AdvancedSystem-Design
45 min
Design a Distributed Search Engine
SearchIndexingShardingReplication
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Interview Question
Design a distributed search engine like Elasticsearch that supports indexing, querying, replication, and relevance ranking.
Key Points to Cover
- Indexing pipeline with analyzers, tokenization, stop-words
- Inverted index data structures, postings, compression
- Sharding and replication strategies for query load
- Relevance scoring models (TF-IDF, BM25, learning-to-rank)
- Cluster management, rebalancing, failover
Evaluation Rubric
Solid indexing pipeline & structures25% weight
Efficient query processing & ranking25% weight
Scalable sharding/replication model25% weight
Cluster mgmt and failover strategy25% weight
Hints
- 💡Think about write amplification and index refresh trade-offs.
Common Pitfalls to Avoid
- ⚠️Inefficient indexing pipeline leading to slow ingestion and high CPU usage.
- ⚠️Poor choice of sharding strategy, leading to hot spots and unbalanced load.
- ⚠️Replication lag or synchronization issues causing data inconsistency or availability problems.
- ⚠️Suboptimal query optimization, resulting in slow search results and excessive resource consumption.
- ⚠️Lack of robust error handling and monitoring, making it difficult to diagnose and resolve issues in production.
Potential Follow-up Questions
- ❓How do you support phrase queries?
- ❓How to scale for multi-tenant workloads?
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