<Apache Spark Graph Processing>中文版前4章,有需要的朋友拿去 [问题点数:20分]

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Apache Spark Graph Processing. pdf
Apache Spark Graph Processing. pdf 是用于Spark图计算的书,高大上必备
Apache_Spark_Graph_Processing
Apache_Spark_Graph_Processing 原版英文书
Apache Spark Graph Processing中文版前4章带目录修正版
Databricks推荐的Spark GraphX库的入门学习资料。原书共7章,这里翻译了前4章。后面几章有时间的话可能会继续,可以关注我的blog了解进度,但不保证哈~。昨晚上传了一版后发现保存后的目录乱的很,这是修改过的,重新上传。
Apache Spark Graph Processing PDF
This book is intended to present the GraphX library for Apache Spark and to teach the fundamental techniques and recipes to process <em>graph</em> data at scale. It is intended to be a self-study step-by-step guide for anyone new to Spark with an interest in or need for large-scale <em>graph</em> <em>processing</em>.
Graph Algorithms:Practical Examples in Apache Spark and Neo4j+高清无码书签完整内容可编辑完美资源
Graph Algorithms by Mark Needham and Amy E. Hodler Copyright © 2019 Amy Hodler and Mark Needham. All rights reserved. What’s in This Book This book is a practical guide to getting started with <em>graph</em> algorithms for developers and data scientists who have experience using Apache Spark™ or Neo4j. Although our algorithm examples utilize the Spark and Neo4j platforms, this book will also be helpful for understanding more general <em>graph</em> concepts, regardless of your choice of <em>graph</em> technologies. The first two chapters provide an introduction to <em>graph</em> analytics, algorithms, and theory. The third chapter briefly covers the platforms used in this book before we dive into three chapters focusing on classic <em>graph</em> algorithms: pathfinding, centrality, and community detection. We wrap up the book with two chapters showing how <em>graph</em> algorithms are used within workflows: one for general analysis and one for machine learning. At the beginning of each category of algorithms, there is a reference table to help you quickly jump to the relevant algorithm. For each algorithm, you’ll find: • An explanation of what the algorithm does • Use cases for the algorithm and references to where you can learn more • Example code providing concrete ways to use the algorithm in Spark, Neo4j, or both 图方法方面最新的参考书,本文理论实践兼备(看标题就知道了),内容高清无码书签完整诚不我欺,强烈推荐给<em>需要</em>的<em>朋友</em>!
Large-Scale Graph Processing on Spark
Large-Scale Graph Processing on Spark
Graph Algorithms: Practical Examples in Apache Spark and Neo4j
Graph Algorithms: Practical Examples in Apache Spark and Neo4j By 作者: Mark Needham – Amy E. Hodler ISBN-10 书号: 1492047686 ISBN-13 书号: 9781492047681 Edition 版本: 1 出版日期: 2019-01-04 pages 页数: (217) Discover how <em>graph</em> algorithms can help you leverage the relationships within your data to develop more intelligent solutions and enhance your machine learning models. You’ll learn how <em>graph</em> analytics are uniquely suited to unfold complex structures and reveal difficult-to-find patterns lurking in your data. Whether you are trying to build dynamic network models or forecast real-world behavior, this book illustrates how <em>graph</em> algorithms deliver value—from finding vulnerabilities and bottlenecks to detecting communities and improving machine learning predictions. This practical book walks you through hands-on examples of how to use <em>graph</em> algorithms in Apache Spark and Neo4j—two of the most common choices for <em>graph</em> analytics. Also included: sample code and tips for over 20 practical <em>graph</em> algorithms that cover optimal pathfinding, importance through centrality, and community detection. Learn how <em>graph</em> analytics vary from conventional statistical analysis Understand how classic <em>graph</em> algorithms work, and how they are applied Get guidance on which algorithms to use for different types of questions Explore algorithm examples with working code and sample datasets from Spark and Neo4j See how connected feature extraction can increase machine learning accuracy and precision Walk through creating an ML workflow for link prediction combining Neo4j and Spark
Stream Processing with Apache Flink
Stream Processing with Apache Flink,2019年Flink最新英文书籍,epub格式,欢迎下载
Big Data Processing with Apache Spark by Srini Penchikala
ig Data Processing with Apache Spark By 作者: Srini Penchikala ISBN-10 书号: 1387659952 ISBN-13 书号: 9781387659951 出版日期: 2018-05-08 pages 页数: (104 ) $19.99 Apache Spark is a popular open-source big-data <em>processing</em> framework that’s built around speed, ease of use, and unified distributed computing architecture. Not only it supports developing applications in different languages like Java, Scala, Python, and R, it’s also hundred times faster in memory and ten times faster even when running on disk compared to traditional data <em>processing</em> frameworks. Whether you are currently working on a big data project or interested in learning more about topics like machine learning, streaming data <em>processing</em>, and <em>graph</em> data analytics, this book is for you. You can learn about Apache Spark and develop Spark programs for various use cases in big data analytics using the code examples provided. This book covers all the libraries in Spark ecosystem: Spark Core, Spark SQL, Spark Streaming, Spark ML, and Spark GraphX.
