spark data lineage

resultCars.foreach (NewVoidfunction(){ Private Static Final LongSerialversionuid = 1L; @Override Public voidCall (String s)throwsException {System.out.println (s); } }); ; // //Print out the final result//list//For (String s:output) {//System.out.println (s);// } } Catch(Exception e) {Log.warn (e); } } /*** Spark If the calculation is not written in main, the im

def main (args:array[string]): Unit = {val sparkconf = new sparkconf (). Setappname ("DecisionTree1"). Setmaster ("local[2") ") Sparkconf.set (" Es.index.auto.create "," true ") Sparkconf.set (" Es.nodes "," 10.3.162.202 ") Sparkconf.set (" Es.port "," 9200 ") val sc = new Sparkcontext (sparkconf)//write2es (SC) read4es (SC); } def write2es (sc:sparkcontext) = {val numbers = Map ("One", 1, "One", "2", "three", 3) Val Airports = Map ("OTP", "Otopeni", "SFO", "San Fran") var Rdd = Sc.makerdd (Seq

Part 1W3cschool's MongoDB java:http://www.w3cschool.cc/mongodb/mongodb-java.htmlMongoDB Java Drive use collation: http://blog.163.com/wm_at163/blog/static/132173490201110254257510/MongoDB Java version driver: http://www.aichengxu.com/view/13226Mongo-java-driver Download: http://central.maven.org/maven2/org/mongodb/mongo-java-driver/Part 2MongoDB Hadoop Driver Introduction: http://blog.csdn.net/amuseme_lu/article/details/6584661MongoDB Connector for Hadoop (GitHub): Https://github.com/mongodb/mon

89 90 9 1 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 As you can see, using Spark's operators for data analysis is very easy.Spark docking Kafka, database, etc. can be seen on the Spark website, which is very easy.Let's take a look at the results of writing to the database in this example: +----+----------+--------+---------------------+ | id | location | counts | ac

Video materials are checked one by one, clear high quality, and contains a variety of documents, software installation packages and source code! Perpetual FREE Updates!Technical teams are permanently free to answer technical questions: Hadoop, Redis, Memcached, MongoDB, Spark, Storm, cloud computing, R language, machine learning, Nginx, Linux, MySQL, Java EE,. NET, PHP, Save your time!Get video materials and technical support addresses----------------

(including HTTP): Step on the Pit: Val conf = new sparkconf (). Setmaster ("local[2]"). Setappname ("Printwebsites") Here the Setmaster parameter must be local[2], for here to open two processes, one to receive, if the default local will not receive data. After compiling, you can run it and find that printing this information: Using Spark ' s default log4j profile:org/apache/

1. Data partitioning To reduce the cost of distributed application communication, control data partitioning for minimal network transmissionAll key values in spark can be partitioned for RDD There are requirements for users to access their non-subscribed pagesStatistics to better recommend content to the user. There is a large User information table (userid,user

implies, is additional information Overwrite is covered Ignore is ignored if present In addition, if you do not specify a storage mode, the default should be savemode.errorifexists, because I repeatedly saved the report: already exists error. How to use: Import Org.apache.spark.sql._ val sqlcontext = new Org.apache.spark.sql.SQLContext (SC) val df = Sqlcontext.load ("/opt/modules/spark1.3.1/examples/src/main/resources/people.json") df.save ("/OPT/TEST/1", " JSON ", Savemode.overwrite) //Can

() {return This. Name; }} //function and date objects cannot be used2.3 ArraysAn array is also a complex data type that represents a list of ordered sets of values that can be accessed by a numeric index. The value of an array can also be any type-simple value, object, or arrayJSON arrays also have no variables and semicolons, which combine arrays and objects to form more complex collections of data[note]

Spark Machine Learning Mllib Series 1 (for Python)--data type, vector, distributed matrix, API Key words: Local vector,labeled point,local matrix,distributed Matrix,rowmatrix,indexedrowmatrix,coordinatematrix, Blockmatrix.Mllib supports local vectors and matrices stored on single computers, and of course supports distributed matrices stored as RDD. An example of a supervised machine learning is called a la

1. Join for different time slice data streams After the first experience, I looked at Spark WebUi's log and found that because spark streaming needed to run every second to calculate the data in real time, the program had to read HDFs every second to get the data for the inn

Spark SQL Load DataSparksql data input and output mainly Dataframe,dataframe provides some common load and save operations.You can create a dataframe by using the load, save the Dataframe data to a file or in a specific format to indicate what format the file is to be read or what format the output data is, and directl

Big Data Architecture Development mining analysis Hadoop HBase Hive Storm Spark Flume ZooKeeper Kafka Redis MongoDB Java cloud computing machine learning video tutorial, flumekafkastorm Training big data architecture development, mining and analysis! From basic to advanced, one-on-one training! Full technical guidance! [Technical QQ: 2937765541] Get the big

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For complex data types, such as IP and Geopoint, they are only valid in Elasticsearch, and are converted to commonly used string types when they are read with spark.Geo types. It is worth mentioning that rich data types available only in Elasticsearch, such as GeoPoint or be GeoShape supported by Conver Ting their structure into the primitives available in the table above. For example, based in its storage

Note:1. The second chapter of this book the sample data because of the short link, the domestic users may not be able to download. I copied the data set to the Baidu network disk. You can download from this place:Http://pan.baidu.com/s/1pJvjHA7Thank you reader Mr. Qian for pointing out the problem.2.P11, remember to set the Log4j.properties file, change the log level to warn, or the output may not look the

Training Big Data architecture development, mining and analysis!from zero-based to advanced, one-to-one training! [Technical qq:2937765541]--------------------------------------------------------------------------------------------------------------- ----------------------------Course System:get video material and training answer technical support addressCourse Presentation ( Big Data technology is very wi

Bonding data of spark. components. DataGrid public var _X:int;public var _Y:int;private function yinjiDG_itemClickHandler(event:GridSelectionEvent):void{_X = event.currentTarget.dataProvider[event.selectionChange.rowIndex].Longitude;_Y = event.currentTarget.dataProvider[event.selectionChange.rowIndex].Latitude;var PointID:String = event.currentTarget.dataProvider[event.selectionChange.rowIndex].EmergencyC

| 27| null| no| 4| 14| 6| null| | 0| null| 32| null| yes| 1| 12| 1| null| | 0| null| 57| null| yes| 5| 18| 6| null| | 0| null| 22| null| no| 2| 17| 6| null| | 0| null| 32| null| no| 2| 17| 5| null|+-------+------+---+------------+--------+-------------+---------+----------+------+scala> data1.f

def main (Args:array[string]) {val sparkconf=NewSparkconf (). Setmaster ("local"). Setappname ("Cocapp"). Set ("Spark.kryo.registrator")., Classof[hbaseconfiguration].getname). Set ("Spark.executor.memory", "4g") Val Sc:sparkcontext=NewSparkcontext (sparkconf) Val SqlContext=NewHivecontext (SC) Val mysqlurl= "Jdbc:mysql://localhost:3306/yangsy?user=rootpassword=yangsiyi"val Rows= Sqlcontext.jdbc (Mysqlurl, "person") Val TableName= "Spark"Val columnfam