How would you assess and improve a service’s performance under high load? Define the key SLIs/SLOs you would set (e.g., latency percentiles, throughput, error rate), describe how you would instrument and profile the system, outline a load-testing plan, identify likely bottlenecks (CPU, I/O, database, cache, network), and propose concrete optimizations with trade-offs.

This question evaluates performance engineering and observability skills—specifically SLIs/SLOs definition, instrumentation and profiling, load-testing, bottleneck identification, and optimization trade-offs for stateless HTTP microservices.

How do I approach System Design interview questions?

System Design questions require understanding of core concepts and practice. PracHub provides solutions with explanations to help you master system design interviews.

What difficulty level is this interview question?

This is a hard difficulty System Design question, commonly asked during Technical Screen rounds at TikTok.

What role is this question designed for?

This question is commonly asked for Software Engineer candidates at TikTok during technical interviews.

Improve and measure service performance | TikTok Interview Question

Assessing and Improving a Service’s Performance Under High Load

Context

You are responsible for a user-facing, stateless HTTP microservice that handles read-heavy traffic with bursty patterns (e.g., push-notification spikes). The service depends on a cache (e.g., Redis) and a database (e.g., MySQL/Postgres), is deployed behind a load balancer, and auto-scales.

Define how you would assess and improve this service’s performance under high load. Address the following:

SLIs/SLOs

Define the key Service Level Indicators (SLIs) and Service Level Objectives (SLOs), including latency percentiles, throughput, error rate, availability, and saturation.

Instrumentation and Profiling

Describe how you would instrument the service (metrics, traces, logs) and how you would profile it (CPU, memory, locks, I/O) to find hotspots.

Load-Testing Plan

Outline a realistic load-testing plan: workload modeling, test types (ramp, spike, soak, stress), environment setup, data representativeness, and pass/fail criteria.

Likely Bottlenecks

Identify common bottlenecks under high load (CPU, I/O, database, cache, network, GC, locks, pools) and symptoms you’d look for.

Concrete Optimizations

Propose specific optimizations and discuss trade-offs (e.g., caching strategies, query/index tuning, batching, backpressure, retries, timeouts, concurrency control, network/protocol tuning, autoscaling).

Make any minimal assumptions you need and be explicit about them.

Context

Define how you would assess and improve this service’s performance under high load. Address the following:

SLIs/SLOs

Define the key Service Level Indicators (SLIs) and Service Level Objectives (SLOs), including latency percentiles, throughput, error rate, availability, and saturation.

Instrumentation and Profiling

Describe how you would instrument the service (metrics, traces, logs) and how you would profile it (CPU, memory, locks, I/O) to find hotspots.

Load-Testing Plan

Outline a realistic load-testing plan: workload modeling, test types (ramp, spike, soak, stress), environment setup, data representativeness, and pass/fail criteria.

Likely Bottlenecks

Identify common bottlenecks under high load (CPU, I/O, database, cache, network, GC, locks, pools) and symptoms you’d look for.

Concrete Optimizations

Propose specific optimizations and discuss trade-offs (e.g., caching strategies, query/index tuning, batching, backpressure, retries, timeouts, concurrency control, network/protocol tuning, autoscaling).

Make any minimal assumptions you need and be explicit about them.

Improve and measure service performance

Quick Overview

Assessing and Improving a Service’s Performance Under High Load

Context

Solution

Comments (0)

Improve and measure service performance

Quick Overview

Assessing and Improving a Service’s Performance Under High Load

Context

Solution

Comments (0)