Stop Chaining HTTP Requests. It’s 2025.

I still see it. Microservices calling other microservices synchronously via REST. Service A calls Service B, which calls Service C. If Service C hiccups, the whole chain collapses, and your checkout page spins until the browser times out. We called this "distributed monoliths" back in 2018, and it’s still a plague today.

You need an event-driven architecture. You know this. But the thought of managing a Kafka cluster—with or without ZooKeeper—makes you want to quit tech and become a goat farmer in Telemark. I get it. The JVM overhead, the memory requirements, the operational complexity... it's overkill for 95% of use cases.

Enter NATS JetStream. It’s the answer to "I want persistence and at-least-once delivery, but I don't want to need a PhD in Java garbage collection tuning."

Why NATS JetStream?

NATS started as a "fire and forget" messaging system. It was fast—blazingly fast—but if no one was listening, the message was gone. JetStream changed the game by adding persistence. It writes messages to disk (streams) so consumers can replay them later. It competes directly with Kafka and RabbitMQ but runs as a single static binary of about 20MB.

The CoolVDS Edge: JetStream relies heavily on write speed for persistence. If your underlying storage is spinning rust or throttled network storage, your message throughput tanks. We equip every CoolVDS instance with enterprise-grade NVMe storage specifically to handle high-IOPS workloads like stream ingestion.

Scenario: The Norwegian Order Processor

Let's build a system where an order comes in and needs to be processed by shipping, invoicing, and analytics. We want these to be decoupled. If the invoicing service is down for maintenance, the order shouldn't fail; the event should sit in the stream until the service comes back online.

1. The Infrastructure Setup

First, we need a solid foundation. Since we are dealing with customer data (orders), we need to worry about GDPR and Schrems II. Hosting this on a US hyperscaler is a legal headache you don't need. A CoolVDS instance in Oslo keeps the data within Norwegian jurisdiction.

Deploying NATS Server (v2.10.x):

Don't use Docker for the stateful core unless you have persistent volumes tuned perfectly. Bare metal or high-performance VPS is better for the broker.

# Download the latest release (approximate for mid-2025)
wget https://github.com/nats-io/nats-server/releases/download/v2.10.14/nats-server-v2.10.14-linux-amd64.zip
unzip nats-server-v2.10.14-linux-amd64.zip
cp nats-server /usr/local/bin/

2. Configuration: Enabling JetStream

Create a configuration file `nats.conf`. This is where we define the storage directory. On CoolVDS, map this to your NVMe mount point.

# nats.conf
server_name: "norway-broker-01"
listen: 0.0.0.0:4222

# JetStream Configuration
jetstream {
    store_dir: "/var/lib/nats-data"
    max_mem: 1G
    max_file: 10G
}

# Security (Simplified for demo)
authorization {
    token: "s3cr3t_token_for_internal_services"
}

Create the systemd service. We want this to survive reboots.

[Unit]
Description=NATS Server
After=network.target

[Service]
Type=simple
ExecStart=/usr/local/bin/nats-server -c /etc/nats/nats.conf
Restart=always
RestartSec=3
User=nats
LimitNOFILE=8192

[Install]
WantedBy=multi-user.target

3. Defining the Stream

You don't need to write code to define infrastructure. Use the `nats` CLI tool. It’s a lifesaver.

# Install CLI
curl -sf https://binaries.nats.dev/nats-io/nats-by-example/info@latest | sh

# Create the ORDERS stream
# We capture all subjects matching 'orders.>'
nats stream add ORDERS \
  --subjects="orders.>" \
  --storage=file \
  --replicas=1 \
  --retention=limits \
  --max-msgs=100000 \
  --discard=old \
  --server=localhost:4222

We chose `file` storage. This is where your disk I/O matters. If you are on a cheap VPS with shared HDD, your `Ack` latency will spike. On CoolVDS NVMe, this operation is effectively instant.

4. The Publisher (Golang)

Here is how a clean, battle-tested producer looks in Go. We use the NATS Go client. Note the use of `PublishAsync` for performance, but we handle the future to ensure the broker actually received it.

package main

import (
	"context"
	"log"
	"time"

	"github.com/nats-io/nats.go"
	"github.com/nats-io/nats.go/jetstream"
)

func main() {
	// Connect to the local CoolVDS NATS instance
	nc, err := nats.Connect("nats://127.0.0.1:4222", nats.Name("OrderService"))
	if err != nil {
		log.Fatal(err)
	}
	defer nc.Close()

	// Create JetStream Context
	js, _ := jetstream.New(nc)

	ctx, cancel := context.WithTimeout(context.Background(), 10*time.Second)
	defer cancel()

	// Publish an order event
	// Subject structure: orders.{region}.{type}
	_, err = js.Publish(ctx, "orders.oslo.created", []byte(`{"order_id": 101, "amount": 599}`))
	if err != nil {
		log.Fatal("Failed to publish order:", err)
	}

	log.Println("Order 101 published to JetStream via NVMe storage.")
}

5. The Durable Consumer

This is critical. If your consumer crashes, it must pick up where it left off. We use a "Durable" consumer name.

func consume() {
	nc, _ := nats.Connect("nats://127.0.0.1:4222")
	js, _ := jetstream.New(nc)
	ctx := context.Background()

    // Create a consumer that remembers its state
	consumer, _ := js.CreateOrUpdateConsumer(ctx, "ORDERS", jetstream.ConsumerConfig{
		Durable:   "InvoiceProcessor",
		AckPolicy: jetstream.AckExplicitPolicy,
	})

	iter, _ := consumer.Messages()
	for {
		msg, err := iter.Next()
		if err != nil { break }

		log.Printf("Processing Invoice for Order: %s", string(msg.Data()))

		// Simulate processing
		time.Sleep(50 * time.Millisecond)

		// Acknowledge. Only then does NATS know we are done.
		msg.Ack()
	}
}

Latency, Geography, and NIX

Why host this in Norway? Speed and Law. If your consumer is in Oslo and your broker is in Frankfurt, you are adding 20-30ms of round-trip time (RTT) to every message acknowledgment. In high-throughput systems, that latency accumulates.

CoolVDS peers directly at NIX (Norwegian Internet Exchange). If your customers are Norwegian businesses, the latency is practically zero (1-2ms). This tight loop allows NATS to process tens of thousands of messages per second without the network becoming the bottleneck.

The Verdict

Kafka is great if you are LinkedIn. For the rest of us building efficient, compliant systems in 2025, NATS JetStream is the superior choice. It lowers your TCO (Total Cost of Ownership) because it uses less CPU and RAM, meaning you can fit it on a smaller VPS.

However, software efficiency implies hardware competence. NATS JetStream will expose slow I/O immediately. Don't cripple it with budget hosting.

Ready to build a resilient event mesh? Don't let slow I/O kill your message throughput. Deploy a high-performance VPS Norway instance on CoolVDS in 55 seconds and see the difference NVMe makes.