#28 Designing a Search System – Elasticsearch, Inverted Index, Ranking Algorithms

The Challenge of Fast & Relevant Search

A large e-commerce store had millions of products. Customers searched for “wireless headphones”, but results were slow and often irrelevant.

The problem? Traditional databases aren’t optimized for full-text search.

The solution? Search engines like Elasticsearch with an inverted index, ranking algorithms, and efficient data retrieval techniques.

How Does a Search System Work?

A search system processes queries, retrieves relevant documents, and ranks results based on relevance.

Key Steps:

Indexing: Converts raw text into a structured searchable format.
Query Processing: Breaks down search terms and applies filters.
Ranking: Scores documents to return the most relevant results.

1. Indexing – Making Data Searchable

Instead of scanning entire documents, search engines use an inverted index, which maps words to their locations in documents.

Example:

“wireless headphones” appears in:
- Doc #3 (title: Wireless Bluetooth Headphones)
- Doc #7 (description: Noise-canceling wireless headphones)

Key Benefits:

Faster lookups – Searches work in milliseconds.
Efficient storage – Only indexes important terms.
Scalability – Supports millions of documents.

Elasticsearch – Distributed Search Engine

Elasticsearch is an open-source, scalable search engine based on Apache Lucene.

Key Features:

Full-text search with advanced ranking.
Distributed & scalable across multiple nodes.
Fuzzy search & autocomplete for better user experience.

Example Elasticsearch Query:

{
  "query": {
    "match": {
      "title": "wireless headphones"
    }
  }
}

Use Case: Amazon uses Elasticsearch to power fast product search and recommendations.

2. Query Processing – Understanding User Intent

Before searching, queries are processed to extract meaning and optimize results.

Key Steps:

Tokenization: Splitting text into words.
Stemming/Lemmatization: Converting words to their base forms (“running” → “run”).
Synonyms & Stopwords Removal: Handling words like “the”, “is”, “a”.

Example:

User Query: “best wireless headphones”
→ Tokenized: [best, wireless, headphones]
→ Stopwords removed: [wireless, headphones]
→ Stemmed: [wireless, headphone]

3. Ranking Algorithms – Sorting Relevant Results

Not all results are equally relevant. Search engines rank documents based on multiple factors.

Common Ranking Algorithms:

TF-IDF (Term Frequency-Inverse Document Frequency):
- Prioritizes frequently occurring words in a document but not across all documents.
BM25 (Best Matching 25):
- Advanced TF-IDF with length normalization and tuning parameters.
Vector Search (Semantic Search):
- Uses AI-based embeddings for context-aware search.

Example – TF-IDF Calculation:

Term: “wireless”
- Appears in 5 out of 100 documents → Lower rank.
- Appears in 1 out of 100 documents → Higher rank.

Handling Real-World Search Challenges

1. Autocomplete & Suggestive Search

Elasticsearch n-grams generate predictions while typing.
Example: Typing “iph” suggests “iPhone”, “iPhone 13”, etc.

2. Fuzzy Matching & Spell Correction

Handles typos and variations (e.g., “headphons” → “headphones”).

3. Personalization & Contextual Search

Uses user history and preferences to rank results.
Example: A gamer searching for “mouse” gets gaming mice first.

Choosing the Right Search Strategy

Feature

SQL Databases

Elasticsearch

Full-Text Search

Slow

Fast & optimized

Ranking Results

Limited

Advanced ranking (BM25, TF-IDF)

Autocomplete

Yes

Scalability

Limited

Distributed & scalable

Real-World Use Cases

1. E-Commerce Platforms (Amazon, eBay)

Elasticsearch powers product search & filtering.
Uses BM25 to rank best-selling and highly-rated products first.

2. Content Platforms (YouTube, Netflix)

Query processing improves video title searches.
Personalized search ranks content based on watch history.

3. Enterprise Search (Google Drive, Notion)

Full-text search indexes documents, notes, and PDFs.
OCR (Optical Character Recognition) extracts text from images.

Conclusion

A well-designed search system combines fast indexing, smart query processing, and ranking algorithms to deliver accurate, relevant, and real-time results.

Elasticsearch provides distributed, full-text search.
Inverted Index speeds up lookups.
Ranking Algorithms ensure the best results appear first.

Next, we’ll explore Designing a Scalable URL Shortener – Hashing, Database Choices, Redirection Optimization.

#code #system-design

3/6/2025

#28 Designing a Search System – Elasticsearch, Inverted Index, Ranking Algorithms

The Challenge of Fast & Relevant Search

A large e-commerce store had millions of products. Customers searched for “wireless headphones”, but results were slow and often irrelevant.

The problem? Traditional databases aren’t optimized for full-text search.

The solution? Search engines like Elasticsearch with an inverted index, ranking algorithms, and efficient data retrieval techniques.

How Does a Search System Work?

