Load Balancing¶

Prerequisites: HTTP & TCP, Consistent Hashing

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Why This Exists¶

You have three app servers behind api.shop.com. Friday 19:12, N0 dies.

Three questions interviewers (and 3 a.m. pages) actually ask:

How does the load balancer know? It does not. It only knows what its health check last observed — a failed TCP connect, a timeout, a non-200 on /health, or a connection reset mid-request.
What happens to in-flight requests? They error. The LB cannot teleport those TCP connections to N1. The client sees a reset or a 502. If the request was not idempotent, a blind retry may double-charge.
What about sessions stored in N0's memory? They are gone. Sticky sessions (cookie or IP affinity) now pin the user to a corpse until the affinity expires or the LB forgets the dead member.

A load balancer exists to hide a pool of backends behind one VIP. Everything interesting is what it does when the pool is uneven, unhealthy, or stateful.

Mental Model

A nightclub bouncer with a clipboard. People (requests) arrive at one door. The bouncer picks a room (backend) using a rule. If a room's lights go out, the bouncer stops sending people — but only after the next walk-by. Anyone already inside is on their own.

Abstraction Levels¶

Mental ModelInterview SimplificationProduction RealityWhere This Stops Being True

One VIP, a pool of backends, a health check that is always slightly stale, and a rule for picking a room.

"L4 for raw TCP, L7 for HTTP routing; least-conn or consistent hash for sticky-ish traffic; take dead nodes out via health checks." Fine as the opening.

In-flight requests to a dead node error — the LB does not teleport TCP. Sticky sessions pin users to a corpse until the cookie dies. Least-conn lies if connections are long-lived (gRPC, websockets) and cheap.

When the bottleneck is a key (one tenant) not a node, adding backends does nothing. When health checks hit a shared dependency, the whole pool goes unready together. DNS round-robin is not a load balancer.

Architecture¶

flowchart LR
    C[Clients] --> DNS[DNS A/AAAA]
    DNS --> VIP[LB VIP]
    VIP --> HC{Health check}
    HC -->|pass| N0[N0]
    HC -->|pass| N1[N1]
    HC -->|fail| N2["N2 DEAD"]
    N0 --> App[(App + local state)]
    N1 --> App
    style N2 fill:#b71c1c,color:#fff
    style VIP fill:#6a1b9a,color:#fff

sequenceDiagram
    participant C as Client
    participant LB
    participant N0
    participant N1
    C->>LB: GET /checkout
    LB->>N0: proxy (least-conn)
    N0-->>LB: 200
    LB-->>C: 200
    Note over N0: N0 kernel panic
    LB->>N0: GET /health
    N0--xLB: timeout
    Note over LB: 2–3 failed probes → OUT
    C->>LB: GET /checkout (in-flight still on N0)
    LB--xC: 502 / RST
    C->>LB: retry GET /checkout
    LB->>N1: only healthy member
    N1-->>C: 200

Interactive Simulation¶

Three nodes. Change the algorithm. Kill N0. Watch new requests skip it and in-flight evaporate.

Load Balancer Visualizer

Algo

rr

Nodes

3

Routed

0

Try: Run → Kill N0. Then switch to consistent hash and add a node — only a slice of keys should move, unlike round robin which immediately reshuffles everyone.

Run it yourself

labs/load-balancer is a real nginx upstream block in front of three real backends (one deliberately slow) — swap round robin for weighted or least-connections by editing config and reloading, kill a backend and watch nginx route around it with zero client-visible errors, and measure how least_conn actually treats the slow backend differently under concurrent load.

Algorithms¶

Algorithm	Rule	Good for	Breaks when
Round robin	Next healthy member, cycle	Homogeneous, stateless, similar latency	One box is slower — it still gets 1/N
Weighted RR	Cycle, but bigger boxes appear more often	Mixed instance sizes	Weights stale after a noisy neighbor
Random	Pick uniformly	Cheap, surprisingly even at high QPS	Same as RR under skew; no connection awareness
Least connections	Lowest outstanding requests	Long-lived or uneven request cost	Slow node accumulates conns and still looks "busy" correctly — until health checks lag
Least latency	EWMA of response time	Geo-uneven pools, mixed hardware	Measurement is the product; a cached 2ms path starves a correct-but-slower shard
Consistent hash	`hash(key)` on a ring → same backend for same key	Caches, sticky-without-cookies, gRPC streams	Hot key pins one node; membership change remaps K/N

Interview Insight 🎯

Least connections is not "smarter round robin." It is a closed feedback loop. A backend that starts swapping will hold connections longer, receive fewer new ones, and may recover. A backend that fails open (accepts then hangs) looks idle after those conns time out — unless your health check hits the same hang.

