How do you handle large datasets in full-text search?

Handling large datasets in full-text search requires a combination of efficient indexing, partitioning strategies, and distributed systems. The goal is to balance speed, scalability, and resource usage while ensuring queries return results quickly. This involves optimizing how data is stored, processed, and retrieved, often leveraging specialized tools and techniques to manage the volume without compromising performance.

One critical approach is using inverted indexes combined with optimizations to reduce index size. Inverted indexes map terms to the documents containing them, enabling fast lookups. For large datasets, this index can become massive, so techniques like tokenization (splitting text into words or phrases), stop-word removal (ignoring common words like “the” or “and”), and stemming (reducing words to roots, like “running” to “run”) help minimize the index footprint. Compression algorithms, such as those using delta encoding or variable-length integers, further reduce storage needs. For example, Elasticsearch uses these optimizations and stores indexes in segments, allowing incremental updates and efficient merging. This ensures the system remains responsive even as data grows.

Another key strategy is partitioning data across multiple nodes (sharding) and distributing queries. Sharding splits the dataset into smaller chunks stored on different servers, enabling parallel processing. For instance, a dataset with 100 million documents might be split into 10 shards, each handled by a separate node. When a search query is executed, it’s sent to all shards, and results are aggregated. Tools like Apache Solr and Elasticsearch automate sharding and provide replication for fault tolerance. Additionally, caching frequently accessed results (e.g., using Redis or in-memory caches) and optimizing hardware (SSDs for faster disk access, sufficient RAM for caching) further enhance performance. By combining these methods, systems scale horizontally to handle terabytes of data while maintaining low latency for user queries.