spark streaming tutorial

Original link: Spark Streaming performance tuning　The Spark streaming provides an efficient and convenient streaming mode, but in some scenarios the default configuration is not optimal, and even the external data cannot be processed in real time, and we need to make relevan

This article reprint please from: Http://qifuguang.me/2015/12/24/Spark-streaming-kafka actual combat Course/ Overview Kafka is a distributed publish-subscribe messaging system, which is simply a message queue, and the benefit is that the data is persisted to disk (the focus of this article is not to introduce Kafka, not much to say). Kafka usage scenarios are still relatively large, such as buffer queues

Preface:　　　　Recently in the research Spark also has Kafka, wants to pass the data which the Kafka end obtains, uses the spark streaming to carry on some computation, but constructs the entire environment is really not easy, therefore hereby writes down this process, shares to everybody, hoped that everybody may take a little detour, can help everybody!Environment

Spark Streaming supports the scalable (scalable), high throughput (high-throughput), fault tolerant (fault-tolerant) stream processing (stream processing) for real-time data streams.Spark Streaming supports the scalable (scalable), high throughput (high-throughput), fault tolerant (fault-tolerant) stream processing (stream processing) for real-time data streams.A

 Exactly once Output is not duplicatedA. Purpose of the course: Customize and develop the spark version you need, including fixes for spark Bugs , improvements in performance, expansion of functionality, as per your business needs In short, suitable for their own company's maintenance, easy easy to understand, easy to maintain. B. transaction processing, such as bank transfer, transaction input an

Run the first sparkstreaming program (and problem solving in the process)Debug Spark Standalone in Windows IntelliJ ideaSbt-assembly launches Scala ProjectDevelop and test Spark's environment and simple tests using ideaRunning Scala programs based on Spark (SBT and command line methods) is to practice the process of developing a Scala project to create a project Create a Scala project named

Spark streaming can receive streaming data from any arbitrary data source beyond the one's for which it has in-built support (that is, beyond flume, Kafka, files, sockets, etc .). this requires the developer to implementCyclerThat is customized for processing data from the concerned data source. This Guide walks through the process of implementing a custom proces

This article explains from two aspects:Advanced Features:1. Dynamic distribution of Spark streaming resources2, Spark streaming dynamic control consumption ratePrinciple analysis, dynamic control consumption rate there is a set of theories behind it, resource dynamic distribution also has a theory.Let's start with the

Contents of this issue: Spark Streaming+spark SQL Case Show Based on the case running source of spark streaming First, the case code elaborated:　　Dynamically calculate the hottest product rankings in different categories of e-commerce, such as the hottest t

Design BackgroundSpark Thriftserver currently has 10 instances on the line, the past through the monitoring port survival is not accurate, when the failure process does not quit a lot of situations, and manually to view the log and restart processing services This process is very inefficient, so design and use spark Streaming to the real-time acquisition of the spark