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Dynamic Resource Allocation in Apache Spark
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Yuta Imai
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アドテク業界でのHadoopの利用についてまとめてました。前半はHadoopとは?という話で、後半がよくある使われ方です。
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Hadoop in adtech
Yuta Imai
Hadoopソースコードリーディング 第21回、Hadoop Summit San Jose 2016報告会で発表した資料です。 http://www.eventbrite.com/e/hadoop-21-tickets-26913657474
Hadoop/Spark セルフサービス系の事例まとめ
Hadoop/Spark セルフサービス系の事例まとめ
Yuta Imai
下記の勉強会で話した、IoTアプリケーションで実際に活用されるApache NiFiのデザインパターンについての資料です。 Apache NiFi 勉強会 〜データフローの自動化〜 http://futureofdata.connpass.com/event/35428/
IoTアプリケーションで利用するApache NiFi
IoTアプリケーションで利用するApache NiFi
Yuta Imai
Introducing Apache Kylin and Apache Druid, those are the open source OLAPs around hadoop world.
OLAP options on Hadoop
OLAP options on Hadoop
Yuta Imai
Hortonworks JapanのメンバーによるApache AmbariについてのWebinarで利用した資料です。
Apache ambari
Apache ambari
Yuta Imai
Hortonworks JapanのSparkについてのWebinarで話した資料です。以下、Webinarの概要。 このセッションでは、前半でApache Sparkの概要・仕組みと、ユースケースや使い所などをご紹介していきます。後半では、YARN上でSparkを動かす際のスケーラビリティや柔軟性についてのメリットについてお話します。これからSparkを始めたいと思っている方、Sparkは知っているけれども知識についての整理をしたい方、バッチ処理の高速化、ストリーム処理、機械学習などに興味のある方が対象です。Hadoopの知識は不要です。
Spark at Scale
Spark at Scale
Yuta Imai
今回のウェビナーでは、Hadoop1.xからみなさまに深く親しまれてきたApache Hiveが昨今、どのような形で高速化されてきたかについて話します。MapReduceからTezに変わった実行エンジン、インデックスを持ったカラムナーファイルフォーマットであるORC、モダンなCPUを最大限に活用するVectorization、Apache Calciteを利用したCost Based Optimizerによる実行計画の最適化、そして1秒以下のクエリレスポンスを実現するLLAPについて説明します。いずれの機能も数行の設定やコマンドで活用可能なものばかりですが、今回はそれらの背景でどんな仕組みが動いているのか、どんな仕組みで実現されているのかということについて話します。
Apache Hiveの今とこれから - 2016
Apache Hiveの今とこれから - 2016
Yuta Imai
Hadoopを取り巻く最新事情や事例をまとめてお伝えするとともに、Hortonworks Data Platform (HDP) の最新版であるHDP2.4を始めとし、データフローの管理ソフトウェアパッケージであるHortonworks DataFlow (HDF)、クラウド環境へのHDPのデプロイを簡単に行うCloudBreakなど、Hortonworksの提供するソフトウェアをひと通りご紹介いたします。Hadoopについて知りたい方から、普段Hadoopを触っている方にもお楽しみいただけるよう、できるだけ技術的な話を中心に説明いたします。
Hadoop最新事情とHortonworks Data Platform
Hadoop最新事情とHortonworks Data Platform
Yuta Imai
The story about how to figure out what to measure, and how you can benchmark that. This slide deck tells the idea of benchmarking and does not tell actual commercial/open source benchmark tools.
Benchmark and Metrics
Benchmark and Metrics
Yuta Imai
Security Recap and Updates for Hadoop. it's all based on Kerberos.
