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teslim
2025-02-13 22:18:51 +01:00
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commit b8fc924e7e
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README.md
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@@ -14,7 +14,7 @@ Your task is to extract the high-throughput ticket purchasing component and exte
- `GET /metrics`: Expose real-time metrics (e.g., tickets sold, available tickets per event) in a format compatible with Prometheus. - `GET /metrics`: Expose real-time metrics (e.g., tickets sold, available tickets per event) in a format compatible with Prometheus.
- **Atomic Ticket Purchase:** Implement the purchase logic using a Redis Lua script to atomically verify and pop a ticket from the ticket pool. This ensures no duplicate ticket sales even under massive concurrent access. - **Atomic Ticket Purchase:** Implement the purchase logic using a Redis Lua script to atomically verify and pop a ticket from the ticket pool. This ensures no duplicate ticket sales even under massive concurrent access.
- **Fallback Mechanism:** If Redis is unavailable or fails during a purchase operation, gracefully fallback to an in-memory store (with appropriate warnings and logs), ensuring the system remains responsive (for demonstration purposes only, as in-memory stores are not persistent). - **Fallback Mechanism:** If Redis is unavailable or fails during a purchase operation, gracefully fallback to an in-memory store (with appropriate warnings and logs), ensuring the system remains responsive (for demonstration purposes only, as in-memory stores are not persistent).
- **Performance and Load Testing:** The service must be designed to handle tens of thousands of requests and be tested under a simulated load of at least 2000 concurrent connections. You should include logging of key performance metrics and purchase statistics. - **Performance and Load Testing:** The service must be designed to handle tens of thousands of requests and be tested under a simulated load of at least 5000 concurrent connections. You should include logging of key performance metrics and purchase statistics.
- **Design Documentation:** Provide a detailed design document (`design.md`) that explains your architectural decisions, how you ensure scalability, measures to handle potential bottlenecks, and details on your fallback strategy. - **Design Documentation:** Provide a detailed design document (`design.md`) that explains your architectural decisions, how you ensure scalability, measures to handle potential bottlenecks, and details on your fallback strategy.
- **Dockerization:** Extend the docker-compose setup to include not only Redis but also (optionally) a Prometheus container to scrape and monitor the metrics from your service. - **Dockerization:** Extend the docker-compose setup to include not only Redis but also (optionally) a Prometheus container to scrape and monitor the metrics from your service.
- **PDF Ticket Generation:** For every successful ticket purchase, a PDF receipt must be generated. You are free to choose any open-source PDF generation package of your choice (e.g., pdfkit, jsPDF, etc.) to implement this functionality. Ensure that PDF generation is integrated into the purchase flow without significant performance degradation. - **PDF Ticket Generation:** For every successful ticket purchase, a PDF receipt must be generated. You are free to choose any open-source PDF generation package of your choice (e.g., pdfkit, jsPDF, etc.) to implement this functionality. Ensure that PDF generation is integrated into the purchase flow without significant performance degradation.
@@ -26,7 +26,7 @@ Your task is to extract the high-throughput ticket purchasing component and exte
3. **Atomic Operations with Lua:** Replace simple atomic operations (like LPOP) with a Redis Lua script that handles the ticket purchase process atomically. 3. **Atomic Operations with Lua:** Replace simple atomic operations (like LPOP) with a Redis Lua script that handles the ticket purchase process atomically.
4. **Fallback to In-Memory Store:** Implement a fallback mechanism that activates if Redis operations fail, ensuring continued functionality with clear logging that this is a non-persistent backup. 4. **Fallback to In-Memory Store:** Implement a fallback mechanism that activates if Redis operations fail, ensuring continued functionality with clear logging that this is a non-persistent backup.
5. **Metrics Endpoint:** Provide a `/metrics` endpoint that returns JSON data with real-time statistics (tickets sold, tickets remaining per event, errors, etc.). 5. **Metrics Endpoint:** Provide a `/metrics` endpoint that returns JSON data with real-time statistics (tickets sold, tickets remaining per event, errors, etc.).
6. **Robust Testing:** Write comprehensive unit tests and integration tests. The integration tests must simulate high load (>=2000 concurrent requests) and prove that no ticket is sold more than once. 6. **Robust Testing:** Write comprehensive unit tests and integration tests. The integration tests must simulate high load (>=5000 concurrent requests) and prove that no ticket is sold more than once.
7. **Logging:** Implement detailed logging for purchase operations, errors, and fallback activations. 7. **Logging:** Implement detailed logging for purchase operations, errors, and fallback activations.
8. **Design Document:** Include a `design.md` file that outlines your architecture, scalability considerations, and design rationale. 8. **Design Document:** Include a `design.md` file that outlines your architecture, scalability considerations, and design rationale.
9. **Docker Support:** Update the docker-compose file to run Redis and optionally Prometheus. Provide clear instructions for running the entire stack. 9. **Docker Support:** Update the docker-compose file to run Redis and optionally Prometheus. Provide clear instructions for running the entire stack.