Stream Processing with Apache Flink(Early Release)
Stream Processing with Apache Flink(Early Release)
[OSDI 14] GraphX 基于Spark-Core下的分布式大图处理系统 学习总结
    今天要讲的文章是OSDI 2010年的一篇文章,GraphX:  Graph Processing in a Distributed Dataflow Framework。本文主要想解决的问题就是:先有的专用图系统能够实现广泛的系统优化,但也是有代价的。 图只是较大的分析过程的一部分,通常将非结构化的图数据和表格式数据组合在一起。 因此,分析dataflow被迫组成多个系统,这增加了复杂性...
即将发布的 Apache Spark 2.4 都有哪些新功能
n n n nn nn n n 本文来自于2018年09月19日在 Adobe Systems Inc 举行的 Apache Spark Meetup。即将发布的 Apache Spark 2.4 版本...
Large-Scale Graph Processing Using Apache Giraph
Large-Scale Graph Processing Using Apache Giraph
spark graphx 图计算demo,结果展现
<em>spark</em> <em>graph</em>x 图计算官网实例练习:rnhttp://<em>spark</em>.<em>apache</em>.org/docs/latest/<em>graph</em>x-programming-guide.htmlrnrnrnrnrnimport org.<em>apache</em>.<em>spark</em>._rnimport org.<em>apache</em>.<em>spark</em>.<em>graph</em>x._rn// To make some of the examples work we
Graph_Algorithms_Neo4j.epub
Graph Algorithms Practical Examples in Apache Spark & Neo4j
Frank Kane's Taming Big Data with Apache Spark and Python 【含代码】
Frank Kane's Taming Big Data with Apache Spark and Python English | 2017 | ISBN-10: 1787287947 | 296 pages | AZW3/PDF/EPUB (conv) | 6.12 Mb Key Features Understand how Spark can be distributed across computing clusters Develop and run Spark jobs efficiently using Python A hands-on tutorial by Frank Kane with over 15 real-world examples teaching you Big Data <em>processing</em> with Spark Book Description Frank Kane's Taming Big Data with Apache Spark and Python is your companion to learning Apache Spark in a hands-on manner. Frank will start you off by teaching you how to set up Spark on a single system or on a cluster, and you'll soon move on to analyzing large data sets using Spark RDD, and developing and running effective Spark jobs quickly using Python. Apache Spark has emerged as the next big thing in the Big Data domain – quickly rising from an ascending technology to an established superstar in just a matter of years. Spark allows you to quickly extract actionable insights from large amounts of data, on a real-time basis, making it an essential tool in many modern businesses. Frank has packed this book with over 15 interactive, fun-filled examples relevant to the real world, and he will empower you to understand the Spark ecosystem and implement production-grade real-time Spark projects with ease. What you will learn Find out how you can identify Big Data problems as Spark problems Install and run Apache Spark on your computer or on a cluster Analyze large data sets across many CPUs using Spark's Resilient Distributed Datasets Implement machine learning on Spark using the MLlib library Process continuous streams of data in real time using the Spark streaming module Perform complex network analysis using Spark's GraphX library Use Amazon's Elastic MapReduce service to run your Spark jobs on a cluster About the Author My name is Frank Kane. I spent nine years at Amazon and IMDb, wrangling millions of customer ratings and customer transactions to produce things such as personalized recommendations for movies and products and "people who bought this also bought." I tell you, I wish we had Apache Spark back then, when I spent years trying to solve these problems there. I hold 17 issued patents in the fields of distributed computing, data mining, and machine learning. In 2012, I left to start my own successful company, Sundog Software, which focuses on virtual reality environment technology, and teaching others about big data analysis. Table of Contents Getting Started with Spark Spark Basics and Simple Examples Advanced Examples of Spark Programs Running Spark on a Cluster SparkSQL, Dataframes and Datasets Other Spark Technologies and Libraries Where to Go From Here? - Learning More About Spark and Data Science
spark graphx实现共同好友的聚合
<em>spark</em> <em>graph</em>x是一款优秀的图计算框架,对于批量计算图计算借助于<em>spark</em>的计算引擎,实现数据的快速聚合。rn对于最基本的 共同好友推荐可以很方便的实现,一下为实现代码:rn数据源的数据格式:rn 1 2rnrnrn2 4rn。。。rnrnrnrnpackage mobnnimport org.<em>apache</em>.<em>spark</em>.<em>graph</em>x.{GraphLoader, VertexRDD}nimp
Apache Spark Graph Processing(PACKT,2015)
