← Glossary / REST API Data Delivery

What is REST API Data Delivery?

REST API data delivery is the synchronous method of serving extracted web data to downstream consumers via standard HTTP endpoints. Unlike asynchronous batch delivery to S3 or event-driven webhooks, a REST API allows your application to query, filter, and paginate scraped records on demand. It shifts the burden of state management and storage to the scraping provider, but introduces latency and rate-limit constraints into your critical path.

Data DeliveryRESTEndpointsPaginationJSON
// 02 — definitions

Pulling data
on demand.

Why synchronous API delivery changes the architecture of a scraping pipeline from a batch ETL process to a live microservice.

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TL;DR

REST API delivery exposes scraped datasets as queryable endpoints. It's ideal for low-volume, high-frequency lookups where the client only needs specific records (like a live price check) rather than the entire dataset. However, it requires the scraping infrastructure to maintain a high-availability serving layer decoupled from the extraction workers.

01Definition & structure
REST API data delivery provides a standard HTTP interface (GET requests, JSON responses, Bearer token auth) to query scraped data. Instead of receiving a massive CSV file once a day, the client application queries the API for specific records exactly when needed. The API acts as a serving layer, sitting between the client and the scraping provider's database.
02How it works in practice
The client sends an authenticated GET request with query parameters (e.g., ?category=electronics&limit=50). The API gateway validates the token, checks the rate limit, and routes the request to a read replica database or an in-memory cache. The data is serialized into JSON and returned. If the data is stale or missing, the API may either return a 404 or trigger a synchronous live-fetch, depending on the configuration.
03Pagination and filtering
Because APIs cannot return millions of records in a single response, data is paginated. Modern scraping APIs use cursor-based pagination (passing a pointer to the last seen record) rather than offset pagination, ensuring stable performance even when querying deep into a dataset. Filtering is handled via query parameters, allowing clients to offload search logic to the provider's database.
04How DataFlirt handles it
We treat our REST delivery layer as a tier-1 microservice, completely isolated from the extraction fleet. Scrapers write to an operational datastore, which continuously syncs to edge-cached read replicas. This architecture guarantees 99.99% uptime and sub-50ms latency for API queries, insulating your application from the inherent instability and latency of the actual web scraping process.
05The synchronous trap
The biggest mistake engineering teams make is using a REST API for bulk ETL ingestion. Writing a script to loop through 100,000 pages of an API to reconstruct a dataset locally is an anti-pattern. It wastes bandwidth, hits rate limits, and takes hours. If you need the whole dataset, use S3 batch delivery. If you need a single record right now, use the REST API.
// 03 — serving metrics

Measuring API
performance.

REST delivery shifts the bottleneck from extraction throughput to serving latency. DataFlirt monitors these metrics per endpoint to ensure client applications don't block waiting for scraped data.

Cache hit ratio = H = hits / (hits + misses)
High cache hit ratio is critical for scraping APIs to avoid live-fetch latency. Serving Layer SLO
Effective latency = Leff = (H × Lcache) + ((1 − H) × Lfetch)
Live-fetch latency can be 2–5 seconds; caching is mandatory for sub-100ms response times. DataFlirt Gateway Metrics
Pagination depth limit = Dmax = max_offset × page_size
Deep offset pagination degrades DB performance; cursor pagination is preferred. Database Query Optimization
// 04 — api request trace

Querying the
serving layer.

A client application requesting pricing data via a DataFlirt REST endpoint. The request hits the edge cache, misses, triggers a fast-path DB lookup, and returns the structured JSON.

GET /v1/productsBearer AuthCursor Pagination
edge.dataflirt.io — live
CAPTURED
// inbound request
GET /v1/products?sku=TS-150&limit=100 HTTP/1.1
Authorization: Bearer df_live_8f92...

// gateway routing
auth.status: ok
rate_limit.remaining: 4,992
cache.lookup: miss

// backend query
db.query_time: 14ms
records.found: 1

// response generation
payload.size: 1.2KB
response.status: 200 OK
response.time: 32ms
// 05 — delivery constraints

Where REST APIs
bottleneck.

