Scalable Search Inverted Indexes and Beyond
Inverted indexes speed up text search by listing which documents contain each word. This lets you answer queries fast without scanning every page. As your data grows, the index must scale without slowing down users. The challenge is to keep insertions fast, queries responsive, and storage affordable across many machines.
One common approach is to split the data into shards. Each shard holds a portion of documents and its own posting lists. With 10 shards, ten million documents can be searched in parallel, and the results are merged in the end. Sharding also helps with distribution and resilience.
Different design choices affect performance. You can shard by document, shard by term, or use a hybrid strategy. Add replicas for fault tolerance. Decide between real-time indexing and batch indexing based on your needs. Index data stays compact with posting lists that store document IDs and optional term frequency. Compression saves space. In practice, you combine skip pointers, delta encoding, and binary packing to speed up scans.
Beyond the basic index, many teams add a forward index, which lists the terms for each document. This helps rewrite queries and ranks results faster. Simple ranking, like BM25 or TF-IDF, combines term importance with document age, popularity, and user signals. Real-time feedback can guide tuning and updates.
Operational tips: monitor latency per shard, rebalance when a shard grows too large, cache hot terms, and plan for failure. Start with a simple setup, then grow as traffic and data rise.
Example: 1 million documents with 50k unique terms can work well with 5 shards. Each shard holds a portion of postings and a small forward index. With compression and smart caching, average query latency stays low, while throughput improves as you add shards.
Real-world search systems blend indexing with ranking, filters, and result caching to deliver useful answers quickly. They require ongoing monitoring, testing, and tuning as data and usage change.
Key Takeaways
- Scale with shard architecture and parallel queries
- Use compression and incremental indexing to control storage and downtime
- Balance speed, relevance, and cost through careful tuning