Katkat

Topic

System Design interview questions

1311 System Design questions from 230 companies, with hints, runnable code and solution walkthroughs.

Questions1311

60 easy819 medium432 hard

Your progress0/1311

System Design interview questions by the numbers

Last updated

Katkat has 1,311 System Design interview questions from 230 companies; the most come from OpenAI (8%), Meta (5%) and Amazon (4%). By difficulty: 5% easy, 62% medium and 33% hard. The most common round is Onsite (55%). Frequent concepts include System Design, Distributed Systems, Scalability and Caching.

Companies (230)

Difficulty

  • Easy5% · 60
  • Medium62% · 819
  • Hard33% · 432

Interview rounds

  • Onsite55% · 721
  • Technical Screen39% · 517
  • Online Assessment3% · 38
  • Other2% · 21
  • HR Screen1% · 13

Roles

  • Software Engineer91% · 1189
  • Machine Learning Engineer2% · 31
  • Data Engineer2% · 26
  • Backend Engineer2% · 25
  • Frontend Engineer1% · 19

Frequent concepts

  • System Design31% · 400
  • Distributed Systems24% · 316
  • Scalability15% · 196
  • Caching7% · 95
  • Concurrency7% · 91
  • system-design6% · 83

Showing 1141–1170 of 1311 results

  • This question tests the ability to design a distributed system for tracking the mapping between ephemeral devices and their currently signed-in users, focusing on handling event concurrency and high-throughput read/write operations.

    SnapchatSoftware EngineerSystem DesignOnsite
  • This question evaluates system design competencies such as scalable time-series data modeling, ingestion and storage architecture, query latency versus retention trade-offs, and alert evaluation logic within the System Design domain.

    SnapchatSoftware EngineerSystem DesignOnsite
  • This question tests the ability to design a high-throughput, low-latency ranking system that balances user experience with monetization goals through multi-stage retrieval and bidding mechanics.

    SnapchatMachine Learning EngineerSystem DesignOnsite
  • This question evaluates a candidate's competency in designing low-latency, scalable, and privacy-compliant ad-insertion platforms, encompassing distributed systems, real-time bidding integration, ad format and measurement standards, content and identity signal processing, pacing/frequency control, and multi-region fault tolerance.

    SnapchatSoftware EngineerSystem DesignOnsite
  • This question evaluates system-design competence for building scalable, low-latency backend services that handle feed vending, pagination, personalized ranking, caching, storage, and content eligibility; it is categorized under System Design and tests practical application of distributed systems and backend data engineering principles.

    SnapchatSoftware EngineerSystem DesignOnsite
  • This question evaluates design skills for RESTful APIs and backend systems, covering API schema and versioning, authentication and rate limiting, caching and cache invalidation, storage models, precomputation pipelines, consistency and staleness bounds, monitoring, and operational concerns.

    SnapchatSoftware EngineerSystem DesignOnsite
  • This question tests the ability to design a high-throughput, fault-tolerant system capable of tracking massive volumes of ad impression events and calculating real-time budget spending.

    SnapchatSoftware EngineerSystem DesignOnsite
  • This question evaluates a candidate's competency in large-scale system design, including distributed architecture, real-time data processing, geo-spatial indexing and low-latency matching for ride-hailing, plus scalable ingestion, time-series storage, rule evaluation, and reliable notification pipelines for price-tracking, within the System Design domain. It is commonly asked to assess architectural trade-offs around scalability, consistency, availability, fault tolerance and operational concerns, testing practical application of architectural patterns alongside conceptual understanding of data modeling and real-time constraints.

    SnapchatSoftware EngineerSystem DesignOnsite
  • This question tests the ability to design a distributed URL shortening service, focusing on system architecture, database partitioning, and strategies for high-frequency write operations like click tracking.

    SnapchatSoftware EngineerSystem DesignTechnical Screen
  • Parallelize the lock BFS safely evaluates requirements, scale assumptions, API/data design, architecture, trade-offs, failure modes, and rollout in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

    SnapchatSoftware EngineerSystem DesignTechnical Screen
  • This question evaluates skills in designing reliable distributed schedulers, including durable state modeling, concurrency and race-condition reasoning, idempotency, and fault-tolerant handoff semantics, and is commonly asked to assess a candidate's ability to guarantee correctness and coordination when multiple replicas must manage recurring job triggers. Categorized under system design and distributed systems with overlap into data modeling and consistency, it emphasizes practical application of architectural and operational trade-offs rather than purely theoretical concepts.

