This document provides information about running Spark on YARN including:
- Spark allows processing of large datasets in a distributed manner using Resilient Distributed Datasets (RDDs).
- When running on YARN, Spark is able to leverage existing Hadoop clusters for locality-aware processing, resource management, and other benefits while still using its own execution engine.
- Running Spark on YARN provides advantages like shipping code to where the data is located instead of moving large amounts of data, leveraging existing Hadoop cluster infrastructure, and allowing Spark workloads to run natively within Hadoop.
Productionizing Spark and the Spark Job ServerEvan Chan
You won't find this in many places - an overview of deploying, configuring, and running Apache Spark, including Mesos vs YARN vs Standalone clustering modes, useful config tuning parameters, and other tips from years of using Spark in production. Also, learn about the Spark Job Server and how it can help your organization deploy Spark as a RESTful service, track Spark jobs, and enable fast queries (including SQL!) of cached RDDs.
Spark Summit Europe: Building a REST Job Server for interactive Spark as a se...gethue
This document describes building a REST job server for interactive Spark as a service using Livy. It discusses the history and challenges of running Spark jobs in Hue, introduces Livy as a Spark server, and details its local and YARN-cluster modes as well as session creation, execution flows, and interpreter support for Scala, Python, R and more. Magic commands are also covered for JSON, table, plotting and other output formats.
Hadoop and Spark Analytics over Better StorageSandeep Patil
This document discusses using IBM Spectrum Scale to provide a colder storage tier for Hadoop & Spark workloads using IBM Elastic Storage Server (ESS) and HDFS transparency. Some key points discussed include:
- Using Spectrum Scale to federate ESS with existing HDFS or Spectrum Scale filesystems, allowing data to be seamlessly accessed even if moved to the ESS tier.
- Extending HDFS across multiple HDFS and Spectrum Scale clusters without needing to move data using Spectrum Scale's HDFS transparency connector.
- Integrating ESS tier with Spectrum Protect for backup and Spectrum Archive for archiving to take advantage of their policy engines and automation.
- Examples of using the unified storage for analytics workflows, life
Spark Compute as a Service at Paypal with Prabhu KasinathanDatabricks
Apache Spark is a gift to the big data community, which adds tons of new features on every release. However, it’s difficult to manage petabyte-scale Hadoop clusters with hundreds of edge nodes, multiple Spark releases and demonstrate operational efficiencies and standardization. In order to address these challenges, Paypal has developed and deployed a REST0based Spark platform: Spark Compute as a Service (SCaaS),which provides improved application development, execution, logging, security, workload management and tuning.
This session will walk through the top challenges faced by PayPal administrators, developers and operations and describe how Paypal’s SCaaS platform overcomes them by leveraging open source tools and technologies, like Livy, Jupyter, SparkMagic, Zeppelin, SQL Tools, Kafka and Elastic. You’ll also hear about the improvements PayPal has added, which enable it to run greater than 10,000 Spark applications in production effectively.
Introduction to Machine Learning in Spark. Presented at Bangalore Apache Spark Meetup by Shashank L and Shashidhar E S on 17/10/2015.
http://www.meetup.com/Bangalore-Apache-Spark-Meetup/events/225649429/
This document discusses Netflix's use of Spark on Yarn for ETL workloads. Some key points:
- Netflix runs Spark on Yarn across 3000 EC2 nodes to process large amounts of streaming data from over 100 million daily users.
- Technical challenges included optimizing performance for S3, dynamic resource allocation, and Parquet read/write. Improvements led to up to 18x faster job completion times.
- Production Spark applications include recommender systems that analyze user behavior and personalize content across billions of profiles and titles.
This document provides information about running Spark on YARN including:
- Spark allows processing of large datasets in a distributed manner using Resilient Distributed Datasets (RDDs).
- When running on YARN, Spark is able to leverage existing Hadoop clusters for locality-aware processing, resource management, and other benefits while still using its own execution engine.
- Running Spark on YARN provides advantages like shipping code to where the data is located instead of moving large amounts of data, leveraging existing Hadoop cluster infrastructure, and allowing Spark workloads to run natively within Hadoop.
Productionizing Spark and the Spark Job ServerEvan Chan
You won't find this in many places - an overview of deploying, configuring, and running Apache Spark, including Mesos vs YARN vs Standalone clustering modes, useful config tuning parameters, and other tips from years of using Spark in production. Also, learn about the Spark Job Server and how it can help your organization deploy Spark as a RESTful service, track Spark jobs, and enable fast queries (including SQL!) of cached RDDs.
