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This is a medium difficulty Coding & Algorithms question, commonly asked during Onsite rounds at Coinbase.

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This question is commonly asked for Software Engineer candidates at Coinbase during technical interviews.

Design order stream with state transitions

Quick Overview

Design order stream with state transitions evaluates algorithm design, data structures, correctness, complexity, edge cases, and implementation details in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

Company: Coinbase

Role: Software Engineer

Category: Coding & Algorithms

Difficulty: medium

Interview Round: Onsite

Implement an in-memory order stream service with operations: add(order), delete(order_id), pause(order_id), resume(order_id); follow-up: delete_all_by_user(user_id). Define the order schema and valid state transitions, ensure operations are idempotent, and support efficient lookups by order_id and by user_id. Describe chosen data structures, expected time/space complexity, and provide example tests. Discuss how you would extend it for persistence and concurrency if required.

Quick Answer: Design order stream with state transitions evaluates algorithm design, data structures, correctness, complexity, edge cases, and implementation details in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

Part 1: In-Memory Order Stream with State Transitions

Implement an in-memory order stream simulator. The service starts empty and processes operations in order. Assumptions: each order has schema order_id, user_id, and state; order_id and user_id are strings and do not contain ':' or ','; order IDs are globally unique and cannot be reused after deletion. Valid states are ACTIVE, PAUSED, and DELETED. Valid transitions are: add creates ACTIVE; pause changes ACTIVE to PAUSED and is an idempotent no-op on PAUSED; resume changes PAUSED to ACTIVE and is an idempotent no-op on ACTIVE; delete changes ACTIVE or PAUSED to DELETED and is an idempotent no-op for missing or already deleted orders. Deleted orders remain as tombstones for order_id lookup and idempotency, but they do not appear in user lookups. Use hash maps for efficient lookup by order_id and by user_id.

Constraints

0 <= len(operations) <= 200000
order_id and user_id are non-empty strings of length at most 64
order_id and user_id do not contain ':' or ','
All operation names are valid and each operation has the correct number of fields
At most 200000 distinct order IDs and 200000 distinct user IDs appear

Examples

Input: ([['add', 'o1', 'u1'], ['get_order', 'o1'], ['pause', 'o1'], ['get_order', 'o1'], ['resume', 'o1'], ['get_user_orders', 'u1']],)

Expected Output: ['OK', 'o1:u1:ACTIVE', 'OK', 'o1:u1:PAUSED', 'OK', 'o1:ACTIVE']

Explanation: A single order is added, paused, resumed, and then found through the user index.

Input: ([['add', 'o1', 'u1'], ['add', 'o1', 'u1'], ['pause', 'o1'], ['pause', 'o1'], ['add', 'o1', 'u2'], ['get_user_orders', 'u1']],)

Expected Output: ['OK', 'OK', 'OK', 'OK', 'INVALID', 'o1:PAUSED']

Explanation: Duplicate add with the same user and duplicate pause are idempotent. Reusing the same order_id for another user is invalid.

Hints

Keep one dictionary from order_id to the order record, and another dictionary from user_id to the set of currently non-deleted order IDs.
Deletion should update both indexes, but keep a tombstone in the order_id dictionary so repeated deletes and later get_order calls are well-defined.

Part 2: In-Memory Order Stream with Bulk Delete by User

Extend the in-memory order stream from Part 1 with delete_all_by_user(user_id). The service starts empty and supports all previous operations plus bulk deletion. Assumptions: each order has schema order_id, user_id, and state; order_id and user_id are strings and do not contain ':' or ','; order IDs are globally unique and cannot be reused after deletion. delete_all_by_user marks every currently non-deleted order for that user as DELETED, removes those orders from the user index, and returns the number of orders newly deleted. Repeating delete_all_by_user for the same user is idempotent and returns zero if no non-deleted orders remain. Keep tombstones in the order_id dictionary.

Constraints

0 <= len(operations) <= 200000
order_id and user_id are non-empty strings of length at most 64
order_id and user_id do not contain ':' or ','
All operation names are valid and each operation has the correct number of fields
At most 200000 distinct order IDs and 200000 distinct user IDs appear

Examples

Input: ([['add', 'o1', 'u1'], ['add', 'o2', 'u1'], ['pause', 'o2'], ['add', 'o3', 'u2'], ['delete_all_by_user', 'u1'], ['get_user_orders', 'u1'], ['get_order', 'o2'], ['get_user_orders', 'u2']],)

Expected Output: ['OK', 'OK', 'OK', 'OK', 'DELETED 2', 'EMPTY', 'o2:u1:DELETED', 'o3:ACTIVE']

Explanation: Bulk delete removes both ACTIVE and PAUSED orders for u1 without affecting u2.

Input: ([['add', 'o1', 'u1'], ['delete', 'o1'], ['delete_all_by_user', 'u1'], ['delete_all_by_user', 'u1'], ['get_order', 'o1']],)

Expected Output: ['OK', 'OK', 'DELETED 0', 'DELETED 0', 'o1:u1:DELETED']

Explanation: An individually deleted order is already absent from the user index, so bulk delete has nothing new to delete.

Hints

The user_id index should contain only currently non-deleted orders, so bulk deletion can iterate exactly the affected orders.
Convert the affected set to a list before mutating or deleting the set from the index.