JP Morgan·Software Engineer·Onsite - System Design / Architecture·Senior

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Jun 2026

Summary

JP Morgan system design round, one big question about building an autocomplete service from scratch. The whole session was basically a deep dive into trie internals and tradeoffs, which I was not fully prepared for.

Questions Asked (1)

Design and implement an autocomplete service using a trie. It needs to support inserting words with optional weights, and a suggest function that returns the top-k completions for a given prefix ranked by weight. Walk through the node structure, how you'd cache or precompute top-k results at each node, how updates to weights are handled, and how you'd keep memory usage bounded.

System DesignAlgorithms & Data StructuresTechnical Trade-offs

Author's notes

This one went longer than I expected.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements (scale, latency, update frequency) and then describe the trie node structure with children map and cached top-k list. Explain how to maintain the top-k cache during insertions and weight updates, and discuss memory bounding techniques like pruning, compression, and eviction policies.

Pro tip: Emphasize the trade-off between precomputing top-k at every node (fast queries, higher memory) versus computing on the fly (slower queries, lower memory), and propose a hybrid approach based on access patterns. Also mention that for financial applications like JP Morgan, consistency and low latency are critical, so consider read-heavy optimizations and eventual consistency for updates.

1. Clarify Requirements and Constraints

Ask about expected scale (number of words, query rate), latency requirements, update frequency, and memory limits. This shapes design decisions.

2. Design Trie Node Structure

Define node with children map (e.g., hash map or array), isEndOfWord flag, weight, and a cached list of top-k completions (word and weight) for the subtree.

3. Implement Insert and Update Operations

Insert words character by character, updating weights and propagating changes to update the top-k cache along the path. For weight updates, traverse to the node and update ancestors' caches.

4. Implement Suggest Function

Traverse to the prefix node and return its cached top-k list. If cache is stale or not present, compute by traversing subtree and using a heap to find top-k.

5. Address Memory Bounding and Optimization

Discuss techniques: pruning low-weight or infrequent words, using a bounded cache size, compressing trie (radix tree), and evicting least recently used prefixes.

Key Points to Mention

Trie node with children map, end-of-word flag, weight, and cached top-k list
Updating top-k cache during insertion and weight changes: merge child caches and current word
Trade-off between precomputation (fast queries) and memory usage; hybrid approach
Memory bounding: pruning, compression (radix tree), LRU eviction, and limiting cache size
Handling weight updates: propagate changes up the trie, possibly using a priority queue to merge
Concurrency and consistency considerations for read-heavy workloads

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