Spark Summit Europe: Building a REST Job Server for interactive Spark as a se...gethue
This document describes building a REST job server for interactive Spark as a service using Livy. It discusses the history and challenges of running Spark jobs in Hue, introduces Livy as a Spark server, and details its local and YARN-cluster modes as well as session creation, execution flows, and interpreter support for Scala, Python, R and more. Magic commands are also covered for JSON, table, plotting and other output formats.
Hadoop and Spark Analytics over Better StorageSandeep Patil
This document discusses using IBM Spectrum Scale to provide a colder storage tier for Hadoop & Spark workloads using IBM Elastic Storage Server (ESS) and HDFS transparency. Some key points discussed include:
- Using Spectrum Scale to federate ESS with existing HDFS or Spectrum Scale filesystems, allowing data to be seamlessly accessed even if moved to the ESS tier.
- Extending HDFS across multiple HDFS and Spectrum Scale clusters without needing to move data using Spectrum Scale's HDFS transparency connector.
- Integrating ESS tier with Spectrum Protect for backup and Spectrum Archive for archiving to take advantage of their policy engines and automation.
- Examples of using the unified storage for analytics workflows, life
Spark Compute as a Service at Paypal with Prabhu KasinathanDatabricks
Apache Spark is a gift to the big data community, which adds tons of new features on every release. However, it’s difficult to manage petabyte-scale Hadoop clusters with hundreds of edge nodes, multiple Spark releases and demonstrate operational efficiencies and standardization. In order to address these challenges, Paypal has developed and deployed a REST0based Spark platform: Spark Compute as a Service (SCaaS),which provides improved application development, execution, logging, security, workload management and tuning.
This session will walk through the top challenges faced by PayPal administrators, developers and operations and describe how Paypal’s SCaaS platform overcomes them by leveraging open source tools and technologies, like Livy, Jupyter, SparkMagic, Zeppelin, SQL Tools, Kafka and Elastic. You’ll also hear about the improvements PayPal has added, which enable it to run greater than 10,000 Spark applications in production effectively.
Introduction to Machine Learning in Spark. Presented at Bangalore Apache Spark Meetup by Shashank L and Shashidhar E S on 17/10/2015.
http://www.meetup.com/Bangalore-Apache-Spark-Meetup/events/225649429/
This document discusses Netflix's use of Spark on Yarn for ETL workloads. Some key points:
- Netflix runs Spark on Yarn across 3000 EC2 nodes to process large amounts of streaming data from over 100 million daily users.
- Technical challenges included optimizing performance for S3, dynamic resource allocation, and Parquet read/write. Improvements led to up to 18x faster job completion times.
- Production Spark applications include recommender systems that analyze user behavior and personalize content across billions of profiles and titles.
Dynamically Allocate Cluster Resources to your Spark ApplicationDataWorks Summit
The document discusses the benefits of exercise for mental health. Regular physical activity can help reduce anxiety and depression and improve mood and cognitive functioning. Exercise stimulates the production of endorphins in the brain which elevate mood and reduce stress levels.
Spark-on-YARN: Empower Spark Applications on Hadoop ClusterDataWorks Summit
This document discusses Apache Spark-on-YARN, which allows Spark applications to leverage existing Hadoop clusters. Spark improves efficiency over Hadoop via in-memory computing and supports rich APIs. Spark-on-YARN provides access to HDFS data and resources on Hadoop clusters without extra deployment costs. It supports running Spark jobs in YARN cluster and client modes. The document describes Yahoo's use of Spark-on-YARN for machine learning applications on large datasets.
ETL with SPARK - First Spark London meetupRafal Kwasny
The document discusses how Spark can be used to supercharge ETL workflows by running them faster and with less code compared to traditional Hadoop approaches. It provides examples of using Spark for tasks like sessionization of user clickstream data. Best practices are covered like optimizing for JVM issues, avoiding full GC pauses, and tips for deployment on EC2. Future improvements to Spark like SQL support and Java 8 are also mentioned.
The document discusses Spark exceptions and errors related to shuffling data between nodes. It notes that tasks can fail due to out of memory errors or files being closed prematurely. It also provides explanations of Spark's shuffle operations and how data is written and merged across nodes during shuffles.
A proxy server acts as an intermediary between a client and the internet. It allows enterprises to ensure security, administrative control, and caching services. There are different types of proxy servers such as caching proxies, web proxies, content filtering proxies, and anonymizing proxies. Proxy servers can operate in either a transparent or opaque mode. They provide benefits like security, performance improvements through caching, and load balancing but also have disadvantages like creating single points of failure.
