apache spark wiki

,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

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 conversion(4) CountCount returns the number of element

the container. It is the responsibility of AM to monitor the working status of the container. 4. Once The AM is-is-to-be, it should unregister from the RM and exit cleanly. Once am has done all the work, it should unregister the RM and clean up the resources and exit. 5. Optionally, framework authors may add controlflow between their own clients to report job status andexpose a control plane.7 ConclusionThanks to the decoupling of resource management and programming framework, yarn provides: Be

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

( 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/ScalaApacheAccessLogParserNext time thank you. How to analyze logs b

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 use is in-heap memory. Reference:https://www.ibm.com/developerworks/cn/

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

After integrating the Scala environment into eclipse, I found an error in the imported spark package, and the hint was: Object Apache is not a member of packages Org, the net said a big push, in fact the problem is very simple;Workaround: When creating a Scala project, the next step in creating the package is to choose:Instead of creating a Java project that is the package type of the Java program, and then

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

"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

The creation of an RDDTwo ways to create an rdd:1) created by an already existing Scala collection2) created by the data set of the external storage system, including the local file system, and all data sets supported by Hadoop, such as HDFs, Cassandra, HBase, Amazon S3, etc.The RDD can only be created based on deterministic operations on datasets in stable physical storage and other existing RDD. These deterministic operations are called transformations, such as map, filter, GroupBy, join.The c

Article titleApache Spark as a compiler:joining a billion Rows per Second on a LaptopDeep dive into the new tungsten execution engineAbout the authorSameer Agarwal, Davies Liu and Reynold XinArticle textReference documents Https://databricks.com/blog/2016/05/23/apache-spark-as-a-compiler-joining-a-billion-rows-per-second-on-a-laptop.html