We extract make-model-year fitment data, part numbers, pricing tiers, and OEM cross-references from RockAuto's complex drill-down catalogue. Delivered as clean JSON, CSV, or Parquet to S3, BigQuery, or Snowflake on your cadence.
Structured, schema-consistent data across all major object types — delivered clean, typed, and ready to query.
Complete list of extractable fields for Part Listings objects from rockauto.com. All fields typed and schema-versioned.
"part_number": "15372", "manufacturer": "BOSCH", "category": "Exhaust & Emission", "sub_category": "Oxygen (O2) Sensor", "base_price": 42.79, "stock_status": "In Stock", "wholesale_closeout": false
| # | part_number | manufacturer | category | sub_category | description | base_price |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Fitment Data objects from rockauto.com. All fields typed and schema-versioned.
"part_number": "15372", "make": "HONDA", "model": "CIVIC", "year": "2018", "engine": "1.5L L4 Turbocharged", "position": "Upstream", "fitment_notes": "Actual OE Part"
| # | part_number | make | model | year | engine | fitment_notes |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Pricing & Core objects from rockauto.com. All fields typed and schema-versioned.
"part_number": "18B4734", "base_price": 54.79, "core_charge": 35.0, "total_price": 89.79, "currency": "USD", "wholesale_closeout": false, "price_timestamp": "2026-05-12T09:14:00Z"
| # | part_number | base_price | core_charge | total_price | currency | wholesale_closeout |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Cross-Reference objects from rockauto.com. All fields typed and schema-versioned.
"part_number": "15372", "manufacturer": "BOSCH", "oem_part_numbers": "['36531-5A2-A01', '36531-5PA-A01']", "interchange_numbers": "['234-9119', '250-25001']", "upc": "028851153725", "cross_reference_type": "Direct Fit"
| # | part_number | manufacturer | oem_part_numbers | alternate_part_numbers | interchange_numbers | upc |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Taxonomy & Navigation objects from rockauto.com. All fields typed and schema-versioned.
"node_id": "nav_1_2_3", "category_name": "Brake Pad", "level": 4, "has_children": false, "navigation_path": "HONDA > 2018 > CIVIC > 1.5L L4 Turbocharged > Brake & Wheel Hub > Brake Pad", "scraped_at": "2026-05-12T09:14:33Z"
| # | node_id | parent_node | category_name | level | url_path | has_children |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
RockAuto's nested drill-down structure is notoriously difficult to scrape. Our pipeline navigates the complex JavaScript tree, parses archaic HTML tables, and outputs clean, relational fitment and pricing data.
Navigate the infamous nested tree structure programmatically. We maintain stateful sessions to expand nodes from Make to Model to Year to Part Category.
Capture exact Make, Model, Year, and Engine combinations. Output relational tables linking specific part numbers to compatible vehicles.
Extract base prices, core charges, and total calculated costs. Identify wholesale closeouts and inventory clearance flags.
Scrape interchange numbers, alternate part numbers, and OEM equivalents to build comprehensive cross-reference databases.
Extract rich data from the pop-up 'Info' pages, including detailed specifications, warranty information, and manufacturer notes.
Identify and track heavily discounted inventory items marked as wholesale closeouts for margin-arbitrage opportunities.
Extract high-resolution image URLs and schematic diagrams associated with part numbers and info pages.
Execute the client-side scripts required to expand tree nodes and load dynamic pricing data without triggering bot detection.
Run one-off bulk exports or configure continuous pipelines to detect pricing deltas and inventory stock-outs.
Brief in. Clean data out.
Provide specific makes, years, or part categories. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, proxy rotation, session management, and CAPTCHA handling for rockauto.com.
Schema validation, null-rate checks, price-outlier detection, and sample fitment mapping before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
RockAuto relies on strict IP rate limits and an archaic, state-heavy DOM structure. Here is how we maintain reliable extraction.
