Myriad·Software Engineer·Onsite - System Design / Architecture·Intermediate

Intermediate

Jun 2026

Summary

Had a system design question at Myriad that was framed around planets of all things. Pretty light on details from what I remember but the core problem was interesting enough.

Questions Asked (1)

Design the infrastructure needed to query and sort data about planets, such as their sizes, distances from Earth, and similar attributes.

System DesignData ModelingAPI & Integrations

Author's notes

I went straight to a relational schema with indexed columns for the numeric fields, which felt right.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements: data volume, query patterns, and consistency needs. Then propose a data model and storage solution that supports efficient querying and sorting, followed by an API design and scalability considerations.

Pro tip: Mention that planetary data is relatively static and read-heavy, so caching and denormalization can be leveraged, but also discuss how to handle updates (e.g., new discoveries) without downtime.

1. Clarify Requirements

Ask about data volume, query patterns (e.g., sort by size, distance), latency requirements, and consistency needs. Determine if data is static or dynamic.

2. Design Data Model

Define a schema with attributes like name, size, distance, and other relevant fields. Consider using a relational database for structured queries or a NoSQL store for flexibility.

3. Choose Storage and Indexing

Select a database that supports efficient sorting and filtering (e.g., PostgreSQL with B-tree indexes, or Elasticsearch for full-text and sorting). Plan indexes on sortable fields.

4. Design API

Define RESTful endpoints (e.g., GET /planets?sort=size&order=desc) with pagination. Consider GraphQL for flexible queries. Ensure API supports filtering and sorting parameters.

5. Address Scalability and Caching

Since data is read-heavy, implement caching (e.g., Redis) and possibly a CDN. Discuss sharding or replication if data grows, and how to handle updates.

Key Points to Mention

Data modeling: attributes like size, distance, and possibly mass, with appropriate data types.
Indexing strategy: B-tree indexes for sorting, composite indexes for multi-column sorts.
API design: query parameters for sorting and filtering, pagination, and error handling.
Caching: use Redis or in-memory cache to reduce database load for frequent queries.
Scalability: read replicas, sharding by planet name or region, and handling updates.
Consistency: eventual consistency vs. strong consistency based on requirements.

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