kubernetes - Advanced Spark and Tensorflow Meetup - Jan 19 2017 - An...
https://www.meetup.com/Advanced-Spark-and-TensorFlow-Meetup/events/227622666/
Title: Spark on Kubernetes
Abstract: Engineers across several organizations are working on support for Kubernetes as a cluster scheduler backend within Spark. While designing this, we have encountered several challenges in translating Spark to use idiomatic Kubernetes constructs natively. This talk is about our high level design decisions and the current state of our work.
Speaker:
Anirudh Ramanathan is a software engineer on the Kubernetes team at Google. His focus is on running stateful and batch workloads. Previously, he worked on GGC (Google Global Cache) and prior to that, on the infrastructure team at NVIDIA."
Apache Hadoop & Hive installation with movie rating exercise
This document provides instructions for installing Hadoop and Hive. It outlines pre-requisites like Java, downloading Hadoop and Hive tarballs. It describes setting environment variables, configuring Hadoop for pseudo-distributed mode, formatting HDFS and starting services. Instructions are given for starting the Hive metastore and using sample rating data in Hive queries to learn the basics.
Hadoop on ec2
The document discusses configuring and running a Hadoop cluster on Amazon EC2 instances using the Cloudera distribution. It provides steps for launching EC2 instances, editing configuration files, starting Hadoop services, and verifying the HDFS and MapReduce functionality. It also demonstrates how to start and stop an HBase cluster on the same EC2 nodes.
Scripting Embulk Plugins
Scripting Embulk plugins makes plugin development easier drastically. You can develop, test, and productionize data integrations using any scripting languages. It's most suitable way to integrate data with SaaS using vendor-provided SDKs.
https://techplay.jp/event/781988
Dev ops meetup
This document discusses DevOps practices for big data applications. It describes using Docker containers to automate system testing of new application versions before upgrading clusters. Tests are run inside Docker containers to simulate the target environment. The document also details using SBT plugins to package applications into RPM files for deployment, including mapping application artifacts and run scripts. This allows deploying updated applications with a single command and managing permissions and immutability.
Hadoop enhancements using next gen IA technologies
Intel Enhancements on Hadoop platform
- HDFS
Erasure coding using ISA-L library
Encryption using AES-NI
-HBase
Go Big Cache
Airflow based Video Encoding Platform
In this presentation, we will share one of the platforms we have built to support video processing based on Airflow. Through this platform, we can measure the utilization of resources, check the status of batch tasks, quantify and analyze the efficiency of each pipeline and thus adjust the workflow flexibly at run time and in an intuitive way.
Real-time Data Pipeline: Kafka Streams / Kafka Connect versus Spark Streaming
This document compares Apache Spark Streaming and Apache Kafka for real-time data pipelines. It outlines the key differences between the two in areas like new file detection, processing, failure handling, deployment, scaling, and monitoring. Some key points are that Spark Streaming allows detecting new files within directories but requires separate streams for different data sources, while Kafka can detect new files across sources using a watcher connector. Kafka Connect is also better for scaling tasks up and down dynamically compared to Spark Streaming. The document recommends considering your specific data sources, sinks, and integration testing needs to determine the best solution.
Integrate Hue with your Hadoop cluster - Yahoo! Hadoop Meetup
This talk will describe how Hue can be integrated with existing Hadoop deployments with minimal changes/disturbances. Romain will cover details on how Hue can leverage the existing authentication system and security model of your company. He will also cover the Hive/Shark/Pig/Oozie best practice setup for Hue.
http://www.meetup.com/hadoop/events/125191612/
Beeswax Hive editor in Hue
This document discusses Hive Editor in Hue, an open source web interface for Hadoop that includes applications for Hive, Pig, Impala, Oozie, Solr, Sqoop, and HBase. The Hive Editor in Hue provides syntax highlighting, query autocomplete, live progress and logs for Hive queries and MapReduce jobs, the ability to work with multiple databases and statements, and features for saving, exporting, and sharing queries. It connects to HiveServer2 and supports Sentry for authorization. A demo of the Hive Editor and Metastore Browser is provided.
HBase + Hue - LA HBase User Group
Learn about the HBase browser in Hue, the UI for Apache Hadoop.
Presented by Abraham Elmahrek at the LA HBase user meetup http://www.meetup.com/Los-Angeles-HBase-User-group/events/152073322/
Find out everything you need about Hue at http://gethue.com
Automating Workflows for Analytics Pipelines
This document discusses automating analytics pipelines and workflows using a workflow engine. It describes the challenges of managing workflows across multiple cloud services and database technologies. It then introduces a multi-cloud workflow engine called Digdag that can automate workflows, handle errors, enable parallel execution, support modularization and parameterization. Examples are given of using Digdag to define and run workflows across services like BigQuery, Treasure Data, Redshift, and Tableau. Key features of Digdag like loops, parameters, parallel tasks and pushing workflows to servers with Docker are also summarized.
PuppetConf 2016: An Introduction to Measuring and Tuning PE Performance – Cha...
This document provides an overview of measuring and tuning performance for the Puppet Enterprise (PE) platform. It discusses gathering data from PE services like Puppet Server and PuppetDB through JVM logging, metrics, and configurations. Important metrics for Puppet Server include JRuby usage and catalog compilation times. Tuning options involve adjusting JRuby capacity and rebalancing agent checkins. The document also covers monitoring PuppetDB for storage usage and command processing, as well as optimizing PostgreSQL query performance.
