1.overview in wordcount
-memory tips:
Job > Stage > Rdd > Dependency RDDs are linked by Dependencies.
2.terms
-RDD is associated by Dependency,ie Dependency is a warpper of RDD.
Stage contains corresponding rdd; dependency contains parent rdd also
-Stage is a wrapper of same-func tasks; second,used to schedule by DAGScheduler
-job.numTasks = resultStage.numPartitions.if ‘spark.default.parallelism’ not set,resultStage.numPartitions = ShuffleRDD.parts=hadoopRDD.splits
so resultstage.numPartitons is deternmined by ShuffleRDD#getDependencies()
TODO check in page
-ShuffleMapStage.partitions = rdd.partition (ie. MapPartitionsRDD[3] here,similar to resultStage except ‘spark.default.parallelism’ case)
-ShuffleRDD will be parted into the ResultStage instead of ShuffleMapStage
cirtical concepts
-spark.default.parallelism-determine how many result tasks to be run,see PariRDDFunctions#reduceByKey()
-each stage has one direct corresponding rdd.
-spark.cores.max in standalnoe mode,this property will limit the maximum of cores in cluster to be utilized .
-ShuffleDependecy.shuffleId == ShuffleMapStage.id?yes,ie ShuffleDependency:ShuffleMapStage = 1:1,
see shuffleToMapStage(shuffleDep.shuffleId) = stage
相关推荐
There is no better time to learn Spark than...This chapter provides a high-level overview of Spark, including the core concepts, the architecture, and the various components inside the Apache Spark stack
•Learn core concepts such as Spark RDDs, Spark Streaming clusters, and the fundamentals of a DStream •Discover how to create a robust deployment •Dive into streaming algorithmics •Learn how to ...
Master the techniques and sophisticated analytics used to construct Spark-based solutions that scale to deliver production-grade data science products About This Book Develop and apply advanced ...
Work with RDD and DataFrame to learn Spark's core abstractions Analysing structured and unstructured data using SparkSQL and GraphX Scalable and fault-tolerant streaming application development using ...
Process data using different file formats such as XML, JSON, CSV, and plain and delimited text, using the Spark core Library. Perform analytics on data from various data sources such as Kafka, and ...
The core challenge are they key capabilities in Spark (Spark SQL, Spark Streaming, Spark ML, Spark R, Graph X) etc. Having understood the key capabilities, it is important to understand how Spark can...
This book discusses various components of Spark such as Spark Core, DataFrames, Datasets and SQL, Spark Streaming, Spark MLib, and R on Spark with the help of practical code snippets for each topic....
Data-intensive systems are a technological building block supporting Big Data and Data Science applications.This book familiarizes readers with core concepts that they should be aware of before ...
Hands-On Data Science and Python Machine Learning gives you the tools that you need to understand and explore the core topics in the field, and the confidence and practice to build and analyze your ...
Understand core concepts behind Hadoop and cluster computing Use design patterns and parallel analytical algorithms to create distributed data analysis jobs Learn about data management, mining, and ...
It will take you from data analysis and data visualization to the core concepts and advantages of machine learning, real-life usage of regression and classification using Naïve Bayes, a deep ...
Understand core concepts behind Hadoop and cluster computing Use design patterns and parallel analytical algorithms to create distributed data analysis jobs Learn about data management, mining, and ...
The book is structured around examples, so you will grasp core concepts via case studies and Python 3 code. As data science projects gets continuously larger and more complex, software engineering ...
The practical examples will help you understand the core concepts of Java, Scala, Kotlin, Clojure, and Groovy Work with various programming paradigms and gain knowledge about imperative, object ...