Designing typeahead search or autocomplete

Requirements

• realtime / low-latency typeahead and autocomplete service for social networks, like Linkedin or Facebook
• search social profiles with prefixes
• newly added account appear instantly in the scope of the search
• not for “query autocomplete” (like the Google search-box dropdown), but for displaying actual search results, including
  • generic typeahead: network-agnostic results from a global ranking scheme like popularity.
  • network typeahead: results from user’s 1st and 2nd-degree network connections, and People You May Know scores.

Architecture

Multi-layer architecture

• browser cache
• web tier
• result aggregator
• various typeahead backend

Result Aggregator

The abstraction of this problem is to find documents by prefixes and terms in a very large number of elements. The solution leverages these four major data structures:

1. InvertedIndex<prefixes or terms, documents>: given any prefix, find all the document ids that contain the prefix.
2. for each document, prepare a BloomFilter<prefixes or terms>: with user typing more, we can quickly filter out documents that do not contain the latest prefixes or terms, by check with their bloom filters.
3. ForwardIndex<documents, prefixes or terms>: previous bloom filter may return false positives, and now we query the actual documents to reject them.
4. scorer(document):relevance: Each partition return all of its true hits and scores. And then we aggregate and rank.

Performance

• generic typeahead: latency < = 1 ms within a cluster
• network typeahead (very-large dataset over 1st and 2nd degree network): latency < = 15 ms
• aggregator: latency < = 25 ms