Tech-Talk at Bay Area Spark Meetup
Apache Spark(tm) has rapidly become a key tool for data scientists to explore, understand and transform massive datasets and to build and train advanced machine learning models. The question then becomes, how do I deploy these model to a production environment. How do I embed what I have learned into customer facing data applications. Like all things in engineering, it depends.
In this meetup, we will discuss best practices from Databricks on how our customers productionize machine learning models and do a deep dive with actual customer case studies and live demos of a few example architectures and code in Python and Scala. We will also briefly touch on what is coming in Apache Spark 2.X with model serialization and scoring options.
The document discusses proxy servers, specifically HTTP and FTP proxy servers. It defines a proxy server as a server that acts as an intermediary for requests from clients to other servers. It describes the main purposes of proxy servers as keeping machines behind it anonymous for security purposes and speeding up access to resources via caching. It also provides details on the mechanisms, types, protocols (HTTP and FTP), and functions of proxy servers.
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.
[2017년 SW 마에스트로 100+ 컨퍼런스]
- 발표자: 오픈스택 한국 커뮤니티 조성수
- 행사 정보: https://www.facebook.com/swmaestro/photos/a.816861878341341.1073741828.812223648805164/1832957773398408/?type=3&theater&ifg=1
Dynamically Allocate Cluster Resources to your Spark ApplicationDataWorks Summit
The document discusses the benefits of exercise for mental health. Regular physical activity can help reduce anxiety and depression and improve mood and cognitive functioning. Exercise stimulates the production of endorphins in the brain which elevate mood and reduce stress levels.
Spark-on-YARN: Empower Spark Applications on Hadoop ClusterDataWorks Summit
This document discusses Apache Spark-on-YARN, which allows Spark applications to leverage existing Hadoop clusters. Spark improves efficiency over Hadoop via in-memory computing and supports rich APIs. Spark-on-YARN provides access to HDFS data and resources on Hadoop clusters without extra deployment costs. It supports running Spark jobs in YARN cluster and client modes. The document describes Yahoo's use of Spark-on-YARN for machine learning applications on large datasets.
ETL with SPARK - First Spark London meetupRafal Kwasny
The document discusses how Spark can be used to supercharge ETL workflows by running them faster and with less code compared to traditional Hadoop approaches. It provides examples of using Spark for tasks like sessionization of user clickstream data. Best practices are covered like optimizing for JVM issues, avoiding full GC pauses, and tips for deployment on EC2. Future improvements to Spark like SQL support and Java 8 are also mentioned.
The document discusses Spark exceptions and errors related to shuffling data between nodes. It notes that tasks can fail due to out of memory errors or files being closed prematurely. It also provides explanations of Spark's shuffle operations and how data is written and merged across nodes during shuffles.
A proxy server acts as an intermediary between a client and the internet. It allows enterprises to ensure security, administrative control, and caching services. There are different types of proxy servers such as caching proxies, web proxies, content filtering proxies, and anonymizing proxies. Proxy servers can operate in either a transparent or opaque mode. They provide benefits like security, performance improvements through caching, and load balancing but also have disadvantages like creating single points of failure.
Tech-Talk at Bay Area Spark Meetup
Apache Spark(tm) has rapidly become a key tool for data scientists to explore, understand and transform massive datasets and to build and train advanced machine learning models. The question then becomes, how do I deploy these model to a production environment. How do I embed what I have learned into customer facing data applications. Like all things in engineering, it depends.
In this meetup, we will discuss best practices from Databricks on how our customers productionize machine learning models and do a deep dive with actual customer case studies and live demos of a few example architectures and code in Python and Scala. We will also briefly touch on what is coming in Apache Spark 2.X with model serialization and scoring options.
The document discusses proxy servers, specifically HTTP and FTP proxy servers. It defines a proxy server as a server that acts as an intermediary for requests from clients to other servers. It describes the main purposes of proxy servers as keeping machines behind it anonymous for security purposes and speeding up access to resources via caching. It also provides details on the mechanisms, types, protocols (HTTP and FTP), and functions of proxy servers.
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.
