Design Search Autocomplete System
Last Updated: January 22, 2026
What is an Autocomplete System?
Search Autocomplete is a widely-used feature in modern applications like Google, Amazon, and YouTube. It enhances user experience by providing real-time suggestions based on partial input, helping users complete queries faster and discover popular or relevant search terms.
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As the user types a query character by character, the system should return the top N suggestions that match the current prefix. For example, typing "app" might yield results like "apple", "app store", or "application".
These suggestions are typically ranked by relevance, popularity, frequency, or recency.
In this chapter, we will explore the low-level design of a Search Autocomplete system in detail.
Let’s start by clarifying the requirements:
1. Clarifying Requirements
Before diving into the design, it’s important to clarify how the autocomplete system is expected to behave. Asking targeted questions helps refine assumptions, define the scope, and align on core expectations for the system.
Candidate: Should the autocomplete system be case-sensitive?
Interviewer: No, all inputs should be treated as lowercase. The system should be case-insensitive.
Candidate: Should the system only support English, or do we need to account for Unicode/multilingual input?
Interviewer: Let’s assume only English characters for now.
Candidate: How should suggestions be ranked—alphabetically, by frequency of use, or both?
Interviewer: Good question. The system should support both strategies. The user of the system should be able to configure the ranking strategy.
Candidate: How many suggestions should be returned per prefix?
Interviewer: That should be configurable, perhaps a default of 10, but the system should allow specifying a custom limit.
Candidate: How does the system learn word frequencies? Are we tracking every time a word is added or searched?
Interviewer: Let’s increment the frequency every time a word is inserted into the system..
Candidate: Can users input new words over time, or is the dictionary fixed at initialization?
Interviewer: Words can be added dynamically during runtime.
Candidate: Should we support deleting a word or updating its frequency?
Interviewer: No, we can skip delete and update functionality for now.
After gathering the details, we can summarize the key system requirements.
1.1 Functional Requirements
- Support inserting words into an internal dictionary.
- Return suggestions when a user types a prefix.
- Suggestions should be ranked based on a configurable strategy (alphabetical or frequency-based).
- The number of suggestions returned should be configurable.
- Frequency count is incremented each time a word is added.
- Words and prefixes are treated case-insensitively.
1.2 Non-Functional Requirements
- The system should be optimized for fast prefix lookups
- The design should follow object-oriented principles with clear separation of concerns
- The system should be modular and extensible to support new ranking strategies
- The components should be testable in isolation
- The system can assume in-memory storage (no persistence required)
Now that we understand what we're building, let's identify the building blocks of our system.