Apache Spark is the next standard of open-source cluster-computing engine for <em>processing</em> big data. Many practical computing problems concern large <em>graph</em>s, like the Web <em>graph</em> and various social networks. The scale of these <em>graph</em>s – in some cases billions of vertices, trillions of edges – poses challenges to their efficient <em>processing</em>. Apache Spark GraphX API combines the advantages of both data-parallel and <em>graph</em>-parallel systems by efficiently expressing <em>graph</em> computation within the Spark data-parallel framework. This book will teach the user to do <em>graph</em>ical programming in Apache Spark, apart from an explanation of the entire process of <em>graph</em>ical data analysis. You will journey through the creation of <em>graph</em>s, its uses, its exploration and analysis and finally will also cover the conversion of <em>graph</em> elements into <em>graph</em> structures. This book begins with an introduction of the Spark system, its libraries and the Scala Build Tool. Using a hands-on approach, this book will quickly teach you how to install and leverage Spark interactively on the command line and in a standalone Scala program. Then, it presents all the methods for building Spark <em>graph</em>s using illustrative network datasets. Next, it will walk you through the process of exploring, visualizing and analyzing different network characteristics. This book will also teach you how to transform raw datasets into a usable form. In addition, you will learn powerful operations that can be used to transform <em>graph</em> elements and <em>graph</em> structures. Furthermore, this book also teaches how to create custom <em>graph</em> operations that are tailored for specific needs with efficiency in mind. The later chapters of this book cover more advanced topics such as clustering <em>graph</em>s, implementing <em>graph</em>-parallel iterative algorithms and learning methods from <em>graph</em> data.
[Spark共同好友查找]
共同好友的概念nn      在一个庞大的社交网络中,两个互相认识的<em>朋友</em>之间的也会存在共同好友。在这个庞大的社交网络总,对所有的用户对中找到”共同好友”,这是一个复杂及有趣的事情。假设,U为一个用户及其所有好友的一个集合:{U1,U2,U3,…Un},我们要从每组集合(Ui,Uj)(i != j)找出共同好友关系。nn       在如今的大多数社交网络(Facebook,LinkedIn,QQ)...
Spark: The Definitive Guide: Big Data Processing Made Simple 英文高清.pdf版
Spark: The Definitive Guide: Big Data Processing Made Simple English | Oct. 2017 | ISBN-10: 1491912219 | 450 pages | PDF | 4.46 Mb Learn how to use, deploy, and maintain Apache Spark with this comprehensive guide, written by the creators of this open-source cluster-computing framework. With an emphasis on improvements and new features in Spark 2.0, authors Bill Chambers and Matei Zaharia break down Spark topics into distinct sections, each with unique goals. You'll explore the basic operations and common functions of Spark's structured APIs, as well as Structured Streaming, a new high-level API for building end-to-end streaming applications. Developers and system administrators will learn the fundamentals of monitoring, tuning, and debugging Spark, and explore machine learning techniques and scenarios for employing MLlib, Spark's scalable machine learning library. Get a gentle overview of big data and Spark Learn about DataFrames, SQL, and Datasets-Spark's core APIs-through worked examples Dive into Spark's low-level APIs, RDDs, and execution of SQL and DataFrames Understand how Spark runs on a cluster Debug, monitor, and tune Spark clusters and applications Learn the power of Spark's Structured Streaming and MLlib for machine learning tasks Explore the wider Spark ecosystem, including SparkR and Graph Analysis Examine Spark deployment, including coverage of Spark in the Cloud
Mastering.Apache.Spark.178397146
