How do you implement autocomplete in full-text search?

Implementing autocomplete in full-text search typically relies on prefix matching and efficient data structures to quickly retrieve suggestions as users type. The core idea is to index terms in a way that allows partial input (like “app” for “apple”) to match potential completions. Common approaches include using prefix-based structures like tries, n-grams, or tools built into search engines like Elasticsearch’s completion suggester. These methods balance speed, storage, and accuracy to deliver real-time results.

One effective method is using edge n-grams during indexing. For example, the term “search” would be split into prefixes like "s", "se", "sea", etc., and stored in the index. When a user types "se", the system matches these precomputed prefixes. This approach works well with standard full-text engines like Elasticsearch or Solr, which support n-gram tokenizers. Another option is a trie (prefix tree), where each node represents a character, enabling fast traversal of possible completions. For instance, a trie storing “apple” and “apply” would allow navigating from “a” → “p” → “p” to find both terms. Some engines, like Elasticsearch, use finite state transducers (FSTs) under the hood for their completion suggester, which compress terms efficiently while enabling fast lookups.

Implementation steps vary based on the tool. In Elasticsearch, you’d define a field with a “completion” type, which builds an FST-based index. During queries, the suggest API returns matches from the indexed prefixes. For custom solutions, you might build a trie or use edge n-grams with a standard inverted index. Performance optimizations include caching frequent queries, limiting the number of suggestions, and using filters (e.g., prioritizing recent or popular terms). Handling typos often involves incorporating fuzzy matching—Elasticsearch’s suggester, for example, supports a fuzziness parameter to tolerate minor spelling errors. The trade-offs include index size (n-grams increase storage) versus query speed, making it critical to test and tune based on the dataset size and latency requirements.