RockAuto aggressively blocks datacenter IPs and monitors request velocity. Our crawlers use US residential ISP proxies with realistic browser fingerprints and request delays modelled on human click patterns.
The catalogue relies on AJAX calls tied to specific session states. We maintain continuous Playwright sessions to replicate the exact click sequences required to expand the Make > Year > Model > Engine tree without breaking state.
RockAuto's DOM relies heavily on deeply nested, non-semantic HTML tables. Our extraction logic uses robust XPath fallback chains and structural pattern matching to isolate part rows, prices, and core charges regardless of layout quirks.
For large automotive catalogues, we maintain a hash index of last-seen values per part. Subsequent runs only push diffs for pricing or stock status changes, reducing compute costs and downstream processing load.
Every run emits structured logs to our observability stack. We alert on null-rate spikes, category structure changes, and coverage drops. SLA uptime is contractual.
Auto parts retailers and manufacturers monitor competitor pricing, core charges, and closeout discounts on equivalent parts.
Data teams extract Make-Model-Year relationships to build or validate ACES/PIES compatible fitment databases for their own eCommerce stores.
Distributors map OEM part numbers to aftermarket equivalents using RockAuto's extensive interchange data.
Category managers identify coverage gaps in specific vehicle years or models by analysing the breadth of RockAuto's catalogue.
Retailers sync pricing and availability signals to automate their own storefront pricing and prevent out-of-stock orders.
Analysts track brand saturation and manufacturer representation across specific automotive sub-categories.
"RockAuto holds the most comprehensive vehicle fitment and cross-reference catalogue on the web, but its archaic nested-table structure makes it notoriously hostile to automated extraction."
Building a reliable RockAuto scraper requires maintaining stateful navigation through its heavily nested JavaScript tree, bypassing strict IP rate limits, and normalising decades of inconsistent part formatting. DataFlirt manages this infrastructure so your engineers can focus on integrating the fitment data, not maintaining fragile parsing scripts.
Everything supported by our rockauto.com scraper — rendered SPA elements, auth walls, rate-limit evasion and beyond.
Open-source tooling on proven cloud infra — no vendor lock-in, full observability.
Scrapy handles crawl orchestration and deduplication. Playwright manages the stateful JavaScript interactions required to expand the RockAuto catalogue tree.
We maintain pools of US-based residential ISP proxies. Rotation happens intelligently to avoid rate limits while maintaining necessary session stickiness.
Pipelines run on AWS Lambda and ECS. Airflow handles scheduling, dependency management, and SLA alerting. All state stored in managed Postgres.
Data delivered to where your team already works — no new tooling required.
About rockauto.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information from RockAuto is generally permissible under applicable law. DataFlirt targets only public, non-authenticated fitment, pricing, and cross-reference data. We do not extract personal data or circumvent authentication walls. Clients should review Terms of Service and consult legal counsel for specific use cases.
We use headless Playwright instances to programmatically click through the Make > Year > Model > Engine navigation tree, managing the required session state and AJAX responses to expose the final part listings.
RockAuto employs strict rate limiting. We distribute requests across large pools of US residential ISP proxies, mimicking human navigation delays and randomising user-agent fingerprints to avoid detection.
We extract the raw relational fitment data (Make, Model, Year, Engine, Part Number) into structured JSON or CSV. While we do not natively output strict ACES/PIES XML files, our output schemas are designed to be easily mapped into those industry standards by your data team.
We configure pipeline cadences based on your requirements. For targeted subsets of parts, we can run daily or intra-day price checks. Full catalogue sweeps typically run weekly due to the sheer volume of navigation nodes.
Absolutely. We provide a sample run for a specific vehicle make and model as part of the pre-engagement scoping process — so you can validate schema fit, field completeness, and data quality before signing any contract.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a full catalogue extraction for cross-referencing or continuous price monitoring for specific part categories — we scope, build, and operate the pipeline. Tell us what you need.
