Design an API to display the total number of iOS downloads from different devices in a graph.

API Design: iOS Download Statistics Graph

This document outlines the design of an API to display the total number of iOS downloads from different devices in a graph format. This API will provide the data necessary to visualize download trends across various iOS devices, helping understand user adoption and platform preferences.

1. Requirements

• Functional Requirements:
  • Provide the total number of downloads for a given time range.
  • Categorize downloads by specific iOS device types (e.g., iPhone 13, iPad Pro).
  • Support filtering by date ranges (e.g., daily, weekly, monthly, custom).
  • Return data in a format suitable for graph plotting (e.g., JSON).
• Non-Functional Requirements:
  • Performance: The API should respond quickly, ideally within a few hundred milliseconds.
  • Scalability: The system should handle a large number of requests and data volume.
  • Security: The API should be secure and only accessible to authorized users/systems.

2. High-Level Design

The system will consist of the following components:

1. API Gateway: Entry point for all API requests.
2. Authentication/Authorization Service: Verifies the identity and permissions of the requester.
3. Download Statistics Service: Processes requests for download statistics, retrieves data from the database, and formats it for the API response.
4. Database: Stores download event data, including device type and timestamp.
5. Caching Layer: Improves response time by caching frequently requested data.

Diagram:

[API Gateway] --> [Authentication/Authorization Service]
[API Gateway] --> [Download Statistics Service]
[Download Statistics Service] --> [Caching Layer]
[Download Statistics Service] --> [Database]

3. Data Model

We will use a relational database to store the download event data. Here's the schema for the downloads table:

4. Endpoints

Endpoint: /api/v1/downloads/ios/stats

• Method: GET

• Description: Retrieves download statistics for iOS devices.

• Request Parameters:

  • start_date (string, optional): Start date for the statistics (YYYY-MM-DD). Defaults to 30 days ago.
  • end_date (string, optional): End date for the statistics (YYYY-MM-DD). Defaults to today.
  • device_type (string, optional): Filter by a specific device type (e.g., iPhone 13). If not provided, returns data for all devices.
  • granularity (string, optional): Granularity of the data (daily, weekly, monthly). Defaults to daily.

• Request Example:

  /api/v1/downloads/ios/stats?start_date=2024-01-01&end_date=2024-01-31&device_type=iPhone%2013&granularity=monthly
  

• Response JSON:

  {
    "status": "success",
    "data": [
      {
        "date": "2024-01-01",
        "device_type": "iPhone 13",
        "downloads": 12345
      },
      {
        "date": "2024-01-08",
        "device_type": "iPhone 13",
        "downloads": 67890
      },
         {
        "date": "2024-01-15",
        "device_type": "iPhone 13",
        "downloads": 45678
      }
    ]
  }
  

5. Tradeoffs

6. Other Approaches

• NoSQL Database (e.g., MongoDB): Could be used for storing download events. Pros: More flexible schema, easier horizontal scaling. Cons: Weaker consistency, requires more application-level logic for data integrity.
• Pre-Aggregated Data: Pre-calculate and store aggregated statistics in a separate table. Pros: Faster retrieval for common queries. Cons: Increased storage requirements, requires maintaining the aggregated data.
• Data Warehouse (e.g., Snowflake): Suitable for large-scale data analysis and reporting. Pros: Optimized for analytical queries, supports complex aggregations. Cons: Higher cost, more complex setup.

7. Edge Cases

• Sudden Spike in Downloads: Implement rate limiting to prevent the API from being overwhelmed. Use caching to serve pre-computed results during peak times.
• Invalid Date Ranges: Validate the input date ranges to ensure they are valid. Return an appropriate error message if the date range is invalid.
• Missing Data: Handle cases where data is missing for certain device types or date ranges. Return a default value (e.g., 0) or indicate that data is unavailable.
• Timezone Issues: Ensure all timestamps are stored in a consistent timezone (e.g., UTC) and handle timezone conversions appropriately.
• Data Inconsistency: Implement data validation and integrity checks to ensure the accuracy of the download statistics.

8. Future Considerations

• Real-time Data: Implement real-time data streaming using technologies like Kafka or RabbitMQ to provide up-to-the-minute download statistics.
• Advanced Analytics: Integrate with a data analytics platform to perform more advanced analysis, such as cohort analysis and user segmentation.
• Personalized Dashboards: Allow users to create personalized dashboards with custom metrics and visualizations.
• Support for More Platforms: Extend the API to support other platforms, such as Android and web, to provide a comprehensive view of application downloads.