A search system processes queries, retrieves relevant documents, and ranks results based on relevance.

Key Steps:

Indexing: Converts raw text into a structured searchable format.
Query Processing: Breaks down search terms and applies filters.
Ranking: Scores documents to return the most relevant results.

1. Indexing – Making Data Searchable

Instead of scanning entire documents, search engines use an inverted index, which maps words to their locations in documents.

Example:

“wireless headphones” appears in:
- Doc #3 (title: Wireless Bluetooth Headphones)
- Doc #7 (description: Noise-canceling wireless headphones)

Key Benefits:

Faster lookups – Searches work in milliseconds.
Efficient storage – Only indexes important terms.
Scalability – Supports millions of documents.

Elasticsearch – Distributed Search Engine

Elasticsearch is an open-source, scalable search engine based on Apache Lucene.

Key Features:

Full-text search with advanced ranking.
Distributed & scalable across multiple nodes.
Fuzzy search & autocomplete for better user experience.

Example Elasticsearch Query:

{
  "query": {
    "match": {
      "title": "wireless headphones"
    }
  }
}

Use Case: Amazon uses Elasticsearch to power fast product search and recommendations.

2. Query Processing – Understanding User Intent

Before searching, queries are processed to extract meaning and optimize results.

Key Steps:

Tokenization: Splitting text into words.
Stemming/Lemmatization: Converting words to their base forms (“running” → “run”).
Synonyms & Stopwords Removal: Handling words like “the”, “is”, “a”.

Example:

User Query: “best wireless headphones”
→ Tokenized: [best, wireless, headphones]
→ Stopwords removed: [wireless, headphones]
→ Stemmed: [wireless, headphone]

3. Ranking Algorithms – Sorting Relevant Results

Not all results are equally relevant. Search engines rank documents based on multiple factors.

Common Ranking Algorithms:

TF-IDF (Term Frequency-Inverse Document Frequency):
- Prioritizes frequently occurring words in a document but not across all documents.
BM25 (Best Matching 25):
- Advanced TF-IDF with length normalization and tuning parameters.
Vector Search (Semantic Search):
- Uses AI-based embeddings for context-aware search.

Example – TF-IDF Calculation:

Term: “wireless”
- Appears in 5 out of 100 documents → Lower rank.
- Appears in 1 out of 100 documents → Higher rank.

Handling Real-World Search Challenges

1. Autocomplete & Suggestive Search

Elasticsearch n-grams generate predictions while typing.
Example: Typing “iph” suggests “iPhone”, “iPhone 13”, etc.

2. Fuzzy Matching & Spell Correction

Handles typos and variations (e.g., “headphons” → “headphones”).

3. Personalization & Contextual Search

Uses user history and preferences to rank results.
Example: A gamer searching for “mouse” gets gaming mice first.

Choosing the Right Search Strategy

Feature

SQL Databases

Elasticsearch

Full-Text Search

Slow

Fast & optimized

Ranking Results

Limited

Advanced ranking (BM25, TF-IDF)

Autocomplete

Yes

Scalability

Limited

Distributed & scalable

Real-World Use Cases

1. E-Commerce Platforms (Amazon, eBay)

Elasticsearch powers product search & filtering.
Uses BM25 to rank best-selling and highly-rated products first.

2. Content Platforms (YouTube, Netflix)

Query processing improves video title searches.
Personalized search ranks content based on watch history.

3. Enterprise Search (Google Drive, Notion)

Full-text search indexes documents, notes, and PDFs.
OCR (Optical Character Recognition) extracts text from images.

Conclusion

A well-designed search system combines fast indexing, smart query processing, and ranking algorithms to deliver accurate, relevant, and real-time results.

Elasticsearch provides distributed, full-text search.
Inverted Index speeds up lookups.
Ranking Algorithms ensure the best results appear first.

Next, we’ll explore Designing a Scalable URL Shortener – Hashing, Database Choices, Redirection Optimization.

#code #system-design

3/6/2025

#8 Database Design & Optimizations – Indexing (B-Trees, Hash), Normalization vs Denormalization

So, your database is slow? No worries, we've all been there. We'll walk through how indexes (like B-trees and hash indexes) speed things up, and when it makes sense to normalize or denormalize your data. Think of it as learning the tricks to keep your app running smoothly.

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#27 Graph Databases & NoSQL Alternatives – Neo4j, MongoDB, DynamoDB, Time-Series DBs

Want to make your app's data storage smarter? Let's talk NoSQL. We'll cover graph databases, document stores, key-value stores, and time-series databases. Think of it as having the right tool for every data task.

Read Full Story

#29 Designing a Scalable URL Shortener – Hashing, Database Choices, Redirection Optimization

Want to build a URL shortener that scales? Let's talk design. We'll cover everything from encoding to database choices. Think of it as building a super-efficient link management system.

Read Full Story