Reactive Spring Boot with WebFlux and R2DBC

Requirements

x86-64
Linux/Unix
Docker
JDK 17
Maven

Startup

The script "up" creates our database container and starts up our application:

1. docker-compose -f docker/cars/docker-compose.yml up -d
2. mvn spring-boot:run

The associated docker-compose also contains initialization scripts for creating our database and inserting test rows.

Shutdown

The script "down" removes our database container:

1.docker-compose -f docker/cars/docker-compose.yml down

Load Testing using Bombardier

In this project we utilize Bombardier to conduct load-testing.

The subject of our tests is the /car-details endpoint, which is responsible for retrieving various vehicle-related data from different repositories and responding with an aggregate object.

Test 1: 10 Concurrent Connections, 1000 Requests (Baseline)

Command: bombardier -m GET localhost:8080/car-details/1 -c 10 -n 1000
Statistics:
  Reqs/sec: 509.64
  Latency: 21.40ms (Avg)
  HTTP codes: 2xx - 1000
  Throughput: 392.74KB/s

Test 2: 100 Concurrent Connections, 1000 Requests

Command: bombardier -m GET localhost:8080/car-details/1 -c 100 -n 1000
Statistics:
  Reqs/sec: 844.22 (↑65.48% from Test 1)
  Latency: 116.03ms (↑440.74% from Test 1)
  HTTP codes: 2xx - 1000
  Throughput: 713.68KB/s (↑81.26% from Test 1)

Test 3: 1000 Concurrent Connections, 2000 Requests

Command: bombardier -m GET localhost:8080/car-details/1 -c 100 -n 1000
Statistics:
  Reqs/sec: 770.96 (↑51.27% from Test 1)
  Latency: 1.17s (↑5392.52% from Test 1)
  HTTP codes: 2xx - 2000
  Throughput: 633.57KB/s (↑61.06% from Test 1)

Test Result Analysis

Baseline (C10, N1000)

The baseline test (Test 1), which serves as our reference point, simulates a moderate load with 10 concurrent connections and 1000 requests. In this scenario:

The application achieved a request rate of 509.64 requests per second.
The average latency was measured at 21.40ms.
All 1000 requests resulted in successful HTTP 2xx responses.

Test 2 (C100, N1000)

In Test 2, where 100 concurrent connections and 1000 requests were applied:

The request rate increased significantly by 65.48% compared to the baseline (Test 1), suggesting improved scalability.
Throughput also showed a notable increase of 81.26% compared to the baseline.
However, the average latency experienced a substantial rise of 440.74% from the baseline, indicating a trade-off between response rate and response time.

Test 3 (C1000, N2000)

In Test 3, with 1000 concurrent connections and 2000 requests:

The request rate maintained a relatively high level, increasing by 51.27% compared to the baseline.
The average latency saw a dramatic increase of 5392.52% compared to the baseline, suggesting significant delays in response times.
Throughput showed a considerable increase of 61.06% compared to the baseline.
This test also successfully processed all 2000 requests with HTTP 2xx responses.

Comparison of test #3 with imperative Spring Boot

This section is dedicated to the comparison of our application with a more traditional, imperative and thus blocking Spring Boot application utilizing JDBC.

Traditional, blocking Spring Boot with JDBC

Command: bombardier -m GET localhost:8080/car-details/1 -c 1000 -n 2000
Statistics:
  Reqs/sec: 98.17
  Latency: 8.72s
  HTTP codes: 2xx - 362, Errors: Timeout - 1638
  Throughput: 43.78KB/s