spark and cassandra

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

The task scheduling system for Spark is as follows:From the Chinese Academy of Sciences to see the cause rddobject generated DAG, and then entered the Dagscheduler stage, Dagscheduler is the state-oriented high-level scheduler, Dagscheduler the DAG split into a lot of tasks, Each group of tasks is a state, whenever encountering shuffle will produce a new state, you can see a total of three state;dagscheduler need to record those rdd is deposited into

You are welcome to reprint it. Please indicate the source, huichiro.Summary This article will give a brief review of the origins of the quasi-Newton method L-BFGS, and then its implementation in Spark mllib for source code reading.Mathematical Principles of the quasi-Newton Method Code Implementation The regularization method used in the L-BFGS algorithm is squaredl2updater. The breezelbfgs function in the breeze library of the scalanlp member

After starting Hadoop and then starting Spark JPS, the master process and worker process are found to be present, and a half-day configuration file is debugged.The test found that when I shut down Hadoop the worker process still exists,However, when I shut down spark again and then JPS, I found that the worker process still exists.Then remembered in the ~/spark/c

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

Provides various official and user release code examples. For code reference, you are welcome to exchange and learn about spark grassland system development, spark grassland system source code, distribution system micro-distribution, it is a three-level distribution mall based on the public platform. The three-level distribution should achieve an infinite loop model, and an innovation of the enterprise mark

3, hands-on generics in Scalageneric generic classes and generic methods, that is, when we instantiate a class or invoke a method, you can specify its type, because Scala generics and Java generics are consistent and are not mentioned here. 4, hands on. Implicit conversions, implicit parameters, implicit classes in Scalaimplicit conversion is one of the key points that many people learn about Scala, which is the essence of Scala:Let's take a look at the example of hidden parameters: The

3, hands-on generics in Scala generic generic classes and generic methods, that is, when we instantiate a class or invoke a method, you can specify its type, because Scala generics and Java generics are consistent and are not mentioned here. 4, hands on. Implicit conversions, implicit parameters, implicit classes in Scala Implicit conversion is one of the key points that many people learn about Scala, which is the essence of Scala: Let's take a look at the example of hidden parameters:

Http://spark.apache.org/docs/1.2.1/streaming-programming-guide.htmlHow to shard data in sparkstreamingLevel of Parallelism in Data processingCluster resources can be under-utilized if the number of parallel tasks used on any stage of the computation are not high E Nough. For example, for distributed reduce operations like reduceByKey reduceByKeyAndWindow and, the default number of parallel tasks are controlled by The spark.default.parallelism configuration property. You can pass the level of par

configuration file are: Run the ": WQ" command to save and exit. Through the above configuration, we have completed the simplest pseudo-distributed configuration. Next, format the hadoop namenode: Enter "Y" to complete the formatting process: Start hadoop! Start hadoop as follows: Use the JPS command that comes with Java to query all daemon processes: Start hadoop !!! Next, you can view the hadoop running status on the Web page used to monitor the cluster status in hadoop. The specific pa