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Selected problem: Collaborative Document Editing

Scope: Design a Google Docs-style collaborative editor for text documents that supports concurrent editing, revision history, permissions, offline recovery, and low-latency presence without losing user edits.

Source grounding: Grokking’s Dropbox chapter supplies the interview frame for sync, offline edits, client metadata, long polling, queues, chunking, dedupe, metadata partitioning, and bottleneck analysis; Alex Xu’s Google Drive chapter adds sync conflicts, revision history, notification service, offline backup queue, strong metadata consistency, and failure handling; DDIA supplies the deeper model for multi-leader-style collaboration, write conflicts, causality, version vectors, operational transformation, CRDTs, total ordering, append-only logs, stream-derived state, and the distinction between timeliness and integrity. Modern refresh: current Yjs and Automerge documentation show practical CRDT-backed collaborative application patterns, while MDN’s WebSocket documentation is a useful reminder that browser WebSocket APIs do not provide built-in backpressure.

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Selected problem: URL Shortener Analytics

Scope: Design the analytics subsystem for a URL shortener: capture click events, compute useful aggregates, and serve owner dashboards without slowing down redirects.

Source grounding: Grokking frames analytics as an extended URL shortener requirement and calls out the danger of updating a shared counter row on every popular redirect; Alex Xu highlights that redirect choice affects analytics fidelity; DDIA supplies the storage and processing lens for event logs, OLTP versus OLAP separation, materialized aggregates, stream windows, partitioning, replication, and idempotent recovery.

Generated by Codex with gpt-5

Selected problem: Distributed Queue

Scope: Design a durable multi-tenant queue service for asynchronous jobs, service decoupling, retries, and fan-out-style workflows, with at-least-once delivery as the default and per-key ordering where callers explicitly need it.

Problem framing

This is the classic “design a distributed message queue” interview problem. Grokking’s interview process is the right starting point: clarify whether the interviewer wants a task queue, a pub/sub broker, or a retained event log before debating Kafka, SQS, or RabbitMQ. Alex Xu’s system design framing treats a message queue as the durability and decoupling layer between producers and consumers, and his notification, news feed, web crawler, chat, and video chapters show the same pattern repeatedly: put slow, unreliable, or bursty work behind a queue so producers and consumers can scale independently. DDIA adds the critical storage lesson: a queue is not just an in-memory list. It is a data system with ordering, replication, durability, retention, acknowledgements, and replay tradeoffs.

Generated by Codex with gpt-5

Selected problem: Payment System

Scope: Design a card-first online payment platform that creates payment intents, authorizes and captures funds, issues refunds, maintains an auditable ledger, and reconciles asynchronous processor outcomes without double-charging customers.

Problem framing

This is the classic “design a Stripe / Adyen / PSP-style payment platform for merchant checkout” interview problem. Grokking’s interview flow still applies directly: define the API contract early, size the system before arguing about storage, and then walk through the write path, read path, partitioning, caching, and failure handling. Alex Xu’s usual system design patterns also fit well here: stateless API servers on the edge, durable primary storage for transactional state, caches for read-heavy metadata, and queues or streams for slow or retryable side effects. DDIA adds the most important payment-specific lesson: integrity matters more than timeliness. A payment status can be briefly stale, but money must not be created, lost, or charged twice.

Generated by Codex with gpt-5

Selected problem: Ad Click Aggregation

Scope: Design a high-throughput ad event aggregation pipeline that ingests impression and click events, deduplicates them, produces near-real-time campaign rollups, and supports slower reconciled reporting for analytics, optimization, and billing-adjacent use cases.

Problem framing

This interview problem is less about serving ads and more about turning a firehose of raw events into trustworthy aggregates. Grokking and Alex Xu push the same early habit here: clarify scope, keep the write path simple and durable, and decouple slow downstream consumers with queues or logs. DDIA adds the deeper constraint: aggregated counters are derived data, so correctness depends on event ordering, idempotence, late-event handling, and replay, not on pretending one synchronous write magically updates every store exactly once.

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Selected problem: Video Streaming Platform

Scope: Design a global video-on-demand platform that supports resumable creator uploads, transcoding, metadata/search, and adaptive-bitrate playback on web, mobile, and TV clients; exclude recommendations, ads, and live streaming from the first version.

Problem framing

This is the classic “design YouTube / Netflix-style VOD platform” interview. Grokking is useful for the interview flow: clarify scope, define APIs early, size uploads versus reads, and separate large media blobs from metadata. Alex Xu adds the practical serving path: object storage for source and encoded media, an asynchronous transcoding pipeline, CDN-based delivery, and a metadata control plane. DDIA sharpens the deeper answer: treat search indexes, counters, and playback-ready state as derived data maintained from durable logs and background workflows, not as one giant transactional database.

Generated by Codex with gpt-5.4

Selected problem: ChatGPT

Scope: Design a text-first ChatGPT-style assistant that supports multi-turn conversations, streamed responses, document-backed answers, bounded tool use, and durable conversation history for consumers and teams.

Problem framing

This interview problem combines ideas from several classic designs instead of mapping neatly to one old chapter. Grokking’s framework still applies: clarify scope, define APIs, estimate scale, pick a data model, then explain the high-level design and bottlenecks. Alex Xu’s chat-system chapter is useful for session handling, streaming delivery, and multi-device conversation sync; the YouTube chapter is useful for large-object storage and asynchronous processing; DDIA adds the deeper foundation for logs, caches, replication, partitioning, derived data, and backpressure. The key modern shift is that the assistant is not only a chat transport problem. It is a stateful product sitting on top of an expensive inference pipeline.

Generated by Codex with gpt-5

Selected problem: Distributed File Storage

Scope: Design a cloud file storage and sync service that stores large files durably, keeps file and folder metadata consistent, and propagates changes across devices and shared workspaces.

Problem framing

This is the classic Dropbox / Google Drive interview problem. Grokking and Alex Xu both push the same core split early: separate the metadata and synchronization plane from the binary file storage plane. DDIA adds the more durable framing: the namespace, versions, and permissions are the system of record, while notifications, sync cursors, search indexes, thumbnails, and audit views are derived data built from change streams.

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Selected problem: Distributed Job Scheduler

Scope: Design a multi-tenant scheduler that supports one-off and recurring jobs, dispatches them reliably to worker fleets, and gives operators clear control over retries, overlap, and execution history.

Problem framing

This interview problem sits between a cron service and a workload orchestrator. Grokking and Alex Xu repeatedly lean on the same building blocks across crawler, notification, and media-processing systems: stateless control planes, durable metadata, queues, workers, and explicit retry paths. DDIA adds the harder part: lease-based coordination, partition ownership, hot-spot avoidance, and clear delivery semantics.

Generated by Codex with gpt-5

Selected problem: Metrics/Logging Pipeline

Scope: Design a centralized telemetry pipeline that ingests high-volume metrics and logs from many services, powers near-real-time dashboards and alerts, and retains searchable history without letting the write path collapse under bursts.

Problem framing

Grokking and Alex Xu both push the same interview habit first: clarify scope before naming technology. Here the system is not “the dashboard product”; it is the backend write and query path behind centralized observability.