spark mllib example

voidPrint (javarddparseddata, Generalizedlinearmodel model) {Javapairrdd { DoublePrediction = Model.predict (Point.features ());//predicting training data with models return NewTuple2(Point.label (), prediction); }); Double MSE= Valuesandpreds.maptodouble ((tuple2//calculates the mean of the squared value of the difference between the predicted value and the actual valueSystem.out.println (Model.getclass (). GetName () + "training Mean squared Error =" +MSE);} Run result Linea

). Mininfogain: Type: double-precision. Meaning: The minimum information gain required to split a node. Mininstancespernode: Type: integer type. Meaning: The minimum number of instances that are included in a node since splitting. Predictioncol: Type: String type. Meaning: The forecast result column name. Rawpredictioncol: Type: String type. Meaning: Original forecast. Seed Type: Long integral type. Meaning: Random seeds. Subsamplingrate: Type: double-precision. Meaning: Learn a decision tree us

Spark example and spark example 1. Set up the Spark development environment in Java (fromHttp://www.cnblogs.com/eczhou/p/5216918.html) 1.1 jdk Installation Install jdk in oracle. I installed jdk 1.7. After installing the new system environment variable JAVA_HOME, the variabl

Spark example: Sorting by array and spark example Array sorting is a common operation. The lower performance limit of a comparison-based sorting algorithm is O (nlog (n), but in a distributed environment, we can improve the performance. Here we show the implementation of array sorting in

[Spark] [Python]spark example of obtaining Dataframe from Avro fileGet the file from the following address:Https://github.com/databricks/spark-avro/raw/master/src/test/resources/episodes.avroImport into the HDFS system:HDFs Dfs-put Episodes.avroRead in:Mydata001=sqlcontext.read.format ("Com.databricks.spark.avro"). Loa

[Spark] [Python] Example of Spark accessing MySQL, generating dataframe:Mydf001=sqlcontext.read.format ("jdbc"). Option ("url", "Jdbc:mysql://localhost/loudacre") \. Option ("DBTable", "accounts"). Option ("User", "training"). Option ("Password", "training"). Load ()In []: Mydf001=sqlcontext.read.format ("jdbc"). Option ("url", "Jdbc:mysql://localhost/loudacre")

Examples of non-interactive running spark application$ cat count.pyImport SysFrom Pyspark import Sparkcontextif __name__ = = "__main__":sc = Sparkcontext ()LogFile = sys.argv[1]Count = Sc.textfile (logfile). Filter (Lambda line: '. jpg '). Count ()Print "JPG requests:", CountSc.stop ()$$ spark-submit--master yarn-client count.py/test/weblogs/*Number of JPG requests:10258$[

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

[Email protected] ~]$ HDFs dfs-cat People.json{"Name": "Alice", "Pcode": "94304"}{"Name": "Brayden", "age": +, "Pcode": "94304"}{"Name": "Carla", "age": +, "Pcoe": "10036"}{"Name": "Diana", "Age": 46}{"Name": "Etienne", "Pcode": "94104"}[Email protected] ~]$HDFs Dfs-cat Pcodes.json{"Pcode": "10036", "City": "New York", "state": "NY"}{"Pcode:" 87501 "," City ":" Santa Fe "," state ":" NM "}{"Pcode": "94304", "City": "Palo Alto", "state": "CA"}{"Pcode": "94104", "City": "San Francisco", "state": "

[Spark] [Hive] [Python] [SQL] A small example of Spark reading a hive table$ cat Customers.txt1Alius2Bsbca3Carlsmx$ hiveHive>> CREATE TABLE IF not EXISTS customers (> cust_id String,> Name string,> Country String>)> ROW FORMAT delimited fields TERMINATED by ' \ t ';hive> Load Data local inpath '/home/training/customers.txt ' into table customers;Hive>exit$pyspark

. This chapter explains how to use external data sources to manipulate data in hive, parquet, MySQL, and integrated use8th Chapter Sparksql VisionThis chapter will explain the Spark's vision: Write less code, read less data, and let the optimizer automatically optimize the programThe 9th Chapter MU Lesson Net Diary actual combatThis chapter uses spark SQL to perform statistical analysis of each dimension of the access log for the master station, which

I. Introduction to Spark SQL External datasourceWith the release of Spark1.2, Spark SQL began to formally support external data sources. Spark SQL opens up a series of interfaces for accessing external data sources to enable developers to implement them.This allows spark SQL to support more types of data sources, such

Example of integrated development of Spring Boot with Spark and Cassandra systems, sparkcassandra This article demonstrates how to use Spark as the analysis engine and Cassandra as the data storage, and use Spring Boot to develop the driver. 1. Prerequisites Install Spark (Spark