Spark 2.x Troubleshooting Guide
From common errors seen in running Spark applications, e.g., OutOfMemory, NoClassFound, disk IO bottlenecks, History Server crash, cluster under-utilization to advanced settings used to resolve large-scale Spark SQL workloads such as HDFS blocksize vs Parquet blocksize, how best to run HDFS Balancer to re-distribute file blocks, etc. you will get all the scoop in this information-packed presentation.
Digdagによる大規模データ処理の自動化とエラー処理
Digdag can automate large-scale data processing and handle errors. It provides constructs like operators, parameters, and task groups to organize workflows. Operators package tasks to run queries or process data. Parameters allow passing variables between tasks. Task groups modularize and organize workflows. Digdag supports error handling, monitoring, parallelization, versioning, and reproducing workflows across environments.
Understanding Memory Management In Spark For Fun And Profit
1) The document discusses memory management in Spark applications and summarizes different approaches tried by developers to address out of memory errors in Spark executors.
2) It analyzes the root causes of memory issues like executor overheads and data sizes, and evaluates fixes like increasing memory overhead, reducing cores, frequent garbage collection.
3) The document dives into Spark and JVM level configuration options for memory like storage pool sizes, caching formats, and garbage collection settings to improve reliability, efficiency and performance of Spark jobs.
Building the Right Platform Architecture for Hadoop
Rommel Garcia
Senior Solution Engineer/Global SME Security Lead for Hortonworks
All Things Open
October 26-27, 2016
Raleigh, North Carolina
January 2011 HUG: Pig Presentation
1) Pig 0.8 focuses on usability, integration, performance, and backwards compatibility with 0.7.
2) It allows UDFs to be written in scripting languages like Jython and provides better statistics and integration with Hadoop job history.
3) Other new features include invoking static Java functions as UDFs, improved HBase integration, and casting relations to scalars.
Hadoop Israel - HBase Browser in Hue
Learn about the HBase browser in Hue, the UI for Apache Hadoop.
Presented by Abraham Elmahrek at Hadoop Israel www.meetup.com/HadoopIsrael/events/161701092/
Find out everything you need about Hue at http://gethue.com
(APP310) Scheduling Using Apache Mesos in the Cloud | AWS re:Invent 2014
"How can you reliably schedule tasks in an unreliable, autoscaling cloud environment? This presentation talks about the design of our Fenzo scheduler, built on Apache Mesos, that serves as the core of our stream-processing platform, Mantis, designed for real-time insights. We focus on the following aspects of the scheduler:
- Resource granularity
- Fault tolerance
- Bin packing, task affinity, stream locality
- Autoscaling of the cluster and of individual service jobs
- Constraints (hard and soft) for individual tasks such as zone balancing, unique, and exclusive instances
This talk also includes detailed information on a holistic approach to scheduling in a distributed, autoscaling environment to achieve both speed and advanced scheduling optimizations."
Taming GC Pauses for Humongous Java Heaps in Spark Graph Computing-(Eric Kacz...
The document discusses tuning the Garbage First (G1) garbage collector in Java 8 to reduce garbage collection pauses for large heaps used in Spark graph computing workloads. It was found that the default G1 settings resulted in lengthy full garbage collections over 100 seconds. After analyzing the garbage collection logs, the main issue was identified as the concurrent marking phase not completing before a full collection was needed. Increasing the number of concurrent marking threads from 8 to 20 addressed this by speeding up the concurrent phase. With this tuning, no full collections occurred and total stop-the-world pause time was reduced to under a minute, a significant improvement over the original implementation.
E-books and App::Pod2Epub
A lightning talk about e-book readers, formats and one way to create your own e-books in epub format.
hello
Genetic programming is a machine learning technique developed in the 1990s in the USA by J. Koza. It uses populations of thousands of computer programs represented as tree structures to solve problems through genetic operations like recombination, mutation, and selection based on fitness. Programs are randomly varied and combined over multiple generations to gradually improve a solution to a problem.
Documenting Code - Patterns and Anti-patterns - NLPW 2016
How do we document code? A good solution is to create self-explanatory code; this somewhat eliminates the need to document the code. We still need some very basic documentation. Code comments are one form of documentation, that is often misused. This talk is about self-explanatory code, the documentation we need and code comment patterns and anti-patterns.
E2 meetup paris 16 0 ct-2013
Presentation given at the Enterprise 2.0 Meet-up in Paris, Oct 16th. The objective was to trigger discussion about social collaboration, adoption, obstacles, approaches.
Documenting code yapceu2016
How do we document code? A good solution is to create self-explanatory code; this somewhat eliminates the need to document the code. We still need some very basic documentation. Code comments are one form of documentation, that is often misused. This talk is about self-explanatory code, the documentation we need and code comment patterns and anti-patterns.
More Related Content
What's hot
Dev ops meetup
This document discusses DevOps practices for big data applications. It describes using Docker containers to automate system testing of new application versions before upgrading clusters. Tests are run inside Docker containers to simulate the target environment. The document also details using SBT plugins to package applications into RPM files for deployment, including mapping application artifacts and run scripts. This allows deploying updated applications with a single command and managing permissions and immutability.
Hadoop enhancements using next gen IA technologies
Intel Enhancements on Hadoop platform
- HDFS
Erasure coding using ISA-L library
Encryption using AES-NI
-HBase
Go Big Cache
Airflow based Video Encoding Platform
In this presentation, we will share one of the platforms we have built to support video processing based on Airflow. Through this platform, we can measure the utilization of resources, check the status of batch tasks, quantify and analyze the efficiency of each pipeline and thus adjust the workflow flexibly at run time and in an intuitive way.
