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Waymo Common Problems

Simulation Logging and Statistics System Design

System DesigneasyLast reported April 2026
By AceOffer · Updated April 2026 · Reported 1× across 190+ candidate reports

Understanding the Problem

Design a data system to collect logs produced by autonomous driving simulations and aggregate/compute statistics from those logs. The system must handle the high volume of simulation-generated data. An embedded follow-up asks: given that simulation can generate abundant data but real-world on-vehicle test data is much more limited, how do you reconcile or bridge the two data sources when computing meaningful statistics?

Functional Requirements

Structured requirements coming soon. For now, see the full problem statement above and the deep-dive prompts below.

Non-Functional Requirements

Latency, throughput, availability, consistency targets — being authored.

The Set Up

Defining the Core Entities

Core entities (Request, Batch, Worker, Cache, etc.) — being authored.

The API

POST /endpoint → describe request shape GET /endpoint → describe response shape (API spec being authored)

High-Level Design

Component diagram + walkthrough mapping each functional requirement to a system flow — being authored.

Potential Deep Dives

These are the directions the interviewer is likely to push you. Each one has multiple valid solutions at different quality tiers.

1)Simulation can generate far more data than real-world on-vehicle testing. How do you handle the statistical imbalance and ensure your aggregated metrics are meaningful for real-world performance? (when: After candidate sketches the basic pipeline)

Bad
Naive approach with serious trade-off — being authored.
Good
Solid baseline with reasonable trade-offs — being authored.
Great
Production-grade approach with explicit trade-off rationale — being authored.

What is Expected at Each Level?

L4 / Mid-level
Cover happy path. Clarify scope. Identify the obvious bottleneck. Pick a reasonable storage and reasonable scaling approach.
L5 / SeniorTarget
All of the above plus: explicit failure handling, durability vs latency trade-offs, choose the right batching/caching strategy, articulate why.
L6 / Staff+
All of the above plus: organizational concerns (rollout, migration, on-call), quantitative analysis, multi-region considerations, what could go wrong with the proposed solution at 10x scale.

Insider Notes

**Common mistakes:** Focusing only on storage/ingestion without addressing how statistics are computed or served; Ignoring the sim-to-real gap sub-problem entirely; Not defining the schema/structure of simulation log events before designing the pipeline **What passers do:** Explicitly separating the ingestion, storage, computation, and serving layers; Proactively raising the sim-to-real gap issue and proposing concrete reconciliation mechanisms (importance weighting, source tagging) **Why people fail:** Treating it as a generic log aggregation problem without addressing autonomous-driving-specific concerns; Struggling to articulate how to handle the data imbalance between simulation and real-world sources **Edge cases probed:** Sim vs. real data volume imbalance and how it skews aggregate statistics; Schema evolution of log formats across simulation versions; Handling partial or corrupted simulation runs in aggregation **Alternative approaches:** Lambda architecture (batch + speed layer) (Provides both real-time and historical aggregation but increases operational complexity with two code paths to maintain.); Pure streaming with stateful aggregation (Flink/Dataflow) (Lower latency for live simulation runs; harder to reprocess historical logs and manage exactly-once semantics at scale.); Offline-only batch pipeline (Spark on object storage) (Simpler to implement and reprocess, but no real-time visibility into ongoing simulation campaigns.)
Waymo · System Design · Last reported April 2026