h2o spark

Overview A spark job is divided into multiple stages. The last stage contains one or more resulttask. The previous stages contains one or more shufflemaptasks. Run resulttask and return the result to the driver application. Shufflemaptask separates the output of a task from Multiple Buckets Based on the partition of the task. A shufflemaptask corresponds to a shuffledependency partition, and the total number of partition is the same as that of parall

Spark is especially suitable for multiple operations on specific data, such as mem-only and MEM disk. Mem-only: high efficiency, but high memory usage, high cost; mem Disk: After the memory is used up, it will automatically migrate to the disk, solving the problem of insufficient memory, it brings about the consumption of Data replacement. Common spark tuning workers include nman, jmeter, and jprofile. Th

Listen to Liaoliang's spark the IMF saga 19th lesson: Spark Sort, job is: 1, Scala two order, use object apply 2; read it yourself RangepartitionerThe code is as follows:/*** Created by Liaoliang on 2016/1/10.*/Object Secondarysortapp {def main (args:array[string]) {val conf=NewSparkconf ()//Create a Sparkconf objectConf.setappname ("Secondarysortapp")//set the application name, the program run monitoring i

The code is as follows:Packagecom.dt.spark.streamingimportorg.apache.spark.sql.sqlcontextimportorg.apache.spark. {sparkcontext,sparkconf}importorg.apache.spark.streaming. {streamingcontext,duration}/*** logs are analyzed using sparkstreaming combined with sparksql. * assuming e-commerce website click Log Format (Simplified) The following:*userid,itemid,clicktime* requirements: processing the item click order within 10 minutes Top10, and display the name of the product. The correspondence between

Reference: Https://spark.apache.org/docs/latest/sql-programming-guide.html#overviewhttp://www.csdn.net/article/2015-04-03/2824407Spark SQL is a spark module for structured data processing. IT provides a programming abstraction called Dataframes and can also act as distributed SQL query engine.1) in Spark, Dataframe is a distributed data set based on an RDD, similar to a two-dimensional table in a traditiona

STEP1: Start the Spark cluster, which is very detailed in the third lecture, after the start of the WebUI as follows: STEP2: Start the spark Shell: You can now view the shell situation through the following Web console: STEP3: Copy the Spark installation directory "README.MD" to the HDFS system Start a new command terminal on the master node and go to the

This article is mainly from two aspects:Contents of this issue1 exactly Once2 output is not duplicated1 exactly OnceTransaction:　　Bank Transfer For example, a user to transfer to the User B, if the B users confiscated, or received multiple accounts, is to undermine the consistency of the transaction. Transactions are handled and processed only once, that is, a is only turned once and B is only received once.　　Decrypt the sparkstreaming schema from a transactional perspective:　　The sparkstreaming

Learn Spark 2.0 (new features, real projects, pure Scala language development, CDH5.7)Share the network disk download--https://pan.baidu.com/s/1c2f9zo0 password: pzx9Spark entered the 2.0 era, introducing many excellent features, improved performance, and more user-friendly APIs. In the "unified programming" is very impressive, the implementation of offline computing and Flow computing API unification, the implementation of the

IntroducedIn general, there are two ways to fault-tolerant distributed datasets: data checkpoints and the updating of record data .For large-scale data analysis, data checkpoint operations are costly and require a large data set to be replicated between machines through a network connection in the data center, while network bandwidth tends to be much lower than memory bandwidth and consumes more storage resources.Therefore, Spark chooses how to record

Problem 1:reduce task number not appropriateSolution: Need to adjust the default configuration according to the actual situation, the adjustment method is to modify the parameter spark.default.parallelism. Typically, the reduce number is set to 2-3 times the number of cores. The number is too large, causing a lot of small tasks, increasing the overhead of starting tasks, the number is too small, the task runs slowly. Therefore, the number of tasks to reasonably modify reduce is spark.default.pa

First Test the spark API in Spark's native mode and run Spark-shell as Local:Let's start with the parallelize:Results after map operation:Below is a look at the filter operation:Filter execution Results:We use the most authentic Scala functional style of programming:Execution Result:As you can see from the results, the results are the same as that of the previous step.But in this way, the style of the compo

To operate HDFs: first make sure that HDFs is up:To start the Spark cluster:Run on the Spark cluster with Spark-shell:View the "LICENSE.txt" file that was uploaded to HDFs before:Read this file with Spark:Count the number of rows in the file using the Counts:We can see that count time is 0.239708sCaches the RDD and executes count to make the cache effective:The e

Application:Application is the spark user who created the Sparkcontext instance object and contains the driver program:Spark-shell is an application because Spark-shell created a Sparkcontext object when it was started, with the name SC:Job:As opposed to Spark's action, each action, such as Count, Saveastextfile, and so on, corresponds to a job instance that contains multi-tasking parallel computations.Driv

"Original Hadoopspark hands-on Practice 10" Spark SQL Programming Basics and hands-on practice (bottom)Goal:1. Deep understanding of the principles of spark SQL programming2. Use simple commands to verify how spark SQL works3. Use a complete case to verify how spark SQL works, and actually do it yourself4. Successful c

First, prepareUpload apache-hive-1.2.1.tar.gz and Mysql--connector-java-5.1.6-bin.jar to NODE01Cd/toolsTAR-ZXVF apache-hive-1.2.1.tar.gz-c/ren/Cd/renMV apache-hive-1.2.1 hive-1.2.1This cluster uses MySQL as the hive metadata storeVI Etc/profileExport hive_home=/ren/hive-1.2.1Export path= $PATH: $HIVE _home/binSource/etc/profileSecond, install MySQLYum-y install MySQL mysql-server mysql-develCreating a hive Database Create databases HiveCreate a hive user grant all privileges the hive.* to [e-mai

[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"). Load ("Episodes.avro")Interactive Run Results:In

Label:Spark1.2 1. Text Import Create the form of an RDD, test txt text master=spark://master:7077 ./bin/spark-shell scala> val sqlcontext = new Org.apache.spark.sql.SQLContext (SC) sqlContext:org.apache.spark.sql.SQLContext = [email protected] scala> import sqlcontext.createschemardd Import Sqlcontext.createschemardd scala> case Class Pe Rson (name:string, age:int) defined class person scala> val people = s

Spark Learning III: Installing and Importing source code for spark schedule and ideatags (space delimited): Spark Spark learns to install and import source code for three spark schedule and idea Data location during an RDD operation Two

The content of this lecture:A. Jobscheduler Insider implementationB. Jobscheduler Deep ThinkingNote: This lecture is based on the spark 1.6.1 version (the latest version of Spark in May 2016).Previous section ReviewLast lesson, we take the Jobgenerator class as the center of gravity, for everyone left and right extension, decryption job dynamic generation, and summed up the job dynamic generation of the thr

The spark kernel is developed by the Scala language, so it is natural to develop spark applications using Scala. If you are unfamiliar with the Scala language, you can read Web tutorials A Scala Tutorial for Java programmers or related Scala books to learn. This article will introduce 3 Scala spark programming examples, WordCount, TOPK, and Sparkjoin, representi