hortonworks yarn

dfs.domain.socket.path . Zero copy: Avoids repeated copy of the data between the kernel buffer and the user buffer, which has already been implemented in earlier HDFs. Disk-aware scheduling: By knowing each block's disk, you can schedule CPU resources to have different CPUs read different disks and avoid the IO competition between queries and queries. The HDFs parameter is dfs.datanode.hdfs-blocks-metadata.enabled . Storage formatFor the analysis type of workload, the best storage

-tests.jar:/usr/hadoop-2.2.0/share/hadoop/hdfs/hadoop-hdfs-nfs-2.2.0.jar:/usr/hadoop-2.2.0/ share/hadoop/hdfs/hadoop-hdfs-2.2.0.jar:/usr/hadoop-2.2.0/share/hadoop/yarn/lib/guice-servlet-3.0.jar:/usr/ hadoop-2.2.0/share/hadoop/yarn/lib/hadoop-annotations-2.2.0.jar:/usr/hadoop-2.2.0/share/hadoop/yarn/lib/ jackson-core-asl-1.8.8.jar:/usr/hadoop-2.2.0/share/hadoop/

Hadoop memory configuration There are two methods to configure the Hadoop memory: manually install the hadoop help script; manually calculate the yarn and mapreduce memory size for configuration. Only the script calculation method is recorded here: Use the wget command to download the script from hortonworks Python hdp-configuration-utils.py Wget http://public-repo-1.hortonworks.com/HDP/tools/2.1.1.0/hdp_m

Big data why Spark is chosenSpark is a memory-based, open-source cluster computing system designed for faster data analysis. Spark, a small team based at the University of California's AMP lab Matei, uses Scala to develop its core code with only 63 Scala files, very lightweight. Spark provides an open-source cluster computing environment similar to Hadoop, but based on memory and iterative optimization design, Spark is performing better on some workloads.In the first half of 2014, the spark open

HadoopYARN supports both memory and CPU scheduling (by default, only memory is supported. If you want to further schedule the CPU, You need to configure it yourself ), this article describes how YARN schedules and isolates these resources. In YARN, resource management is completed by ResourceManager and NodeManager. Hadoop YARN supports both memory and CPU schedu

Reference http://www.cnblogs.com/shishanyuan/p/4721326.html1. Spark Run architecture 1.1 Terminology DefinitionsThe concept of Lapplication:spark application is similar to that in Hadoop MapReduce, which refers to a user-written Spark application,Contains acode for a driver functionand distributed in the clusterExecutor code that runs on multiple nodesThe driver in Ldriver:spark is the main () function that runs the application above and creates Sparkcontext,The purpose of creating sparkcontext

dispatches the Task in Slot. But the Task here is different from what we understand in Hadoop. For Flink's JobManager, it dispatches a Pipeline Task, not a point. For example, in Hadoop, Map and Reduce are two tasks that are scheduled independently and will take up compute resources. For Flink, MapReduce is a Pipeline Task that occupies only one compute resource. In a similar case, if there is a MRR Pipeline task, it is also a Pipeline task that is dispatched collectively in Flink. In TaskManag

In the first blog article of article 2014, we will gradually write a series of New Year's news. Deb/rpm of hadoop and its peripheral ecosystems is of great significance for automated O M. The rpm and deb of the entire ecosystem are established and then the local yum or apt source is created, this greatly simplifies hadoop deployment and O M. In fact, both cloudera and hortonworks do this. I wanted to write both rpm and deb, but it is estimated tha

knows).Storm is the solution for streaming hortonworks Hadoop data platforms, and spark streaming appears in MapR's distributed platform and Cloudera's enterprise data platform. In addition, Databricks is a company that provides technical support for spark, including the spark streaming. While both can run in their own cluster framework, Storm can run on Mesos, while spark streaming can run on yarn and Me

clusters that are difficult to install and manage. And to deal with different big data use cases, you need to integrate many different tools (such as mahout for machine learning and storm for streaming data processing).If you want to do more complex work, you must concatenate a series of mapreduce jobs and execute them sequentially. Each job is Gao Shiyan, and the next job can start only after the previous one has completed.Spark, however, allows program developers to develop complex multi-step

.jar to/usr/local/hadoop2x-eclipse-plugin/build/ Contrib/eclipse-plugin/lib/hadoop-hdfs-2.6.0.jar[Copy] Copying/usr/local/hadoop/share/hadoop/hdfs/hadoop-hdfs-nfs-2.6.0.jar To/usr/local/hadoop2x-eclipse-plugin/build /contrib/eclipse-plugin/lib/hadoop-hdfs-nfs-2.6.0.jar[Copy] Copying Files To/usr/local/hadoop2x-eclipse-plugin/build/contrib/eclipse-plugin/lib[Copy] Copying/usr/local/hadoop/share/hadoop/yarn/hadoop-y