[2017년 SW 마에스트로 100+ 컨퍼런스]
- 발표자: 오픈스택 한국 커뮤니티 조성수
- 행사 정보: https://www.facebook.com/swmaestro/photos/a.816861878341341.1073741828.812223648805164/1832957773398408/?type=3&theater&ifg=1
This study analyzed influential Twitter users discussing Sejong City, South Korea by identifying key users, examining their activities, relationships, and keywords. The researchers found that influential users included media outlets and ordinary users who frequently posted and had many followers and followings, indicating they addressed public issues and had influence. Influential users tended to interact more than media outlets. Keyword analysis showed discussions focused on politicians, the Sejong City law, and other social and political issues.
The document discusses a research meeting looking at the relationship between web visibility and election outcomes. It poses two research questions around whether indices of visibility on platforms like Twitter, blogs and news are related to vote results, and what differences exist in communication patterns across these platforms during local elections. It then provides data on mentions of several politicians on Twitter, blogs and news sources to analyze trends and patterns of visibility.
The document discusses a research meeting looking at the relationship between web visibility and election outcomes. It poses two research questions around whether indices of visibility on Twitter, blogs and news are related to vote results, and what differences exist in communication patterns across these platforms during local elections. It then provides data on the Twitter, blog and news mentions of several Korean politicians during their elections. The document concludes by noting the meeting will track trends in visibility for these politicians across the different web platforms over time.
1) The document analyzes comments posted on Korean politicians' mini-homepages on Cyworld, a Korean social networking site.
2) It finds that frequently used words in comments include references to collective society and positive emotion.
3) A sentiment analysis found that ruling party politicians received more negative comments than opposition politicians.
1) The document analyzes political communication on Cyworld, a Korean social networking site. It examines comments posted on politicians' profiles to understand public sentiment.
2) A mixed analysis of word frequencies, networks, and sentiment classification found terms representing collectivism and positive/negative emotions.
3) Ruling party politicians received more negative comments, suggesting social media impacts governing officials. Analyzing user comments provides insight into political landscapes.
This document analyzes the relationship between political blog posts and voter turnout in South Korean national assembly elections. The study monitored over 62,000 blog posts related to 29 candidates over a 12-day period. It found a positive correlation between the number of blog posts about a candidate and the number of votes received. A simple regression model indicated the number of blog posts was a significant predictor of votes. The results suggest political blogs can influence elections and real-time blog monitoring provides insights into socio-political issues and campaigns.
The document discusses different types of network analysis including social network analysis, semantic network analysis, and neural network analysis. It provides examples of how each has been used to analyze communication networks, language use in documents, and cognitive structures. Specific techniques are described like social network surveys, semantic network software, and the Galileo model for analyzing attitudes.
This document summarizes research using digital tools to study social science in South Korea and Taiwan. It describes how e-research allows the automation of research processes and analysis of new data sources like social media. Two case studies are presented: one analyzing politicians' use of the microblogging site Plurk in Taiwan, finding progressive politicians used it more; the other examining the changing network structures of South Korean politicians from Web 1.0 homepages to Web 2.0 blogs to Twitter, finding networks became denser over time. The research demonstrates how digital tools can provide new insights into political discussions and connections online.
The document introduces an API-based webometrics tool called WeboNaver that was created for the Korean search engine Naver. WeboNaver allows researchers to automatically collect large amounts of web data and distinguish different types of information. It provides an interface that allows users to select search queries, run them, and output the results. The tool provides a way to systematically analyze web presence and trends using data from the popular Korean search engine Naver.
The document summarizes an investigation of internet-based Korean politics using e-research tools. It discusses the development of tools like WeboNaver, Cyworld Extractor, and Twitter Extractor to analyze online prominence of politicians across platforms, semantic networks, and sentiment analysis. It also covers the theoretical framework and current status of e-research in South Korea.
1. The document summarizes a study that used webometrics analysis to map the e-science landscape in South Korea by analyzing over 1,000 webpages and 800 websites related to terms like e-research, e-science, and cyberinfrastructure.
2. The analysis found that media sites made up the majority of retrieved websites, and keywords like "cyberinfrastructure" were more prominent than terms like "e-science."
3. Co-link and inter-link analyses revealed collaboration structures but also an absence of links between universities researching digital humanities and government e-science institutions.
This document summarizes a research study analyzing the content of tweets sent by politicians from several countries including Korea, the UK, US and Canada. The researchers gathered Twitter data for the politicians using an API tool and conducted a preliminary content analysis of tweets from Korean politicians. They categorized the tweets into three types: socio-political messages about events and policies, private personal messages, and conversational messages intended for others using the "@" feature. The results of their content analysis on 878 tweets from Korean politicians found statistically significant differences in the frequencies of these three tweet types.
