Docker FAQ

What is Docker?

Docker is a container management platform for automating deployments of re-usable environemnets.

What are Containers? Containers vs VM

Containers are a type of virtualization. They virtualize environments at the operating system level by sharing the base libraries. This removes the need for boot drives and hardware interfaces for each environment. This makes a container use much less resources than a virtual machine (VM) which can make environments more efficient because lack of duplication of resources.

Why Docker?

Using Docker we can keep a fresh deployment of our Hadoop/Jupyter/Spark environment readily avaliable. Taking around 2 minutes to remove and launch a new environment with the same initial configuration everytime.

Installing Docker

Download Docker Community Edition from Docker

Pull the Class Image

docker pull w261/w261-environment

Things to know

In the container for W261 we use docker-compose to build our container. Let's review the configuration file docker-compose.yml in its current state at the time of writing

version: '2'
services:
  quickstart.cloudera:
    # from repo
    image: w261/w261-environment:latest
    # from local
    #image: w261:latest
    hostname: quickstart.cloudera
    privileged: true
    command: bash -c "/root/start-notebook.sh;/usr/bin/docker-quickstart"
    ports:
      - "8887:8888"   # Hue server
      - "8889:8889"   # jupyter
      - "10020:10020" # mapreduce job history server
      - "8022:22"     # ssh
      - "7180:7180"   # Cloudera Manager
      - "11000:11000" # Oozie
      - "50070:50070" # HDFS REST Namenode
      - "50075:50075" # hdfs REST Datanode
      - "8088:8088"   # yarn resource manager webapp address
      - "19888:19888" # mapreduce job history webapp address
      - "8983:8983"   # Solr console
      - "8032:8032"   # yarn resource manager access
      - "8042:8042"   # yarn node manager
      - "60010:60010" # hbase
      - "4040:4040"   # Spark UI
      - "8080:8080"   # Hadoop Job Tracker
    tty: true
    stdin_open: true
    volumes: 
      # windows example
      # - C:\Users\winegarj\w261:/media/notebooks
      # linux example
      #- /home/jameswine/notebooks:/media/notebooks
      - C:\Users\jwinegar\w261:/media/notebooks

• version: this item says use v2 syntax
• services: list of containers
  • quickstart.cloudera: the name of a container, the label being quickstart.cloudera
    • image: use this base container
    • hostname: DNS name for the container
    • privledged: allow access to other machines such as the local machine
    • commands: run this commands on start
    • ports: map ports so that services running on the container are accessible from the local computer
      • remote port:local port
    • tty: allow a shell to be initiated
    • stdin_open: allow interactivity with the shell
    • volumes: location to map from local computer to the docker container so they can share.
      • /local/path:/media/notebook

If we review the bash scripts startup.sh we can see that the jupyter notebook is launched from the /media/notebook directory. This is very important for our deployment.

How to Use

1. Install Docker (Restart as needed)
2. Copy the contents of the docker-compose.yml file in this repo to somewhere on your computer with the same name
3. Edit the Volume section to match where you want to store your notebooks/homework locally
4. Open up a terminal or Powershell windows and change directory to where you stored your docker-compose.yml file
5. Run docker-compose up
6. Open your browser and go to localhost:8889 for regular Python, MRJob, and Pyspark
7. If using spark a sparkContext is automatically defined during initialization, in the sc variable

General Issues

Using python packages against HDFS

Add the following parameter to Map Reduce Streaming and MRJob commands: -cmdenv PATH=/opt/anaconda/bin:$PATH

Hostname mapping

Apply the quickstart.cloudera alias for 127.0.0.1 aka localhost

• Linux & Mac
  1. Open Terminal
  2. Open hostfile by running sudo nano /etc/hosts
  3. Append the following line, then save: 127.0.0.1 quickstart.cloudera
  4. Refresh DNS with sudo killall -HUP mDNSResponder
• Windows:
  1. Open notepad as administrator (otherwise you'll not be able to save the file)
  2. Open C:\Windows\System32\drivers\etc\hosts in notepad. Note the file has no extension
  3. Append the following line, then save: 127.0.0.1 quickstart.cloudera
  4. Refresh DNS by running ipconfig /flushdns in command prompt or powershell

Minimum System Requirements for MIDS W261 Cloudera Hadoop Container

Docker needs 2 CPUs and 4 GB of RAM to ensure resource managers don't crash during normal operation.

• Linux
  1. By default Docker shares the same resources as the local computer.
• Windows
  1. Right click Docker in the notification area
  2. Click Settings
  3. Click Advanced
  4. Slide Memory to 4096 MB
• Mac OS
  1. Click Docker in the clock(?) area
  2. Click Settings
  3. Click Advanced
  4. Slide Memory to 4096 MB

Using Spark inside the notebook

from pyspark.sql import SparkSession
app_name = "example_notebook"
master = "local[*]"
spark = SparkSession\
        .builder\
        .appName(app_name)\
        .master(master)\
        .getOrCreate()
sc = spark.sparkContext

spark is the general session manager for dataframes and the newer style introduced in Spark 2.0

sc is a Spark context sets up internal services and establishes a connection to a Spark execution environment.

Linux Issues

Windows Issues

Minimum OS requirement

• Windows 10 Pro/Education is required to run Docker on Windows. A free license of Windows 10 Education is avaliable to all students through UCB Software Central

Auto-save/Overwrite Issues

• jupyter/notebook#484 This has been addressed with Jupyter 5.1.0 which is being used for latest build of the container.

Mac Issues

• Macs require a computer capable of virtualization to test this run sysctl kern.hv_support in a terminal.
  • If 1 then good to go
  • If 0 then you need a new computer