rdd meaning

Basic how Spark works1. Distributed2, mainly based on memory (few cases disk-based)3. Iterative calculationThe RDD and its features1. RDD is the core abstraction provided by Spark, all known as the Resillient distributed dataset, or elastic distributed data set.2. The RDD is a collection of elements that contain data in abstract terms. It is partitioned, divided

Spark Source Analysis Reproduced: Http://blog.sina.com.cn/s/articlelist_2628346427_2_1.html Http://blog.sina.com.cn/s/blog_9ca9623b0102webd.html Spark Performance Optimization-------Development tuning reprint 2016-05-15 12:58:17 Development tuning, know spark basic development principles, including: RDD lineage design, operator rational use, special operation optimization. Principle one: Do not repeat the creation of

As we all know, Apache Spark has built in a lot of API to manipulate data. But many times, when we develop applications in reality, we need to solve real-world problems that might not be available in Spark , and we need to extend the Spark API to implement our own approach.There are two ways to extend the Spark API, (1), one of which is to add a custom method to the existing Rdd , and (2) The second is to create our own

The following are lessons learned from the Spark Rdd decryption course:Before introducing the spark Rdd, simply say Hadoop MapReduce, which is calculated based on the data flow, loads the data from the physical storage, and then operates the data.The last write to the physical storage device, such a pattern will produce a large number of intermediate results.MapReduce is not suitable for scenes: 1. Not suit

About SparkSpark is a large data distributed computing framework based on memory computing. Spark is based on memory computing, which improves the real-time processing in big data environments while guaranteeing high fault tolerance and high scalability.In spark, calculations are performed through the RDD (resilient distributed dataset, Elastic distributed DataSet), which are distributed across the cluster in parallel. Rdds is the underlying abstract

Tags: effect generated memory accept compile check coder heap JVM The Rdd, DataFrame, and dataset in Spark are the data collection abstractions of Spark, and the RDD is for each object, but DF and DS are for row RDD Advantages:Compile-Time type safetyThe type error can be checked at compile timeObject-oriented Programming styleManipulate data directly from the c

' ve got big RDD (1GB) in yarn cluster. On local machine, which use this cluster I has only MB. I ' d like to iterate over the values in the RDD on my local machine. I can ' t use Collect (), because it would create too big array locally which the then my heap. I need some iterative. There is method iterator (), but it requires some additional information, I can ' t provide.Udp:commited Tolocaliterator meth

Spark Fast Big Data analytics8.4.2 Critical performance considerations for memory managementMemory for Spark several different uses, understanding and tuning Spark's memory usageCan help optimize your spark application. In each actuator process, there is a list of centralized uses. RDD Storage When the persist () or cache () method of the Rdd is called, the

Spark loads JSON files from HDFS files to SQL tables through RDDRDD Definition RDD stands for Resilient Distributed Dataset, which is the core abstraction layer of spark. It can be used to read multiple files. Here we demonstrate how to read hdfs files. All spark jobs occur on RDD. For example, you can create a new RDD, convert the existing

From Https://databricks.gitbooks.io/databricks-spark-knowledge-base/content/performance_optimization/how_many_ Partitions_does_an_rdd_have.htmlFor tuning and troubleshooting, it's often necessary to know what many paritions an RDD represents. There is a few ways to find this information:View Task execution against partitions Using the UIWhen a stage is executes, you can see the number of partitions for a given stage in the Spark UI. For example, the f

Sparkcontext.scala implements a Sparkcontext class and Object,sparkcontext spark-like portals that connect spark clusters, create RDD, accumulate amounts, and broadcast volumes.In the spark framework, the class is loaded only once in a JVM. In the stage of loading classes, the properties, code blocks, and functions defined in the Sparkcontext class are loaded.(1) class Sparkcontext (config:sparkconf) extends Logging with Executoallocationclient, The d

Narrow dependence Narrow dependencyMap,filter,union,Join (co-partitioned) formulates which unique sub-rdd The Shard in the parent RDD is specifically assigned toIn parallel, the Rdd shard is independent.Shards that rely on the same ID onlyRange ShardOne to DependencyRange dependencyInside can previously computed partitionThe computation can be merged, can greatly

Why are two APIs of Spark RDD fold and aggregate? Why is it not a foldLeft ?, Rddfoldleft Welcome to my new blog address: http://cuipengfei.me/blog/2014/10/31/spark-fold-aggregate-why-not-foldleft/ As we all know, the List of Scala standard library has a foldLeft Method Used for aggregation operations. For example, I define a company class: 1 case class Company(name:String, children:Seq[Company]=Nil) It has a name and a subsidiary. T

The RDD is the most basic and fundamental data abstraction for spark, which has the fault tolerance of data flow models like MapReduce, and allows developers to perform memory-based computations on large clusters.To effectively implement fault tolerance, the RDD (see http://www.cnblogs.com/zlslch/p/5718799.html) provides a highly restricted shared memory that the RDD