stay at home for 10 hours, stay in the company for 8 hours, and may be passing by some base station in the car.
Ideas:
For each cell phone number under which base station to stay the longest time, in the calculation, with "mobile phone number + base station" in order to locate under which base station stay at the time,
Because there will be a lot of user log data under each base station.
The country has a lot of base stations, each telecommunications branch is only responsible for calcula
Transfer from http://www.cnblogs.com/hseagle/p/3664933.htmlVersion: UnknownWedgeSource reading is a very easy thing, but also a very difficult thing. The easy is that the code is there, and you can see it as soon as you open it. The hard part is to understand the reason why the author should have designed this in the first place, and what is the main problem to solve at the beginning of the design.It's a good idea to read the spark paper from Matei Za
Tags: android http io using AR java strong data spSpark SQL Architecture and case drill-down video address:http://pan.baidu.com/share/link?shareid=3629554384uk=4013289088fid=977951266414309Liaoliang Teacher (e- mail:[email protected] QQ: 1740415547)President and chief expert, Spark Asia-Pacific Research Institute, China's only mobile internet and cloud computing big data synthesizer.In Spark, Hadoop, Androi
In addition to my consent, prohibited all reprint, emblem Shanghai one lang.ProfileAfter you have written a standalone spark application, you need to commit it to spark cluster, and generally use Spark-submit to submit your application, what do you need to be aware of in the process of using spark-submit?This article t
Real-time streaming processing complete flow based on flume+kafka+spark-streaming
1, environment preparation, four test server
Spark Cluster Three, SPARK1,SPARK2,SPARK3
Kafka cluster Three, SPARK1,SPARK2,SPARK3
Zookeeper cluster three, SPARK1,SPARK2,SPARK3
Log Receive server, SPARK1
Log collection server, Redis (this machine is used to do redis development, now used to do log collection test, the hostname
Original linkWhat is SparkApache Spark is a large data processing framework built around speed, ease of use, and complex analysis. Originally developed in 2009 by Amplab of the University of California, Berkeley, and became one of Apache's Open source projects in 2010.Compared to other big data and mapreduce technologies such as Hadoop and Storm, Spark has the following advantages.First,
The task scheduling system for Spark is as follows:From the Chinese Academy of Sciences to see the cause rddobject generated DAG, and then entered the Dagscheduler stage, Dagscheduler is the state-oriented high-level scheduler, Dagscheduler the DAG split into a lot of tasks, Each group of tasks is a state, whenever encountering shuffle will produce a new state, you can see a total of three state;dagscheduler need to record those rdd is deposited into
You are welcome to reprint it. Please indicate the source, huichiro.Summary
This article will give a brief review of the origins of the quasi-Newton method L-BFGS, and then its implementation in Spark mllib for source code reading.Mathematical Principles of the quasi-Newton Method
Code Implementation
The regularization method used in the L-BFGS algorithm is squaredl2updater.
The breezelbfgs function in the breeze library of the scalanlp member
Spark StreamingSpark streaming uses the spark API for streaming calculations, which means that streaming and batching are done on spark. So you can reuse batch code, build powerful interactive applications using Spark streaming, and not just analyze data.
Spark Streaming Ex
You are welcome to reprint it. Please indicate the source, huichiro.Summary
The previous blog shows how to modify the source code to view the call stack. Although it is also very practical, compilation is required for every modification, which takes a lot of time and is inefficient, it is also an invasive modification that is not elegant. This article describes how to use intellij idea to track and debug spark source code.Prerequisites
This document a
The spark version tested in this article is 1.3.1Spark Streaming programming Model:The first step:A StreamingContext object is required, which is the portal to the spark streaming operation, and two parameters are required to build a StreamingContext object:1, Sparkconf object: This object is configured by the Spark program settings, such as the master node of th
Content:1, the traditional spark memory management problem;2, Spark unified memory management;3, Outlook;========== the traditional Spark memory management problem ============Spark memory is divided into three parts:Execution:shuffles, Joins, Sort, aggregations, etc., by default, spark.shuffle.memoryfraction default i
transformation processing, the contents of the dataset are changed, the dataset A is converted to DataSet B, and the contents of the dataset are then normalized to a specific value after action has been processed. Only if there is an action on the RDD, all operation on the RDD and its parent RDD will be submitted to cluster for real execution.From code to dynamic running, the components involved are as shown.New Sparkcontext ("spark://...", "MyJob"
Label:Spark Learning five: Spark SQLtags (space delimited): Spark
Spark learns five spark SQL
An overview
Development history of the two spark
Three spark SQL and hive comparison
Quad
Provides various official and user release code examples. For code reference, you are welcome to exchange and learn about spark grassland system development, spark grassland system source code, distribution system micro-distribution, it is a three-level distribution mall based on the public platform. The three-level distribution should achieve an infinite loop model, and an innovation of the enterprise mark
3, hands-on generics in Scalageneric generic classes and generic methods, that is, when we instantiate a class or invoke a method, you can specify its type, because Scala generics and Java generics are consistent and are not mentioned here. 4, hands on. Implicit conversions, implicit parameters, implicit classes in Scalaimplicit conversion is one of the key points that many people learn about Scala, which is the essence of Scala:Let's take a look at the example of hidden parameters:
The
3, hands-on generics in Scala generic generic classes and generic methods, that is, when we instantiate a class or invoke a method, you can specify its type, because Scala generics and Java generics are consistent and are not mentioned here. 4, hands on. Implicit conversions, implicit parameters, implicit classes in Scala Implicit conversion is one of the key points that many people learn about Scala, which is the essence of Scala: Let's take a look at the example of hidden parameters:
Http://spark.apache.org/docs/1.2.1/streaming-programming-guide.htmlHow to shard data in sparkstreamingLevel of Parallelism in Data processingCluster resources can be under-utilized if the number of parallel tasks used on any stage of the computation are not high E Nough. For example, for distributed reduce operations like reduceByKey reduceByKeyAndWindow and, the default number of parallel tasks are controlled by The spark.default.parallelism configuration property. You can pass the level of par
configuration file are:
Run the ": WQ" command to save and exit.
Through the above configuration, we have completed the simplest pseudo-distributed configuration.
Next, format the hadoop namenode:
Enter "Y" to complete the formatting process:
Start hadoop!
Start hadoop as follows:
Use the JPS command that comes with Java to query all daemon processes:
Start hadoop !!!
Next, you can view the hadoop running status on the Web page used to monitor the cluster status in hadoop. The specific pa
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