Building app-ads-sdk-crawler: A Developer's Journey into App Store Scraping

Project Genesis

Unveiling the Secrets of App Advertising: My Journey with App-Ads-SDK-Crawler

From Idea to Implementation

1. Initial Research and Planning

2. Technical Decisions and Their Rationale

• Choice of Programming Language: Python was selected for its rich ecosystem of libraries for web scraping, data manipulation, and database interaction. The use of Python 3.11/3.12 ensured access to the latest features and performance improvements.

• Database Selection: PostgreSQL was chosen for its robustness and support for complex queries, which would be necessary for managing the large datasets generated by the crawlers. The decision to use pg-ddl for database initialization allowed for a structured approach to database schema management.

• Crawling Strategy: The project was designed to include multiple scrapers to pull apps from various sources, including top lists from app stores and third-party stores. This multi-faceted approach was intended to maximize the number of apps discovered and analyzed.

• App-ads.txt Collection Process: The decision to implement a multi-step process for collecting app-ads.txt files was based on the need for accuracy and compliance with IAB specifications. This involved obtaining store IDs, developer URLs, and then crawling those URLs for the app-ads.txt files.

3. Alternative Approaches Considered

• Single Store Focus: Initially, there was a consideration to focus solely on either the Google Play Store or the Apple App Store. However, this was quickly dismissed in favor of a dual approach to ensure comprehensive coverage of the mobile app ecosystem.

• Using Existing APIs: The possibility of leveraging existing APIs for app data retrieval was explored. However, many APIs had limitations in terms of data access and rate limits, which could hinder the project’s ability to scale. Thus, a custom scraping solution was deemed more flexible and powerful.

• Different Database Solutions: Other database systems, such as MySQL or NoSQL databases, were considered. However, PostgreSQL’s advanced features, such as support for JSONB and complex queries, made it the preferred choice for handling the diverse data structures involved in the project.

4. Key Insights That Shaped the Project

• Importance of Data Quality: The realization that the quality of the data collected would directly impact the utility of the app-ads.txt files led to a focus on building robust error handling and data validation mechanisms within the scrapers.

• Scalability Considerations: As the project evolved, it became clear that scalability would be a critical factor. This insight prompted the implementation of options for limiting processes and managing concurrent scraping tasks to prevent overwhelming the target servers.

• User-Centric Design: Understanding the end-users of the data—advertisers, app developers, and researchers—shaped the design of the output and reporting features. Ensuring that the results were easily accessible and interpretable was a key consideration.

• Iterative Development: The project benefited from an iterative development approach, allowing for continuous testing and refinement of the scrapers and database interactions. This flexibility enabled the team to adapt to challenges and incorporate feedback effectively.

Under the Hood

Technical Deep-Dive: Crawl App-Adst.txt and App Store Apps

1. Architecture Decisions

• Modular Design: The project is structured into distinct modules for scraping, data processing, and database interaction. This modularity allows for easier maintenance and scalability. For example, separate scrapers are implemented for different app stores, which can be independently updated or replaced.

• Database-Driven Approach: PostgreSQL is used as the primary database to store app details and app-ads.txt entries. This choice allows for robust data management and querying capabilities. The use of SQL DDL scripts for database initialization ensures that the database schema can be easily set up and modified.

• Asynchronous Processing: The architecture supports asynchronous operations, particularly in the app discovery and scraping processes. This is crucial for handling the potentially long wait times associated with network requests to app stores.

2. Key Technologies Used

• Python: The primary programming language for the project, chosen for its rich ecosystem of libraries and ease of use in web scraping and data manipulation.

• Node.js: Utilized for the google-play-scraper package, which provides a convenient way to interact with the Google Play Store API.

• PostgreSQL: A powerful relational database system used to store app data and app-ads.txt entries. Its support for complex queries and transactions makes it suitable for this project.

• Virtual Environments: Python’s virtual environments are used to manage dependencies, ensuring that the project runs in an isolated environment without conflicts with other Python projects.

