# 🇨🇳 JD.com Product Scraper · 京东商品数据采集 (`nexgendata/jd-com-product-scraper`) Actor Scrape JD.com (京东) — China #2 e-commerce. Product data: pricing, brand, JD自营 vs 第三方, reviews, ratings, JD Logistics flag. Built for cross-border e-commerce, supply chain analytics, brand monitoring, Chinese-market intelligence. Alibaba 1688 / Taobao alternative for retail-side intelligence. - **URL**: https://apify.com/nexgendata/jd-com-product-scraper.md - **Developed by:** [Stephan Corbeil](https://apify.com/nexgendata) (community) - **Categories:** E-commerce - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing Pay per event This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. 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 ## JD.com Product Scraper 🇨🇳🛒 **Scrape JD.com (京东) — China's #2 e-commerce platform — for product pricing, brand data, seller information, reviews, ratings, and JD自营 (self-operated) vs 第三方 (third-party) classification.** Built for cross-border e-commerce sellers, supply-chain analysts, brand monitoring teams, and Chinese-market intelligence operators who need clean, structured product data without spinning up their own Chinese-internet scraping stack. JD.com is the workhorse of Chinese e-commerce for electronics, appliances, FMCG, and premium goods. Where Taobao/Tmall is the long tail, JD is curated, fast-shipping, and largely first-party — which makes its pricing, stock signals, and review data unusually high-signal for sourcing and competitive intelligence. This actor turns `search.jd.com` and `item.jd.com` into a structured product dataset you can ingest into BigQuery, Snowflake, Postgres, an internal dashboard, or a Claude/GPT-powered analyst workflow with one HTTP call. --- ### Who this is for - **Cross-border e-commerce operators** sourcing products in China to resell on Amazon, Shopify, eBay, TikTok Shop, or Temu — and who need real-time JD pricing as a price-floor reference. - **Supply-chain analysts** tracking JD自营 first-party listings as a leading indicator of category demand and inventory health. - **Brand monitoring & MAP enforcement teams** watching how their brand appears on JD — unauthorized resellers, gray-market listings, MAP violations, and counterfeits. - **Chinese-market intelligence consultancies** producing competitive landscape reports on consumer categories (mobile, appliances, beauty, FMCG, maternity, fresh). - **Investors and equity researchers** tracking JD-listed brand performance, discount intensity, and review velocity as alt-data for JD.com (NASDAQ: JD) and competitor coverage. - **AI agents and LLM pipelines** that need a clean `tool_call` against JD product data without manually managing a Playwright cluster behind the Great Firewall. --- ### What you get For every product matched, the actor returns a structured record: | Field | Type | Description | |---|---|---| | `sku_id` | string | JD product ID — stable, used in `item.jd.com/{sku_id}.html` | | `name` | string | Full product title (Chinese, occasionally English) | | `brand` | string | Brand parsed from the product spec sheet | | `category` | string | Breadcrumb path (e.g. `家用电器 > 大家电 > 平板电视`) | | `current_price_cny` | float | Live price in CNY (¥) | | `original_price_cny` | float | Pre-discount / MSRP price in CNY | | `discount_pct` | float | Computed discount percentage | | `seller_type` | string | `JD自营` (self-operated) or `第三方` (marketplace) | | `seller_name` | string | Shop / merchant name | | `rating_avg` | float | Average product rating (1–5 stars) | | `review_count` | int | Total review count | | `is_jd_logistics` | boolean | Whether 京东物流 (JD Logistics) ships the SKU | | `image_url` | string | Primary product image | | `product_url` | string | Canonical `item.jd.com/{id}.html` link | | `data_source` | string | Always `jd.com` for live records | --- ### Input filters | Field | Default | Notes | |---|---|---| | `keywords` | `["手机"]` | Array of search terms. Chinese keywords work best. | | `category` | `""` | Optional JD category ID (e.g. `9987,653,655`). | | `min_price` | `0` | Minimum CNY price filter. | | `max_price` | `0` | Maximum CNY price filter. | | `sort` | `default` | `default` / `sales` (销量) / `price` (low→high). | | `limit` | `30` | Total products across all keywords. | Example input: ```json { "keywords": ["空气炸锅", "扫地机器人"], "min_price": 200, "max_price": 2000, "sort": "sales", "limit": 100 } ```` *** ### Pricing **$0.01** per run (platform overhead) **+ $0.05 per product record.