spark and cassandra

This project mainly explains a set of big data statistical analysis platform which is applied in Internet e-commerce enterprise, using Java, Spark and other technologies, and makes complex analysis on the various user behaviors of e-commerce website (Access behavior, page jump behavior, shopping behavior, advertising click Behavior, etc.). Use statistical analysis data to assist PM (product manager), data analyst, and management to analyze existing pr

As a memory-based distributed computing engine, Spark's memory management module plays a very important role in the whole system. Understanding the fundamentals of spark memory management helps to better develop spark applications and perform performance tuning. The purpose of this paper is to comb out the thread of Spark memory management, and draw the reader's

Introduction: This paper introduces Baidu based on spark heterogeneous distributed depth learning system, combining spark and depth learning platform paddle to solve the data access problem between paddle and business logic, on the basis of using GPU and FPGA heterogeneous computing to enhance the data processing capability of each machine, Use yarn to allocate heterogeneous resources, support multi-tenancy

Spark standalone cluster is a cluster mode in the master-slaves architecture. Like most master-slaves cluster clusters, there is a single point of failure (spof) in the master node. Spark provides two solutions to solve this single point of failure problem: Single-node recovery with local file system) Zookeeper-based standby Masters (standby masters with zookeeper) Zookeeper provides a leader election m

First of all, of course, is to download a spark source code, in the http://archive.cloudera.com/cdh5/cdh/5/to find their own source code, compiled their own packaging, about how to compile packaging can refer to my original written article: http://blog.csdn.net/xiao_jun_0820/article/details/44178169 After execution you should be able to get a compressed package similar to SPARK-1.6.0-CDH5.7.1-BIN-CUSTOM-SP

above instance again prompts an error and needs to be ./output removed first.Rm-r./outputInstall SparkVisit spark official, download and unzip as follows.sudo tar-zxf ~/download/spark-1.6. 2-bin-without-hadoop.tgz-c/usr/local//usr/localsudo mv. /spark-1.6. 2-bin-without-hadoop/./-R hadoop:hadoop./spark # Here

Reprinted from: http://www.cnblogs.com/spark-china/p/3941878.html Prepare a second, third machine running Ubuntu system in VMware; Building the second to third machine running Ubuntu in VMware is exactly the same as building the first machine, again not repeating it.Different points from installing the first Ubuntu machine are:1th: We name the second to third Ubuntu machine for Slave1, Slave2, as shown in:There are three virtual machines

spark2.3.0+kubernetes Application Deployment Spark can be run in Kubernetes managed clusters, using native kubernetes scheduling features have been added to spark. At present, kubernetes scheduling is experimental, in future versions, Spark may have behavioral changes in configuration, container images, and portals. (1) Prerequisites. Run on

This article from the official blog, slightly added: https://github.com/mesos/spark/wiki/Spark-Programming-GuideSpark sending Guide From a higher perspective, in fact, every Spark application is a Driver class that allows you to run user-defined main functions and perform various concurrent operations and calculations on the cluster. The most important abstracti

Spark is a class mapred computing framework developed by UC Berkeley Amplab. The Mapred framework applies to batch jobs, but because of its own framework constraints, first, pull-based heartbeat job scheduling. Second, the shuffle intermediate results all landed disk, resulting in high latency, start-up overhead is very large. And the spark is for iterative, interactive computing generation. First, it uses

Submitting applicationsScripts in the script in Spark bin directory are spark-submit used with the launch application on the cluster. It can use all Spark-supported cluster managers through a single interface, so you don't need to configure your application specifically for each cluster managers.Packaging app DependenciesIf your code relies on other projects, in

For the past few months, we had been busy working on the next major release of the big data open source software we love: Apache Spark 2.0. Since Spark 1.0 came out both years ago, we have heard praises and complaints. Spark 2.0 builds on "What do we have learned in the past" years, doubling down "What are users love and improving on?" RS Lament. While this blog

Spark Pseudo-distributed fully distributed Installation GuidePosted 4 months ago (2015-04-02 03:58) Read (3891) | Comments (5) 156 People favorite This article, I want to Favorites 6 Catalog [-] 0, preface 1, Installation Environment 2, pseudo-distributed installation 2.1 decompression, configuration environment variables can 2.2 let the configuration effective 2.3 start spark 2.4 Run the

Spark SQL is one of the most widely used components of Apache Spark, providing a very friendly interface for distributed processing of structured data, with successful production practices in many applications, but on hyper-scale clusters and datasets, Spark SQL still encounters a number of ease-of-use and scalability challenges. To address these challenges, the

Today, some friends asked how to perform unit tests on spark. Write the SBT test method as follows: When testing the spark test case, you can use the SBT test command:1. test all test cases SBT/SBT Test 2. Test a single test case SBT/SBT "test-only * driversuite *" The following is an example: This test case is located at $ spark_home/CORE/src/test/Scala/org/Apache/spa