spark kafka example

[Spark] [Python] Example of a dataframe in which a limited record is taken:SqlContext = Hivecontext (SC)PEOPLEDF = SqlContext.read.json ("People.json")Peopledf.limit (3). Show ()===[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":

[Spark] [Python] An example of opening a JSON file in a dataframe way:[email protected] ~]$ 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] ~]$[Email protected] ~]$ HDFs dfs-put People.json[Email protected] ~]$ HDFs dfs-cat People.jso

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 development environment," which speci

probability of B. Bayesian FormulaBayesian formula provides a method to calculate the posterior probability P (B | A) from the prior probability P (A), P (B), and P (A | B ). Bayesian theorem is based on the following Bayesian formula: P (A | B) increases with the growth of P (A) and P (B | A), and decreases with the growth of P (B, that is, if B is more likely to be observed when it is independent of A, then B's support for a is smaller. Naive Bayes The naive Bayes algorithm uses Bayesian fo

allowlocal * flag Specifies whether the scheduler can run the computation on the driver rather than * shipping it Out to the cluster, for short actions like first (). */def Runjob[t, U:classtag] (Rdd:rdd[t], func: (Taskcontext, iterator[t]) = = U, Partitions:seq[int] , Allowlocal:boolean, Resulthandler: (Int, U) = = Unit) {if (Stopped.get ()) {throw new illegalstate Exception ("Sparkcontext have been Shutdown")} val callSite = getcallsite val cleanedfunc = Clean (func) loginfo ("Starting job

[Example of a limited record taken in Spark][python]dataframethe continuationIn [4]: Peopledf.select ("Age")OUT[4]: Dataframe[age:bigint]In [5]: Mydf=people.select ("Age")---------------------------------------------------------------------------Nameerror Traceback (most recent)----> 1 Mydf=people.select ("Age")Nameerror:name ' People ' is not definedIn [6]: Mydf=peopledf.select ("Age")In [7]: Mydf.take (3)

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-$SCALA _version.tgz http://downloads.typesafe.com/scala/$SCALA _version/scala-$SCALA _version.tgz \ Tar-XF scala-$SCALA _version.tgz RMscala-$SCALA _version.tgz Echo>>/ROOT/.BASH

To see the next simplest example. 1. Increase in Pom.xml 2. Create a new class Import static Spark. spark.*; public class HelloWorld {public static void Main (string[] args) {Get ("/hello", (req, res)-> "Hello World");}}Run HelloWorld directly, visit Http://localhost:4567/hello, and the page will show Hello World Even Java can write so concise ... Two.

1. Operator Classification From the general direction, the Spark operator can be broadly divided into the following two types of transformation: The operation is deferred calculation, that is, the conversion from one RDD to another rdd is not executed immediately, it is necessary to wait until there is an action action to actually trigger the operation. Action: Triggers the Spark submission job (job) and o

Org.apache.spark.api.java.function.function#call (java.lang.Object)*/ PublicQualify Call (String v1)throwsException {//TODO auto-generated Method StubString s[] = V1.split (","); Qualify Q=NewQualify (); Q.seta (Integer.parseint (s[0])); Q.setb (Long.parselong (s[1])); Q.SETC (s[2]); returnQ; } }); Map.persist (Storagelevel.memory_and_disk_ser ()); System.out.println (Map.count ()); }}ImportOrg.apache.spark.serializer.KryoRegistrator;ImportCom.esotericsoftwar

Operating EnvironmentCluster Environment: CDH5.3.0The specific jar versions are as follows:Spark version: 1.2.0-cdh5.3.0Hive Version: 0.13.1-cdh5.3.0Hadoop version: 2.5.0-cdh5.3.0Simple Java version of Spark SQL sample Spark SQL directly queries JSON-formatted data Custom functions for Spark SQL Spark

Start by creating a new Maven project in Eclipse Java EE with the following specific optionsClick Finish to create a success, then change the default jdk1.5 to jdk1.8Then edit Pom.xml Join Spark-core DependencyThen copy the source code sample program in the book, because the spark version in the book is 1.2 My environment spark is 2.2.1 so need to modify the code

Operating system: Windows 10Idea:idea 14.1.41: Use idea to import the Spark 1.5 source, note that MAVEN is configured to import automatically2: Check the options for Hadoop, Hive, Hive-thriftserver,yarn in the profiles under the Maven window.3: Check the genertate sourec command under the Maven window4: Change all dependency of the module example to compileReplace Pom.xml First, then the missing one which m

[Spark] [Python] [DataFrame] [Rdd] Example of getting an RDD from Dataframe$ 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"}$pysparkSqlContext = Hivecontext (SC)PEOPLEDF = SqlContext.read.json ("People.json")Peoplerdd = Peopledf.rddPeoplerdd.

[Continuation of the Spark][python]sortbykey example:[Spark] [Python]groupbykey ExampleIn []: Mydata003.collect ()OUT[29]:[[u ' 00001 ', U ' sku933 '],[u ' 00001 ', U ' sku022 '],[u ' 00001 ', U ' sku912 '],[u ' 00001 ', U ' sku331 '],[u ' 00002 ', U ' sku010 '],[u ' 00003 ', U ' sku888 '],[u ' 00004 ', U ' sku411 ']in [+]: Mydata005=mydata003.groupbykey ()in [+]

[Spark] [Python] [RDD] [DataFrame] from the RDD construction DataFrame ExampleFrom pyspark.sql.types Import *schema = Structtype ([Structfield ("Age", Integertype (), True),Structfield ("Name", StringType (), True),Structfield ("Pcode", StringType (), True)])Myrdd = Sc.parallelize ([(+, "Abram", "01601"), (+, "Lucia", "87501")])MYDF = Sqlcontext.createdataframe (Myrdd,schema)Mydf.limit (5). Show ()+---+-----+-----+|age| name|pcode|+---+-----+-----+| 4

[Spark] [Python]sortbykey Example:[Email protected] ~]$ HDFs dfs-cat test02.txt00002 sku01000001 sku93300001 sku02200003 sku88800004 sku41100001 sku91200001 sku331[Email protected] ~]$Mydata001=sc.textfile ("Test02.txt")Mydata002=mydata001.map (Lambda line:line.split ("))Mydata002.take (3)OUT[4]: [[u ' 00002 ', U ' sku010 '], [u ' 00001 ', U ' sku933 '], [u ' 00001 ', U ' sku022 ']Mydata003=mydata002.sortby

type, which is slightly different from Updatestatebykey. Here is an example /** Mapwithstate.function is the state pair (K,V) of each key to map * Each of the input (Stockmame,stockprice) key value pairs, using the state of each key to map, Returns the new results * Here the state is the last price of each stockname * with the input (Stockname,stockprice) StockPrice the last price in the state ( state.update function) * Mapping res

An official example of this articlehttp://blog.csdn.net/dahunbi/article/details/72821915Official examples have a disadvantage, used for training data directly on the load came in, do not do any processing, some opportunistic. Load and parse the data file. Val data = Mlutils.loadlibsvmfile (SC, "Data/mllib/sample_libsvm_data.txt") In practice, our spark are all architectures on Hadoop systems, and t