← Glossary / Load Balancer

What is Load Balancer?

A load balancer is the traffic cop of a distributed scraping architecture, sitting between your job scheduler and the fleet of worker nodes. It distributes outbound fetch requests and inbound data processing tasks across available compute resources to prevent any single node from bottlenecking. In high-throughput pipelines, an intelligent load balancer doesn't just route round-robin — it routes based on target domain rate limits, proxy pool health, and worker memory pressure.

InfrastructureTraffic RoutingConcurrencyHigh AvailabilityWorker Nodes
// 02 — definitions

Distribute
the load.

How traffic routing keeps high-volume scraping pipelines stable when fetching millions of pages per hour.

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TL;DR

A load balancer distributes scraping tasks across a cluster of worker nodes. Without one, a single slow target or memory-heavy extraction job can crash your primary instance. Production scraping requires load balancing at two layers: task distribution (sending URLs to workers) and network egress (routing requests through proxy gateways).

01Definition & structure
A load balancer in a scraping context is a system that distributes incoming jobs (URLs to fetch, data to parse) across a pool of worker nodes. It ensures no single server is overwhelmed while others sit idle. It typically consists of a listener that accepts tasks, a routing algorithm (like round-robin or least-connections), and a health-checking mechanism to remove dead nodes from the pool.
02Task vs. Network load balancing
Scraping requires two distinct types of load balancing. Task load balancing (often handled by message brokers like RabbitMQ) distributes the compute work — rendering JavaScript, parsing DOMs, executing extraction logic. Network load balancing (handled by proxy gateways) distributes the outbound HTTP requests across thousands of IP addresses to manage target rate limits and avoid bans.
03Routing algorithms in scraping
Standard round-robin routing fails in scraping because tasks are not uniform. Fetching a static JSON API takes 50ms; rendering a heavy React SPA takes 5 seconds and 200MB of RAM. Modern scraping infrastructure uses resource-aware routing, dispatching tasks based on the real-time memory and CPU availability of the worker nodes to prevent Out-Of-Memory (OOM) crashes.
04How DataFlirt handles it
We run a decoupled architecture. Our task router monitors worker memory pressure at 500ms intervals, applying strict backpressure if a node exceeds 85% RAM utilization. Simultaneously, our egress router tracks global rate limits per target domain. If the fleet is instructed to crawl at 10 req/s, the egress load balancer enforces that cap globally, regardless of how many worker nodes are active.
05The "thundering herd" problem
If a target site goes down and returns 503s, scraping workers will finish their tasks instantly and immediately ask the load balancer for more work. This causes a massive spike in request volume that can look like a DDoS attack. Proper load balancers implement circuit breakers — pausing task distribution for a specific domain if error rates spike, protecting both your fleet and the target.
// 03 — the routing math

How do we
route requests?

Load balancing for scraping isn't just about CPU — it's about respecting target rate limits across a distributed fleet. DataFlirt's scheduler calculates worker capacity dynamically.

Worker Capacity = C = (Memtotal − Membase) / Membrowser
Max concurrent headless browsers a single node can safely run. Infrastructure sizing model
Target Rate Limit Distribution = Rworker = Rtarget_max / Nactive_workers
Ensuring aggregate requests from all nodes don't trigger 429s. Distributed rate limiting
Queue Backpressure = P = Tasksqueued / (Throughput × Timemax)
Triggers auto-scaling when P > 1.0. DataFlirt auto-scaler
// 04 — load balancer trace

Routing 10k tasks
across 40 nodes.

A live trace from DataFlirt's internal routing layer during a burst crawl of a major retail catalog. Shows health checks, backpressure, and egress routing.

Layer 7Least ConnectionsAuto-scaling
edge.dataflirt.io — live
CAPTURED
// inbound job batch
job.id: "crawl_retail_US_042"
tasks.queued: 10,000

// worker health check
node_group.alpha: 20/20 healthy
node_group.beta: 18/20 healthy // 2 cordoned (OOM)

// routing execution (least_conn)
route -> worker-a01: 250 tasks [mem: 42%]
route -> worker-b12: 250 tasks [mem: 38%]
route -> worker-a14: 0 tasks [mem: 94%] // backpressure applied

// egress proxy routing
egress.gateway: "proxy_pool_us_residential"
gateway.status: 2,400 IPs active

// status
batch.completion: 100%
pipeline.state: healthy
// 05 — bottleneck vectors

Where routing
breaks down.