Hadoop and Kerberos
Hadoop and Kerberos
Yuta Imai
Spark Casual Talk #1( http://connpass.com/event/15575/ )で発表したSpark Streaming + Amazon Kinesisの話。
Spark Streaming + Amazon Kinesis
Spark Streaming + Amazon Kinesis
Yuta Imai
オンラインゲームの仕組みや工夫を調べてみたのを社内勉強会で発表した。ときのスライド。の公開用。 オンラインゲームの種別とそれぞれの仕組みについての話と、オープンソースになっているQuakeの仕組みの話、という2つの話が主なトピック
オンラインゲームの仕組みと工夫
オンラインゲームの仕組みと工夫
Yuta Imai
Amazon Machine Learningの紹介資料です。機械学習とは?というところから入り、Amazon Machine Learningでどんなことができるのは、何に使えるのか、という点について解説しています。
Amazon Machine Learning
Amazon Machine Learning
Yuta Imai
OGC2015のプレゼンで使ったスライド。AWSのゲームSA、Nate WigerのGDC2015でのプレゼンを元にしています。
Global Gaming On AWS
Global Gaming On AWS
Yuta Imai
Developers IO 2015 Business Day( http://devio2015.classmethod.jp/about/business/ )で話した「デジタルマーケティングon AWS」のスライドです。
Digital marketing on AWS
Digital marketing on AWS
Yuta Imai
よくわかるAmazon EC2セミナーにて使用した資料です。
EC2のストレージどう使う? -Instance Storageを理解して高速IOを上手に活用!-
EC2のストレージどう使う? -Instance Storageを理解して高速IOを上手に活用!-
Yuta Imai
発表資料@ 第27回 データマイニング+WEB@東京( #TokyoWebmining 27th) -WEB解析・オープンデータ・クラウド 祭り-
クラウドネイティブなアーキテクチャでサクサク解析
クラウドネイティブなアーキテクチャでサクサク解析
Yuta Imai
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Node-RED on device to Apache NiFi on cloud, via SORACOM Canal, with no Internet
Node-RED on device to Apache NiFi on cloud, via SORACOM Canal, with no Internet
HDP2.5 Updates
HDP2.5 Updates
Deep Learning On Apache Spark
Deep Learning On Apache Spark
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Hadoop in adtech
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Hadoop/Spark セルフサービス系の事例まとめ
IoTアプリケーションで利用するApache NiFi
IoTアプリケーションで利用するApache NiFi
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Apache ambari
Apache ambari
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Spark at Scale
Apache Hiveの今とこれから - 2016
Apache Hiveの今とこれから - 2016
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Hadoop最新事情とHortonworks Data Platform
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Benchmark and Metrics
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Spark Streaming + Amazon Kinesis
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EC2のストレージどう使う? -Instance Storageを理解して高速IOを上手に活用!-
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Building Digital Trust in a Digital Economy Veronica Tan, Director - Cyber Security Agency of Singapore Apidays Singapore 2024: Connecting Customers, Business and Technology (April 17 & 18, 2024) ------ Check out our conferences at https://www.apidays.global/ Do you want to sponsor or talk at one of our conferences? https://apidays.typeform.com/to/ILJeAaV8 Learn more on APIscene, the global media made by the community for the community: https://www.apiscene.io Explore the API ecosystem with the API Landscape: https://apilandscape.apiscene.io/
Apidays Singapore 2024 - Building Digital Trust in a Digital Economy by Veron...
Apidays Singapore 2024 - Building Digital Trust in a Digital Economy by Veron...
apidays
In an era where artificial intelligence (AI) stands at the forefront of business innovation, Information Architecture (IA) is at the core of functionality. See “There’s No AI Without IA” – (from 2016 but even more relevant today) Understanding and leveraging how Information Architecture (IA) supports AI synergies between knowledge engineering and prompt engineering is critical for senior leaders looking to successfully deploy AI for internal and externally facing knowledge processes. This webinar be a high-level overview of the methodologies that can elevate AI-driven knowledge processes supporting both employees and customers. Core Insights Include: Strategic Knowledge Engineering: Delve into how structuring AI's knowledge base is required to prevent hallucinations, enable contextual retrieval of accurate information. This will include discussion of gold standard libraries of use cases support testing various LLMs and structures and configurations of knowledge base. Precision in Prompt Engineering: Learn the art of crafting prompts that direct AI to deliver targeted, relevant responses, thereby optimizing customer experiences and business outcomes. Unified Approach for Enhanced AI Performance: Explore the intersection of knowledge and prompt engineering to develop AI systems that are not only more responsive but also aligned with overarching business strategies. Guiding Principles for Implementation: Equip yourself with best practices, ethical guidelines, and strategic considerations for embedding these technologies into your business ecosystem effectively. This webinar is designed to empower business and technology leaders with the knowledge to harness the full potential of AI, ensuring their organizations not only keep pace with digital transformation but lead the charge. Join us to map a roadmap to fully leverage Information Architecture (IA) and AI chart a course towards a future where AI is a key pillar of strategic innovation and business success.
