Apache Spark Components

The Big data field of the 2014, Apache Spark (hereinafter referred to as Spark) is undoubtedly the most attention. Spark, from the hand of the family of Berkeley Amplab, at present by the commercial company Databricks escort. Spark has become one of ASF's most active projects since March 2014, and has received extensive support in the industry-the spark 1.2 release in December 2014 contains more than 1000 contributor contributions from 172-bit TLP ...

Http://www.aliyun.com/zixun/aggregation/13383.html ">spark is a cluster computing platform originating from the Amplab of the University of California, Berkeley, which is based on memory computing and has more performance than Hadoop , even with disk, the calculation of the iteration type will increase by 10 times times. Spark is a rare all-round player, starting from multiple iterations, eclectic data Warehouse, stream processing and graph calculation. Spar ...

The Apache Software Foundation has officially announced that Spark's first production release is ready, and this analytics software can greatly speed up operations on the Hadoop data-processing platform. 　　As a software project with the reputation of a "Hadoop Swiss Army Knife", Apache Spark can help users create performance-efficient data analysis operations that are faster than they would otherwise have been on standard Apache Hadoop mapreduce. Replace MapReduce ...

Cloudera has courted four mainstream companies to work together to push for a combination of two big open source projects to further improve the planning of the Hadoop community's power. Cloudera, IBM, Intel, Databricks and MAPR have established a partnership to migrate Apache Hive to Apache Spark, which was released at the Spark Summit in San Francisco this week. We have received the news last week, there are rumors that Cloudera will recommend the hive with ...

Currently, the Hadoop distribution has an open source version of Apache and a Hortonworks distribution (HDP Hadoop), MapR Hadoop, and so on. All of these distributions are based on Apache Hadoop.

Hadoop is a large data distributed system infrastructure developed by the Apache Foundation, the earliest version of which was the 2003 original Yahoo! Doug cutting is based on Google's published academic paper. Users can easily develop and run applications that process massive amounts of data in Hadoop without knowing the underlying details of the distribution. The features of low cost, high reliability, high scalability, high efficiency and high fault tolerance make Hadoop the most popular large data analysis system, yet its HDFs and mapred ...

1. As with most other distributed systems, the Apache Mesos, in order to simplify the design, also employs a master/slave structure that, in order to solve the master single point of failure, makes master as lightweight as possible, and the above number It can be reconstructed through various slave, so it is easy to solve the single point of failure by zookeeper. (What is Apache Mesos?) Reference: "Unified resource management and scheduling platform (System) Introduction", this article analysis based on MES ...

1. The introduction of Mesos is mainly composed of four components, respectively, Mesos-master,mesos-save,scheduler and executor, each component is based on protocal buffer actor Model for communication (using Open Source Library libprocess). In other words, each module is a server (in fact, the socket server), listening to messages from other modules, once received a message ...

"Editor's note" Recently, MAPR has formally integrated the Apache drill into the company's large data-processing platform, and opened up a series of large database-related tools. Today, in the highly competitive field of Hadoop, open source has become a tool for many companies, they have to contribute more code to protect themselves, but also through open source to attack other companies. In this case, Derrick Harris made a brief analysis on Gigaom. Recently, Mapr,apache Drill Project founder, has ...

This time, we share the 13 most commonly used open source tools in the Hadoop ecosystem, including resource scheduling, stream computing, and various business-oriented scenarios. First, we look at resource management.