L4 vs L7¶

flowchart TB
    subgraph L4["L4 — TCP/UDP"]
      A1[See 5-tuple] --> A2[Splice or NAT to backend]
      A2 --> A3[Cannot read Host / path / cookie]
    end
    subgraph L7["L7 — HTTP/gRPC"]
      B1[Terminate TCP/TLS] --> B2[Read method, path, headers]
      B2 --> B3[Route, auth, retry, WAF]
    end

	L4 (NLB, IPVS, Maglev)	L7 (ALB, Envoy, nginx, HAProxy HTTP)
Sees	IP, port, protocol	HTTP/gRPC after TLS terminate (or with TLS inspect)
Cost	Cheap; millions of flows	Decrypt + parse; CPU-bound
Routing	Per connection	Per request (`/payments` vs `/search`)
Retries	Dangerous (may replay bytes)	Possible if method is idempotent / has idempotency key
Headers	Cannot add `X-Request-Id`	Can
Timeouts	Idle TCP	Idle + request + upstream + stream

Local vs global:

Local (regional) LB — one VIP per AZ/region, cheap, low RTT. A regional outage takes the VIP with it.
Global (GSLB / anycast) — DNS or anycast steers to the nearest healthy region. Failover is DNS TTL + resolver cache (seconds to minutes) or BGP withdrawal (faster, but coarser). Users with a sticky resolver cache will keep hammering a dying region.

How the LB Knows a Server Is Dead¶

Health checks are the entire control plane for "who is in the pool."

Passive — observe real traffic: connect errors, 5xx, latency outliers. Fast, but a bad deploy that returns 200 with empty bodies looks healthy.
Active — periodic probe (GET /healthz, TCP connect, gRPC health). Independent of user traffic. Slow: 3 fails × 5s interval = 15s of 502s after a crash.
Out-of-band — service discovery (K8s Endpoints, Consul). The LB removes a pod when readiness fails, before it is dead — if you wired readiness correctly.

Concrete timeline for 3 servers, N0 dead:

t	What is true	What clients see
0	N0 kernel panic	In-flight to N0: RST / timeout
0–5s	LB still routes new work to N0 (last probe was OK)	~1/3 of new requests fail
5–15s	Failed probes accumulate	Same, plus retries amplify
15s	N0 marked down	Traffic on N1, N2. Capacity −33%
later	N0 sticky cookies still sent	LB must ignore affinity for down members or users loop 502

Local sessions on N0: shopping carts, WebSocket rooms, in-memory rate-limit counters — gone. Design: store session in Redis / the client, or accept reconnect.

Sticky Sessions¶

See Stateless vs Stateful Applications for why this trade-off exists in the first place. Affinity maps a client to one backend:

Cookie (AWSALB, SRV) — L7 inserts a cookie. Survives NAT. Breaks if the box dies and you don't fall back.
IP hash — L4, hash(client_ip) % N. Collapses behind CGNAT / corporate egress. One IP = millions of mobiles.
Consistent hash on a request key — user_id or session_id. Same as cache sharding; membership change remaps a slice.

Production Trap

Sticky sessions are a crutch for server-side memory. They turn a stateless pool into N tiny monoliths. Autoscale, deploys, and AZ loss all become user-visible. Prefer externalizing state. If you must stick (WebSockets, gRPC streams), plan drain: stop new affinity, wait in-flight, then terminate.

Failure Modes¶

Dead backend, live VIP¶

Symptom: 502/504 spike at ~1/N of traffic, then recovery after probe threshold
Impact: Error budget, retry storms onto remaining boxes
Detection: LB target UnHealthyHostCount, backend 5xx vs LB 5xx
Mitigation: Fast probes, fail-open vs fail-closed policy, capacity headroom ≥ 1 AZ

Slow backend (not dead)¶

Symptom: p99 explodes; least-conn helps, RR hurts
Impact: Thread/connection pool exhaustion on callers
Detection: Per-target latency histogram, not just average
Mitigation: Outlier ejection (Envoy), request hedging on idempotent reads, separate pools for slow tenants

All health checks fail¶

Symptom: Empty pool. Fail-closed → 503 for everyone. Fail-open → traffic to corpses.
Impact: Total outage vs serving garbage
Detection: healthy_hosts == 0 page
Mitigation: Last-known-good, panic threshold (keep last N), synthetic canary that is not the user path

