Full-Chain Load Testing Theory, Model Design (DESP) and New Oriental Continuation Class Case Study

What is Full‑Chain Load Testing

Full‑chain load testing simulates massive user requests based on real production scenarios, system environments and actual data to stress the entire business chain, continuously tuning performance; its core consists of business scenarios, data links, pressure models and environment topology, and it encompasses automation, performance analysis, scaling solutions, and more.

Why Perform Full‑Chain Load Testing

High‑traffic services are difficult to stabilize; large distributed micro‑service systems have complex call chains; test environments often differ from production in version, data, and configuration, making it impossible to accurately predict real‑world performance from isolated module tests.

Suitable Scenarios for Full‑Chain Load Testing

Regular online promotional activities.

Complex business and data call chains that are core to the system.

Significant growth in real traffic compared to historical patterns.

Frequent business iteration causing performance fluctuations.

Test environments that cannot match production resources.

Full‑Chain Load Testing Model Design (DESP)

The DESP model abstracts four dimensions: Data Simulation (D), Environment Simulation (E), Scenario Simulation (S), and Pressure Simulation (P). Optimizing these dimensions determines the overall fidelity of the test.

Data Simulation (D)

Test data includes background data (historical/stock data) and request parameter data (masked production data or constructed per scenario). Both data scale and complexity affect performance; for example, varying the number of classes per student dramatically changes load.

Environment Simulation (E)

The test environment should closely mirror production, preferably by testing online with traffic isolation; if offline, resources and topology should be proportionally scaled to production.

Scenario Simulation (S)

Identify core business chains, define all involved interfaces, call modes (sync/async, sequential/parallel) and parameters; focus on high‑frequency core paths while deprioritizing low‑frequency operations.

Pressure Simulation (P)

Define load parameters such as concurrency, target TPS, think time, warm‑up duration, and test length; choose between concurrent‑user mode or TPS‑target mode based on testing goals.

Case Study: New Oriental Continuation‑Class System

1. System Overview

The continuation‑class workflow involves account validation, student and class checks, rule verification, discount calculation, and order processing.

2. Test Scenario

Identify core business chain and related services.

Build a dedicated testing channel to isolate traffic and data.

Mark test traffic via custom headers and propagate the marker through downstream services.

3. Test Data

Two data sources are used: automatically generated test accounts (with real‑like class and rule data) and extracted real user traffic for replay. Primitive data includes accounts and students (both virtual and real); business data includes classes, continuation rules, discount rules, and linked data constructed automatically.

4. Load Design

Analyze production logs to determine peak concurrency and request distribution, then construct load profiles that reflect real traffic ratios, including full‑volume cart data simulation for peak periods.

5. Automation

Baseline scenarios are triggered automatically via a task scheduler, with data construction, trial runs, custom tasks (e.g., data warm‑up), and result comparison; alerts are sent via email or DingTalk when performance deviates.

Full‑Chain Load Testing Platform Construction

System Architecture

Key Features

Supports HTTP/HTTPS, online script creation and debugging.

One‑click test data generation.

Flexible test modeling.

Unified load‑generator management.

Real‑time monitoring, alerting, and circuit breaking.

Result aggregation and performance baseline library.

Task‑driven baseline monitoring.

Recruitment

The testing team is expanding and seeks Java developers or performance testing engineers. Interested candidates can send resumes to [email protected] . Contact: Zheng Huaining.