This guide describes how to download and use QuickStart dockers, which provide everything you need to try CDH, Cloudera Manager, Impala, Cloudera Search and PySpark 2.4 with Jupyter Notebook

This section describes how to deploy a single-host Cloudera open-source distribution, including CDH and Cloudera Manager. You can use this environment to learn Hadoop, try new ideas, and test and demonstrate your application.

Requirements

• At least 8 GB of RAM and 2 virtual CPU. To increase the default configuration see here

Download the cloudera docker image:

$ wget https://downloads.cloudera.com/demo_vm/docker/cloudera-quickstart-vm-5.13.0-$ 0-beta-docker.tar.gz
$ tar xzf cloudera-quickstart-vm-5.13.0-0-beta-docker.tar.gz
$ cd cloudera-quickstart-vm-5.13.0-0-beta-docker
$ docker import cloudera-quickstart-vm-5.13.0-0-beta-docker.tar cloudera_513

Run the container:

$ docker run --hostname=quickstart.cloudera --privileged=true -t -i -p 8888:8888 -p 80:80 -p 7180:7180 cloudera_513 /usr/bin/docker-quickstart

Required flags and options

Start Cloudera Manager service:

[root@quickstart /]$ sudo /home/cloudera/cloudera-manager --express --force

Note The container contains a relational dataset retail_dba in as mysql db. To export it on Hive run:

[cloudera@quickstart ~]$ sqoop import-all-tables \
   -m 1 \
   --connect jdbc:mysql://quickstart:3306/retail_db \
   --username=retail_dba \
   --password=cloudera \
   --compression-codec=snappy \
   --as-parquetfile \
   --warehouse-dir=/user/hive/warehouse \
   --hive-import

Now you can find the dataset's tables as parquet files on HDFS:

[root@quickstart /]$ hdfs dfs -ls /user/hive/warehouse/

and Hive:

This section describes how to deploy a single-host Hadoop 2.7 distribution, including Spark 2.2.0. You can use this environment to learn PySpark on small, local data through Jupyter Notebook.

The following command starts a container with the Notebook server listening for HTTP connections on port 8888 with a randomly generated authentication token configured.

docker run -it --rm -p 8888:8888 jupyter/pyspark-notebook

Take note of the authentication token included in the notebook startup log messages (e.g. http://localhost:8888/?token=e0967346858196004e70c70d2ee05e56743caab6e2a6eb5e). Include it in the URL you visit to access the Notebook server or enter it in the Notebook login form.