About This Book Explore the integration of Apache Spark with third party applications such as H20, Databricks and Titan Evaluate how Cassandra and Hbase can be used for storage An advanced guide with a combination of instructions and practical examples to extend the most up-to date Spark functionalities Who This Book Is For If you are a developer with some experience with Spark and want to strengthen your knowledge of how to get around in the world of Spark, then this book is ideal for you. Basic knowledge of Linux, Hadoop and Spark is assumed. Reasonable knowledge of Scala is expected. What You Will Learn Extend the tools available for <em>processing</em> and storage Examine clustering and classification using MLlib Discover Spark stream <em>processing</em> via Flume, HDFS Create a schema in Spark SQL, and learn how a Spark schema can be populated with data Study Spark based <em>graph</em> <em>processing</em> using Spark GraphX Combine Spark with H20 and deep learning and learn why it is useful Evaluate how <em>graph</em> storage works with Apache Spark, Titan, HBase and Cassandra Use Apache Spark in the cloud with Databricks and AWS In Detail Apache Spark is an in-memory cluster based parallel <em>processing</em> system that provides a wide range of functionality like <em>graph</em> <em>processing</em>, machine learning, stream <em>processing</em> and SQL. It operates at unprecedented speeds, is easy to use and offers a rich set of data transformations. This book aims to take your limited knowledge of Spark to the next level by teaching you how to expand Spark functionality. The book commences with an overview of the Spark eco-system. You will learn how to use MLlib to create a fully working neural net for handwriting recognition. You will then discover how stream <em>processing</em> can be tuned for optimal performance and to ensure parallel <em>processing</em>. The book extends to show how to incorporate H20 for machine learning, Titan for <em>graph</em> based storage, Databricks for cloud-based Spark. Intermediate Scala based code examples are provided for Apache Spark module <em>processing</em> in a CentOS Linux and Databricks cloud environment. Table of Contents Chapter 1: Apache Spark Chapter 2: Apache Spark Mllib Chapter 3: Apache Spark Streaming Chapter 4: Apache Spark Sql Chapter 5: Apache Spark Graphx Chapter 6: Graph-Based Storage Chapter 7: Extending Spark With H2O Chapter 8: Spark Databricks Chapter 9: Databricks Visualization
HeadFirstJava中文版
HeadFirstJava<em>中文版</em>,<em>需要</em>的<em>朋友</em>可以<em>拿去</em>
Graph Algorithms Practical Examples in Apache Spark and Neo4j
Graph Algorithms Practical Examples in Apache Spark and Neo4j 2019-04-15 月的书
Cooperative and Graph Signal Processing
CONTENT PART 1 BASICS OF INFERENCE OVER NETWORKS CHAPTER 1 Asynchronous Adaptive Networks CHAPTER 2 Estimation and Detection Over Adaptive Networks CHAPTER 3 Multitask Learning Over Adaptive Networks With Grouping CHAPTER 4 Bayesian Approach to Collaborative Inference in Networks CHAPTER 5 Multiagent Distributed Optimization CHAPTER 6 Distributed Kalman and Particle Filtering CHAPTER 7 Game Theoretic Learning PART 2 SIGNAL PROCESSING ON GRAPHS CHAPTER 8 Graph Signal Processing . CHAPTER 9Sampling and Recovery of Graph Signals CHAPTER 10 Bayesian Active learning on Graphs . CHAPTER 1 1 Design of Graph Filters and Filterbanks CHAPTER 12 Statistical Graph Signal Processing: Stationarity and Spectral Estimation CHAPTER 1 3 Inference of Graph Topology CHAPTER 14 Partially Absorbing Ranclom Walks: A Unifiecl Framework for Learning on Graphs PART 3 DISTRIBUTED COMMUNICATIONS, NETWORKING, AND SENSING . . . . .
Mastering Apache Spark(PACKT,2015)
Apache Spark is an in-memory cluster based parallel <em>processing</em> system that provides a wide range of functionality like <em>graph</em> <em>processing</em>, machine learning, stream <em>processing</em> and SQL. It operates at unprecedented speeds, is easy to use and offers a rich set of data transformations. This book aims to take your limited knowledge of Spark to the next level by teaching you how to expand Spark functionality. The book commences with an overview of the Spark eco-system. You will learn how to use MLlib to create a fully working neural net for handwriting recognition. You will then discover how stream <em>processing</em> can be tuned for optimal performance and to ensure parallel <em>processing</em>. The book extends to show how to incorporate H20 for machine learning, Titan for <em>graph</em> based storage, Databricks for cloud-based Spark. Intermediate Scala based code examples are provided for Apache Spark module <em>processing</em> in a CentOS Linux and Databricks cloud environment.
graph algorithms neo4j
图计算 请查看,真的很不错。你指的拥有哈!
