In essence, this application was centered around inheriting Getsy's legacy codebase and then extending the capabilities of the database and server layer to support production-level traffic while maintaining its service-oriented architecture. The task entitled implementing techniques such as indexing, compression, clustering, horizontal scaling, and caching. To learn more about the journey, feel free to reach out to me at jasonhong1240@gmail.com.
After horizontally scaling the service on 4 AWS EC2 t2.micro instances and utilizing round robin load balancing with content caching on my NGINX proxy server, I was able to handle 10,000 requests per second with a 0.0% error rate and an average latency of 62 ms.
This is in comparison to a 45.3% error rate and an average latency of 3727 ms before all the optimizations.
In conclusion:
| BEFORE | AFTER |
|---|---|
| 3727 ms | 62 ms |
| 45.3% error rate | 0.0% error rate |
This equates to...
| SPEED | ERROR RATE |
|---|---|
| 98.34% improvement in speed | 100% error reduction |
| It takes 1.66% of the time it did before! |
| Technologies Used |
|---|
| PostgreSQL |
| MongoDB |
| NGINX |
| AWS EC2 |
| New Relic |
| Loader.io |
| k6.io |
| PM2 |
| Node.js |
| Express.js |
Open up the terminal and from within the root directory, run the following commands:
npm install
npm run csvThe "npm run csv" script creates a CSV file in the root directory named "entries.csv".
Navigate to the database directory and import the schema into Postgres by running the command:
psql postgres < schema.sqlAnd then the seeding script:
npm run seedGo back to the root diretory and run these scripts to start the server and serve the static files:
npm run build
npm startOpen up your browser and navigate to http://localhost:8001/