3. Interesting Implementation Details

• Crawling Logic: The project includes two crawlers specifically designed for the Apple iTunes and Google Play stores. The crawling logic is implemented to first pull the top apps from the stores and then extract their store IDs. This is done using the following code snippet:

  from google_play_scraper import app
  
  def fetch_top_apps():
      top_apps = app('top', category='APPLICATION')
      return [app['appId'] for app in top_apps]

• App-ads.txt Scraping: The process of scraping app-ads.txt files is more complex than regular ads.txt files. The project includes a method to crawl developer URLs to find the app-ads.txt files. This is achieved through a series of steps that include fetching store IDs and their corresponding developer URLs:

  def fetch_app_ads_txt(developer_url):
      response = requests.get(developer_url + '/app-ads.txt')
      if response.status_code == 200:
          return response.text
      return None

• Command-Line Interface: The project provides a command-line interface (CLI) for users to interact with the scrapers. Options such as --new-apps-check and --app-ads-txt-scrape allow users to specify the desired operation, enhancing usability.

4. Technical Challenges Overcome

• Rate Limiting and Throttling: One of the significant challenges faced during the scraping process is the rate limiting imposed by app stores. To overcome this, the project implements a throttling mechanism that introduces delays between requests to avoid being blocked. This can be seen in the following code:

  import time
  
  def safe_request(url):
      time.sleep(1)  # Delay to avoid rate limiting
      return requests.get(url)

• Data Consistency: Ensuring data consistency when updating app details was another challenge. The project uses a combination of timestamps and version checks to determine whether to update existing records in the database. This is crucial for maintaining accurate and up-to-date information.

• Error Handling: The project includes robust error handling to manage network issues and unexpected responses from the app stores. This is implemented using try-except blocks to catch exceptions and log errors without crashing the entire scraping process.

  try:
      response = safe_request(app_url)
      response.raise_for_status()
  except requests.exceptions.RequestException as e:
      logging.error(f"Error fetching {app_url}: {e}")

Conclusion

Lessons from the Trenches

1. Key Technical Lessons Learned

• Complexity of Data Collection: Collecting app-ads.txt files is more complex than regular ads.txt files due to the need for multiple steps (store IDs, developer URLs, and crawling). This highlights the importance of understanding the data structure and requirements before starting a scraping project.
• Database Management: Using PostgreSQL for managing app data proved effective, but it required careful setup and management of schemas. Understanding SQL DDL and how to initialize the database was crucial for smooth operation.
• Environment Setup: The necessity of a specific Python version and NodeJS for dependencies emphasized the importance of maintaining a consistent development environment. Using virtual environments helped isolate project dependencies.
• Performance Optimization: The slow nature of app discovery led to the realization that pre-existing lists of store IDs can significantly speed up the process. This suggests that leveraging existing data can enhance efficiency in data collection tasks.

2. What Worked Well

• Modular Design: The project’s modular approach, with separate scripts for different tasks (scraping, database management, etc.), made it easier to maintain and debug. Each component could be tested independently.
• Use of Established Libraries: Utilizing libraries like google-play-scraper simplified the scraping process and reduced the amount of custom code needed, allowing for quicker implementation and fewer bugs.
• Clear Documentation: The README provided clear instructions for setup, installation, and running the application, which facilitated onboarding for new developers and users. This clarity helped in reducing setup time and confusion.

3. What You’d Do Differently

• Error Handling and Logging: Implementing more robust error handling and logging mechanisms would improve the ability to diagnose issues during scraping and database interactions. This would help in identifying problems without needing to manually check logs.
• Rate Limiting and Throttling: To avoid being blocked by app stores, implementing rate limiting and throttling mechanisms would be beneficial. This would ensure that the scrapers operate within acceptable limits and reduce the risk of IP bans.
• Automated Testing: Incorporating automated tests for the scraping functions and database interactions would help ensure that changes do not introduce new bugs. This could include unit tests and integration tests to validate the entire workflow.

4. Advice for Others

• Start with a Clear Plan: Before diving into coding, outline the data you need, the sources, and the steps required to collect it. This will save time and effort in the long run.
• Leverage Existing Resources: Don’t reinvent the wheel. Use existing libraries and tools whenever possible to speed up development and reduce complexity.
• Document Everything: Maintain thorough documentation throughout the project. This includes not only setup instructions but also code comments and explanations of the logic behind complex functions.
• Be Prepared for Changes: App stores frequently update their APIs and structures. Build flexibility into your scrapers to accommodate changes without requiring a complete rewrite.
• Engage with the Community: Participate in forums or communities related to web scraping and app development. Sharing experiences and learning from others can provide valuable insights and solutions to common challenges.

What’s Next?

Conclusion

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