User: you need to build a scrapper service which scrapes data and push it in db after cleaning 
so will look on google for more than 200 cities one by one , pupolar resutrant in bangalore then, we scrap first two pages of google and we will prepare a list of 10-15 popular websites list which cover about resurants , scrap list of resturants from there. then push it in db like resturant name , and its component like - review , price etc.
then we will give ranking to each resturant according to number of times it appears in each 10 websites .
Assistant: To build a scraper service that gathers data on popular restaurants from more than 200 cities and processes the data for storage in a database, you need to follow a structured approach. Here is a step-by-step guide to help you:

### Step 1: Define Requirements and Setup
- **Scope**: Scrape data for restaurants in 200+ cities.
- **Data Points**: Restaurant name, reviews, price, etc.
- **Ranking**: Rank restaurants based on frequency across multiple websites.
- **Tools**: Choose your tech stack, such as Python with libraries like BeautifulSoup and Requests, and a database like MongoDB or PostgreSQL.

### Step 2: Perform Initial Google Search
- **Automation**: Use an automated tool or library like Selenium or Puppeteer to perform Google searches for keywords such as "popular restaurants in Bangalore", "famous restaurants in London", etc. for each city.
- **Results Curation**: Extract the URLs from the first two pages of search results.

### Step 3: Identify Popular Websites
- **Web Page Analysis**: Analyze the search results and identify 10-15 popular and relevant websites that list restaurants.
- **Website Selection**: Choose websites based on relevance and data availability.

### Step 4: Extract Data from Websites
- **Scraping Logic**: Use web scraping libraries like BeautifulSoup, Scrapy, or Selenium to extract required data from selected websites.
  - **Restaurant Information**: Gather restaurant name, number of reviews, average rating, price range, etc.
- **Data Cleaning**: Validate and clean the extracted data by removing duplicates and correcting formats.

### Step 5: Store Data in Database
- **Database Design**: Create a database schema to fit your data model, e.g., tables for restaurants, reviews, and ranks.
- **Inserting Data**: Use database connectors (e.g., PyMySQL, SQLAlchemy, PyMongo) to insert the cleaned data into the database.

### Step 6: Implement Ranking System
- **Frequency Analysis**: Count the number of occurrences of each restaurant across the websites.
- **Ranking Calculation**: Assign a rank to each restaurant based on its frequency—the more a restaurant appears, the higher its rank.

### Step 7: Automate & Schedule
- **Automation**: Set up a cron job or use a task scheduler to automate the scraping process at specified intervals.
- **Error Handling**: Implement logging and exception handling to ensure robust operations.

### Step 8: Monitor & Maintain
- **Monitoring**: Regularly monitor the performance and logs of the scraper service.
- **Adaptation**: Update scraping scripts as websites change their structure over time.

### Considerations
- **Legal Compliance**: Check the legality and terms of service of scraping the websites.
- **Scalability**: Design the service to handle scaling, as it will be scraping and processing data for many cities.
- **Data Quality**: Ensure consistent and high-quality data for meaningful ranking outcomes.

By following these steps, you can create an effective web scraper service that gathers restaurant information from a large number of cities and provides useful rankings based on data frequency across different platforms.
User: can you name me some websites which list major cities restirants on it . like delhi , pune , hydrabsad , london , paris etc 
Assistant: