Key Considerations for Database Design
When designing the database for a Typeahead Suggestion system, the following considerations are critical:
- Speed: Typeahead suggestions need to be provided in real-time with low latency. Hence, the database design should be optimized for fast reads.
- Scalability: The system must handle large volumes of queries, user data, and stored search terms.
- Indexing: Creating appropriate indexes is essential for efficient search queries.
- Data Consistency: Maintaining consistency of search terms across different users and ensuring that new terms are added quickly.
Entities in the Typeahead System
The following are the key entities that will be involved in the database design:
Search Terms
This table stores the actual terms that users are typing into the search box.
User Search History
This table stores individual user search history, which can be used for personalized suggestions.
Popular Queries
This table stores the most popular search queries (the most commonly searched terms).
Suggestions Index
This stores the indexed search terms or suggestions to speed up querying and returning suggestions in real-time.
Schema Design
1. Search Terms Table
This table stores the search terms (queries) that are being typed in the system. It keeps track of every search term that is used, along with metadata.
| Column Name | Data Type | Description |
|---|---|---|
id |
INT (Primary Key, Auto Increment) | Unique identifier for each search term. |
term |
VARCHAR(255) | The search term (e.g., “New York”, “weather”). |
created_at |
DATETIME | The timestamp when the search term was first entered. |
last_used_at |
DATETIME | The last time this search term was used for suggestions. |
usage_count |
INT | The number of times this search term has been used. |
Indexes:
termshould be indexed for fast lookups.usage_countcould be indexed if we want to fetch the most popular terms quickly.
2. User Search History Table
This table stores the search history for each user. This is useful for providing personalized suggestions based on the user’s previous search behavior.
| Column Name | Data Type | Description |
|---|---|---|
id |
INT (Primary Key, Auto Increment) | Unique identifier for the history entry. |
user_id |
INT | Unique identifier for the user. |
query |
VARCHAR(255) | The query typed by the user (e.g., “New York”). |
timestamp |
DATETIME | The timestamp when the user made the search. |
Indexes:
- Index
user_idfor fast lookups of user history. - Index
queryto speed up search term lookups for personalized suggestions.
3. Popular Queries Table
This table stores a set of popular queries. These are the most commonly searched terms, which can be used as quick suggestions or to show trending searches.
| Column Name | Data Type | Description |
|---|---|---|
id |
INT (Primary Key, Auto Increment) | Unique identifier for the popular query. |
term |
VARCHAR(255) | The search term that is trending. |
usage_count |
INT | The number of times this term has been searched. |
created_at |
DATETIME | The timestamp when the query was added to the popular list. |
Indexes:
- Index
termto speed up lookups for popular queries. - Index
usage_countto quickly find the most popular queries.
4. Suggestions Index Table (Optional)
This table could be used to create an index that stores a faster way to retrieve suggestions based on partial query input. It could be helpful when you’re trying to perform quick lookups for autocompletion or autocomplete suggestions.
| Column Name | Data Type | Description |
|---|---|---|
id |
INT (Primary Key, Auto Increment) | Unique identifier for the suggestion entry. |
term |
VARCHAR(255) | A stored term that is used for suggestions. |
suggestion |
VARCHAR(255) | The suggestion that will be returned when the user types this prefix. |
popularity_score |
INT | A score representing how popular or relevant the suggestion is. |
Indexes:
- Index
termto speed up lookups. - Index
popularity_scorefor faster retrieval of the most relevant suggestions.
Relationships Between Tables
- Search Terms: This is the foundational table that stores all search terms across the system.
- User Search History: This is related to the
Search Termstable through thequerycolumn. Every time a user makes a search, it is logged in this table. - Popular Queries: This table could be updated based on
Search Terms, as popular search terms should be added to the popular queries list. - Suggestions Index: This table stores terms for faster lookup, and may reference
Search Termsfor autocompletion or prefix matching.
Sample Queries
Query 1: Get Suggestions Based on User Input
To get suggestions based on partial user input (e.g., typing “New Y” would return [“New York”, “New Year”]):
Query 2: Get User's Search History
To get the search history for a user with user_id = 123:
Query 3: Get Popular Queries
To get the most popular search terms that have been used more frequently:
Scaling Considerations
Indexing:
Index the term column for fast lookups in the Search Terms and Popular Queries tables.
Create an index on user_id and query for fast lookup in User Search History.
Sharding:
If your data grows very large, you may need to partition or shard the database to handle the scale. For example, you could shard the data by user_id or split the Search Terms table based on alphabets or regions.
Cache Layer:
Use a cache (like Redis) for frequently queried suggestions or popular queries to reduce the load on the database and improve response time.
Real-Time Updates:
To keep the suggestions up-to-date, consider real-time indexing with a search engine like Elasticsearch or Solr, and sync this index with your database.
