← Harvey AI Interview Insights
Start by clarifying requirements and constraints (e.g., scale, consistency, durability), then propose a high-level architecture with separate API, metadata, and content layers. Dive into the tricky parts: enforcing the 5-entry limit with concurrency control and implementing OS-style duplicate renaming. Finally, discuss trade-offs and potential bottlenecks.
Pro tip: Emphasize idempotency and atomicity in your API design—interviewers love hearing about how you handle retries and partial failures. Also, mention that the 5-entry limit is per directory, so you'll need a distributed lock or transactional mechanism to enforce it.
Ask about expected scale (number of files, directories, users), consistency requirements (strong vs eventual), durability, and latency. Confirm that the 5-entry limit is hard and applies to all directories.
Outline the three main components: API layer (handles requests, auth, validation), metadata service (stores directory structure, file metadata, enforces limits), and content store (stores file blobs, e.g., S3 or HDFS). Explain how they interact.
Define addFile(path) and list(path) semantics. For addFile, describe the duplicate renaming algorithm (e.g., file.txt -> file (1).txt) and how to make it atomic. For list, discuss pagination and consistency.
Explain how to store metadata (e.g., relational DB or distributed KV store) and enforce the 5-entry limit. Discuss locking strategies (e.g., per-directory locks, optimistic concurrency) to prevent race conditions.
Describe blob storage, deduplication, and consistency between metadata and content. Discuss trade-offs: strong vs eventual consistency, latency vs durability, and how to handle failures (e.g., orphaned blobs).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the service's access patterns, consistency needs, and scale before proposing a schema or backend. Then compare relational and NoSQL options against those requirements, and justify your choice with concrete trade-offs. For Harvey AI, emphasize how metadata supports document ingestion, versioning, and retrieval for legal AI workloads.
Pro tip: Show that you understand the operational reality: relational databases are often the right default for metadata because of transactions and query flexibility, but you can layer a NoSQL store or search index for scale or full-text needs. Mention that schema evolution and migrations are a key part of the decision, not an afterthought.
Ask about read/write ratio, query patterns (point lookups vs. complex joins vs. full-text search), consistency requirements, and expected scale. This ensures your choice is grounded in the actual service needs.
Outline core entities (e.g., documents, versions, users, permissions, tags) and their relationships. Mention fields like IDs, timestamps, ownership, and version history, and note whether the schema is fixed or evolving.
Discuss relational strengths (ACID, joins, mature tooling) and NoSQL strengths (horizontal scale, flexible schema, high write throughput). Tie each to the requirements from step 1.
Pick one primary store (e.g., PostgreSQL for metadata) and explain why it fits. Optionally mention complementary stores (e.g., Elasticsearch for search, S3 for blobs) and how they integrate.
Acknowledge downsides of your choice (e.g., scaling limits, migration complexity) and how you'd mitigate them. Show awareness of future growth and schema changes.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Talked about multipart uploads and storing a reference to object storage rather than the bytes in the metadata DB.
Start by contrasting small vs. large file uploads: for small files, buffering in memory is fine, but for large files, you need streaming and chunking. Then walk through a scalable architecture that handles large files without exhausting memory, covering client-side chunking, server-side streaming, storage, and resumability.
Pro tip: Emphasize that the real challenge isn't just memory—it's reliability and user experience. Mention how you'd handle network failures, resume uploads, and provide progress feedback, which shows you think beyond the happy path.
Ask about file size limits, expected concurrency, latency requirements, and whether the upload is user-facing or machine-to-machine. This shows you tailor solutions to context.
Describe how buffering the entire file in memory (e.g., reading into a byte array) works for small files but leads to OOM errors, high GC pressure, and poor scalability for large files.
Detail client-side chunking (e.g., splitting into 5-10MB parts), server-side streaming to disk or object storage (e.g., S3 multipart upload), and using streams to avoid loading the whole file into memory.
Discuss how to handle failures: retries with exponential backoff, resumable uploads via chunk checksums or upload IDs, and idempotency to avoid duplicate chunks.
Mention asynchronous processing (e.g., virus scan, transcoding) via queues, and trade-offs like increased complexity, latency, and storage costs versus memory efficiency.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
I leaned on two-phase commit between the metadata DB and object storage, then immediately second-guessed myself out loud because 2PC across external systems is painful.
Start by clarifying the system's requirements and constraints, then discuss consistency models and trade-offs. Next, outline failure scenarios and how to detect and handle them, and finally explain rollback strategies and their implications.
Pro tip: Tie your answer to Harvey AI's domain (legal AI) by emphasizing data integrity and auditability, and mention how you'd validate rollbacks with canary deployments or feature flags.
Ask about the system's consistency needs (strong vs. eventual), SLAs, and data criticality to frame your answer.
Discuss trade-offs between strong and eventual consistency, and how to implement them (e.g., quorum reads/writes, CRDTs).
Identify potential failures (network partitions, node crashes) and propose detection and mitigation strategies (timeouts, retries, circuit breakers).
Explain how to safely roll back changes (e.g., versioned deployments, database migrations with down scripts) and ensure idempotency.