Synchronous delivery is convenient but fragile at scale. These are the most common failure modes when clients consume scraped data via REST endpoints instead of batch files.

AVG LATENCY ·  ·  ·  ·    45ms (cached)
MAX PAYLOAD ·  ·  ·  ·    5MB per page
UPDATED ·  ·  ·  ·  ·  ·  2026-05-19
01

Deep offset pagination

performance killer · DB table scans degrade response times on high page numbers
02

Rate limit exhaustion

429 Too Many Requests · Aggressive polling by client apps without backoff logic
03

Live-fetch latency

timeout risk · Cache misses forcing real-time scrapes block the client
04

Payload size limits

memory bloat · Attempting to pull too many records per page
05

Schema drift

parsing errors · Unversioned endpoints breaking downstream client parsers
// 06 — serving architecture

Decouple extraction,

from delivery.

Never expose your extraction workers directly to client API requests. DataFlirt's REST delivery layer is strictly decoupled: scrapers write to a normalized operational database, and the API reads from an edge-cached replica. This ensures that a sudden spike in client API requests doesn't trigger a DDoS attack on the target website, and target website downtime doesn't result in 500 errors for the client application.

api.dataflirt.com/v2

Live metrics for a dedicated client REST endpoint.

endpoint /v2/real-estate/listings
auth_mode Bearer Token
rate_limit 50 req/s
cache_hit_rate 94.2%
p95_latency 42ms
live_fetch_fallback disabled
status operational

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// 07 — FAQ

Common
questions.

Common questions about REST API delivery, batch vs synchronous, and how DataFlirt manages high-availability endpoints.

Ask us directly →
Should I use REST API or S3 batch delivery? +
Use REST APIs for targeted lookups (e.g., "get me the current price of this specific SKU"). Use S3 batch delivery for bulk ingestion (e.g., "give me the entire product catalog"). Trying to pull 10 million records through a paginated REST API is inefficient, error-prone, and puts unnecessary load on the serving layer.
How do you handle pagination? +
We enforce cursor-based pagination (e.g., ?after=cursor_id) rather than offset-based pagination (?page=500). Deep offsets require the database to scan and discard thousands of rows before returning results, which kills API performance at scale. Cursors provide constant-time lookups regardless of depth.
What happens if the target site is down? +
Because our extraction and delivery layers are decoupled, target site downtime does not break the API. The endpoint will return the last known good cached record, accompanied by a _meta.stale flag and a last_extracted_at timestamp. Your application stays up even when the source goes down.
Can the API trigger a real-time scrape? +
Yes, via a specific force_refresh=true parameter. However, this bypasses the cache and routes the request directly to an extraction worker. It incurs high latency (often 2–5 seconds) and is subject to strict, separate rate limits to prevent abuse and target overload.
How do you version the API when schemas change? +
We use URI versioning (e.g., /v1/, /v2/). Each API version maps to a specific extraction schema contract. When a target site changes significantly and requires a new schema, we deploy a new API version. The old version remains active, returning cached or mapped data, giving your engineering team time to migrate.
What are the rate limits? +
Rate limits are configurable per client and tier, typically ranging from 10 to 100 requests per second. They are enforced at the edge using Redis-backed token buckets. Exceeding the limit returns a standard 429 Too Many Requests response with a Retry-After header.
$ dataflirt scope --new-project --target=rest-api-data-delivery READY

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// 08 — keep reading

Related glossary terms

Data Delivery

Webhook Data Push

Event-driven delivery where the scraper pushes data to your endpoint upon completion.

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Data Delivery

S3 Data Delivery

The standard batch delivery method for high-volume, full-catalog scraping pipelines.

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Data Delivery

JSON Lines Format

The preferred serialization format for streaming large datasets without memory bloat.

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Data Delivery

Data as a Service (DaaS)

The business model of providing continuous, clean data feeds via APIs and bulk exports.

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