Real-time Data Pipeline: Kafka Streams / Kafka Connect versus Spark Streaming
This document compares Apache Spark Streaming and Apache Kafka for real-time data pipelines. It outlines the key differences between the two in areas like new file detection, processing, failure handling, deployment, scaling, and monitoring. Some key points are that Spark Streaming allows detecting new files within directories but requires separate streams for different data sources, while Kafka can detect new files across sources using a watcher connector. Kafka Connect is also better for scaling tasks up and down dynamically compared to Spark Streaming. The document recommends considering your specific data sources, sinks, and integration testing needs to determine the best solution.
Integrate Hue with your Hadoop cluster - Yahoo! Hadoop Meetup
This talk will describe how Hue can be integrated with existing Hadoop deployments with minimal changes/disturbances. Romain will cover details on how Hue can leverage the existing authentication system and security model of your company. He will also cover the Hive/Shark/Pig/Oozie best practice setup for Hue.
http://www.meetup.com/hadoop/events/125191612/
Beeswax Hive editor in Hue
This document discusses Hive Editor in Hue, an open source web interface for Hadoop that includes applications for Hive, Pig, Impala, Oozie, Solr, Sqoop, and HBase. The Hive Editor in Hue provides syntax highlighting, query autocomplete, live progress and logs for Hive queries and MapReduce jobs, the ability to work with multiple databases and statements, and features for saving, exporting, and sharing queries. It connects to HiveServer2 and supports Sentry for authorization. A demo of the Hive Editor and Metastore Browser is provided.
HBase + Hue - LA HBase User Group
Learn about the HBase browser in Hue, the UI for Apache Hadoop.
Presented by Abraham Elmahrek at the LA HBase user meetup http://www.meetup.com/Los-Angeles-HBase-User-group/events/152073322/
Find out everything you need about Hue at http://gethue.com
Automating Workflows for Analytics Pipelines
This document discusses automating analytics pipelines and workflows using a workflow engine. It describes the challenges of managing workflows across multiple cloud services and database technologies. It then introduces a multi-cloud workflow engine called Digdag that can automate workflows, handle errors, enable parallel execution, support modularization and parameterization. Examples are given of using Digdag to define and run workflows across services like BigQuery, Treasure Data, Redshift, and Tableau. Key features of Digdag like loops, parameters, parallel tasks and pushing workflows to servers with Docker are also summarized.
PuppetConf 2016: An Introduction to Measuring and Tuning PE Performance – Cha...
This document provides an overview of measuring and tuning performance for the Puppet Enterprise (PE) platform. It discusses gathering data from PE services like Puppet Server and PuppetDB through JVM logging, metrics, and configurations. Important metrics for Puppet Server include JRuby usage and catalog compilation times. Tuning options involve adjusting JRuby capacity and rebalancing agent checkins. The document also covers monitoring PuppetDB for storage usage and command processing, as well as optimizing PostgreSQL query performance.
Spark 2.x Troubleshooting Guide
From common errors seen in running Spark applications, e.g., OutOfMemory, NoClassFound, disk IO bottlenecks, History Server crash, cluster under-utilization to advanced settings used to resolve large-scale Spark SQL workloads such as HDFS blocksize vs Parquet blocksize, how best to run HDFS Balancer to re-distribute file blocks, etc. you will get all the scoop in this information-packed presentation.
Digdagによる大規模データ処理の自動化とエラー処理
Digdag can automate large-scale data processing and handle errors. It provides constructs like operators, parameters, and task groups to organize workflows. Operators package tasks to run queries or process data. Parameters allow passing variables between tasks. Task groups modularize and organize workflows. Digdag supports error handling, monitoring, parallelization, versioning, and reproducing workflows across environments.
Understanding Memory Management In Spark For Fun And Profit
1) The document discusses memory management in Spark applications and summarizes different approaches tried by developers to address out of memory errors in Spark executors.
2) It analyzes the root causes of memory issues like executor overheads and data sizes, and evaluates fixes like increasing memory overhead, reducing cores, frequent garbage collection.
3) The document dives into Spark and JVM level configuration options for memory like storage pool sizes, caching formats, and garbage collection settings to improve reliability, efficiency and performance of Spark jobs.
Building the Right Platform Architecture for Hadoop
Rommel Garcia
Senior Solution Engineer/Global SME Security Lead for Hortonworks
All Things Open
October 26-27, 2016
Raleigh, North Carolina
January 2011 HUG: Pig Presentation
1) Pig 0.8 focuses on usability, integration, performance, and backwards compatibility with 0.7.
2) It allows UDFs to be written in scripting languages like Jython and provides better statistics and integration with Hadoop job history.
3) Other new features include invoking static Java functions as UDFs, improved HBase integration, and casting relations to scalars.
Hadoop Israel - HBase Browser in Hue
Learn about the HBase browser in Hue, the UI for Apache Hadoop.
Presented by Abraham Elmahrek at Hadoop Israel www.meetup.com/HadoopIsrael/events/161701092/
Find out everything you need about Hue at http://gethue.com
(APP310) Scheduling Using Apache Mesos in the Cloud | AWS re:Invent 2014
"How can you reliably schedule tasks in an unreliable, autoscaling cloud environment? This presentation talks about the design of our Fenzo scheduler, built on Apache Mesos, that serves as the core of our stream-processing platform, Mantis, designed for real-time insights. We focus on the following aspects of the scheduler:
- Resource granularity
- Fault tolerance
- Bin packing, task affinity, stream locality
- Autoscaling of the cluster and of individual service jobs
- Constraints (hard and soft) for individual tasks such as zone balancing, unique, and exclusive instances
This talk also includes detailed information on a holistic approach to scheduling in a distributed, autoscaling environment to achieve both speed and advanced scheduling optimizations."