The most common failure modes when scaling a scraping fleet behind a load balancer. Uneven memory distribution is the primary cause of node death.

FLEET SIZE ·  ·  ·  ·  ·  800+ nodes
ROUTING ALGO ·  ·  ·  ·   Least-loaded
UPDATED ·  ·  ·  ·  ·  ·  2026-05-19
01

Uneven memory distribution

OOM kills · Heavy pages crash nodes if routed blindly
02

Target-side rate limit breaches

429 Too Many Requests · Workers unaware of peers' request rates
03

Proxy gateway saturation

Connection drops · Too many concurrent connections to the proxy
04

Queue broker latency

Task timeouts · Redis/RabbitMQ struggling to dispatch tasks
05

Sticky session failures

Auth drops · Login state lost when routed to new node
// 06 — our architecture

Decoupled routing,

for both tasks and network egress.

DataFlirt separates task load balancing from network load balancing. Tasks are distributed via message queues to workers based on available memory and CPU. Network requests from those workers are then routed through a secondary egress load balancer that manages proxy rotation, IP cooldowns, and target-specific rate limits. This prevents a heavy extraction job from blocking network I/O, and vice versa.

LB Telemetry

Live metrics from a production routing layer.

active.nodes 38healthy
tasks.queued 1,204
routing.algo least_memory
avg.node.cpu 62%optimal
avg.node.mem 78%
cordoned.nodes 2
egress.rate 4,200 req/s

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// 07 — FAQ

Common
questions.

About load balancing, proxy gateways, stateful scraping, and how DataFlirt manages distributed fleets.

Ask us directly →
What is the difference between a load balancer and a proxy? +
A load balancer distributes your internal workload across your own servers. A proxy masks your identity and routes your outbound requests to the target website. In a scraping pipeline, you use a task load balancer to manage your workers, and those workers send requests through a proxy gateway to reach the target.
Do I need a load balancer if I only have one scraping server? +
No. If you run a single server, a simple queue (like Celery or Bull) is enough to manage concurrency. Load balancers become necessary when you scale horizontally to multiple servers and need a central point to distribute tasks and monitor node health.
How does load balancing affect IP rotation? +
It complicates it if not handled correctly. If worker A and worker B both hit the same target simultaneously through different IPs, the target might flag the sudden spike in distributed traffic. Your egress load balancer must enforce global rate limits per target, regardless of which worker initiated the request.
How does DataFlirt handle stateful scraping across multiple nodes? +
We use sticky sessions backed by a centralized Redis cache. If a scraping job requires a login, the session cookie and browser state are stored centrally. When a subsequent task for that session is routed to a different worker, the worker hydrates its browser context from the cache before making the request.
What happens when a worker node dies mid-scrape? +
The load balancer's health checks detect the failure (usually via missed heartbeats). The node is cordoned off, and any tasks that were assigned to it but unacknowledged are returned to the queue to be picked up by healthy workers. This ensures zero data loss during infrastructure scaling events.
Is it legal to use distributed scraping to bypass rate limits? +
Distributing requests to avoid crashing a target server is good citizenship. Distributing requests specifically to evade a target's stated rate limits (e.g., in robots.txt) is a ToS violation and can lead to IP bans. We use load balancing to scale our extraction throughput while strictly enforcing aggregate rate limits at the egress layer to remain compliant.
$ dataflirt scope --new-project --target=load-balancer READY

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hello@dataflirt.com  ·  Bengaluru  ·  IST  ·  typical reply < 4h
// 08 — keep reading

Related glossary terms

Scraping Infrastructure

Horizontal Scaling

Adding more worker nodes to your fleet, which requires a load balancer to manage.

Read →
IP Proxies

Proxy Gateway

The egress load balancer that distributes your outbound requests across proxy IPs.

Read →
Scraping Infrastructure

Worker Concurrency

How many parallel tasks a single node can handle before the load balancer must route elsewhere.

Read →
Scraping Infrastructure

Auto-Scaling

Dynamically provisioning new nodes when the load balancer detects queue backpressure.

Read →