@@ -59,9 +59,9 @@ Your task is to extract the high-throughput ticket purchasing component and exte
### Load Testing ### Load Testing
Simulate high load using a tool like [autocannon](https://github.com/mcollina/autocannon) or [wrk](https://github.com/wg/wrk). For example, to simulate 2000 concurrent connections on event 1: Simulate high load using a tool like [autocannon](https://github.com/mcollina/autocannon) or [wrk](https://github.com/wg/wrk). For example, to simulate 5000 concurrent connections on event 1:
npx autocannon -c 2000 -d 30 http://localhost:3049/buy/1 npx autocannon -c 5000 -d 30 http://localhost:3049/buy/1
### Metrics ### Metrics
ticket.html
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<html lang="en"> <html lang="en">
<head> <head>
<meta charset="UTF-8" /> <meta charset="UTF-8" />
<meta <meta http-equiv="X-UA-Compatible" content="IE=edge" />
http-equiv="X-UA-Compatible" <meta name="viewport" content="width=device-width, initial-scale=1.0" />
content="IE=edge" <link rel="preconnect" href="https://fonts.googleapis.com" />
/> <link rel="preconnect" href="https://fonts.gstatic.com" crossorigin />
<meta
name="viewport"
content="width=device-width, initial-scale=1.0"
/>
<link
rel="preconnect"
href="https://fonts.googleapis.com"
/>
<link
rel="preconnect"
href="https://fonts.gstatic.com"
crossorigin
/>
<link <link
href="https://fonts.googleapis.com/css2?family=Roboto:wght@100;500;700;900&display=swap" href="https://fonts.googleapis.com/css2?family=Roboto:wght@100;500;700;900&display=swap"
rel="stylesheet" rel="stylesheet"
@@ -28,39 +15,39 @@
<style> <style>
body { body {
font-family: 'Roboto', sans-serif; font-family: "Roboto", sans-serif;
} }
.ferrari { .ferrari {
background-image: url(https://s3.us-west-1.amazonaws.com/com.mkdlab.images/baas/nordin/076532395068ferrari.png); background-image: url(https://placehold.co/600x400/EEE/31343C);
background-repeat: no-repeat; background-repeat: no-repeat;
background-position: center left; background-position: center left;
} }
.wing { .wing {
background-image: url(https://s3.us-west-1.amazonaws.com/com.mkdlab.images/baas/nordin/076532395068wing.png); background-image: url(https://placehold.co/600x400/EEE/31343C);
background-repeat: no-repeat; background-repeat: no-repeat;
background-position: center right; background-position: center right;
} }
</style> </style>
</head> </head>
<body class="h-screen w-screen h-[800px] overflow-x-hidden "> <body class="h-screen w-screen h-[800px] overflow-x-hidden">
<div class=" js bg-white grid grid-cols-3 gap-1 bg-[red] p-3 "></div> <div class="js bg-white grid grid-cols-3 gap-1 bg-[red] p-3"></div>
<script> <script>
const js = document.querySelector(".js") const js = document.querySelector(".js");
for(let i = 0 ; i < 240; i++) { for (let i = 0; i < 240; i++) {
js.innerHTML += ` <div class="border-2 border-black border-dashed relative "> js.innerHTML += ` <div class="border-2 border-black border-dashed relative ">
<div class="h-[12px] w-full bg-[#B3E1AB]"></div> <div class="h-[12px] w-full bg-[#B3E1AB]"></div>
<div class="flex h-[245px] bg-[#EDF0F3] w-[100%] "> <div class="flex h-[245px] bg-[#EDF0F3] w-[100%] ">
<div class="w-[25%] z-[100]"> <div class="w-[25%] z-[100]">
<img <img
class="block w-[96%] h-[50%]" class="block w-[96%] h-[50%]"
src="https://s3.us-west-1.amazonaws.com/com.mkdlab.images/baas/nordin/076532395068redLottery.png" src="https://placehold.co/600x400/EEE/31343C"
alt="lottery car" alt="lottery car"
/> />
<img <img
class="block w-[98%] h-[50%]" class="block w-[98%] h-[50%]"
src="https://s3.us-west-1.amazonaws.com/com.mkdlab.images/baas/nordin/076532395068darkLottery.png" src="https://placehold.co/600x400/EEE/31343C"
alt="lottery car" alt="lottery car"
/> />
</div> </div>
@@ -101,7 +88,7 @@
</h5> </h5>
<img <img
src="https://s3.us-west-1.amazonaws.com/com.mkdlab.images/baas/nordin/076532395068appLogo.svg" src="https://placehold.co/600x400/EEE/31343C"
class="h-[35px]" class="h-[35px]"
alt="" alt=""
/> />
@@ -114,14 +101,13 @@
</div> </div>
<div class="h-[12px] w-full bg-[#B3E1AB]"></div> <div class="h-[12px] w-full bg-[#B3E1AB]"></div>
<img <img
src="https://s3.us-west-1.amazonaws.com/com.mkdlab.images/baas/nordin/076532395068barcode.png" src=https://placehold.co/600x400/EEE/31343C"
alt="barcode" alt="barcode"
class="absolute top-1/2 right-0 transform -translate-y-1/2" class="absolute top-1/2 right-0 transform -translate-y-1/2"
/> />
</div> </div>
` `;
}
}
</script> </script>
</body> </body>
</html> </html>