Taming GC Pauses for Humongous Java Heaps in Spark Graph Computing-(Eric Kacz...
The document discusses tuning the Garbage First (G1) garbage collector in Java 8 to reduce garbage collection pauses for large heaps used in Spark graph computing workloads. It was found that the default G1 settings resulted in lengthy full garbage collections over 100 seconds. After analyzing the garbage collection logs, the main issue was identified as the concurrent marking phase not completing before a full collection was needed. Increasing the number of concurrent marking threads from 8 to 20 addressed this by speeding up the concurrent phase. With this tuning, no full collections occurred and total stop-the-world pause time was reduced to under a minute, a significant improvement over the original implementation.
What's hot (17)
Hadoop enhancements using next gen IA technologies
Hadoop enhancements using next gen IA technologies
Real-time Data Pipeline: Kafka Streams / Kafka Connect versus Spark Streaming
Real-time Data Pipeline: Kafka Streams / Kafka Connect versus Spark Streaming
Integrate Hue with your Hadoop cluster - Yahoo! Hadoop Meetup
Integrate Hue with your Hadoop cluster - Yahoo! Hadoop Meetup
PuppetConf 2016: An Introduction to Measuring and Tuning PE Performance – Cha...
PuppetConf 2016: An Introduction to Measuring and Tuning PE Performance – Cha...
Understanding Memory Management In Spark For Fun And Profit
Understanding Memory Management In Spark For Fun And Profit
Building the Right Platform Architecture for Hadoop
Building the Right Platform Architecture for Hadoop
(APP310) Scheduling Using Apache Mesos in the Cloud | AWS re:Invent 2014
(APP310) Scheduling Using Apache Mesos in the Cloud | AWS re:Invent 2014
Taming GC Pauses for Humongous Java Heaps in Spark Graph Computing-(Eric Kacz...
Taming GC Pauses for Humongous Java Heaps in Spark Graph Computing-(Eric Kacz...
Viewers also liked
E-books and App::Pod2Epub
A lightning talk about e-book readers, formats and one way to create your own e-books in epub format.
hello
Genetic programming is a machine learning technique developed in the 1990s in the USA by J. Koza. It uses populations of thousands of computer programs represented as tree structures to solve problems through genetic operations like recombination, mutation, and selection based on fitness. Programs are randomly varied and combined over multiple generations to gradually improve a solution to a problem.
Documenting Code - Patterns and Anti-patterns - NLPW 2016
How do we document code? A good solution is to create self-explanatory code; this somewhat eliminates the need to document the code. We still need some very basic documentation. Code comments are one form of documentation, that is often misused. This talk is about self-explanatory code, the documentation we need and code comment patterns and anti-patterns.
E2 meetup paris 16 0 ct-2013
Presentation given at the Enterprise 2.0 Meet-up in Paris, Oct 16th. The objective was to trigger discussion about social collaboration, adoption, obstacles, approaches.
Documenting code yapceu2016
How do we document code? A good solution is to create self-explanatory code; this somewhat eliminates the need to document the code. We still need some very basic documentation. Code comments are one form of documentation, that is often misused. This talk is about self-explanatory code, the documentation we need and code comment patterns and anti-patterns.
Basic testing with selenium
The document discusses using Selenium IDE to record and play test cases for web applications. It provides an overview of key web technologies like HTTP, HTML, DOM, CSS, and JavaScript. It then covers how to install and use Selenium IDE to record tests, modify test cases, handle random input, and run tests on a standalone Selenium server in different browsers.
Apache JMeter Introduction
The document discusses the Apache JMeter tool, which is open source software for load testing and measuring the performance of web applications. It summarizes JMeter's capabilities for web testing, load testing, and performance testing. The presentation focuses on using JMeter for testing HTTP/HTTPS responses, using test data from CSV files, and generating randomization through scripting. It also provides an overview of how to test websites through black box testing and record test scripts using developer tools. The presentation concludes with a demonstration of JMeter and an invitation for questions.
Viewers also liked (7)
Documenting Code - Patterns and Anti-patterns - NLPW 2016
Documenting Code - Patterns and Anti-patterns - NLPW 2016
Similar to Playing with Hadoop 2013-10-31
Big data with hadoop Setup on Ubuntu 12.04
This document provides an overview of Hadoop and how to set it up. It first defines big data and describes Hadoop's advantages over traditional systems, such as its ability to handle large datasets across commodity hardware. It then outlines Hadoop's components like HDFS and MapReduce. The document concludes by detailing the steps to install Hadoop, including setting up Linux prerequisites, configuring files, and starting the processes.
Cloudera hadoop installation
The document discusses installing Cloudera Hadoop (CDH 4) on Ubuntu 12.04 LTS. It provides an overview of Hadoop and its components. It then outlines the installation steps for Cloudera Hadoop which include preparing the system by installing prerequisites like OpenSSH, configuring password-less SSH and sudo, editing the host file, installing MySQL and the JDBC connector, and downloading and running the Cloudera Manager installer.
Single node hadoop cluster installation
This document provides instructions for installing a single-node Hadoop cluster on Ubuntu. It outlines downloading and configuring Java, installing Hadoop, configuring SSH access to localhost, editing Hadoop configuration files, and formatting the HDFS filesystem via the namenode. Key steps include adding a dedicated Hadoop user, generating SSH keys, setting properties in core-site.xml, hdfs-site.xml and mapred-site.xml, and running 'hadoop namenode -format' to initialize the filesystem.
