avro spark

1. Install Hadoop clusterReference: http://www.cnblogs.com/wcwen1990/p/6739151.html2. Installing hiveReference: http://www.cnblogs.com/wcwen1990/p/6757240.html3. Installation configuration SparkCompiling spark:http://www.cnblogs.com/wcwen1990/p/7688027.htmlDeployment reference: Http://www.cnblogs.com/wcwen1990/p/6889521.html4. Spark-sql Integrated HiveCopy the Hdfs-site.xml, hive-site.xml configuration file to the

Scenario: Use spark streaming to receive real-time data and query operations related to tables in the relational database;Using technology: Spark streaming + spark JDBC External datasourcesCode prototype: Packagecom.luogankun.spark.streamingImportorg.apache.spark.SparkConfImportorg.apache.spark.streaming. {Seconds, StreamingContext}ImportOrg.apache.spark.sql.hive

Contents of this issue:1 Spark streaming Alternative online experiment2 instantly understand the nature of spark streamingQ: Why cut into spark source version from spark streaming? Spark did not start with spark streamin

Below is a look at the use of Union:Use the collect operation to see the results of the execution:Then look at the use of Groupbykey:Execution Result:The join operation is the process of a Cartesian product operation, as shown in the following example:To perform a join operation on RDD3 and RDD4:Use collect to view execution results:It can be seen that the join operation is exactly a Cartesian product operation;The reduce itself, which is an action-type operation in an RDD operation, causes the

Copy an objectThe content of the copied "input" folder is as follows:The content of the "conf" file under the hadoop installation directory is the same.Now, run the wordcount program in the pseudo-distributed mode we just built:After the operation is complete, let's check the output result:Some statistical results are as follows:At this time, we will go to the hadoop Web console and find that we have submitted and successfully run the task:After hadoop completes the task, you can disable the had

SOURCE Link: Spark streaming: The upstart of large-scale streaming data processingSummary: Spark Streaming is the upstart of large-scale streaming data processing, which decomposes streaming calculations into a series of short batch jobs. This paper expounds the architecture and programming model of spark streaming, and analyzes its core technology with practice,

Overview Flume: A distributed, reliable, and usable service for efficiently collecting, aggregating, and moving large-scale log data We build a flume + Spark streaming platform to get data from flume and process it. There are two ways to do this: Use the push-based method of Flume-style, or use a custom sink to implement the Pull-based method. Approach 1:flume-style push-based Approach Flume is designed to push informati

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