EIS-Webinar-Prompt-Knowledge-Eng-2024-04-08.pptx
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Choosing the right accounts payable services provider is a strategic decision that can significantly impact your business's financial performance and operational efficiency. By considering factors such as expertise, range of services, technology infrastructure, scalability, cost, and reputation, businesses can make informed decisions and select a provider that aligns with their unique needs and objectives. Partnering with the right provider can streamline accounts payable processes, drive cost savings, and position your business for long-term success. https://katprotech.com/accounts-payable-and-purchase-order-automation/
Factors to Consider When Choosing Accounts Payable Services Providers.pptx
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Katpro Technologies
Presentation on the progress in the Domino Container community project as delivered at the Engage 2024 conference
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Martijn de Jong
What are drone anti-jamming systems? The drone anti-jamming systems and anti-spoof technology protect against interference, jamming, and spoofing of the UAVs. To protect their security, countries are beginning to research drone anti-jamming systems, also known as drone strike weapons. The anti-jam and anti-spoof technology protects against interference, jamming and spoofing. A drone strike weapon is a drone attack weapon that can attack and destroy enemy drones. So what is so unique about this amazing system?
What Are The Drone Anti-jamming Systems Technology?
What Are The Drone Anti-jamming Systems Technology?
Antenna Manufacturer Coco
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Heather Hedden, Senior Consultant at Enterprise Knowledge, presented “The Role of Taxonomy and Ontology in Semantic Layers” at a webinar hosted by Progress Semaphore on April 16, 2024. Taxonomies at their core enable effective tagging and retrieval of content, and combined with ontologies they extend to the management and understanding of related data. There are even greater benefits of taxonomies and ontologies to enhance your enterprise information architecture when applying them to a semantic layer. A survey by DBP-Institute found that enterprises using a semantic layer see their business outcomes improve by four times, while reducing their data and analytics costs. Extending taxonomies to a semantic layer can be a game-changing solution, allowing you to connect information silos, alleviate knowledge gaps, and derive new insights. Hedden, who specializes in taxonomy design and implementation, presented how the value of taxonomies shouldn’t reside in silos but be integrated with ontologies into a semantic layer. Learn about: - The essence and purpose of taxonomies and ontologies in information and knowledge management; - Advantages of semantic layers leveraging organizational taxonomies; and - Components and approaches to creating a semantic layer, including the integration of taxonomies and ontologies
The Role of Taxonomy and Ontology in Semantic Layers - Heather Hedden.pdf
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writing some innovation for development and search
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naman860154
An excellent report on AI technology, specifically generative AI, the next step after ChatGPT from Epam. Impact Assessments, Road Charts with fully updated Results and new charts.
A Call to Action for Generative AI in 2024
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Results
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What is a good lead in your organisation? Which leads are priority? What happens to leads? When sales and marketing give different answers to these questions, or perhaps aren't sure of the answers at all, frustrations build and opportunities are left on the table. Join us for an illuminating session with Cian McLoughlin, HubSpot Principal Customer Success Manager, as we look at that crucial piece of the customer journey in which leads are transferred from marketing to sales.
04-2024-HHUG-Sales-and-Marketing-Alignment.pptx
04-2024-HHUG-Sales-and-Marketing-Alignment.pptx
HampshireHUG
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Created by Mozilla Research in 2012 and now part of Linux Foundation Europe, the Servo project is an experimental rendering engine written in Rust. It combines memory safety and concurrency to create an independent, modular, and embeddable rendering engine that adheres to web standards. Stewardship of Servo moved from Mozilla Research to the Linux Foundation in 2020, where its mission remains unchanged. After some slow years, in 2023 there has been renewed activity on the project, with a roadmap now focused on improving the engine’s CSS 2 conformance, exploring Android support, and making Servo a practical embeddable rendering engine. In this presentation, Rakhi Sharma reviews the status of the project, our recent developments in 2023, our collaboration with Tauri to make Servo an easy-to-use embeddable rendering engine, and our plans for the future to make Servo an alternative web rendering engine for the embedded devices industry. (c) Embedded Open Source Summit 2024 April 16-18, 2024 Seattle, Washington (US) https://events.linuxfoundation.org/embedded-open-source-summit/ https://ossna2024.sched.com/event/1aBNF/a-year-of-servo-reboot-where-are-we-now-rakhi-sharma-igalia
A Year of the Servo Reboot: Where Are We Now?
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Igalia
In this session, we will delve into strategic approaches for optimizing knowledge management within Microsoft 365, amidst the evolving landscape of Copilot. From leveraging automatic metadata classification and permission governance with SharePoint Premium, to unlocking Viva Engage for the cultivation of knowledge and communities, you will gain actionable insights to bolster your organization's knowledge-sharing initiatives. In this session, we will also explore how to facilitate solutions to enable your employees to find answers and expertise within Microsoft 365. You will leave equipped with practical techniques and a deeper understanding of how there is more to effective knowledge management than just enabling Copilot, but building actual solutions to prepare the knowledge that Copilot and your employees can use.
Strategies for Unlocking Knowledge Management in Microsoft 365 in the Copilot...