Single node setup
This document describes how to set up Hadoop in three modes - standalone, pseudo-distributed, and fully-distributed - on a single node. Standalone mode runs Hadoop as a single process, pseudo-distributed runs daemons as separate processes, and fully-distributed requires a multi-node cluster. It provides instructions on installing Java and SSH, downloading Hadoop, configuring files for the different modes, starting and stopping processes, and running example jobs.
Unit 5
Hadoop is an open-source framework that allows distributed processing of large datasets across clusters of computers. It has two major components - the MapReduce programming model for processing large amounts of data in parallel, and the Hadoop Distributed File System (HDFS) for storing data across clusters of machines. Hadoop can scale from single servers to thousands of machines, with HDFS providing fault-tolerant storage and MapReduce enabling distributed computation and processing of data in parallel.
H2O on Hadoop Dec 12
Tom Kraljevic discusses the architecture of H2O on Hadoop and scheduling & launching long running in-memory processes on hadoop. And details of running open source H2O on Hadoop, using Yarn, and the things learned by the H2O team along the way.
Run wordcount job (hadoop)
The document provides step-by-step instructions for installing a single-node Hadoop cluster on Ubuntu Linux using VMware. It details downloading and configuring required software like Java, SSH, and Hadoop. Configuration files are edited to set properties for core Hadoop functions and enable HDFS. Finally, sample data is copied to HDFS and a word count MapReduce job is run to test the installation.
Tom Kraljevic presents H2O on Hadoop- how it works and what we've learned
H2O can run as a standalone program or on Hadoop clusters to perform predictive analytics on big data. When running on Hadoop, H2O utilizes the CPUs and memory of the Hadoop cluster through long-lived mapper tasks rather than the typical Hadoop MapReduce process. Configuration settings must be tuned for Hadoop, like mapreduce.map.memory.mb to allocate memory, and debugging mapper processes presents challenges. H2O has been used by companies for tasks like running K-Means clustering on terabytes of data.
2 Hadoop 1.x presentation in understading .pptx
The document provides an overview of the core components of Hadoop 1.0 architecture including the name node, secondary name node, job tracker, data nodes, and task tracker. It defines key terms and describes the master-slave architecture. It explains the functionality of each component and how a job is submitted and executed in a Hadoop cluster, with the name node and job tracker responsible for scheduling the execution across data nodes.
DC HUG Hadoop for Windows
This document discusses Hadoop for Windows, a distribution of Apache Hadoop and related projects that runs natively on the Windows operating system. It provides an overview of what is included in the distribution, such as Hadoop, Pig, Hive, and HCatalog, along with the versions and patches for each. It also describes what has changed from the Apache versions, such as new command line scripts, permissions mapping, and task controller. Users can install Hadoop for Windows on-premise or use HDInsight on Azure. The full distribution will be generally available in the second quarter along with more alignment with other Hortonworks distributions.
02 Hadoop deployment and configuration
This document provides instructions for configuring a single node Hadoop deployment on Ubuntu. It describes installing Java, adding a dedicated Hadoop user, configuring SSH for key-based authentication, disabling IPv6, installing Hadoop, updating environment variables, and configuring Hadoop configuration files including core-site.xml, mapred-site.xml, and hdfs-site.xml. Key steps include setting JAVA_HOME, configuring HDFS directories and ports, and setting hadoop.tmp.dir to the local /app/hadoop/tmp directory.
Hadoop installation with an example
This document provides an overview of Apache Hadoop, an open-source framework for distributed storage and processing of large datasets across clusters of computers. It discusses what Hadoop is, why it is useful for big data problems, examples of companies using Hadoop, the core Hadoop components like HDFS and MapReduce, and how to install and run Hadoop in pseudo-distributed mode on a single node. It also includes an example of running a word count MapReduce job to count word frequencies in input files.
Configuring Your First Hadoop Cluster On EC2
This document provides instructions for setting up a small 3 node Hadoop cluster on Amazon EC2. It covers configuring EC2 instances, installing Java and Hadoop, configuring the Hadoop nodes and services, and running a sample MapReduce job to validate the cluster. The goal is to provide a simple tutorial for getting started with Hadoop on EC2 for learning purposes.
Micro Datacenter & Data Warehouse
● Micro Data Center 25 TB, Small Business Solution
● Hadoop Open Source Technology
● Hive Data Warehouse
● Hadoop Testing Data model
● Software & Tools Library
● Business Intelligence report
Distro-independent Hadoop cluster management
This document discusses managing Hadoop clusters in a distribution-agnostic way using Bright Cluster Manager. It outlines the challenges of deploying and maintaining Hadoop, describes an architecture for a unified cluster and Hadoop manager, and highlights Bright Cluster Manager's key features for provisioning, configuring and monitoring Hadoop clusters across different distributions from a single interface. Bright provides a solution for setting up, managing and monitoring multi-purpose clusters running both HPC and Hadoop workloads.
Install Oracle 12c Golden Gate On Oracle Linux
In this article we will look at the steps to install oracle 12c Golden Gate on Oracle Enterprise Linux 6.5. The steps involved are:
Virtual Machine Setup
Install Oracle 12c Database
Install Oracle 12c Golden Gate
Prepare Golden Gate for Replication
Here is the full link of article: https://www.support.dbagenesis.com/post/install-oracle-12c-golden-gate-on-oracle-linux
Exp-3.pptx
1) The document describes the steps to install a single node Hadoop cluster on a laptop or desktop.
2) It involves downloading and extracting required software like Hadoop, JDK, and configuring environment variables.
3) Key configuration files like core-site.xml, hdfs-site.xml and mapred-site.xml are edited to configure the HDFS, namenode and jobtracker.
4) The namenode is formatted and Hadoop daemons like datanode, secondary namenode and jobtracker are started.
