If you want to build from the source, the software below needs to be installed on your system. bin/install-interpreter.sh -all.Īfter unpacking, jump to Starting Apache Zeppelin with Command Line section. If you have downloaded netinst binary, install additional interpreters before you start Zeppelin. If you want to install Apache Zeppelin with a stable binary package, please visit Apache Zeppelin download Page. The other option is building from the source.Īlthough it can be unstable somehow since it is on development status, you can explore newly added feature and change it as you want. You can download not only the latest stable version but also the older one if you need. One is downloading pre-built binary package from the archive. There are two options to install Apache Zeppelin on your machine. Interpreter & Data Resource AuthorizationĪpache Zeppelin officially supports and is tested on next environments.Watch the AMPLab Docker account for updates. There is more to comeīesides offering lightweight isolated execution of worker processes inside containers via LXC, Docker also provides a kind of combined git and github for container images. If you are running Docker via vagrant on Mac OS make sure to increase the memory allocated to the virtual machine to at least 2GB. Which will kill all Spark and nameserver containers. $ sudo docker-scripts/deploy/kill_all.sh nameserver Scala> textFile.map().collect()Īfter you are done you can terminate the cluster from the outside via $ sudo docker-scripts/deploy/kill_all.sh spark Once the shell is up, let’s run a small example: scala> val textFile = sc.textFile("hdfs://master:9000/user/hdfs/test.txt") You should see something like this: *** Starting Spark 0.8.0 *** docker-scripts/deploy/deploy.sh -i amplab/spark:0.8.0 -c So let’s start up a Spark 0.8.0 cluster with two workers and connect to the Spark shell right away. The shell container is started from the deploy script by passing -c option but can also be attached later. This directory is then mounted on the master and worker containers under /data.īoth the Spark and Shark shells are started in separate containers. If you want to make a directory on the host accessible to the containers - say to import some data into Spark - just pass it with the -v option. All containers are also accessible via ssh using a pre-configured RSA key. Since services depend on a properly configured DNS, one container will automatically be started with a DNS forwarder. Hadoop HDFS services are started as well. The script either starts a standalone Spark cluster or a standalone Shark cluster with a given number of worker nodes. Running the deploy script without arguments shows command line options. During the first execution Docker will automatically fetch container images from the global repository, which are then cached locally. docker-scripts/deploy/deploy.sh -i amplab/spark:0.8.0 -cĪnd get a Spark cluster with two worker nodes and HDFS pre-configured. Start up a Spark 0.8.0 cluster and fall into the Spark shell by running $ sudo. (Contributions from the community are welcome, just send a pull request!) Fast track: deploy a virtual cluster on your laptop $ git clone -b blogpost repository contains deploy scripts and the sources for the Docker image files, which can be easily modified. The next step is to clone the git repository that contains the startup scripts. First run the Docker Hello World! example to get started. In fact, Apache Mesos recently added support for running Docker containers on compute nodes.ĭocker runs on any standard 64-bit Linux distribution with a recent kernel but can also be installed on other systems, including Mac OS, by adding another layer of virtualization. Its main advantage is that the very same container that runs on your laptop can be executed in the same way on a production cluster. You can use our Docker images to create a local development or test environment that’s very close to a distributed deployment.ĭocker provides a simple way to create and run self-sufficient Linux containers that can be used to package arbitrary applications. How fast? When we timed it, we found it took about 42 seconds to start up a pre-configured cluster with several workers on a laptop. This post will teach you how to use Docker to quickly and automatically install, configure and deploy Spark and Shark as well. Is its parent directory writable by the server?Īpache Spark and Shark have made data analytics faster to write and faster to run on clusters. Error: Unable to create directory uploads/2023/05.
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