Apache Spark的设计与实现 PDF中文版
本文主要讨论 Apache Spark 的设计与实现,重点关注其设计思想、运行原理、实现架构及性能调优,附带讨论与 Hadoop MapReduce 在设计与实现上的区别。不喜欢将该文档称之为“源码分析”,因为本文的主要目的不是去解读实现代码,而是尽量有逻辑地,从设计与实现原理的角度,来理解 job 从产生到执行完成的整个过程,进而去理解整个系统。 讨论系统的设计与实现有很多方法,本文选择 问题驱动 的方式,一开始引入问题,然后分问题逐步深入。从一个典型的 job 例子入手,逐渐讨论 job 生成及执行过程中所<em>需要</em>的系统功能支持,然后有选择地深入讨论一些功能模块的设计原理与实现方式。也许这样的方式比一开始就分模块讨论更有主线。 本文档面向的是希望对 Spark 设计与实现机制,以及大数据分布式处理框架深入了解的 Geeks。 因为 Spark 社区很活跃,更新速度很快,本文档也会尽量保持同步,文档号的命名与 Spark 版本一致,只是多了一位,最后一位表示文档的版本号。 由于技术水平、实验条件、经验等限制,当前只讨论 Spark core standalone 版本中的核心功能,而不是全部功能。诚邀各位小伙伴们加入进来,丰富和完善文档。 好久没有写这么完整的文档了,上次写还是三年前在学 Ng 的 ML 课程的时候,当年好有激情啊。这次的撰写花了 20+ days,从暑假写到现在,大部分时间花在 debug、画图和琢磨怎么写上,希望文档能对大家和自己都有所帮助。 内容 本文档首先讨论 job 如何生成,然后讨论怎么执行,最后讨论系统相关的功能特性。具体内容如下: Overview 总体介绍 Job logical plan 介绍 job 的逻辑执行图(数据依赖图) Job physical plan 介绍 job 的物理执行图 Shuffle details 介绍 shuffle 过程 Architecture 介绍系统模块如何协调完成整个 job 的执行 Cache and Checkpoint 介绍 cache 和 checkpoint 功能 Broadcast 介绍 broadcast 功能 Job Scheduling
learning spark 中文版
Spark,是一种通用的大数据计算框架,正如传统大数据技术Hadoop的MapReduce、Hive引擎,以及Storm流式实时计算引擎等。 Spark包含了大数据领域常见的各种计算框架:比如Spark Core用于离线计算,Spark SQL用于交互式查询,Spark Streaming用于实时流式计算,Spark MLlib用于机器学习,Spark GraphX用于图计算。 Spark主要用于大数据的计算,而Hadoop以后主要用于大数据的存储(比如HDFS、Hive、HBase等),以及资源调度(Yarn)。 Spark+Hadoop的组合,是未来大数据领域最热门的组合,也是最有前景的组合 --------------------- 作者:大数据精英 来源:CSDN 原文:https://blog.csdn.net/qq_42107047/article/details/80239094 版权声明:本文为博主原创文章,转载请附上博文链接!
