DeathStarBench - Social Network Setup

This document provides step-by-step instructions on setting up and running the Social Network microservices from the DeathStarBench suite.

Overview of DeathStarBench - Social Network

DeathStarBench is a benchmark suite for microservices-based applications. The Social Network component simulates a real-world social networking service, including user interactions such as:

• User registration and authentication
• Posting and reading timelines
• Following/unfollowing users
• Building social graphs

Architecture Overview

The Social Network is composed of multiple microservices, including:

• User Service: Handles authentication and user management.
• Post Service: Manages user posts.
• Timeline Service: Generates personalized home and user timelines.
• Social Graph Service: Maintains follower/following relationships.
• MongoDB: Stores user and post data.
• NGINX: Serves as an API gateway for requests.

The services communicate using REST APIs and are deployed using Docker containers.

Prerequisites

Ensure you have the following installed:

• Docker & Docker Compose
• Python 3 (with venv support)
• Required system dependencies:

  sudo apt update && sudo apt install -y python3-aiohttp luajit luasocket libssl-dev make

1. Clone the Repository

git clone --recurse-submodules https://github.com/harshpala/DeathStarBench.git
cd DeathStarBench/socialNetwork

2. Start Services Using Docker Compose

docker compose up -d

Check running containers:

docker ps

3. Access MongoDB and Verify Users

Enter the MongoDB container:

docker exec -it socialnetwork-user-mongodb-1 mongosh

Check available databases:

show dbs

Check existing users:

use user
db.user.find().pretty()

4. Register Users & Construct Social Graph

Run the script to initialize a social graph:

python3 scripts/init_social_graph.py --graph=socfb-Reed98

If you get a ModuleNotFoundError for aiohttp, install it in a virtual environment:

python3 -m venv venv
source venv/bin/activate
pip install aiohttp

Then retry running the script.

5. Build the Workload Generator

Ensure dependencies are installed, then build:

cd ../wrk2
make
cd ../socialNetwork

6. Run Workload Generators

• Compose Posts

  ../wrk2/wrk -D exp -t 12 -c 400 -d 300 -L -s ./wrk2/scripts/social-network/compose-post.lua http://localhost:8080/wrk2-api/post/compose -R 10

• Read Home Timelines

  ../wrk2/wrk -D exp -t 12 -c 400 -d 300 -L -s ./wrk2/scripts/social-network/read-home-timeline.lua http://localhost:8080/wrk2-api/home-timeline/read -R 10

• Read User Timelines

  ../wrk2/wrk -D exp -t 12 -c 400 -d 300 -L -s ./wrk2/scripts/social-network/read-user-timeline.lua http://localhost:8080/wrk2-api/user-timeline/read -R 10

7. Debugging & Common Issues

• MongoDB Not Found: Ensure the container is running with docker ps.
• Missing Python Packages: Use a virtual environment (venv) to install required dependencies.
• Build Errors in wrk2: Ensure make and dependencies (libssl-dev) are installed.

8. Stopping and Cleaning Up

To stop all services:

docker compose down

To remove all volumes and clean up:

docker compose down -v

Next Steps

• Test API endpoints with Postman or curl
• Modify configurations for performance tuning
• Scale services using Kubernetes
• Benchmarking and Profiling Performance Across Multiple Machine Configurations

Goal

The primary objective of this project is to gain hands-on experience in profiling, deploying, and integrating microservices within a Docker and Kubernetes-hosted environment. The project involves deploying the system across multiple machines with varying configurations to benchmark and analyze performance metrics effectively.

This process will help in:

• Profiling system performance under different hardware and software environments.
• Optimizing resource utilization for efficient microservices execution.
• Ensuring seamless deployment and integration using containerized solutions.

By leveraging Docker and Kubernetes, the project aims to simulate real-world deployment scenarios and evaluate system scalability, reliability, and performance.

Additional Documentation

For more detailed information, please refer to the following documents:

• Creativity - Creative approaches and innovative solutions implemented in the project
• Deployment - Detailed deployment instructions and configurations
• Microservices Architecture Breakdown - Comprehensive analysis of the microservices architecture
• Social Network README - Detailed documentation for the Social Network microservices
• Helm Chart README - Automate the process of deploying Social Network Microservices on a Kubernetes cluster natively using Helm chart

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