    SnowflakeSoftware EngineerSystem DesignTechnical Screen
  • Design a job scheduler service that runs one-time and cron jobs, supports pausing and resuming, and dispatches work to an unlimited pool of workers. It tests due-job indexing, claim and lease protocols, pause semantics, at-least-once execution with idempotency, and handling top-of-the-hour spikes.

    SnowflakeSoftware EngineerSystem DesignTechnical Screen
  • This question tests your ability to design a scalable spatial indexing system capable of performing range-based queries (e.g., 'find all points of interest within 5km') while maintaining high availability and low latency.

    SnowflakeSoftware EngineerSystem DesignTechnical Screen
  • A backend system design prompt for a photo album service where users upload photos and organize them into albums. It probes keeping a metadata database and blob storage consistent on upload, showing new photos instantly despite processing lag, blocking duplicate uploads, and when a relational database stops scaling.

    SnowflakeSoftware EngineerSystem DesignOnsite
  • This question evaluates system design and API design competencies, focusing on creating a REST API abstraction layer and internal SDK that address cross-cutting concerns like reliability, observability, security, versioning, and contract/interface modeling.

    SnowflakeSoftware EngineerSystem DesignTechnical Screen
  • This problem tests the ability to design a distributed, fault-tolerant job scheduler capable of handling task persistence, concurrency, and strict timing requirements at scale.

    SnowflakeSoftware EngineerSystem DesignOnsite
  • Design a quota service that several services draw from for each user, where the balance must never be overdrawn under highly concurrent requests and must stay strongly consistent. It tests atomic conditional updates, reserve-and-commit APIs with idempotency, replication choices, and scaling a single hot user's quota.

    SnowflakeSoftware EngineerSystem DesignTechnical Screen
  • This problem evaluates your ability to design a distributed, concurrent web crawling architecture that balances high throughput with politeness policies, concurrency control, and robust state management.

    SnowflakeSoftware EngineerSystem DesignOnsite
  • This problem tests the ability to design a persistent key-value store that balances disk I/O, memory caching, and thread safety under high concurrency.

    SnowflakeSoftware EngineerSystem DesignOnsite
  • This question evaluates a candidate's ability to design scalable, multi-tenant distributed analytics platforms, testing competencies in distributed systems architecture, data layout and storage, query planning and execution, metadata management, consistency and replication strategies, fault tolerance, observability, and capacity planning.

    SnowflakeSoftware EngineerSystem DesignTechnical Screen
  • Design a distributed tree node counter evaluates requirements, scale assumptions, API/data design, architecture, trade-offs, failure modes, and rollout in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

    SnowflakeSoftware EngineerSystem DesignTechnical Screen
  • This question evaluates a candidate's ability to design a global multi-tenant quota service, testing competencies in distributed systems, data consistency, concurrency control, API and storage modeling, and scalability within the System Design domain.

    SnowflakeSoftware EngineerSystem DesignTechnical Screen
  • Tests the ability to design an immutable, highly available, multi-tenant audit logging service that supports high-throughput ingestion and tamper-evident storage while providing fast, tenant-scoped querying.

    SnowflakeSoftware EngineerSystem DesignTechnical Screen
  • Tests your ability to design an asynchronous, scalable system for executing interactive SQL queries and streaming execution state and tabular results back to web clients.

    SnowflakeSoftware EngineerSystem DesignTechnical Screen
  • This question evaluates a candidate's ability to design scalable, durable object storage systems with deduplication, touching on storage architecture, data modeling, metadata management, consistency, and garbage collection.

    SnowflakeSoftware EngineerSystem DesignTechnical Screen
  • This question evaluates a candidate's understanding of caching, consistency, versioning, eviction, and update propagation in DAG-based materialized views, testing competencies in distributed systems, data engineering, and storage-hierarchy design.

    SnowflakeSoftware EngineerSystem DesignOnsite
  • This question evaluates scheduling and resource-management skills, including DAG dependency handling, concurrency bounding, timeout/retry strategies, observability, and correctness reasoning for service startup orchestration.

    SnowflakeSoftware EngineerSystem DesignOnsite
  • This question evaluates expertise in distributed system architecture, metadata and schema management, transactional DDL semantics, consistency and replication strategies, API and data-model design, multi-tenant isolation, observability, security, and capacity planning.

    SnowflakeSoftware EngineerSystem DesignTechnical Screen
  • Design a Random Number Generation API evaluates requirements, scale assumptions, API/data design, architecture, trade-offs, failure modes, and rollout in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

    SoFiSoftware EngineerSystem DesignOnsite
  • Design a Real-Time Suggestions Service evaluates requirements, scale assumptions, API/data design, architecture, trade-offs, failure modes, and rollout in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

    SoFiSoftware EngineerSystem DesignOnsite

More topics