transformers spark

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

Content:1, exactly what is page;2, page specific two ways to achieve;3, page of the use of the source of the detailed;What is page============ in ==========tungsten?1, in Spark in fact there is no page this class!!! In essence, page is a data structure (similar to stack, list, etc.), from the OS level, page represents a memory block in the page can store data, there are many different page in the OS, when to get the data, The first thing to do is to l

Tachyon is a killer Technology in the big data era and a technology that must be mastered in the big data era. With tachyon, distributed machines can share data based on the distributed memory file storage system built on tachyon. This is of extraordinary significance for Machine Collaboration, data sharing, and speed improvement of distributed systems; In this course, we will first start with the tachyon architecture, the tachyon architecture and startup principle, then carefully parse the ta

Thanks for the original link: https://www.jianshu.com/p/a1526fbb2be4 Before reading this article, please step into the spark streaming data generation and import-related memory analysis, the article is focused on from the Kafka consumption to the data into the Blockmanager of this line analysis. This content is a personal experience, we use the time or suggest a good understanding of the internal principles, not to copy receiver evenly distributed to

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

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

When a task executes a commit failure, it retries, and the default retry count for the task is 4 times. def this (sc:sparkcontext) = This (SC, sc.conf.getInt ("Spark.task.maxFailures", 4)) (Taskschedulerimpl)(2) Add TasksetmanagerSchedulerbuilder (depending on the Schedulermode, FIFO is different from fair implementation) #addTaskSetManger方法会确定TaskSetManager的调度顺序, Then follow Tasksetmanager's locality aware to determine that each task runs specifically in that executorbackend. The default schedu

This lesson: The use of Scala's implicit in the Spark source code Scala's implicit programming operation combat Scala's implicit enterprise-class best practices The use of Scala's implicit in the Spark source codeThe meaning of this thing is very significant, the RDD itself does not have a key, value, but it is the time of its own interpretation into a key Value of the method to read,

You are welcome to reprint it. Please indicate the source, huichiro.Summary There is nothing to say about source code compilation. For Java projects, as long as Maven or ant simple commands are clicked, they will be OK. However, when it comes to spark, it seems that things are not so simple. According to the spark officical document, there will always be compilation errors in one way or another, which is an

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$[

stay at home for 10 hours, stay in the company for 8 hours, and may be passing by some base station in the car. Ideas: For each cell phone number under which base station to stay the longest time, in the calculation, with "mobile phone number + base station" in order to locate under which base station stay at the time, Because there will be a lot of user log data under each base station. The country has a lot of base stations, each telecommunications branch is only responsible for calcula