Spark GraphX 的数据可视化
Spark GraphX 本身并不提供可视化的支持, 我们通过第三方库 GraphStream 和 Breeze 来实现这一目标
Apache Spark 2 for Beginners [2016]
Apache Spark 2.0 for Beginners English | ISBN: 1785885006 | 2016 | Key Features This book offers an easy introduction to the Spark framework published on the latest version of Apache Spark 2 Perform efficient data <em>processing</em>, machine learning and <em>graph</em> <em>processing</em> using various Spark components A practical guide aimed at beginners to get them up and running with Spark Book Description Spark is one of the most widely-used large-scale data <em>processing</em> engines and runs extremely fast. It is a framework that has tools that are equally useful for application developers as well as data scientists. This book starts with the fundamentals of Spark 2 and covers the core data <em>processing</em> framework and API, installation, and application development setup. Then the Spark programming model is introduced through real-world examples followed by Spark SQL programming with DataFrames. An introduction to SparkR is covered next. Later, we cover the charting and plotting features of Python in conjunction with Spark data <em>processing</em>. After that, we take a look at Spark's stream <em>processing</em>, machine learning, and <em>graph</em> <em>processing</em> libraries. The last chapter combines all the skills you learned from the preceding chapters to develop a real-world Spark application. By the end of this book, you will have all the knowledge you need to develop efficient large-scale applications using Apache Spark. What you will learn Get to know the fundamentals of Spark 2 and the Spark programming model using Scala and Python Know how to use Spark SQL and DataFrames using Scala and Python Get an introduction to Spark programming using R Perform Spark data <em>processing</em>, charting, and plotting using Python Get acquainted with Spark stream <em>processing</em> using Scala and Python Be introduced to machine learning using Spark MLlib Get started with <em>graph</em> <em>processing</em> using the Spark GraphX Bring together all that you've learned and develop a complete Spark application
Spark相关电子书一
1、databricks-<em>spark</em>-reference-applications.pdf 2、Fast Data Processing with Spark(PACKT,2ed,2015).pdf 3、Fast Data Processing with Spark.pdf 4、Machine Learning with Spark.pdf 5、Machine Learning with Spark<em>中文版</em>(Spark机器学习).pdf 6、Mastering Apache Spark.pdf 7、Learning Spark 2015.1.pdf 8、Spark Cookbook.pdf 9、Spark-DevOps-Training.pdf
Spark-编程进阶(Scala版)
累加器nn累加器提供了将工作节点中的值聚合到驱动器程序中的简单语法。累加器的一个常见用法是在调测时对作业执行过程中的时间进行计数。nn例:累加空行nnnval sc = new SparkContext()nval file = sc.textFile(&quot;file.txt&quot;)nval blankLines = sc.accumulator(0)//创建Accumulator[Int]并初始化为0n...
The C Programming Language中文版
貌似只有前4章,是<em>中文版</em>的
Practical Real-time Data Processing and Analytics azw3
Practical Real-time Data Processing and Analytics 英文azw3 本资源转载自网络,如有侵权,请联系上传者或csdn删除 本资源转载自网络,如有侵权,请联系上传者或csdn删除
Apache Spark三种分布式部署方式比较
目前Apache Spark支持三种分布式部署方式,分别是standalone、<em>spark</em> on mesos和 <em>spark</em> on YARNrn其中,第一种类似于MapReduce 1.0所采用的模式,内部实现了容错性和资源管理,后两种则是未来发展的趋势,部分容错性和资源管理交由统一的资源管理系统完成:让Spark运行在一个通用的资源管理系统之上,这样可以与其他计算框架,比如MapReduce,公用
spark-2.3.0 api 文档
<em>spark</em> 2.3.0 api 文档。 Apache Spark is a fast and general-purpose cluster computing system. It provides high-level APIs in Java, Scala, Python and R, and an optimized engine that supports general execution <em>graph</em>s. It also supports a rich set of higher-level tools including Spark SQL for SQL and structured data <em>processing</em>, MLlib for machine learning, GraphX for <em>graph</em> <em>processing</em>, and Spark Streaming
Apache Spark 2.x Cookbook_Cloud-ready recipes for analytics and data science
1: Getting Started with Apache Spark 2: Developing Applications with Spark 3: Spark SQL 4: Working with External Data Sources 5: Spark Streaming 6: Getting Started with Machine Learning 7: Supervised Learning with MLlib Regression 8: Supervised Learning with MLlib Classification 9: Unsupervised Learning 10: Recommendations Using Collaborative Filtering 11: Graph Processing Using GraphX and GraphFrames 12: Optimizations and Performance Tuning
spark 1.2.0 文档(spark-1.2.0-doc)
<em>spark</em>-1.2.0 文档 api Spark Overview Apache Spark is a fast and general-purpose cluster computing system. It provides high-level APIs in Java, Scala and Python, and an optimized engine that supports general execution <em>graph</em>s. It also supports a rich set of higher-level tools including Spark SQL for SQL and structured data <em>processing</em>, MLlib for machine learning, GraphX for <em>graph</em> <em>processing</em>, and Spark Streaming.
Apache Spark 2.0.2 中文文档 | 那伊抹微笑 - ApacheCN(Apache中文网)
ApacheCN(Apache中文网)- 关于我们 : http://www.<em>apache</em>.wiki/pages/viewpage.action?pageId=2887249rnrnApacheCN(Apache中文网)- Apache Sparkrn 2.0.2 中文文档 : http://www.<em>apache</em>.wiki/pages/viewpage.action?pageId=2883613r
ASPNetExt.exe
这是asp.net的扩展插件,有<em>需要</em>的<em>朋友</em><em>拿去</em>!
图数据库——Neo4j(二)Cypher
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