apache spark programming language

Original address: http://blog.jobbole.com/?p=89446I first heard of spark at the end of 2013, when I was interested in Scala, and Spark was written in Scala. After a while, I made an interesting data science project, and it tried to predict surviving on the Titanic . This proves to be a good way to learn more about spark content and

script directly to get the result of the operation. During the run, a bug,org.apache.spark.deploy.yarn.client is found with a parameter "–name" to specify the application name: However, in the course of use, this parameter blocks the application, viewing the source code found to be a bug, which has been submitted to spark Jira: 1 2 3 4 5 6 7 8 9 10 11 12//Location: New-yarn/src/main/scala/org/apache /

You are welcome to reprint it. Please indicate the source.Summary The SQL module was added to the newly released spark 1.0. What's more interesting is that hiveql in hive also provides good support, as a source code analysis control, it is very interesting to know how spark supports hql.Introduction to hive The following part is taken from hive in hadoop definite guide. "Hive was designed by Facebook to all

You are welcome to reprint it. Please indicate the source, huichiro.Wedge Hive is an open source data warehouse tool based on hadoop. It provides a hiveql language similar to SQL, this allows upper-layer data analysts to analyze massive data stored in HDFS without having to know too much about mapreduce. This feature has been widely welcomed. An important module in the overall hive framework is the execution module, which is implemented using the mapr

lot above. To put it bluntly, when writing yarn application, it mainly implementsClientAndApplicatonmaster. For more information, seeSimple-yarn-app.Spark on Yarn Combined with the deployment mode of spark standalone and the requirements of the yarn programming model, a table is provided to show the comparison between spark standalone and

-1.1.0-hadoop2.2.0.jar file, add the finished interface as follows:2.2 Example 1: Run directly"Spark programming Model (top) – Concept and Shell test" using Spark-shell for the search of Sogou logs, here we use idea to re-practice the number of Session query leaderboard, you can find that the use of professional development tools can be convenient and quick many.

the source reading, we need to focus on the following two main lines. static View is RDD, transformation and action Dynamic View is the life of a job, each job is divided into multiple stages, each stage can contain more than one RDD and its transformation, How these stages are mapped into tasks is distributed into cluster References (Reference) Introduction to Spark Internals http://files.meetup.com/3138542/dev-meetup-dec-

Apache Spark Memory Management detailedAs 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 thi

another set of external systems or provide the calculated results to the user. One of the big advantages of the storm ecosystem is that it has a rich mix of stream types enough to fetch data from any type of source. While it is possible to write custom streams for some highly specific applications, we can always find the right solution from the vast existing source types-from the Twitter streaming API to the Apache Kafka to the JMS broker, all covere

documentation.SummaryIn the source reading, we need to focus on the following two main lines. static View is RDD, transformation and action Dynamic View is the life of a job, each job is divided into multiple stages, each stage can contain more than one RDD and its transformation, How these stages are mapped into tasks is distributed into cluster References (Reference) Introduction to Spark Internals http://files.meetup.com

, including transformation and action.When do you use the RDD?General scenarios for using RDD: You need to use low-level's transformation and action to control your data set; Your data sets are unstructured, such as streaming media or text streams; You want to use functional programming to manipulate your data, rather than using a domain-specific language (DSL) to express it; You don't

Spark Cluster Mode Overview Spark Sort Based Shuffle Memory Analysis Spark Off_heap Unified Memory Management in Spark 1.6 Tuning spark:garbage Collection Tuning Spark Architecture Spark