apache spark use cases

built is to run a wordcount on it. $mkdir in$cat > in/fileThis is one lineThis is another line Copy the file to HDFS $bin/hdfs dfs -copyFromLocal in /in Run wordcount bin/hadoop jar ./share/hadoop/mapreduce/hadoop-mapreduce-examples-2.4.0.jar wordcount /in /out View running results bin/hdfs dfs -cat /out/* Take a rest, configure it here, and it will be sweaty. Next, run spark on yarn, and then stick to it for a short time.Run sparkpi on yarn to downl

Zeppelin IntroductionApache Zeppelin provides a web version of a similar Ipython notebook for data analysis and visualization. The back can be connected to different data processing engines, including Spark, Hive, Tajo, native support Scala, Java, Shell, Markdown and so on. Its overall presentation and use form is the same as the Databricks cloud, which comes from the demo at the time.Zeppelin can achieve w

fetch the data when it executes to Shufflerdd The first thing is to consult the location of the data that Mapoutputtrackermaster is going to take. Call Blockmanager.getmultiple to get real data based on the returned results Pseudo code of FETCH function for Blockstoreshufflefetcher val blockManager = SparkEnv.get.blockManager val startTime = System.currentTimeMillis val statuses = SparkEnv.get.mapOutputTracker.getServerStatuses(shuffleId, reduceId) logDeb

Follow the Iteblog_hadoop public number and comment at the end of the "double 11 benefits" comments Free "0 start TensorFlow Quick Start" Comment area comments (seriously write a review, increase the opportunity to list). Message points like the top 5 fans, each free one of the "0 start TensorFlow Quick Start", the event until November 07 18:00. This PPT from Spark Summit EUROPE 2017 (other PPT material is being collated, please pay attention to this

"War of the Hadoop SQL engines. And the winner is ...? "This is a very good question. However, whatever the answer, it's worth a little time to get to know the spark SQL members within the spark family. Originally Apache Spark SQL official online code Snippets (Spark officia

equivalent to ToArray, ToArray is deprecated, collect returns the distributed RDD as a single stand-alone Scala array. Use Scala's functional operation on this array.The left square in Figure 18 represents the RDD partition, and the right square represents an array in the stand-alone memory. The result is returned to the node where the Driver program is located, stored as an array, through a function operation.Figure Collect operator to RDD conversio

"War of the Hadoop SQL engines. And the winner is ...? "This is a very good question. Just. No matter what the answer is. We all spend a little time figuring out spark SQL, the family member inside Spark.Originally Apache Spark SQL official code Snippets on the Web (Spark official online sample has a common problem: do

One of the interface Automation tests using jmeter+ant (Data driven) Describes how to use a CSV file to manage interfaces in bulkThis article then describes how to use Apache-ant to execute test Cases and generate HTML format test reports① downloading and installing apache

,COLLECT,COLLECTASMAP)4. Variable sharingSpark has two different ways to share variablesA. Variables after broadcast broadcast,broadcast each partition will be stored in one copy, but can only be read and cannot be modified >>>NBSP; b Span class= "o" style= "color: #666666;" >= sc broadcast ([ 1 2 3 4 5 ]) >>> SC . parallelize ([0,0]) . FlatMap (Lambdax:b. value )B. Accumulator accumulator, can only write, cannot be read in workerIf the accumulator is just a scalar, it is easy

remember the transition actions that apply to the underlying dataset (such as a file). These conversions will only actually run if a request is taken to return the result to driver. This design allows spark to run more efficiently. For example, we can implement: a new dataset created from map and used in reduce, and ultimately only the result of reduce is returned to driver, not the entire large new dataset. Figure 2 depicts the implementation logic

/jblas/wiki/Missing-Libraries). Due to the license (license) issue, the official MLlib relies on concentration withoutIntroduce the dependency of the Netlib-java native repository. If the runtime environment does not have a native library available, the user will see a warning message. If you need to use Netlib-java libraries in your program, you will need to introduce com.github.fommil.netlib:all:1.1.2 dependencies or reference guides to your project

to see the sample code of the application.Version and APIs The Hadoop ecosystem is filled with different libraries, and the possible APIs conflicts between them will drive people crazy. The main API changes are in Hadoop 0.20. In this version, the old org. apache. hadoop. mapred API is changed to org. apache. hadoop. mapreduce API. API changes in turn affect these libraries: The mongo-hadoop package com. m

mainly shuffle use, Here are two scenarios, shuffle write and shuffle read,write occupy the memory strategy is more complex, if it is the general sort, mainly with the heap memory, if it is tungsten sort, Is the way in which the out-of-heap memory is combined with the memory in the heap (if the external memory is not enough), and whether the sort is a normal sort or tungsten is determined by spark.For shuffle read, the main

Spark supports yarn as a resource scheduler, so the principle of yarn should still be known: http://www.socc2013.org/home/program/a5-vavilapalli.pdf But overall, this is a general paper, Its principles are not particularly prominent, and the data it enumerates are not comparable, and there is almost no advantage in yarn. Anyway, the way I read it is that yarn's resource allocation is poorly estimated on latency. And the actual

( Line= Getstatuscode (P.parserecord ( Line)) =="404"). Map (Getrequest (_)). Countval RECs =Log.Filter( Line= Getstatuscode (P.parserecord ( Line)) =="404"). Map (Getrequest (_)) Val Distinctrecs =Log.Filter( Line= Getstatuscode (P.parserecord ( Line)) =="404"). Map (Getrequest (_)). Distinctdistinctrecs.foreach (println)It's OK! A simple example! The main use of the analysis log package! Address is: Https://github.com/jinhang/ScalaApacheAccessLogPar

Installation: (http://zeppelin.apache.org/docs/0.7.2/manual/interpreterinstallation.html#3rd-party-interpretersThe download is zeppelin-0.7.2-bin-all,package with the all interpreters. Decompression complete.================================================================================Modify configuration. BASHRC# ZeppelinExport Zeppelin_home=/home/raini/app/zeppelinExport path= $ZEPPELIN _home/bin: $PATHModify Zeppelin-env.sh# All configurations are post modifiedExport JAVA_HOME=/HOME/RAINI/A

calculate the small data, observe the effect, adjust the parameters, and then gradually increase the amount of data for large-scale operation by different sampling scales. Sampling can be done via the RDD sample method. WithThe resource consumption of the cluster is observed through the Web UI.1) Memory release: Preserves references to old graph objects, but frees up the vertex properties of unused graphs as soon as possible, saving space consumption. Vertex release through the Unpersistvertice

Basis Spark's shell serves as a powerful interactive data analysis tool, providing an easy way to learn the API. It can use Scala (a good way to run an existing Java library on a Java Virtual machine) or Python. Start running in the Spark directory using the following method: ./bin/spark-shellIn the spark shell, there