From one server to many: scaling and load balancing

Two ways to grow

When one server is not enough you have two options.

Image: Derrick Coetzee · CC0 · Source · Wikimedia Commons: Technician with laptop working on server rack at NERSC

• Vertical scaling ("scale up"): use a bigger machine with more CPU, memory or disk. It is simple because nothing in the design changes.
• Horizontal scaling ("scale out"): add more machines and share the work between them. It needs more design effort but has no hard ceiling.

	Vertical	Horizontal
Effort	Low	Higher
Ceiling	Hardware limit	Practically none
Failure	One machine is a single point of failure	Survives losing a machine
Cost curve	Gets steep for the largest machines	Roughly linear with commodity machines

Real systems use both: scale up until it gets expensive or risky, then scale out.

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Figure SDA-FND-02-1 · Scale up versus scale out · © forcsguy.com

What a load balancer does

Once you have several servers, something must decide which one handles each request. That is the load balancer.

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Figure SDA-FND-02-2 · Load balancer in front of a server pool · © forcsguy.com

It does three jobs:

1. Distributes requests so no server is overloaded.
2. Checks health. It probes each server regularly and stops sending traffic to ones that fail.
3. Hides the pool. Clients talk to one address, and servers can be added or removed without clients noticing.

Common strategies

• Round robin: take turns. Simple and fine when requests are similar.
• Least connections: send to the server with the fewest active requests. Better when request cost varies.
• Hashing (for example on user id or IP): the same key goes to the same server. Useful when a server keeps a local cache for that key.
• Weighted: bigger machines get a bigger share.

Load balancers work at different layers. A layer 4 balancer routes by IP and port and is very fast. A layer 7 balancer reads the HTTP request, so it can route by URL path or header and terminate TLS.

A security benefit comes with this layout: only the balancer needs a public address. The servers behind it use private addresses, reachable from inside the network but not from the internet, so clients cannot bypass the balancer or attack a server directly. If one server goes offline, the balancer sends its traffic to the others and a replacement can join the pool.

Stateless servers make scaling easy

A server is stateless when it keeps nothing about a user between requests. Everything it needs comes from the request or from a shared store.

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Figure SDA-FND-02-3 · Stateful versus stateless servers · © forcsguy.com

With stateless servers any request can go to any server, so you can add servers at will, replace a failed one without losing data, and run auto-scaling. The state does not disappear. It moves to a database, a cache or a session store, which you then have to scale and protect, which is what the next lessons are about.

Putting it together

A typical first scalable layout:

1. Users reach a load balancer.
2. It spreads requests over a pool of stateless application servers.
3. Servers read and write a shared database (and later a cache).
4. Static files such as images and scripts are served from a CDN so the servers do not carry that traffic (lesson 9).

This design handles a surprisingly large amount of traffic. The first thing to give way is usually the database, which is where we go next.

A worked example: how many servers do you need?

Capacity planning is arithmetic you can do on a napkin. Say your product has 5 million daily users and each makes about 20 requests a day.

1. Requests per day: 5,000,000 x 20 = 100 million.
2. Per second on average: 100,000,000 / 86,400 = about 1,160 requests per second.
3. Traffic is not flat. A common rule of thumb is that the busiest second is 2 to 3 times the average, so plan for about 3,500 per second.
4. One server handles about 400 requests per second flat out, but you never want servers at 100%: queues build and latency explodes near full load. Plan for roughly 70% utilisation, so 280 requests per second each.
5. 3,500 / 280 = 12.5, so 13 servers carry the peak.
6. Add spare capacity so you survive failures: with 13 servers, losing 2 leaves 11, which carry only 3,080 requests per second. Plan for N+2: 15 servers.

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Figure SDA-FND-02-4 · From users to a server count · © forcsguy.com

The same method works for any tier. Only the "requests per server" number changes, and you should measure it with a load test rather than guess.

Choosing a balancing method with real numbers

Round robin is fair in the number of requests, not in the work. Imagine three servers and a stream where most requests take 10 ms but every fifth takes 1 second (a report export, say). Round robin gives each server the same count, but by chance one server collects several slow requests at once, its queue grows, and its users wait while the other two are nearly idle.

Least connections notices: the server with the most open requests gets fewer new ones, so the slow requests spread out as they arrive. It costs the balancer a little bookkeeping and helps most when request costs vary a lot. Use plain round robin when requests are similar and servers identical; use least connections, or "power of two choices" (pick two servers at random and send to the less busy one), when they are not. For caches and stateful workloads, consistent hashing keeps the same key on the same server so its memory stays useful.

Health checks and draining

A balancer is only as good as its knowledge of which servers are alive.

• Active health checks call a lightweight endpoint (for example /health) every few seconds. After two or three failures in a row, the server leaves rotation; after a few successes it returns. Make the check reflect real readiness: a server that is up but cannot reach its database should fail the check.
• Passive checks watch real traffic: too many errors or timeouts from one server and it is ejected.
• Connection draining matters at deploys. Before stopping a server, tell the balancer to send it no new requests, let in-flight requests finish (typically 30 to 60 seconds), then stop it. Without draining, every deploy drops a few user requests.

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Figure SDA-FND-02-5 · Rolling out a new version without dropping requests · © forcsguy.com

Mistakes people make

• Treating the balancer as magic. It is a server too and can be a single point of failure. Run at least two, or use a managed one.
• Adding servers behind a single database. App servers scale out easily; if the database is the bottleneck, more app servers only make it fail faster.
• Keeping state on the server. Sessions, uploads and caches held in one server's memory break the moment a request lands elsewhere. Move them to a shared store.
• Sizing for the average. The system fails at peak, which is when it matters.
• Never testing failure. Kill a server during a load test. If users notice, you have found a gap before they did.

Try it yourself

Open the System Builder challenge "One server is not enough". Run the test on one server and watch it fail, add a load balancer without adding servers, then add servers and see where the numbers settle. Or skip the challenge and open a load-balanced system on the free canvas, then change the wiring yourself.

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