If you are a Northeastern PhD student, and you have a compelling need to access these resources, contact Professor Wilson and we may be able to accommodate your request. HDFS is a distributed file system designed to store large files spread across multiple physical machines and hard drives. Spark is a tool for running distributed computations over large datasets.
Spark is a successor to the popular Hadoop MapReduce computation framework. Together, Spark and HDFS offer powerful capabilities for writing simple code that can quickly compute over large amounts of data in parallel.
However, behind the scenes all files stored in HDFS are split apart and spread out over multiple physical machines and hard drives. As a user, these details are transparent; you don't need to know how your files are broken apart or where they are stored. Spark is two things: 1 a set of programming tools for writing parallelizable, data crunching code, and 2 a framework for executing this code in parallel across many physical machines and CPUs.
Spark supports code written in Java, Scala, and Python. Any library that can be used by these languages can also be used in a Spark "job". Furthermore, Spark comes with several libraries that implement parallel machine learning, graph processing, SQL-like querying of data, and date stream processing. The Decepticons cluster is currently composed of 20 Dell R servers, plus two additional servers that serve as the Master and Secondary Master.
All 20 machines are located on a private, 10 Gbps Ethernet network that is only accessible from achtung Once you have sshed into one of the Achtungs, the Decepticons are accessible as [name]. In total, the cluster has CPUs. The servers are running Ubuntu Common Python modules have been pre-installed.
Should you require additional software tools, contact Prof. In order to use HDFS and Spark, you first need to configure your environment so that you have access to the required tools. The easiest way to do this is to modify the. Specifically, you should add the following two lines to your. Before discussing how to use the Spark cluster, we first need to discuss how to have good manners when using these resources.
The fundamental point to keep in mind is that the Decepticons cluster is a shared resource , meaning that if you abuse it, you will have a direct, negative impact on your colleagues. Thus, it is important to plan your usage of the cluster carefully and conscientiously.
First: disk space. The administrators reserve the right to delete your data from HDFS at any time if we find you are taking up too much space, and we are not responsible if you lose critical data due to negligence or hardware failures.
Second: CPU time. This means that if you submit a job that takes hours to complete, nobody else can use the cluster until your job is finished. Thus, please be mindful when submitting large jobs to the cluster. If other students are working on a deadline and need cluster resources, be a good coworker and wait until they are done before starting large jobs. The administrators reserve the right to kill any job at any time, should the need arise.
Wilson and a home directory will be created for you. Most Spark jobs will be doing computations over large datasets. We have preconfigured the hdfs tool so that it will automatically connect to the HDFS storage offered by the Decepticons cluster. In this case, user cbw has three files in their home directory.
Notice how the hdfs utility takes -ls as a command line parameter; this tells it to list the files in an HDFS directory, just like the ls command lists files in a local directory. In addition to -ls , the hdfs utility also includes versions of many common Unix file utilities. These commands include:. In addition to these standard commands, the hdfs utility can also upload files from local storage into HDFS, and download files from HDFS into local storage:.
Other commands are also available; run hdfs dfs with no parameters to see a list of possible commands. You can give people greater access to your files by changing their ownership, or by changing their permissions. Finally, a note on data formats. When files are uploaded to HDFS, they are automatically split up into smaller pieces and distributed throughout the cluster.
Thus, you should be able to upload any data that you have stored in simple text files to HDFS. However, HDFS is not well suited for binary data files.
Download Materials. This recipe demonstrates how a file or directory is removed from the HDFS. Prerequisites: Before proceeding with the recipe, make sure Single node Hadoop is installed on your local EC2 instance. If not installed, please find the links provided above for installations. It is clear from the above result that the file is moved to the trash. Relevant Projects.
The syntax for this is given below:. For example, I want to download the "Test. Then the command would be:. Let us execute the above command and check in the "testing" directory if the "Test. Download Materials. Prerequisites: Before proceeding with the recipe, make sure Single node Hadoop is installed on your local EC2 instance.
If not installed, please find the links provided above for installations. If they are not visible in the Cloudera cluster, you may add them by clicking on the "Add Services" in the cluster to add the required services in your local instance.
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