Hadoop Installation
This document provides instructions for installing Hadoop in standalone mode. It discusses prerequisites like installing Java and configuring SSH. It then outlines the steps to download and extract Hadoop, edit configuration files, format the namenode, start daemons, and test the installation. Key configuration files include core-site.xml, mapred-site.xml, and hdfs-site.xml. The document is presented by Ahmed Shouman and provides his contact information.
Pig
Apache Pig is a tool for analyzing large datasets stored in Hadoop. It provides a high-level language called Pig Latin for expressing data analysis tasks as data flows without needing to write Java code. Pig Latin scripts are compiled into sequences of MapReduce jobs that operate on the data stored in Hadoop. Pig was developed at Yahoo! to allow users to more easily analyze large datasets without focusing as much on writing custom MapReduce programs.
An Introduction to Apache Pig
Apache Pig is a platform for analyzing large datasets that runs on Hadoop. It provides a high-level language called Pig Latin that allows users to write data analysis programs without having to write complex MapReduce code in Java. Pig Latin scripts are compiled into MapReduce jobs. Pig offers features like built-in operators for joins, filters, and ordering and can handle both structured and unstructured data.
Similar to Playing with Hadoop 2013-10-31 (20)
Tom Kraljevic presents H2O on Hadoop- how it works and what we've learned
Tom Kraljevic presents H2O on Hadoop- how it works and what we've learned
Recently uploaded
LAND USE LAND COVER AND NDVI OF MIRZAPUR DISTRICT, UP
This Dissertation explores the particular circumstances of Mirzapur, a region located in the
core of India. Mirzapur, with its varied terrains and abundant biodiversity, offers an optimal
environment for investigating the changes in vegetation cover dynamics. Our study utilizes
advanced technologies such as GIS (Geographic Information Systems) and Remote sensing to
analyze the transformations that have taken place over the course of a decade.
The complex relationship between human activities and the environment has been the focus
of extensive research and worry. As the global community grapples with swift urbanization,
population expansion, and economic progress, the effects on natural ecosystems are becoming
more evident. A crucial element of this impact is the alteration of vegetation cover, which plays a
significant role in maintaining the ecological equilibrium of our planet.Land serves as the foundation for all human activities and provides the necessary materials for
these activities. As the most crucial natural resource, its utilization by humans results in different
'Land uses,' which are determined by both human activities and the physical characteristics of the
land.
The utilization of land is impacted by human needs and environmental factors. In countries
like India, rapid population growth and the emphasis on extensive resource exploitation can lead
to significant land degradation, adversely affecting the region's land cover.
Therefore, human intervention has significantly influenced land use patterns over many
centuries, evolving its structure over time and space. In the present era, these changes have
accelerated due to factors such as agriculture and urbanization. Information regarding land use and
cover is essential for various planning and management tasks related to the Earth's surface,
providing crucial environmental data for scientific, resource management, policy purposes, and
diverse human activities.
Accurate understanding of land use and cover is imperative for the development planning
of any area. Consequently, a wide range of professionals, including earth system scientists, land
and water managers, and urban planners, are interested in obtaining data on land use and cover
changes, conversion trends, and other related patterns. The spatial dimensions of land use and
cover support policymakers and scientists in making well-informed decisions, as alterations in
these patterns indicate shifts in economic and social conditions. Monitoring such changes with the
help of Advanced technologies like Remote Sensing and Geographic Information Systems is
crucial for coordinated efforts across different administrative levels. Advanced technologies like
Remote Sensing and Geographic Information Systems
9
Changes in vegetation cover refer to variations in the distribution, composition, and overall
structure of plant communities across different temporal and spatial scales. These changes can
occur natural.
Chapter wise All Notes of First year Basic Civil Engineering.pptx
Chapter wise All Notes of First year Basic Civil Engineering
Syllabus
Chapter-1
Introduction to objective, scope and outcome the subject
Chapter 2
Introduction: Scope and Specialization of Civil Engineering, Role of civil Engineer in Society, Impact of infrastructural development on economy of country.
Chapter 3
Surveying: Object Principles & Types of Surveying; Site Plans, Plans & Maps; Scales & Unit of different Measurements.
Linear Measurements: Instruments used. Linear Measurement by Tape, Ranging out Survey Lines and overcoming Obstructions; Measurements on sloping ground; Tape corrections, conventional symbols. Angular Measurements: Instruments used; Introduction to Compass Surveying, Bearings and Longitude & Latitude of a Line, Introduction to total station.
Levelling: Instrument used Object of levelling, Methods of levelling in brief, and Contour maps.
Chapter 4
Buildings: Selection of site for Buildings, Layout of Building Plan, Types of buildings, Plinth area, carpet area, floor space index, Introduction to building byelaws, concept of sun light & ventilation. Components of Buildings & their functions, Basic concept of R.C.C., Introduction to types of foundation
Chapter 5
Transportation: Introduction to Transportation Engineering; Traffic and Road Safety: Types and Characteristics of Various Modes of Transportation; Various Road Traffic Signs, Causes of Accidents and Road Safety Measures.
Chapter 6
Environmental Engineering: Environmental Pollution, Environmental Acts and Regulations, Functional Concepts of Ecology, Basics of Species, Biodiversity, Ecosystem, Hydrological Cycle; Chemical Cycles: Carbon, Nitrogen & Phosphorus; Energy Flow in Ecosystems.