** - 30 products ≈ **$1.51** - 100 products ≈ **$5.01** - 1,000 products ≈ **$50.01** This is volume-friendly e-commerce pricing — designed for sourcing teams running daily category sweeps and brand-monitoring teams running rolling 7-day SKU panels. You only pay for products actually returned; if JD blocks a query, you'll see a `data_source: jd.com:maintenance` stub record (one per failed keyword) and no overcharge. *** ### How it works under the hood JD.com has a non-trivial anti-bot layer on plain-HTTP search XHRs. The actor uses a stealthed headless Chromium (Playwright) to fetch `search.jd.com/Search` pages, scrolls to trigger lazy-loaded cards, parses out SKU IDs and card-level pricing, and then enriches each SKU via the public `item.jd.com/{sku_id}.html` endpoint (which serves real HTML to plain HTTP clients) and the JD comment summary API for ratings. Concurrent enrichment uses a 4-way semaphore so you won't hammer JD into rate-limiting you. If JD hard-blocks a keyword, the actor returns a maintenance stub instead of crashing — your downstream pipeline will see the failure clearly without 5xx alerts. *** ### Comparison vs alternatives | Product | JD.com Product Scraper | Alibaba 1688 | Shopify Markets | Sensor Tower | Sinotrust | |---|---|---|---|---|---| | **Focus** | JD.com retail SKUs, real consumer-facing prices | Wholesale / B2B 1688 listings | Your own Shopify storefronts in CN region | Mobile app intelligence | Premium consulting reports | | **Real-time?** | Yes — live JD prices and reviews on every run | Wholesale-tier, slower refresh | Your own data, not market data | Daily | Quarterly reports | | **Brand monitoring** | Yes — MAP, gray market, unauthorized resellers | No (wholesale only) | Your storefronts only | Apps only, no products | Custom engagements | | **JD自营 vs 第三方 flag** | Yes | N/A | N/A | N/A | Custom | | **Review counts + ratings** | Yes | Limited | N/A | App reviews only | Custom | | **Self-serve API** | Apify run = HTTP call | Manual / aggregator APIs | Shopify Admin API | Yes | No | | **Cost** | $0.05 / product, pay-per-result | Aggregator $$$, often $5k+/mo | $0 but only your data | $$$$ ($60k+/yr enterprise) | $$$$$ bespoke | | **Cross-border friendliness** | Plug-and-play Apify run | Wholesale-oriented | Storefront tool, not intel | Apps not products | Reports, not data | If you're doing **cross-border retail arbitrage, MAP enforcement, or category demand analysis**, JD.com Product Scraper gives you live retail-side data that 1688 / Shopify / Sensor Tower / Sinotrust simply don't expose at this price point. *** ### Use cases & playbooks #### 1. Cross-border arbitrage price floor Run daily across your top 50 SKUs. Compare `current_price_cny` × FX × shipping × tariff against your Amazon/TikTok Shop landed cost. Auto-flag SKUs where JD price drops > 10% week-over-week — those are the ones where your retail margins are about to collapse. #### 2. JD自营 vs 第三方 share-of-shelf Track the `seller_type` mix in your category over time. A rising 第三方 share usually signals JD ceding direct distribution to marketplace sellers — which is a sourcing opportunity but also a brand-control risk. #### 3. Brand-protection / MAP enforcement For each of your brand's SKUs, monitor `seller_name`. Flag any non-authorized seller. Combine with `current_price_cny` to detect MAP violations in real time. #### 4. Review-velocity demand signal Track `review_count` deltas weekly per SKU. SKUs with accelerating review counts are gaining demand momentum — leading indicator for restocks and category trend reports. #### 5. New-product launch monitoring Run weekly with `sort=default` and a tight keyword list. Diff against prior runs to surface newly-listed SKUs. Great for category-launch tracking and competitive intelligence. *** ### Companion actors (NexGenData fleet) This actor is part of a Chinese-market / Asia-Pacific intelligence stack. Combine it with: - **[Weibo Hot Search Tracker](https://apify.com/nexgendata/weibo-hot-search-tracker)** — 微博热搜 trending topics, viral signals, and consumer attention as a leading indicator for JD demand. - **[Xiaohongshu Trends Tracker](https://apify.com/nexgendata/xiaohongshu-trends-tracker)** — 小红书 / RED note trends — early demand signals for beauty, fashion, lifestyle SKUs before they show up on JD. - **[China Trends Tracker](https://apify.com/nexgendata/china-trends-tracker)** — cross-platform Chinese trend aggregator for omni-channel demand mapping. - **[Eastmoney China Stock Screener](https://apify.com/nexgendata/eastmoney-china-stock-screener)** — A-share fundamentals for the listed brands (e.g. JD-listed appliance OEMs) behind the SKUs. - **[HKEX Hang Seng Stock Screener](https://apify.com/nexgendata/hkex-hang-seng-stock-screener)** — Hong Kong-listed brand parents (JD.com itself, Tencent-backed brands, etc.). - **[Finance MCP Server](https://apify.com/nexgendata/finance-mcp-server)** — drop the above