8 Questions
Which of the following is NOT a high-level feature to keep in mind while implementing an autocomplete feature?
Updating the list of suggestions within 100 milliseconds
What type of functionality is the search functionality in this context similar to?
Autocomplete feature in a search engine
How many requests will the client make to the backend when the user types the word 'dog'?
3
What is the purpose of the 'frequency' column in the relational table?
To store the number of times a string has been searched
Which data structure is commonly used for designing autocomplete functionality?
Trie
What is the main disadvantage of using SQL for auto suggestion with large datasets?
It does not scale well
Where can the Trie data structure be stored for small datasets?
In memory
How can the Trie data structure be generated from existing search data?
By reading Apache logs and computing the Trie structure
Study Notes
Designing an Auto Suggestion Feature with Trie Data Structure
- The existing solution for auto suggestion involves querying a search history table using SQL.
- The solution works fine for small datasets and low traffic, but does not scale well.
- The Trie data structure is commonly used for designing autocomplete functionality.
- Trie is a tree-like structure where each node represents a letter and leaf nodes represent words.
- The Trie structure allows for efficient retrieval of suggestions based on a given prefix.
- The Trie structure can be generated periodically from existing search data.
- Once the Trie structure is generated, it can be stored in memory for small datasets.
- For larger datasets, the Trie data structure can be stored in a database like MongoDB or Cassandra.
- Caching the most common search strings in Redis or Memcached can improve performance.
- The Trie data structure can be generated using a nightly batch job that reads Apache logs.
- The batch job parses the logs to find the search strings and their frequencies.
- The Trie structure is then computed and stored in a database for efficient retrieval.
Test your knowledge on designing an auto suggestion feature with the Trie data structure. Learn about the limitations of existing solutions, the benefits of using a Trie structure, and how to generate and store Trie data for efficient retrieval. Explore caching options and batch job implementation.
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