Hi @Xuan,
You’ve asked some interesting questions, but in our course, we focus more on explaining how communication happens rather than getting into the nitty-gritty of the search service implementation. We skip the detailed implementation stuff to keep our focus on teaching how clients talk to the backend services. Although, I’ll try to answer all your queries:
Q: Which “backend” component in the given design will implement the bucket pattern?
Search service implements the bucket pattern.
Q: Do we need any change to the database schema to use bucket pattern?
In most cases, you won’t need major schema changes for bucket pagination. You might add fields to documents that indicate the bucket they belong to (e.g., a “bucket_id” field). However, the core data structure usually remains the same.
Q: What happens to the buckets when we have different sorting and filtering for the same query string?
To handle buckets with different sorting/filtering:
- We can create separate sets of buckets for each combination of sorting/filtering options. This increases storage requirements but improves performance for specific queries.
- Re-compute buckets on the fly based on the requested sorting/filtering. This can be less efficient but avoids redundancy.
Q: What are the tradeoffs of bucket pattern, such as data redundancy or else?
Bucket patterns can introduce some data redundancy, especially if sorting criteria change frequently. We can address this issue by opting any option mentioned in the previous answer
Q :Does the system in the book search for exact query string or a combination of keywords in any order?
Our service focuses more on delivering quick and relevant results than exact matches.
Q: Do we return a document which only has the word “data” without the word “structure”?
It returns a document containing both the terms, regardless of the order. The main condition is that both words are mentioned in search results.
Q: How does the bucket pattern work if we now implement a fuzzy search feature?
Generally, buckets are created based on specific criteria, typically for exact matching. For fuzzy search, we need to implement additional algorithms to extend search capabilities beyond exact matches. This involves techniques like measuring closeness through edit distance calculations and incorporating phonetic encoding to identify similar-sounding words.
Lastly, you’re right about explaining details with examples. However, if we provide more details regarding implementation, beginner-level learners might get confused by these intricacies. Therefore, for simplicity, we intentionally hide implementation details.
