# Automobile Catalog — Luxury Car Taxonomy Scraper (`rastriq/automobile-catalog-taxonomy`) Actor Scrapes make/model/generation/variant/specs taxonomy from automobile-catalog.com for luxury and classic car brands. - **URL**: https://apify.com/rastriq/automobile-catalog-taxonomy.md - **Developed by:** [ScrappingLatam](https://apify.com/rastriq) (community) - **Categories:** E-commerce, Automation - **Stats:** 2 total users, 1 monthly users, 0.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $0.20 / 1,000 results This Actor is paid per event and usage. You are charged both the fixed price for specific events and for Apify platform usage. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event ## What's an Apify Actor? Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours, and optionally produces a well-defined JSON output, datasets with results, or files in key-value store. In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server. Actors are written with capital "A". ## How to integrate an Actor? If asked about integration, you help developers integrate Actors into their projects. You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready. The best way to integrate Actors is as follows. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md). If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md). For usage examples, see the [API](#api) section below. For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt). # README ## Automobile Catalog — Luxury Car Taxonomy Scraper Scrapes the complete make/model/generation/variant/specs taxonomy from **automobile-catalog.com** for 65 luxury and classic car brands — Porsche, Ferrari, Mercedes-Benz, Lamborghini, Aston Martin, Bentley, Rolls-Royce, McLaren, Bugatti, and more. ### What does it do? Runs a 3-phase pipeline: 1. **Generations** — downloads all model generations per brand (e.g. Porsche 911 3rd gen, 1973–1989). ~1,500 rows. Fast (~5 min). 2. **Variants** — for each generation, downloads all individual variants (e.g. 911 Carrera RS 2.7 Coupe). ~8,000–12,000 rows. Medium (~3–4 h). 3. **Specs** — for each variant, downloads the full technical spec sheet (engine, power, torque, dimensions, weight, etc.). ~8,000–12,000 rows. Slow (~8–10 h). Run each phase separately using `resume_from_kv=true` to chain runs efficiently. ### Output fields | Field | Description | |---|---| | `_type` | `generation` / `variant` / `spec` | | `make_slug` | Brand slug (`porsche`, `mercedes-benz`) | | `model_slug` | Model/generation slug | | `family_slug` | Family slug (without generation suffix) | | `year_from` / `year_to` | Production years | | `car_id` | Stable numeric ID from automobile-catalog.com | | `variant_slug` | Full variant slug | | `spec_url` | Source URL | | `power_hp` / `power_kw` / `power_ps` | Engine power in 3 units | | `torque_nm` | Torque in Nm | | `displacement_cm3` | Engine displacement | | `transmission_type` | `manual` / `automatic` | | `body_style` | `coupe`, `convertible`, `sedan`, etc. | | `fuel_type` | `gasoline`, `diesel`, `electric`, `hybrid` | | `accel_0_100_kmh_s` | 0–100 km/h acceleration | | `top_speed_kmh` | Top speed | | `curb_weight_kg` | Curb weight in kg | ### Example input ```json { "phase": "generations", "makes": ["porsche", "ferrari", "lamborghini"], "max_items": 0, "resume_from_kv": false, "delay_min_s": 2, "delay_max_s": 4 } ```` ### Recommended run sequence ``` Run 1: phase=generations, resume_from_kv=false → ~5 min Run 2: phase=variants, resume_from_kv=true → ~3-4 h Run 3: phase=specs, resume_from_kv=true → ~8-10 h ``` Each run saves progress to the Key-Value store. If a run times out, re-run with `resume_from_kv=true` and it picks up exactly where it stopped. ### Notes - Uses Apify residential proxies to bypass Cloudflare Turnstile - Rate-limited to 2–4 s between requests (configurable) - Automatically retries on 429/503 with exponential backoff - All data is pushed incrementally — no data is lost on timeout # Actor input Schema ## `phase` (type: `string`): Which scraping phase to execute. Run in order: generations → variants → specs. Use 'all' to run everything in one go (slow). ## `makes` (type: `array`): List of make slugs (e.g. \['porsche', 'ferrari', 'mercedes-benz']). Leave empty to use the default luxury list (65 brands). ## `resume_from_kv` (type: `boolean`): If enabled, reads progress from the Key-Value store of the PREVIOUS run (same Actor) and skips already-scraped items. ## `max_items` (type: `integer`): Stop after processing this many items (generations / variants / specs). 0 = no limit. Useful for testing. ## `delay_min_s` (type: `number`): Minimum wait between HTTP requests. Increase if you see 429 errors. ## `delay_max_s` (type: `number`): Maximum wait between HTTP requests. ## Actor input object example ```json { "phase": "generations", "makes": [], "resume_from_kv": false, "max_items": 0, "delay_min_s": 2, "delay_max_s": 4 } ``` # Actor output Schema ## `results` (type: `string`): Dataset containing all scraped listings # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = {}; // Run the Actor and wait for it to finish const run = await client.actor("rastriq/automobile-catalog-taxonomy").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`); const { items } = await client.dataset(run.defaultDatasetId).listItems(); items.forEach((item) => { console.dir(item); }); // 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs ``` ## Python example ```python from apify_client import ApifyClient # Initialize the ApifyClient with your Apify API token # Replace '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = {} # Run the Actor and wait for it to finish run = client.actor("rastriq/automobile-catalog-taxonomy").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{}' | apify call rastriq/automobile-catalog-taxonomy --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=rastriq/automobile-catalog-taxonomy", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Automobile Catalog — Luxury Car Taxonomy Scraper", "description": "Scrapes make/model/generation/variant/specs taxonomy from automobile-catalog.com for luxury and classic car brands.", "version": "0.1", "x-build-id": "z9HngVtJ4KM8oCKwi" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/rastriq~automobile-catalog-taxonomy/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-rastriq-automobile-catalog-taxonomy", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/rastriq~automobile-catalog-taxonomy/runs": { "post": { "operationId": "runs-sync-rastriq-automobile-catalog-taxonomy", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/rastriq~automobile-catalog-taxonomy/run-sync": { "post": { "operationId": "run-sync-rastriq-automobile-catalog-taxonomy", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "properties": { "phase": { "title": "Phase to run", "enum": [ "generations", "variants", "specs", "all" ], "type": "string", "description": "Which scraping phase to execute. Run in order: generations → variants → specs. Use 'all' to run everything in one go (slow).", "default": "generations" }, "makes": { "title": "Brands to scrape", "type": "array", "description": "List of make slugs (e.g. ['porsche', 'ferrari', 'mercedes-benz']). Leave empty to use the default luxury list (65 brands).", "default": [], "items": { "type": "string" } }, "resume_from_kv": { "title": "Resume from checkpoint", "type": "boolean", "description": "If enabled, reads progress from the Key-Value store of the PREVIOUS run (same Actor) and skips already-scraped items.", "default": false }, "max_items": { "title": "Max items per phase", "minimum": 0, "type": "integer", "description": "Stop after processing this many items (generations / variants / specs). 0 = no limit. Useful for testing.", "default": 0 }, "delay_min_s": { "title": "Min delay between requests (seconds)", "minimum": 0.5, "type": "number", "description": "Minimum wait between HTTP requests. Increase if you see 429 errors.", "default": 2 }, "delay_max_s": { "title": "Max delay between requests (seconds)", "minimum": 1, "type": "number", "description": "Maximum wait between HTTP requests.", "default": 4 } } }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } } } ```