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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
This is a Scala by Example from the official website.The example of how Scala is used in more detail after tutorial1.Programing with Actors and Message2.Expressions and Simple Functions3.first-class Functions4.Class and Objects5.Case Classes and Pattern Matching6.Generic Types and Methods7.Lists8.for-comprehensions9.Mu
). 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] [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")
Mode CREATE temporary TABLE USING OPTIONSAfter Spark1.2, a table that creates an external data source is supported by the DDL syntax for create temporary table USING options.CREATE Temporary TABLE jsontableusing org.apache.spark.sql.jsonOPTIONS ( path '/path/to/data.json ')1. Operation Example:Let's take example down the People.json file to do an example.shengli-mac$ cat/users/shengli/git_repos/spark/exam
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$[
configuration file are: Run the ": WQ" command to save and exit. Through the above configuration, we have completed the simplest pseudo-distributed configuration. Next, format the hadoop namenode: Enter "Y" to complete the formatting process: Start hadoop! Start hadoop as follows: Use the JPS command that comes with Java to query all daemon processes: Start hadoop !!! Next, you can view the hadoop running status on the Web page used to monitor the cluster status in hadoop. The specific pa
From java:openjdk-8ENV hadoop_home/opt/spark/hadoop-2.6.0ENV mesos_native_library/opt/libmesos-0.22.1. soenv sbt_version0.13.8ENV scala_version2.11.7RUNmkdir/opt/Sparkworkdir/opt/spark# Install scalarun cd/root Curl-o scala-$SCALA _version.tgz http://downloads.typesafe.com/scala
, DISTINCT, subtract, sample, takesample Cache type Cache, persist 1.2 transfromation operators for Key-value data types type operator input partition and output partition one-to-one Mapvalues For a single Rdd Combinebykey, Reducebykey, Partitionby Two Rdd aggregation Cogroup Connection Join, Leftoutjoin, Rightoutjoin 1.3 Action operator type operator
[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
There have also been recent studies using spark streaming for streaming. This article is a simple example of how to do spark streaming programming with the flow-based count of word counts.1. Dependent jar PackagesRefer to the article "Using Eclipse and idea to build the Scala+spark
dataset = spark. Read. Format ("libsvm"). Load ("Data/mllib/sample_libsvm_data.txt ")// Split the data into training and Test Sets (30% held out for testing)Val array (tranningdata, testdata) = dataset. randomsplit (Array (0.7, 0.3), seed = 1234l)// Train a naviebayes ModelVal model = new naivebayes (). Fit (tranningdata)// Select example rows to display.Val predictions = model. Transform (testdata)Predict
The program simply reads the data from the file and calculates it.Package com.bill.www/** * Created by Bill on 2016/2/3. * Purpose: Simple data calculation using Scala * source file: Interface record number of 20, including timestamp and floating-point data * execution: Scala Readfile.scala "E:\\spark\\data\\i_22_221000000073_l_ 20151016\\i_22_221000000073_l_2015
One of the simplest examples of Spark's own is mentioned earlier, as well as the section on Sparkcontext, which describes the transformation in the rest of the content.Object SPARKPI { def main (args:array[string]) { val conf = new sparkconf (). Setappname ("Spark Pi") val spark = New Sparkcontext (conf) val slices = if (args.length > 0) args (0). ToInt Else 2 val n = math.min (100000L * Slice
Brief introductionThere is no enumeration type in Scala, but the enumeration class is provided in the standard class library to produce enumerations. After extending the enumeration class, call the value method to initialize the possible values in the enumeration. The inner class value is actually an abstract class, and the real creation is Val. Because it's actually Val, you can pass in the ID and name for value If not specified, the ID is added to t
Brief introductionThere are no enumeration types in Scala, but the enumeration class is provided in the standard class library to produce enumerations. After extending the enumeration class, call the value method class to initialize the possible values in the enumeration.The inner class value is actually an abstract class, and the real creation is Val. Because it is actually Val, you can pass in the ID and name for value. If not specified, the ID is a
Here's a simple Scala call-by-name example. I ' ll show the normal approach to writing a method and passing in a parameter, and then show a call-by-name (pass by name) Example. 1) A "Normal" Scala method Here I show how to pass a parameter to a method "normally", i.e., call by value: Object Test extends App { def ti
Package Yjmyzzimport Java.io.PrintWriterimport Java.util.Dateimport scala.io.Sourceobject ScalaApp02 {def main (args: Array[string]) {tupledemo println mapdemo println arraydemo println filewriteandread println (getu Rlcontent ("http://www.cnblogs.com/yjmyzz/")}/** * Tuple example */def Tupledemo = {//val represents a constant (equivalent to final in Java), VAR represents a variable Val tuple = ("Jimmy", +, New Date ())//This is more concise th
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