into a Claude/GPT agent via MCP for natural-language e-commerce intelligence. The full stack lets you trace consumer attention (Weibo / Xiaohongshu) → product demand (JD) → brand financials (Eastmoney / HKEX) in a single agent workflow. *** ### Operational notes - **Anti-bot:** JD's defenses are real but not draconian. Expect ~85–95% success on normal keywords. Niche or restricted categories (alcohol, pharma) sometimes return shorter result sets. - **Geography:** all prices are CNY, China-region pricing. Cross-border ¥-USD conversion is your job downstream. - **Compliance:** this actor accesses only publicly-served pages on JD.com. Use responsibly and in accordance with JD's terms and your jurisdiction's data-protection regime. - **Throttling:** the actor self-throttles enrichment to 4 concurrent requests. Don't run more than ~5 parallel actor runs against JD or you risk seeing maintenance stubs. *** ### Roadmap - Multi-category sweep mode (auto-walk a category tree) - Stock-availability flag (in stock / 预售 / 缺货) - Historical price tracking via JD's price-history endpoint - Review-text scraping (premium add-on) *** ### Try it now Click **Run** with the default input, get 30 real JD.com products in under 60 seconds, and pipe them into your existing data pipeline. Smoke-tested daily; pricing is transparent and pay-per-result. **[Run the JD.com Product Scraper →](https://apify.com/nexgendata/jd-com-product-scraper)** and start your free trial today. Sign up via the link, run any NexGenData actor, and the free $5 of Apify platform credit covers your first 100 JD products — risk-free. Build your Chinese e-commerce intelligence stack with NexGenData. *** *Built by NexGenData. Questions or custom data needs? Reach out via the Apify Store contact form.* *** ### Why this beats Tmall, 1688, Pinduoduo, and Crunchbase enrichment alone | Capability | NexGenData JD.com Scraper | 1688 Alibaba Browse | Pinduoduo App | Tmall Open Platform | Crunchbase / PitchBook enrichment | |---|---|---|---|---|---| | JD.com SKU-level price + discount | ✅ CNY current + original | ❌ wholesale only | ❌ different catalog | ❌ different storefront | ❌ company-level only | | JD自营 vs 第三方 seller-type flag | ✅ | ❌ | ❌ | ❌ | ❌ | | JD Logistics flag (`is_jd_logistics`) | ✅ | ❌ | ❌ | ❌ | ❌ | | Review count + average rating | ✅ | ❌ public | ⚠️ limited | ⚠️ closed API | ❌ | | Bulk export (JSON / CSV / Parquet) | ✅ via Apify dataset | ❌ HTML only | ❌ app only | ⚠️ closed merchant API | ✅ but at $1500-3000/seat/mo | | Cross-border friendly (no Chinese phone / 营业执照) | ✅ public endpoints | ⚠️ partial | ❌ app-only auth | ❌ requires Tmall merchant account | n/a | | Pricing | $0.05 / SKU, no subscription | Manual browse | n/a | Enterprise contract | $1500-3000/seat/mo flat | For competitive-intel teams comparing brand presence on China's #2 e-commerce platform, this gives you the same SKU-level depth Bloomberg China Consumer Intel and SimilarWeb Shopper Intelligence charge five-figure annual fees for — without the contract. *** ### About NexGenData NexGenData publishes 200+ buyer-intent Apify actors covering SEC filings (Form 4 insider buys, Form D, 13F holdings, 8-K material events, Schedule 13D/G activist tracker), YC alumni, Delaware DOC, lead generation, competitive intelligence, stock fundamentals across 30+ exchanges (SGX, ASX, TSX, B3, TWSE, KOSPI, HKEX, BSE Beijing, STAR Market, NSE India, HOSE Vietnam, Saudi Tadawul, JSE), property & macro data (Singapore HDB / URA, US Treasury yields, FX rates, commodity futures), and Chinese-market intelligence (JD, Douyin, Weibo, Xiaohongshu). All actors are pay-per-result — you only pay for rows you keep. No subscription, no seat licence, no annual contract. **Browse the full catalog and start your free trial:** https://apify.com/nexgendata?fpr=2ayu9b Sign up via that link and the free Apify platform credit covers your first hundred-plus rows on every actor — risk-free evaluation. # Actor input Schema ## `keywords` (type: `array`): List of search keywords to scrape on JD.com. Chinese keywords work best (e.g. '手机', '空气炸锅'). English keywords are also supported. Each keyword runs an independent search. ## `category` (type: `string`): JD category ID (e.g. '9987,653,655' for mobile phones, '670,671,672' for laptops). Leave empty to search all categories. Find IDs at list.jd.com. ## `min_price` (type: `integer`): Minimum product price in CNY (¥). Filters out cheaper products. Leave 0 for no minimum. ## `max_price` (type: `integer`): Maximum product price in CNY (¥). Filters out more expensive products. Leave 0 for no maximum. ## `sort` (type: `string`): Sort results by: 'default' (relevance / JD综合排序), 'sales' (销量 — best-selling first), 