hadoop-testing

This serves as a testing sandbox for Hadoop, equipped with fundamental components of the Hadoop ecosystem to facilitate the rapid establishment of test environments.

We try to deploy a big data ecosystem in multiple Docker containers to simulate the production environment. Generally speaking, it contains two types of deployment modes(standalone and mixed deployed). Standalone mode is just like a SaaS service provided by cloud vendors, while the mixed deployed mode is just like the semi-managed EMR service of cloud vendors. The whole deployment architecture is shown below:

Draw by excalidraw

Features

• Realistic simulation of production environment;
• Lightweight, highly scalable and tailored Hadoop ecosystem;
• Multi-purpose, multi-scenario, suitable for:
  • Component developer: unit and integration testing;
  • DevOps engineer: parameter adjustment verification, compatibility testing of component upgrades;
  • Solution architect: Sandbox simulation of migration work, work shop demonstration;
  • Data ETL engineer: a test environment that is easy to build and destroy;

Prepare

This project uses ansible to render the Dockerfile, shell scripts, and configuration files from the templates. Please make sure you have installed it before building.

Considering, ansible strongly depends on the Python environment. To make the Python environment independent and easy to manage, it is recommended to use pyenv and virtualenv to manage Python environment.

Here we provide guides for macOS and centOS users.

MacOS

Install from Homebrew

brew install pyenv pyenv-virtualenv

Append to ~/.zshrc, and perform source ~/.zshrc or open a new terminal to take effect.

eval "$(pyenv init -)"
eval "$(pyenv virtualenv-init -)"

CentOS

Before installing, we need to install some required packages.

yum install gcc make patch zlib-devel bzip2 bzip2-devel readline-devel sqlite sqlite-devel openssl-devel tk-devel libffi-devel xz-devel

Then, install pyenv:

curl https://pyenv.run | bash

# or

curl -L https://raw.githubusercontent.com/pyenv/pyenv-installer/master/bin/pyenv-installer | bash

If you use bash, add it into ~/.bash_profile or ~/.bashrc:

export PYENV_ROOT="$HOME/.pyenv"
[[ -d $PYENV_ROOT/bin ]] && export PATH="$PYENV_ROOT/bin:$PATH"
eval "$(pyenv init -)"

Add it into ~/.bashrc:

eval "$(pyenv virtualenv-init -)"

After all, source ~/.bash_profile and ~/.bashrc.

Install nc

To let ansible control the host itself and all the hadoop related containers, we need to install nc command:

yum install epel-release && yum install -y nc

Then configure the ~/.ssh/config file in your host:

Host hadoop-*
    Hostname %h.orb.local
    User root
    Port 22
    ForwardAgent yes
    IdentityFile ~/.ssh/id_rsa_hadoop_testing
    StrictHostKeyChecking no
    ProxyCommand nc -x 127.0.0.1:18070 %h %p

Note : DO NOT forget to reduce access permission by invoking this command:

chmod 600 ~/.ssh/id_rsa_hadoop_testing

After all the containers have been launched, test the controllability via this command:

ansible-playbook ping.yaml

It should print all nodes' OS information (include host and hadoop related containers).

If not, use -vvv config option to debug it.

Use pyenv

Create virtualenv

pyenv install 3.9
pyenv virtualenv 3.9 hadoop-testing

Localize virtualenv

pyenv local hadoop-testing

Install packages to the isolated virtualenv

pip install -r requirements.txt

Components

The supported components are listed below:

• Hadoop (3.3.6)
• Hive (2.3.9)
• Iceberg (1.4.2)
• Hudi (0.14.1)
• Kyuubi (1.8.1)
• Spark (3.4.2)
• Flink (1.18.1)
• Trino (436)
• ZooKeeper (3.8.3)
• Ranger (2.4.0)
• Grafana (9.5.2)
• Prometheus (latest)
• Loki (2.8.0)
• Kafka (2.8.1)
• MySQL (8.0)

JDK

• JDK 8 (1.8.0.392, default)
• JDK 17 (17.0.9)
• JDK 21 (21.0.1)

How to use

Firstly, use ansible to render some build files(download.sh, .env, compose.yaml...).

ansible-playbook playbook.yaml

You can add -vvv arg to debug the playbook:

ansible-playbook playbook.yaml -vvv

Download all required artifacts, which will be used for building Docker images.

This scripts will download a large amount of artifacts, depending on your network bandwidth, it may take a few minutes or even hours to complete. You can also download them manually and put them into the download directory, the scripts won't download them again if they already exist.

./download.sh

Build docker images

./build-image.sh

Run the testing plagground

docker compose up

Access services

Networks

For macOS users, it's recommended to use OrbStack as the container runtime. OrbStack provides an out-of-box container domain name resolving feature to allow accessing each container via <container-name>.orb.local.

For other platforms, we provide a socks5 server in a container named socks5, which listens 18070 port and is exposed to the dockerd host by default, you can forward traffic to this socks server to access services run in other containers.

For example, to access service in Browser, use SwitchyOmega to forward traffic of *.orb.local to <dockerd-hostname>:18070.

Service endponits

Once the testing environment is fully operational, the following services will be accessible:

• Grafana: http://grafana.orb.local:3000
• Prometheus: http://prometheus.orb.local:9090
• Kyuubi UI: http://hadoop-master1.orb.local:10099
• Spark History Server: http://hadoop-master1.orb.local:18080
• Flink History Server: http://hadoop-master1.orb.local:8082
• Hadoop HDFS: http://hadoop-master1.orb.local:9870
• Hadoop YARN: http://hadoop-master1.orb.local:8088
• Hadoop MapReduce JobHistory: http://hadoop-master1.orb.local:19888
• Ranger Admin: http://hadoop-master1.orb.local:6080 (admin/Ranger@admin123)
• Trino Web UI: http://hadoop-master1.orb.local:18081 (admin/)

Roadmap

1. Add more components, such as LDAP, Kerberos, HBase, etc.
2. Fully templatized. Leverage Ansible and Jinja2 to templatize the Dockerfiles, shell scripts, and configuration files, so that users can easily customize the testing environment by modifying the configurations, e.g. only enabling a subset of components, and changing the version of the components.
3. Provide user-friendly docs, with some basic tutorials and examples, e.g. how to create a customized testing environment, how to run some basic examples, how to add a new component, etc.
4. Kerberized Hadoop cluster is a common scenario in the production environment, and it's usually a headache to set up a kerberized environment and tackle the Kerberos-related issues. We can provide a kerberized environment for testing and learning.