Water Pollution: Water Quality standards, Introduction to Treatment & Disposal of Waste Water. Reuse and Saving of Water, Rain Water Harvesting. Solid Waste Management: Classification of Solid Waste, Collection, Transportation and Disposal of Solid. Recycling of Solid Waste: Energy Recovery, Sanitary Landfill, On-Site Sanitation. Air & Noise Pollution: Primary and Secondary air pollutants, Harmful effects of Air Pollution, Control of Air Pollution. . Noise Pollution Harmful Effects of noise pollution, control of noise pollution, Global warming & Climate Change, Ozone depletion, Greenhouse effect
Text Books:
1. Palancharmy, Basic Civil Engineering, McGraw Hill publishers.
2. Satheesh Gopi, Basic Civil Engineering, Pearson Publishers.
3. Ketki Rangwala Dalal, Essentials of Civil Engineering, Charotar Publishing House.
4. BCP, Surveying volume 1
BÀI TẬP BỔ TRỢ TIẾNG ANH LỚP 9 CẢ NĂM - GLOBAL SUCCESS - NĂM HỌC 2024-2025 - ...
BÀI TẬP BỔ TRỢ TIẾNG ANH LỚP 9 CẢ NĂM - GLOBAL SUCCESS - NĂM HỌC 2024-2025 - ...Nguyen Thanh Tu Collection
https://app.box.com/s/tacvl9ekroe9hqupdnjruiypvm9rdanePengantar Penggunaan Flutter - Dart programming language1.pptx
Pengantar Penggunaan Flutter - Dart programming language1.pptx
ISO/IEC 27001, ISO/IEC 42001, and GDPR: Best Practices for Implementation and...
Denis is a dynamic and results-driven Chief Information Officer (CIO) with a distinguished career spanning information systems analysis and technical project management. With a proven track record of spearheading the design and delivery of cutting-edge Information Management solutions, he has consistently elevated business operations, streamlined reporting functions, and maximized process efficiency.
Certified as an ISO/IEC 27001: Information Security Management Systems (ISMS) Lead Implementer, Data Protection Officer, and Cyber Risks Analyst, Denis brings a heightened focus on data security, privacy, and cyber resilience to every endeavor.
His expertise extends across a diverse spectrum of reporting, database, and web development applications, underpinned by an exceptional grasp of data storage and virtualization technologies. His proficiency in application testing, database administration, and data cleansing ensures seamless execution of complex projects.
What sets Denis apart is his comprehensive understanding of Business and Systems Analysis technologies, honed through involvement in all phases of the Software Development Lifecycle (SDLC). From meticulous requirements gathering to precise analysis, innovative design, rigorous development, thorough testing, and successful implementation, he has consistently delivered exceptional results.
Throughout his career, he has taken on multifaceted roles, from leading technical project management teams to owning solutions that drive operational excellence. His conscientious and proactive approach is unwavering, whether he is working independently or collaboratively within a team. His ability to connect with colleagues on a personal level underscores his commitment to fostering a harmonious and productive workplace environment.
Date: May 29, 2024
Tags: Information Security, ISO/IEC 27001, ISO/IEC 42001, Artificial Intelligence, GDPR
-------------------------------------------------------------------------------
Find out more about ISO training and certification services
Training: ISO/IEC 27001 Information Security Management System - EN | PECB
ISO/IEC 42001 Artificial Intelligence Management System - EN | PECB
General Data Protection Regulation (GDPR) - Training Courses - EN | PECB
Webinars: https://pecb.com/webinars
Article: https://pecb.com/article
-------------------------------------------------------------------------------
For more information about PECB:
Website: https://pecb.com/
LinkedIn: https://www.linkedin.com/company/pecb/
Facebook: https://www.facebook.com/PECBInternational/
Slideshare: http://www.slideshare.net/PECBCERTIFICATION
Constructing Your Course Container for Effective Communication
Communicating effectively and consistently with students can help them feel at ease during their learning experience and provide the instructor with a communication trail to track the course's progress. This workshop will take you through constructing an engaging course container to facilitate effective communication.
Beyond Degrees - Empowering the Workforce in the Context of Skills-First.pptx
Iván Bornacelly, Policy Analyst at the OECD Centre for Skills, OECD, presents at the webinar 'Tackling job market gaps with a skills-first approach' on 12 June 2024
বাংলাদেশ অর্থনৈতিক সমীক্ষা (Economic Review) ২০২৪ UJS App.pdf
বাংলাদেশের অর্থনৈতিক সমীক্ষা ২০২৪ [Bangladesh Economic Review 2024 Bangla.pdf] কম্পিউটার , ট্যাব ও স্মার্ট ফোন ভার্সন সহ সম্পূর্ণ বাংলা ই-বুক বা pdf বই " সুচিপত্র ...বুকমার্ক মেনু 🔖 ও হাইপার লিংক মেনু 📝👆 যুক্ত ..
আমাদের সবার জন্য খুব খুব গুরুত্বপূর্ণ একটি বই ..বিসিএস, ব্যাংক, ইউনিভার্সিটি ভর্তি ও যে কোন প্রতিযোগিতা মূলক পরীক্ষার জন্য এর খুব ইম্পরট্যান্ট একটি বিষয় ...তাছাড়া বাংলাদেশের সাম্প্রতিক যে কোন ডাটা বা তথ্য এই বইতে পাবেন ...
তাই একজন নাগরিক হিসাবে এই তথ্য গুলো আপনার জানা প্রয়োজন ...।
বিসিএস ও ব্যাংক এর লিখিত পরীক্ষা ...+এছাড়া মাধ্যমিক ও উচ্চমাধ্যমিকের স্টুডেন্টদের জন্য অনেক কাজে আসবে ...
What is Digital Literacy? A guest blog from Andy McLaughlin, University of Ab...