'price' (价格 — low to high). Best-seller data is the most commercially useful for sourcing decisions. ## `limit` (type: `integer`): Maximum number of products to return across all keywords. Each product is billed at $0.05. Start small (10–30) for exploration; scale to hundreds for full sourcing runs. ## `use_proxy` (type: `boolean`): If true, route Playwright traffic through Apify's default proxy pool. Default off — direct egress usually performs better against JD.com from Apify's compute IPs. Turn on if you see anti-bot blocks. ## Actor input object example ```json { "keywords": [ "手机", "空气炸锅" ], "category": "", "min_price": 0, "max_price": 0, "sort": "default", "limit": 30, "use_proxy": false } ``` # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "keywords": [ "手机", "空气炸锅" ], "category": "", "min_price": 0, "max_price": 0, "sort": "default", "limit": 30, "use_proxy": false }; // Run the Actor and wait for it to finish const run = await client.actor("nexgendata/jd-com-product-scraper").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${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 = { "keywords": [ "手机", "空气炸锅", ], "category": "", "min_price": 0, "max_price": 0, "sort": "default", "limit": 30, "use_proxy": False, } # Run the Actor and wait for it to finish run = client.actor("nexgendata/jd-com-product-scraper").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 '{ "keywords": [ "手机", "空气炸锅" ], "category": "", "min_price": 0, "max_price": 0, "sort": "default", "limit": 30, "use_proxy": false }' | apify call nexgendata/jd-com-product-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=nexgendata/jd-com-product-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "🇨🇳 JD.com Product Scraper · 京东商品数据采集", "description": "Scrape JD.com (京东) — China #2 e-commerce. Product data: pricing, brand, JD自营 vs 第三方, reviews, ratings, JD Logistics flag. Built for cross-border e-commerce, supply chain analytics, brand monitoring, Chinese-market intelligence. Alibaba 1688 / Taobao alternative for retail-side intelligence.", "version": "0.1", "x-build-id": "eeD95Ptvp6ri593js" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/nexgendata~jd-com-product-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-nexgendata-jd-com-product-scraper", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/nexgendata~jd-com-product-scraper/runs": { "post": { "operationId": "runs-sync-nexgendata-jd-com-product-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/nexgendata~jd-com-product-scraper/run-sync": { "post": { "operationId": "run-sync-nexgendata-jd-com-product-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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", "required": [ "keywords" ], "properties": { "keywords": { "title": "Keywords (中文 / English)", "type": "array", "description": "List of search keywords to scrape on JD.com. Chinese keywords work best (e.g. '手机', '空气炸锅'). English keywords are also supported. Each keyword runs an independent search.", "default": [ "手机" ], "items": { "type": "string" } }, "category": { "title": "Category ID (optional)", "type": "string", "description": "JD category ID (e.g. '9987,653,655' for mobile phones, '670,671,672' for laptops). Leave empty to search all categories. Find IDs at list.jd.com.", "default": "" }, "min_price": { "title": "Min Price (CNY)", "minimum": 0, "type": "integer", "description": "Minimum product price in CNY (¥). Filters out cheaper products. Leave 0 for no minimum.", "default": 0 }, "max_price": { "title": "Max Price (CNY)", "minimum": 0, "type": "integer", "description": "Maximum product price in CNY (¥). Filters out more expensive products. Leave 0 for no maximum.", "default": 0 }, "sort": { "title": "Sort Order", "enum": [ "default", "sales", "price" ], "type": "string", "description": "Sort results by: 'default' (relevance / JD综合排序), 'sales' (销量 — best-selling first), 'price' (价格 — low to high). Best-seller data is the most commercially useful for sourcing decisions.", "default": "default" }, "limit": { "title": "Product Limit", "minimum": 1, "maximum": 2000, "type": "integer", "description": "Maximum number of products to return across all keywords. Each product is billed at $0.05. Start small (10–30) for exploration; scale to hundreds for full sourcing runs.", "default": 30 }, "use_proxy": { "title": "Use Apify Proxy", "type": "boolean", "description": "If true, route Playwright traffic through Apify's default proxy pool. Default off — direct egress usually performs better against JD.com from Apify's compute IPs. Turn on if you see anti-bot blocks.", "default": false } } }, "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 } } } } } } } } } } ```