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Product / Decision Making questions require understanding of core concepts and practice. PracHub provides solutions with explanations to help you master product / decision making interviews.

What difficulty level is this interview question?

This is a hard difficulty Product / Decision Making question, commonly asked during Onsite rounds at Google.

What role is this question designed for?

This question is commonly asked for Product Manager candidates at Google during technical interviews.

Historical FX-Rate Service – System Design

Quick Overview

This question evaluates system design and product decision-making skills, focusing on designing a high-throughput, low-latency historical FX-rate service with emphasis on time-series data modeling, caching and consistency trade-offs, scalability, and operational monitoring; the domain is product-focused system design (Product / Decision Making).

System Design: Historical FX Rates Service (10k QPS)

Background

You are designing an internal service for engineers and analysts to fetch historical currency exchange rates (e.g., USD→EUR) for analytics, backfills, and financial reporting. The service must be highly available, low latency, and cost-efficient, and will be consumed by internal applications and data pipelines.

Assume the service primarily supports read traffic (10k QPS) with occasional corrections/backfills from upstream providers. Historical rates are mostly immutable but can be revised.

Requirements

Describe a design that covers:

APIs
- Read endpoints for point-in-time and time-series queries.
- Optional write/ingestion endpoints for internal data pipelines.
- Versioning, pagination, auth, rate limits, and error handling.
Data Model
- Entities, fields (e.g., pair, timestamp, rate types), precision, versioning for corrections, and indexing.
Storage Layer
- Hot vs. cold storage choices; partitioning/sharding; replication; retention.
Caching Strategy
- Layers (in-process, distributed, CDN if applicable), keys, TTLs, invalidation, and precomputation.
Consistency Requirements
- Freshness expectations, handling of corrections/backfills, snapshot semantics, and trade-offs (strong vs. eventual consistency).
Scalability Plan
- Capacity estimates for 10k QPS, autoscaling, multi-region strategy, and failure handling.
Monitoring and Operations
- SLIs/SLOs, alerts, tracing, data quality checks, and runbooks.

State reasonable assumptions where needed to make the problem concrete.

Quick Overview

Background

Assume the service primarily supports read traffic (10k QPS) with occasional corrections/backfills from upstream providers. Historical rates are mostly immutable but can be revised.

Requirements

Describe a design that covers:

APIs

Read endpoints for point-in-time and time-series queries.
Optional write/ingestion endpoints for internal data pipelines.
Versioning, pagination, auth, rate limits, and error handling.

Data Model

Entities, fields (e.g., pair, timestamp, rate types), precision, versioning for corrections, and indexing.

Storage Layer

Hot vs. cold storage choices; partitioning/sharding; replication; retention.

Caching Strategy

Layers (in-process, distributed, CDN if applicable), keys, TTLs, invalidation, and precomputation.

Consistency Requirements

Freshness expectations, handling of corrections/backfills, snapshot semantics, and trade-offs (strong vs. eventual consistency).

Scalability Plan

Capacity estimates for 10k QPS, autoscaling, multi-region strategy, and failure handling.

Monitoring and Operations

SLIs/SLOs, alerts, tracing, data quality checks, and runbooks.

State reasonable assumptions where needed to make the problem concrete.

Historical FX-Rate Service – System Design

Quick Overview

System Design: Historical FX Rates Service (10k QPS)

Background

Requirements

Solution

Comments (0)

Historical FX-Rate Service – System Design

Quick Overview

System Design: Historical FX Rates Service (10k QPS)

Background

Requirements

Solution

Comments (0)