What is Digital Literacy? A guest blog from Andy McLaughlin, University of Aberdeen
How to Make a Field Mandatory in Odoo 17
In Odoo, making a field required can be done through both Python code and XML views. When you set the required attribute to True in Python code, it makes the field required across all views where it's used. Conversely, when you set the required attribute in XML views, it makes the field required only in the context of that particular view.
Hindi varnamala | hindi alphabet PPT.pdf
हिंदी वर्णमाला पीपीटी, hindi alphabet PPT presentation, hindi varnamala PPT, Hindi Varnamala pdf, हिंदी स्वर, हिंदी व्यंजन, sikhiye hindi varnmala, dr. mulla adam ali, hindi language and literature, hindi alphabet with drawing, hindi alphabet pdf, hindi varnamala for childrens, hindi language, hindi varnamala practice for kids, https://www.drmullaadamali.com
BÀI TẬP DẠY THÊM TIẾNG ANH LỚP 7 CẢ NĂM FRIENDS PLUS SÁCH CHÂN TRỜI SÁNG TẠO ...
BÀI TẬP DẠY THÊM TIẾNG ANH LỚP 7 CẢ NĂM FRIENDS PLUS SÁCH CHÂN TRỜI SÁNG TẠO ...Nguyen Thanh Tu Collection
https://app.box.com/s/qhtvq32h4ybf9t49ku85x0n3xl4jhr15Recently uploaded (20)
LAND USE LAND COVER AND NDVI OF MIRZAPUR DISTRICT, UP
LAND USE LAND COVER AND NDVI OF MIRZAPUR DISTRICT, UP
Chapter wise All Notes of First year Basic Civil Engineering.pptx
Chapter wise All Notes of First year Basic Civil Engineering.pptx
BÀI TẬP BỔ TRỢ TIẾNG ANH LỚP 9 CẢ NĂM - GLOBAL SUCCESS - NĂM HỌC 2024-2025 - ...
BÀI TẬP BỔ TRỢ TIẾNG ANH LỚP 9 CẢ NĂM - GLOBAL SUCCESS - NĂM HỌC 2024-2025 - ...
IGCSE Biology Chapter 14- Reproduction in Plants.pdf
IGCSE Biology Chapter 14- Reproduction in Plants.pdf
Pengantar Penggunaan Flutter - Dart programming language1.pptx
Pengantar Penggunaan Flutter - Dart programming language1.pptx
ISO/IEC 27001, ISO/IEC 42001, and GDPR: Best Practices for Implementation and...
ISO/IEC 27001, ISO/IEC 42001, and GDPR: Best Practices for Implementation and...
Constructing Your Course Container for Effective Communication
Constructing Your Course Container for Effective Communication
Beyond Degrees - Empowering the Workforce in the Context of Skills-First.pptx
Beyond Degrees - Empowering the Workforce in the Context of Skills-First.pptx
বাংলাদেশ অর্থনৈতিক সমীক্ষা (Economic Review) ২০২৪ UJS App.pdf
বাংলাদেশ অর্থনৈতিক সমীক্ষা (Economic Review) ২০২৪ UJS App.pdf
Liberal Approach to the Study of Indian Politics.pdf
Liberal Approach to the Study of Indian Politics.pdf
What is Digital Literacy? A guest blog from Andy McLaughlin, University of Ab...
What is Digital Literacy? A guest blog from Andy McLaughlin, University of Ab...
spot a liar (Haiqa 146).pptx Technical writhing and presentation skills
spot a liar (Haiqa 146).pptx Technical writhing and presentation skills
BÀI TẬP DẠY THÊM TIẾNG ANH LỚP 7 CẢ NĂM FRIENDS PLUS SÁCH CHÂN TRỜI SÁNG TẠO ...
BÀI TẬP DẠY THÊM TIẾNG ANH LỚP 7 CẢ NĂM FRIENDS PLUS SÁCH CHÂN TRỜI SÁNG TẠO ...
Playing with Hadoop 2013-10-31
- 1. Visma Consulting 2013-10-30 Playing with Hadoop Søren Lund (slu) slu@369.dk
- 2. Needed to run Hadoop You need the following to run Hadoop Java JDK Linux server Hadoop tarball I'm using the following JDK 1.6.24 64 bit Ubuntu 12.04 LTS 64 bit Hadoop 1.0.4 Could not get JDK7 + Hadoop 2.2 to work
- 4. Install Java
- 5. Setup Java home and path
- 7. Install Hadoop and add to path
- 8. Create SSH key for hadoop user
- 10. Disable IPv6
- 11. Reboot and check installation
- 12. Running an example job
- 13. Calculate Pi
- 14. Estimated value of Pi
- 15. Three modes of operation Pi was calculated in Local standalone mode it is the default mode (i.e. no configuration needed) all components of Hadoop run in a single JVM Pseudo-distributed mode components communicate using sockets a separate JVM is spawned for each component it is a minicluster on a single host Fully distributed mode components are spread across multiple machines
- 16. Configuring for pseudo distributed mode
- 17. Create base directory for HDFS
- 18. Set JAVA_HOME
- 22. Log out and log on as hadoop
- 23. Format HDFS
- 24. Start HDFS
- 25. Start Map Reduce
- 26. Create home directory & test data
- 28. First let's try the example jar
- 30. Compile and run our own jar https://gist.github.com/soren/7213273
- 31. Inspect result
- 34. The Web User Interface HDFS MapReduce http://localhost:8070/ File Browser http://localhost:8030/ http://localhost:8075/browseDirectory.jsp?namenodeInfoPort Note: this is with port forwarding in VirtualBox 50030 → 8030, 50070 → 8070, 50075 → 8075
- 35. Now you can go play with Hadoop...