Describe how you'd test failure and rollback scenarios (chaos engineering) and monitor system health to ensure correctness.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Partitioned on user ID for the metadata store, cached directory listings with a short TTL.
Start by clarifying the service's current architecture, scale, and bottlenecks, then propose a layered scaling strategy that addresses data partitioning, sharding, and caching in order of impact. Emphasize trade-offs and iterative improvements rather than a one-size-fits-all solution.
Pro tip: Always tie your scaling choices back to the specific access patterns and SLAs of the service—interviewers at Harvey AI value pragmatic, data-driven decisions over buzzwords. Mention monitoring and the ability to roll back changes as part of your strategy.
Ask about expected scale, read/write ratio, latency SLAs, data size, and existing pain points to ground your answer in reality.
Discuss how to split data by natural boundaries (e.g., tenant, user, region) to enable independent scaling and reduce contention.
Explain sharding strategies (range, hash, consistent hashing), shard key selection, and how to handle rebalancing and hotspots.
Cover client-side, CDN, application-level, and database caching, including cache invalidation, TTLs, and consistency trade-offs.
Propose starting with the simplest effective changes, measuring impact, and evolving the architecture as load grows.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Standard stuff: request latency, error rates, queue depth for async jobs.
Start by clarifying the service's purpose and critical user journeys, then propose a layered observability strategy covering metrics, logs, and traces with specific tools. For rate limiting and quotas, discuss algorithms, enforcement points, and how to handle multi-tenancy and fairness, ensuring alignment with business goals.
Pro tip: Tie rate limiting and quotas to business metrics like cost per query or user satisfaction, and mention how you'd use observability data to continuously tune limits. Also, highlight the importance of graceful degradation and clear communication to users when limits are hit.
Ask questions to understand the service's functionality, expected traffic, user base (e.g., multi-tenant), and business goals. Identify critical paths and potential abuse scenarios.
Propose metrics (e.g., request rate, latency, error rates, resource usage), logging (structured logs with context), and tracing (distributed tracing for request flows). Mention tools like Prometheus, Grafana, ELK, Jaeger, and how to use them for alerting and dashboards.
Choose algorithms (e.g., token bucket, sliding window) based on requirements. Decide enforcement points (API gateway, service mesh, application code) and discuss distributed rate limiting using Redis or similar. Consider per-user, per-IP, or per-API-key limits.
Define quotas (e.g., daily/monthly usage limits) and how to track them (e.g., counters in a database or Redis). Discuss enforcement, reset periods, and handling of overages (e.g., throttling, billing). Consider tiered quotas for different user plans.
Explain how observability data informs tuning of rate limits and quotas. Set up alerts for anomalies, and plan for capacity planning and cost management. Emphasize feedback loops and continuous improvement.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Structure your answer around the CIA triad (Confidentiality, Integrity, Availability) and defense-in-depth, covering authentication, authorization, path traversal, and encryption in a layered manner. Emphasize practical trade-offs between security, performance, and usability, and relate choices to the service's context (e.g., AI document processing at Harvey AI).
Pro tip: Demonstrate awareness of OWASP Top 10 and mention specific mitigations like using a Web Application Firewall (WAF) and regular security audits. Also, highlight the importance of logging and monitoring for security incidents.
Ask clarifying questions about the service's data sensitivity, user roles, and compliance needs. Identify potential threats (e.g., unauthorized access, data leaks, path traversal) and prioritize based on risk.
Propose a robust authentication mechanism (e.g., OAuth 2.0, JWT) and fine-grained authorization (e.g., RBAC, ABAC). Discuss session management, token expiration, and secure storage of credentials.
Explain input validation and sanitization techniques, such as whitelisting allowed characters, using safe APIs for file operations, and normalizing paths before access. Mention avoiding direct user input in file paths.
Cover encryption in transit (TLS 1.3) and at rest (AES-256). Discuss key management (e.g., KMS), hashing passwords with salt (bcrypt, Argon2), and encrypting sensitive fields.
Emphasize continuous monitoring, audit logging, and regular security testing (pen tests, SAST/DAST). Mention incident response and updating defenses based on new threats.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Latency SLO for metadata reads, availability target, and an upload success rate SLI.
Start by clarifying the service's purpose and user expectations, then define SLIs that measure user-perceived performance and reliability. Set SLOs as targets for those SLIs, balancing ambition with feasibility, and explain how they drive error budgets and operational decisions.
Pro tip: Tie SLOs to business impact and user experience, not just technical metrics; for example, an AI product like Harvey should prioritize response accuracy and latency for critical queries over raw uptime.
Ask clarifying questions about the service's functionality, critical user journeys, and dependencies to understand what matters most to users.
Select a small set of SLIs that directly reflect user experience, such as latency, availability, throughput, and correctness, ensuring they are measurable and actionable.
Define SLOs as target percentages or thresholds for each SLI over a window (e.g., 99.9% availability over 30 days), based on user needs and business goals.
Explain how SLOs translate into error budgets that guide release velocity and reliability investments, and what happens when budgets are exhausted.
Emphasize that SLIs/SLOs are not static; they should be reviewed regularly with product and engineering teams to adapt to changing user needs.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.