The document summarizes a proposal to investigate internet-based politics in South Korea using e-research tools. It involves recruiting two foreign scholars, Dr. Greg Elmer and Dr. Maurice Vergeer, to collaborate with the principal investigator, Dr. Han Woo Park, at YeungNam University. Over four years, they will develop new methods and tools to analyze political campaigns on social media and websites. They will also conduct educational seminars and workshops on topics like webometrics, social network analysis, and data visualization techniques.
1. SocsciBot 4 Link crawler for the social sciences 본 매뉴얼은 SocSciBot 3 (http://socscibot.wlv.ac.uk/) 한글버전입니다 . 박한우 ( 영남대학교 언론정보학과 , http://www.hanpark.net) SocSciBot 는 링크 분석 연구 목적으로 만들어진 웹 사이트 crawler 입니다 . 하나 혹은 여러 개의 사이트를 대상으로 한 링크 분석을 처리하는데 사용될 수 있거나 , 여러 개의 사이트를 대상으로 하여 검색 엔진을 실행하는데 사용될 수 있습니다 . 또한 링크 분석과 검색 엔진이 어떻게 작동하는 지 설명하는데 사용할 수 있습니다 .
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3. SocSciBot, ScoSciBot Tools and Cyclist 설치 및 사용 웹사이트 Crawl 하기 조사한 결과에 대한 기초적인 보고서 보기 ……………… . 4 ……………… .................................................................. 4 ……………………………………… 15 ……………………………………………………… . 22 SocsciBot 4 LIST 보길 원하는 부분을 클릭하면 바로 넘어갑니다 . 네트워크 다이어그램 보기 사이트 네트워크 보기 …………………………………………………… ....……. 28 EXIT
4. SocSciBot, ScoSciBot Tools and Cyclist 설치 및 사용 ① ─ 웹사이트 Crawl 하기 이 설명서 소개는 링크 데이터를 Crawl 하는 것에서부터 분석하는 것까지 매우 소규모 SocSciBot 프로젝트의 모든 단계들을 다루고 있습니다 . 이 설명서를 통해서 SocSciBot 이 할 수 있는 일이 무엇인지를 쉽게 알 수 있습니다 . LIST EXIT
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17. SocSciBot, ScoSciBot Tools and Cyclist 설치 및 사용 ② ─ 조사한 결과에 대한 기초 보고서 보기 LIST EXIT 이 설명서 소개는 링크 데이터를 Crawl 하는 것에서부터 분석하는 것까지 매우 소규모 SocSciBot 프로젝트의 모든 단계들을 다루고 있습니다 . 이 설명서를 통해서 SocSciBot 이 할 수 있는 일이 무엇인지를 쉽게 알 수 있습니다 .
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22. SocSciBot, ScoSciBot Tools and Cyclist 설치 및 사용 ③ ─ 네트워크 다이어그램 보기 LIST EXIT 이 설명서 소개는 링크 데이터를 Crawl 하는 것에서부터 분석하는 것까지 매우 소규모 SocSciBot 프로젝트의 모든 단계들을 다루고 있습니다 . 이 설명서를 통해서 SocSciBot 이 할 수 있는 일이 무엇인지를 쉽게 알 수 있습니다 .
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28. SocSciBot, ScoSciBot Tools and Cyclist 설치 및 사용 ④ ─ 사이트 네트워크 보기 LIST EXIT 이 설명서 소개는 링크 데이터를 Crawl 하는 것에서부터 분석하는 것까지 매우 소규모 SocSciBot 프로젝트의 모든 단계들을 다루고 있습니다 . 이 설명서를 통해서 SocSciBot 이 할 수 있는 일이 무엇인지를 쉽게 알 수 있습니다 .
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Editor's Notes
http://people.oii.ox.ac.uk/escher/wp-content/uploads/2007/09/socscibot2pajek_v1.0.zip SocSciBot2Pajek is a Perl script that converts link structure files collected with the SocSciBot crawler of Mike Thelwall into .net files for analysis with the social network analysis application Pajek. It also does some other useful stuff like creating a partition indicating the file types as well as producing a Pajek syntax file that will automatically compute some basic network statistics. You can download it here and let me know if you encounter any problems.
You can rearrange the network by clicking on nodes and dragging them around. This and also try the options in the tab on the right hand side of the screen to make the nodes and arrows bigger and smaller. Also, select some nodes by clicking and dragging across them and then right click to activate a menu of properties that can be changed. Change the colour of the selected nodes to yellow and try out some other changes.