How Does Google work?

Search Engine Architecture Overview

A modern search engine architecture comprises several interconnected systems designed for scalability, performance, and real-time processing. Here’s a high-level overview of the key components and their interactions:

Infrastructure Layer

• Application Servers: Distributed cluster of virtual or physical servers
• Network: High-speed internet connection with redundant paths
• Security: Multi-layered approach including firewalls, DMZ, and Web Application Firewalls (WAF)
• Load Balancers: Geo-distributed for traffic management and application delivery
  • Load balancing is helpful here because it can route the reply from the crawled server to a different process, essentially an HTTP file receiver. Because there are many slow servers and because many other servers become slow – due to hosting too many sites, network clogging, and bad architecture, you don’t want the crawl requester to be delayed – needs to send out billions of URLs across to web servers as requests. By divorcing the request and receive function you remove bottlenecks. The file-receiving servers can accept lots of slow responsive servers because the data demand is low. Other servers can be separated for highly responsive servers because they will quickly reach maximum connection counts. Again, load balancers will distribute that between those servers based on weighing, round robin, health checking
• Storage: Distributed database systems optimized for inverted indices

Crawling Subsystem

Note: most people think that search and ranking happen when you do a search. It’s actually already done. To make the search so fast,  the search layer just returns the next 10 results from a list starting at position 1, and the only logic applied is (a) geo-specific pages are removed, (b) QDF checks pull in items based on a date and time range and (c) real-time data is pulled from a queue which has it ready to go

• Crawler Manager: Maintains URL list with metadata (last crawl date, page speed, robots.txt status)
• Distributed Crawlers: Multiple instances working in parallel
• Prioritization Engine: Determines crawl frequency based on site authority and update patterns
• Real-time Crawlers: Separate system for handling frequently updated content and trusted sources

Content Processing Pipeline

• Essentially you want to break the HTML user interface from the business logic and the databases (the index)
• I would imagine a 3 tier where you have a very basic Apache (or IIS) web application that lets a user enter a search query and then it fires it the search application which sends the results to another 2nd User interface. That way should there be peak demand you can basically disconnect the user operation servers from the functioning search server (read: Apache Load Balancing)
• The business logic layer – the “search” should simply have 2 functions
• Function A – strip the search string in a query of non-value words (a, the, an, and, it, is) where each word is searched and a second query where its searched in different %’s of completeness. For a search for “how much does an iPhone cost” would [much or does or iPhone or cost] and also [much or does or iPhone] and [much or does or cost]
• The query is then handed to the next available search parser. The search parser connects to the next available database server. this is where load balancing comes in – if any single server becomes non-responsive, the load balancer just removes it from the queue and connects the next available healthy server
• The parser requests the top 10 results with those words and is given a list of key IDs which each match a URL
• The magic of rank order isn’t established during search – search is the final leg

1. HTML Stripper: Removes scripts and formatting
2. Language Detector: Identifies content language
3. Security Scanner: Checks for malware and vulnerabilities
4. Spam Detector: Analyzes content for spam signals
5. Content Extractor: Creates searchable version of the page
6. Indexer: Processes words and phrases for the inverted index

Indexing Subsystem

• Inverted Index: Core data structure for efficient retrieval
• Word/Phrase Tables: Separate indices for different linguistic elements
• Ranking Preprocessor: Calculates initial page scores based on various signals
• Geo-Location Indexer: Tags content with relevant geographic data

Query Processing Subsystem

• Query Parser: Strips non-value words and generates search variations
• Load Balancer: Distributes queries across available parsing nodes
• Results Retriever: Fetches top results from pre-ranked indices
• Real-time Updater: Incorporates fresh content from a dedicated queue

Serving Layer

• Web Application: User interface for query input
• Business Logic Layer: Handles query processing and result formatting
• Caching System: Stores frequently accessed results for faster retrieval
• Ad Server Integration: Merges organic results with relevant paid content

Scalability and Performance Considerations

• Auto-scaling: Dynamic resource allocation based on demand
• Asynchronous Processing: Non-blocking operations for improved throughput
• Microservices Architecture: Decomposition of functions into independent, scalable services
• Distributed Caching: Reduces database load and improves response times

Data Flow

1. User submits query through web interface
2. Query parser processes and optimizes the search string
3. Load balancer routes query to available processing nodes
4. Index servers retrieve relevant document IDs
5. Ranking system applies final ordering based on query-specific factors
6. Ad server injects relevant paid content
7. Results are formatted and returned to the user interface

This architecture ensures rapid query processing by pre-computing rankings and storing optimized indices. The system’s modular design allows for independent scaling of components to handle varying loads and data growth.