Instacart·Software Engineer·Technical Phone Screen·Intermediate

Intermediate

May 2026

Summary

Instacart coding round focused on extending an in-memory key-value store, specifically around scan operations with filtering and ordering. The data structure discussion was the real meat of it.

Questions Asked (3)

Given an in-memory database that stores field-value pairs under a key, implement a scan function that returns all field-value pairs for a given key, sorted alphabetically by field name, formatted as 'field(value)' strings. Return an empty list if the key doesn't exist.

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

I went with TreeMap immediately and it felt right.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the data structure and constraints, then outline a solution that retrieves the field-value pairs, sorts them by field name, and formats them as strings. Discuss time and space complexity, and consider edge cases like missing keys or empty values.

Pro tip: Mention that you would use a TreeMap or sort the keys to ensure alphabetical order, and highlight that returning an immutable list or defensive copy prevents unintended modifications. Also, discuss how the solution scales with large numbers of fields.

1. Clarify requirements and constraints

Ask about the expected size of data, whether the database is thread-safe, and if the output should be sorted in ascending order. Confirm the exact format of 'field(value)' strings.

2. Choose data structures and algorithm

Decide on using a hash map for storage and a sorted structure (like TreeMap) or sorting the keys for retrieval. Consider if sorting can be done once or per query.

3. Implement the scan function

Write pseudocode or actual code: check if key exists, retrieve the map of fields, sort fields alphabetically, iterate and format each pair, and collect results in a list.

4. Analyze complexity and trade-offs

Discuss time complexity (O(n log n) for sorting n fields) and space complexity (O(n) for output). Mention alternatives like maintaining sorted order on insert for O(n) retrieval.

5. Test with edge cases

Consider cases: key not found (return empty list), empty field map, fields with special characters, and large datasets. Ensure the function handles nulls gracefully.

Key Points to Mention

Use of appropriate data structures (e.g., HashMap for storage, TreeMap or sorting for retrieval)
Time and space complexity analysis, including trade-offs between sorting on insert vs. on retrieval
Handling of edge cases: missing key, empty values, and null inputs
Immutability and thread-safety considerations if applicable
Formatting details: exact string format 'field(value)' and sorting order (alphabetical, case-sensitive?)
Scalability: how the solution performs with a large number of fields or frequent scans

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.

Extend the scan function to support a prefix filter: given a key and a prefix string, return only the field-value pairs where the field name starts with that prefix, still sorted lexicographically.

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

Pretty straightforward once you have the TreeMap in place.

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AI HintsAI Generated

Suggested Approach

Clarify the existing scan function's signature, data structure, and sorting behavior, then propose a solution that leverages the sorted order to efficiently filter by prefix. Discuss trade-offs between modifying the scan logic versus post-filtering, and consider edge cases like empty prefix and no matches.

Pro tip: Mention that if the underlying data is sorted, you can use binary search to find the first key with the prefix and iterate until the prefix no longer matches, achieving O(log n + k) time. This shows you think about performance beyond the naive O(n) scan.

1. Understand the existing scan function

Ask clarifying questions about the current implementation: what data structure is used, how are field-value pairs stored, and how is sorting achieved? Confirm the function signature and return type.

2. Define the prefix filter behavior

Specify that only field names starting with the given prefix should be included, and the result must remain sorted lexicographically. Consider edge cases: empty prefix (return all), no matches (return empty), and case sensitivity.

3. Choose an efficient algorithm

If the data is sorted, use binary search to find the first key >= prefix, then iterate while keys start with prefix. Otherwise, iterate through all pairs and filter, maintaining order. Discuss time and space complexity.

4. Implement and test

Write clean code that integrates the filter into the scan function, ensuring the output remains sorted. Test with various prefixes, including those that match multiple keys, one key, and none.

5. Discuss trade-offs and optimizations

Compare filtering during scan versus post-filtering. Mention potential optimizations like using a trie or index if prefix queries are frequent, and how this affects memory and update costs.

Key Points to Mention

Time complexity: O(n) for naive scan vs O(log n + k) with binary search on sorted data
Space complexity: O(k) for output, O(1) extra if filtering in place
Leveraging existing sorted order to avoid full scan
Edge cases: empty prefix, no matches, prefix longer than any key
Trade-offs: modifying scan vs separate filter function, and impact on maintainability
Potential use of data structures like trie for frequent prefix queries

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.

Walk through the trade-offs between using a sorted map (like TreeMap) versus a regular HashMap with sorting done at query time, in the context of this in-memory database.

Technical Trade-offsSystem Design

Author's notes

This was the part I actually enjoyed.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the access patterns and workload characteristics of the in-memory database, then compare TreeMap and HashMap+sort across key dimensions like time complexity, memory overhead, and concurrency. Conclude with a recommendation tied to the specific use case, acknowledging that the right choice depends on read/write ratio and query frequency.

Pro tip: Mention that TreeMap's sorted order can be leveraged for range queries and ordered traversals without extra sorting, which is a common pattern in databases for efficient range scans. Also note that if writes are frequent, the O(log n) insertion cost of TreeMap may outweigh its benefits compared to HashMap's O(1) put, especially if sorted access is rare.

1. Clarify requirements and access patterns

Ask about the expected operations: are range queries or ordered traversals common? What is the read/write ratio? This determines whether sorted order is needed at all.

2. Analyze time complexity

Compare TreeMap's O(log n) for put/get/remove and O(log n) for range queries versus HashMap's O(1) average for put/get/remove but O(n log n) for sorting at query time.

3. Consider memory and overhead

Discuss TreeMap's higher memory footprint due to tree nodes and balancing metadata versus HashMap's lower overhead but potential need for temporary sorted structures during queries.

4. Evaluate concurrency and thread-safety

Mention that both can be wrapped for thread-safety (e.g., Collections.synchronizedMap or ConcurrentSkipListMap for sorted), but ConcurrentSkipListMap offers better concurrency for sorted maps.

5. Recommend based on use case

Conclude with a recommendation: if sorted access is frequent, TreeMap is better; if writes dominate and sorted access is rare, HashMap with on-demand sorting may be more efficient.

Key Points to Mention

Time complexity: TreeMap O(log n) vs HashMap O(1) for basic operations; sorting at query time adds O(n log n) per query.
Range queries: TreeMap supports efficient range views (subMap, headMap, tailMap) without extra sorting.
Memory overhead: TreeMap uses more memory per entry due to tree structure; HashMap has lower overhead but may need extra memory for sorting.
Concurrency: ConcurrentSkipListMap provides a concurrent sorted map, while HashMap can be wrapped but sorting still requires synchronization.
Read/write ratio: If writes are frequent, TreeMap's log n insertion cost may be prohibitive; if reads with sorted order are frequent, TreeMap wins.
Caching sorted results: If using HashMap, consider caching sorted results if the data doesn't change often, to avoid repeated sorting.

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.