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Drew Madelung
Explore 'The Codex of Business: Writing Software for Real-World Solutions,' a compelling SlideShare presentation that delves into digital transformation in healthcare. Discover through a detailed case study how Agile methodologies empower healthcare providers to develop, iterate, and refine digital solutions that address real-world challenges. Learn how strategic planning, user feedback, and continuous improvement drive success in deploying technologies that enhance patient care and operational efficiency. Ideal for healthcare professionals, IT specialists, and digital transformation advocates seeking actionable insights and practical examples of technology making a real difference.
The Codex of Business Writing Software for Real-World Solutions 2.pptx
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This project focuses on implementing real-time object detection using Raspberry Pi and OpenCV. Real-time object detection is a critical aspect of computer vision applications, allowing systems to identify and locate objects within a live video stream instantly.
Real Time Object Detection Using Open CV
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Dynamic Resource Allocation in Apache Spark
1.
Dynamic Resource Alloca1on in Apache Spark Yuta Imai @imai_factory
2.
1. RDD Graph val text = "Hello Spark, this is my first Spark application." val textArray = text.split(" ").map(_.replaceAll(" ","")) val result = sc.parallelize(textArray) .map(item => (item, 1)) .reduceByKey((x,y) => x + y) .collect()
3.
Array Array ParallelCollec1onRDD Par11on0 Par11on1 Par11on2 Par11on3 MapPar11onsRDD Par11on0 Par11on1 Par11on2 Par11on3 ShuffledRDD Par11on0 Par11on1 sc.parallelize() .map(…)
.reduceByKey(…) .collect() 2. DAG Scheduler
4.
Array Array ParallelCollec1onRDD Par11on0 Par11on1 Par11on2 Par11on3 MapPar11onsRDD Par11on0 Par11on1 Par11on2 Par11on3 ShuffledRDD Par11on0 Par11on1 sc.parallelize() .map(…)
.reduceByKey(…) .collect() 2. DAG Scheduler Narrow Dependency Shuffle Dependency
5.
Array Array ParallelCollec1onRDD Par11on0 Par11on1 Par11on2 Par11on3 MapPar11onsRDD Par11on0 Par11on1 Par11on2 Par11on3 ShuffledRDD Par11on0 Par11on1 sc.parallelize() .map(…)
.reduceByKey(…) .collect() 2. DAG Scheduler Narrow Dependency Shuffle Dependency Stage0 Stage1 Task0 Task1 Task2 Task3 Task4 Task5
6.
3. Task Scheduler Par11on0 Par11on1 Par11on2 Par11on3 Par11on0 Par11on1 Par11on2 Par11on3 Task0 Task1 Task2 Task3 Executors
7.
Shuffle File iterator.map(…).map(...)... Executor Thread Storage Worker Node iterator.map(…).map(...)... Executor Thread Worker Node
8.
DYNAMIC RESOURCE ALLOCATION
9.
Dynamic Resource Alloca1on • Adds extra executors to an app which has pending tasks. – Offloads challenge for exact resource planning for an app. • Removes idle executors from an app. – Helps a long running app to free idle executors.
10.
Overview Tasks Executors
11.
Overview Tasks Executors Insufficient capacity
12.
Overview Tasks Executors Insufficient capacity
13.
Overview Tasks Executors Insufficient capacity
14.
Overview Tasks Executors Insufficient capacity Op1mal capacity
15.
Overview Tasks Executors ✔ ✔ Insufficient capacity Op1mal capacity
Idle executors
16.
Tasks Executors ✔ ✔ Overview Insufficient capacity Op1mal capacity
Idle executors Op1mal capacity
17.
Request Policy • An app starts with user specified # of executors. ./bin/spark-submit --class <main-class> --master <master-url> --num-executors <# of executors> • Ader spark.dynamicAlloca1on.schedulerBacklogTimeout(sec), App starts reques1ng new executors, if it has pending task(s). •
App requests new executors every spark.dynamicAlloca1on.sustainedSchedulerBacklogTimeout(sec), with doubling # of requests like 1, 2, 4, 8, 16…
18.
Remove Policy • An app removes an executor when it has been idle for more than spark.dynamicAlloca1on.executorIdleTimeout seconds.
19.
External Shuffle Service iterator.map(…).map(...)... Executor Thread Storage Worker Node iterator.map(…).map(...)... Executor Thread Worker Node
20.
External Shuffle Service iterator.map(…).map(...)... Executor Thread Storage Worker Node iterator.map(…).map(...)... Executor Thread Worker Node
21.
External Shuffle Service iterator.map(…).map(...)... Executor Thread Storage Worker Node iterator.map(…).map(...)... Executor Thread Worker Node Shuffle Service Shuffle Service
Download now