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Search Backend

Build a search API with filtering, sorting, and full-text search.

1h 30m
0 problems
Topic Progress0%

Requirements & Scope

Search Features

  1. Full-text search across titles, descriptions
  2. Filtering: category, price range, brand, rating
  3. Sorting: relevance, price, popularity
  4. Autocomplete suggestions
  5. Faceted search (result counts per filter)

Scale

  • p99 < 100ms search latency
  • < 5 seconds index freshness
  • 10K searches/sec throughput

Key Points

  • Understanding Search Backend is essential for production systems
  • Always consider scalability and maintainability
  • Test thoroughly before deploying to production
  • Monitor performance and set up alerting

Common Patterns

  1. Validation: Always validate input at the boundary
  2. Error Handling: Use structured error responses
  3. Logging: Log key events for debugging
  4. Testing: Unit, integration, and load tests
  5. Documentation: Keep docs updated with code changes

Architecture & Elasticsearch

Indexing Strategy

CDC (Change Data Capture):
PostgreSQL -> Debezium -> Kafka -> Elasticsearch Consumer -> ES Index

Elasticsearch Query Example

{
  "query": {
    "bool": {
      "must": [{"match": {"title": "running shoes"}}],
      "filter": [
        {"range": {"price": {"gte": 50, "lte": 150}}},
        {"term": {"category": "shoes"}}
      ]
    }
  },
  "aggs": {
    "brands": {"terms": {"field": "brand"}}
  }
}

Autocomplete

Use completion suggester for prefix matching with fast lookup.

Relevance & Ranking

Ranking Factors

  • Text relevance (title match > description match)
  • Popularity (views, purchases)
  • Recency (newer products boosted)
  • Rating (higher rated boosted)
  • Stock availability (in-stock preferred)

Caching

  • Cache popular queries in Redis
  • TTL: 60 seconds for trending, 5 minutes for stable

Key Points

  • Understanding Search Backend is essential for production systems
  • Always consider scalability and maintainability
  • Test thoroughly before deploying to production
  • Monitor performance and set up alerting

Common Patterns

  1. Validation: Always validate input at the boundary
  2. Error Handling: Use structured error responses
  3. Logging: Log key events for debugging
  4. Testing: Unit, integration, and load tests
  5. Documentation: Keep docs updated with code changes

Practice Problems

0/3solved
Implement Search Backend

Design and implement a solution for Search Backend in a backend system. Consider scalability, error handling, and production readiness.

Solution
// Search Backend implementation
// Key aspects: validation, error handling, logging, testing

public class SearchBackend {
    // Production-ready implementation
}
Search Backend Edge Cases

Identify and handle edge cases for Search Backend. What happens under high load, with invalid input, or during failures?

Solution
// Edge case handling:
// 1. Null/empty input -> validation
// 2. High load -> rate limiting, queuing
// 3. Failures -> retries, circuit breaker
// 4. Concurrent access -> locks, idempotency
Search Backend Testing Strategy

Write a testing strategy for Search Backend. Include unit tests, integration tests, and performance tests.

Solution
// Test plan:
// - Unit: 80% coverage target
// - Integration: API contracts
// - Performance: latency, throughput
// - Chaos: failure injection

Quiz

1. What keeps Elasticsearch in sync with the database?

Question 1 options

2. Why use completion suggester for autocomplete?

Question 2 options

3. What is the primary purpose of Search Backend?

Question 3 options

4. What is a common mistake when implementing Search Backend?

Question 4 options

Flashcards

Question

How to keep ES in sync?

Click to reveal answer

Answer

CDC via Debezium -> Kafka -> Elasticsearch

Question

What is faceted search?

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Answer

Result counts per filter value

Question

What is Search Backend?

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Answer

Search Backend is a key concept in backend development.

Question

When to use Search Backend?

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Answer

Use Search Backend when building production systems that require reliability, scalability, and maintainability.

Question

Search Backend best practices

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Answer

Follow SOLID principles, write clean code, test thoroughly, document decisions, and monitor in production.

Revision Notes

Key Takeaways

  • 1.Elasticsearch for full-text search and filtering
  • 2.CDC keeps search index in sync
  • 3.Completion suggester for autocomplete
  • 4.Cache popular queries

Interview Tips

  • •Explain full search flow from query to results
  • •Discuss relevance customization

Cheat Sheet

Search Backend

  • Engine: Elasticsearch
  • Sync: CDC (Debezium + Kafka)
  • Autocomplete: Completion suggester
  • Cache: Popular queries in Redis