Connection imbalance after deploy¶

Symptom: New pods idle; old pods hold keep-alives for minutes
Fix: Max connection age, graceful drain, Connection: close on shutdown, L7 request balancing not L4 connection balancing

Production Debugging¶

Symptom: 10% 502s after a deploy, then 33% after one AZ blip

1. Split LB 5xx vs backend 5xx
   → ALB "ELB 5XX" vs target 5XX. Different owners.
2. Healthy host count by AZ
   → one AZ at 0? subnet / SG / NACLs, not the app.
3. Target response time vs request count
   → slow vs dead vs never registered.
4. Drain / deregistration delay
   → in-flight killed at SIGTERM? need preStop + sleep ≥ deregistration_delay.
5. Sticky cookie after kill
   → curl -c / -b and watch if you keep hitting the same target.
6. DNS
   → are you even hitting this LB? stale CNAME, two VIPs, IPv6-only client.

Metrics: RequestCount, HTTPCode_ELB_5XX, HealthyHostCount, TargetResponseTime p50/p99, active connection count per target, retry rate.

Scaling Limits¶

L4 Maglev/IPVS: millions of flows; limited by state table and NIC PPS, not HTTP parsing.
L7 Envoy/nginx: CPU for TLS + HPACK + filters. Rule of thumb: a few 10k–100k RPS per fat instance, then scale the proxy tier.
Cross-AZ bytes are a bill. Keep LB and backends in-AZ when you can; use a regional VIP only if you accept the tax.
Global DNS failover cannot beat TTL. Anycast can, but you debug BGP instead of nginx.

Trade-offs¶

Dimension	Round robin	Least conn	Consistent hash	Sticky cookie
Evenness	Good if work is equal	Best for mixed duration	Even keys, not even load	Worst (pinned)
Failover	Next tick	Next tick	Remap slice	Until cookie dies
Cache locality	None	None	Excellent	Accidental
Complexity	Low	Medium	Medium	High (state)
Use	Stateless HTTP	Mixed RPC	Caches, streams	Legacy sessions

Interview Questions¶

FoundationSeniorStaff

Q: Three servers, one dies. How does the load balancer know?

"It doesn't instantly. An active health check — TCP connect or HTTP GET /health — fails a few times in a row, then the target is pulled from the pool. Passive checks can react to real 5xx/timeouts faster. In-flight requests on the dead box fail; the LB cannot migrate a TCP connection. New requests go to the remaining two. If we used sticky sessions pointing at the dead box, those clients keep failing until affinity is dropped."

Q: When do you choose L4 vs L7? When consistent hashing vs least connections?

"L4 when I need raw PPS, TLS passthrough (end-to-end to the app), or UDP. L7 when I need path-based routing, authn at the edge, per-request retries, or header injection. Least connections when request cost varies and backends are interchangeable. Consistent hashing when I want the same key on the same box — cache hit rate, or long-lived streams — and I accept remapping K/N on membership change. I never hash on client IP behind NAT."

Q: We need 'zero-downtime' deploys and an AZ loss without dropping in-flight checkouts. Design the LB policy.

"Three layers. (1) Capacity: N+1 AZ, each AZ can take 50% extra; autoscale on in-flight, not CPU only. (2) Drain: pod/instance gets SIGTERM → fail readiness immediately so the Service/LB stops new work → sleep ≥ deregistration delay → finish in-flight with a long enough graceful period. Checkout must be idempotent (Idempotency-Key) so a 502 can retry once. (3) State: no checkout session in process memory; payment intents live in the DB. Health checks hit a shallow /ready that verifies the process loop, not a deep dependency — otherwise a shared Redis blip removes the entire fleet. I'd also set a panic threshold so we never go to zero hosts because the health endpoint itself is wrong. Measure success as 'in-flight checkout completion rate during deploy,' not 'HTTP 200 on /health.'"

Reasoning Exercises¶

RR vs least-conn when 1 of 10 boxes has a 10× slower disk. Who melts first?
You add a 4th cache node. Modular hash vs consistent hash: how much cache do you throw away?
Anycast VIP, one POP blackholes SYN packets. What does a user in that city see, and how long?
Sticky cookie + blue/green deploy. Why do 30% of users stay on blue after you shifted weight to 0?

Key Takeaways¶

Remember

The LB knows death only through probes and observed errors — budget that delay
In-flight work on a dead box dies; design retries only for idempotent requests
Sticky / in-memory sessions turn one failed node into a product outage
L4 is cheap and blind; L7 is expensive and can retry, route, and drain
Algorithms trade evenness vs locality; pick from the workload, not a blog default

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