# Boliga Scraper – Danish Real Estate Listings & Sold Prices (`haketa/boliga-scraper`) Actor Extract active listings AND sold prices (solgte boliger) from Boliga.dk, Denmark's #1 property portal. Price, m² price, rooms, size, address, energy class, coordinates & full sale history. - **URL**: https://apify.com/haketa/boliga-scraper.md - **Developed by:** [Haketa](https://apify.com/haketa) (community) - **Categories:** Real estate, Lead generation - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $2.00 / 1,000 results This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. Since this Actor supports Apify Store discounts, the price gets lower the higher subscription plan you have. 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 ## Boliga Scraper – Danish Real Estate Listings & Sold Prices (Solgte Boliger) Extract **active property listings** *and* **historical sold prices (solgte boliger / salgspriser)** from [Boliga.dk](https://www.boliga.dk) — Denmark's largest and most-used real estate marketplace. Pull clean, structured JSON, CSV, or Excel data with prices in DKK, price per m², rooms, size, build year, energy class, addresses, coordinates, sale dates and more. Whether you are building a property valuation model, tracking the Danish housing market, generating real-estate leads, or feeding a data product — this scraper turns Boliga into a reliable, on-demand data feed. **No code, no infrastructure, no maintenance.** > 🇩🇰 **The only Boliga scraper that covers BOTH homes for sale *and* the full sold-price history.** Most tools only scrape active listings. Sold prices (solgte boliger) are the single most valuable dataset on Boliga — covering the entire Danish market, going back decades. --- ### ⭐ Why this Boliga scraper? - ✅ **Sold prices (solgte boliger) included** — scrape Denmark's complete sales history with final price, sale date, sale type, price per m² and price change. Perfect for valuations, CMA and market analysis. - ✅ **Active listings (til salg)** — every home currently on the market, with price, m² price, days-on-market and foreclosure flag. - ✅ **Nationwide coverage** — target specific zip codes, cities or regions, or scrape all of Denmark in one run. - ✅ **Powerful filters** — property type, price range, rooms, living area, build year, and (for sold homes) the sale year. - ✅ **25+ structured fields** — addresses, coordinates, energy class, lot size, build year, sale type and more. - ✅ **Reliable at scale** — automatic pagination and de-duplication across thousands of properties. - ✅ **Any format** — export to JSON, CSV, Excel, XML, or pull via API. --- ### 📋 Table of contents 1. [What is Boliga?](#-what-is-boliga) 2. [What data can you extract?](#-what-data-can-you-extract) 3. [Use cases](#-use-cases--who-is-this-for) 4. [How to use the Boliga scraper](#-how-to-use-the-boliga-scraper) 5. [Input examples](#-input-examples) 6. [Output example](#-output-example) 7. [Output fields reference](#-output-fields-reference) 8. [Tips for best results](#-tips-for-best-results) 9. [FAQ](#-frequently-asked-questions) 10. [Integrations](#-integrations--exporting-your-data) --- ### 🏠 What is Boliga? [Boliga](https://www.boliga.dk) is one of Denmark's largest and most popular property platforms. Danes use it to browse homes for sale and — crucially — to look up what properties **actually sold for**. Boliga aggregates the entire Danish housing market across every property type: - 🏡 **Villa** (detached houses) - 🏘️ **Rækkehus** (townhouses) - 🏢 **Ejerlejlighed** (owner-occupied apartments) - 🌲 **Fritidshus** (vacation homes) - 🤝 **Andelsbolig** (cooperative housing) - 🚜 **Landejendom** (country properties) - 🌳 **Grund** (plots of land) Because Boliga combines **current listings** with a deep archive of **final sale prices** from across Denmark, it is the single richest public source of Danish real-estate data. This scraper unlocks that data for analysis, automation and product building. --- ### 📦 What data can you extract? This actor extracts two complementary datasets from Boliga: #### 1. Homes for sale (til salg) Everything currently on the market: price in DKK, price per square meter, rooms, living area, lot size, build year, energy class, days-on-market, foreclosure flag, address, city, zip code, and GPS coordinates. #### 2. Sold prices (solgte boliger) — the money data 💰 Boliga holds the full sales history of the Danish market. For each sold property you get the **sale price**, the **sale date**, the **sale type** (regular, family, or auction), **price per m²**, the **price change**, and full property specs. This is the dataset that powers: - Automated valuation models (AVMs) - Comparative market analysis (CMA) - "What did the neighbor's house sell for?" tools - Investment and yield analysis - Market trend dashboards Nothing else on the market combines both in a single, easy-to-use scraper. --- ### 🎯 Use cases — who is this for? #### 🏦 Real estate investors & analysts Track sold prices across zip codes and cities to spot under- and over-valued areas, calculate yields, and time the market. #### 💼 PropTech & startups Power your app, valuation model, or comparison site with fresh Danish housing data — without building and maintaining a scraper yourself. #### 📊 Market researchers & journalists Analyze price trends, days-on-market, price-per-m² by region, and how sale prices evolve over time. #### 🏘️ Real estate agencies & brokers Benchmark listings against the competition, monitor pricing in your area, and build automated CMA reports for clients. #### 🎯 Lead generation Identify new listings and foreclosure properties for outreach and deal sourcing. #### 🏦 Banks & mortgage lenders Feed Danish sold-price data into risk and collateral valuation models. #### 🤖 Data products & AI Build training datasets and feeds for real-estate machine learning models, chatbots, and analytics dashboards. --- ### 🚀 How to use the Boliga scraper You don't need any coding skills. Just configure the input and click **Start**. 1. **Choose a listing type** — *For sale* (til salg) or *Sold prices* (solgte boliger). 2. **(Optional) target an area** — enter one or more Danish zip codes (e.g. `2100,8000`). Leave empty to cover all of Denmark. 3. **(Optional) add filters** — property types, price range, rooms, living area, build year, and (for sold homes) the sale year. 4. **Set Max items** — limit how many properties to collect (recommended, especially for sold prices which can return huge volumes). 5. Click **Start** and watch the results stream into your dataset. 6. **Export** to JSON, CSV, Excel, or pull via the API. > 💡 Sold-price searches across all of Denmark can contain hundreds of thousands of records — always set **Max items**, or narrow down by zip code and sale year. --- ### 📝 Input examples #### Example 1 — Apartments for sale in Copenhagen ```json { "listingType": "forSale", "zipCodes": "2100,2200,2300", "propertyTypes": ["3"], "maxItems": 200 } ```` #### Example 2 — Villas sold in Aarhus since 2022 💰 ```json { "listingType": "sold", "zipCodes": "8000,8200,8210", "propertyTypes": ["1"], "soldYearMin": 2022, "maxItems": 500 } ``` #### Example 3 — All homes for sale under 3,000,000 DKK nationwide ```json { "listingType": "forSale", "priceMax": 3000000, "maxItems": 1000 } ``` #### Example 4 — Sold properties via a ready-made Boliga URL ```json { "startUrls": [ { "url": "https://api.boliga.dk/api/v2/sold/search/results?zipCodes=2100&propertyType=3" } ], "maxItems": 300 } ``` *** ### 📤 Output example Each property is saved as a clean JSON record. Example of a **sold** apartment: ```json { "listingType": "sold", "id": "1234567", "url": "https://www.boliga.dk/bolig/1234567", "propertyType": "Ejerlejlighed", "propertyTypeCode": 3, "address": "Østerbrogade 100, 2. tv", "zipCode": "2100", "city": "København Ø", "price": 4250000, "squareMeterPrice": 58219, "priceChangePercent": -3.5, "rooms": 3, "size": 73, "lotSize": null, "buildYear": 1932, "energyClass": "C", "soldDate": "2026-04-18", "salesType": "Almindelig handel", "latitude": 55.7095, "longitude": 12.5780, "scrapedAt": "2026-06-03T10:00:00.000Z" } ``` And an **active** listing: ```json { "listingType": "forSale", "id": "7654321", "url": "https://www.boliga.dk/bolig/7654321", "propertyType": "Villa", "propertyTypeCode": 1, "address": "Skovvej 12", "zipCode": "8000", "city": "Aarhus C", "price": 5495000, "squareMeterPrice": 32134, "rooms": 5, "size": 171, "lotSize": 642, "buildYear": 1968, "energyClass": "D", "daysForSale": 23, "isForeclosure": false, "latitude": 56.1572, "longitude": 10.2107, "scrapedAt": "2026-06-03T10:00:00.000Z" } ``` *** ### 📚 Output fields reference | Field | Description | |---|---| | `listingType` | `forSale` or `sold` | | `id` | Boliga property ID | | `url` | Direct link to the property page | | `propertyType` | Villa, apartment, townhouse, etc. | | `propertyTypeCode` | Numeric Boliga property-type code | | `address` | Street address | | `zipCode` | Postal code | | `city` | City / town | | `municipality` | Municipality | | `price` | Price in DKK (sale price for sold homes) | | `squareMeterPrice` | Price per square meter (DKK/m²) | | `priceChangePercent` | Price change in % | | `rooms` | Number of rooms | | `size` | Living area in m² | | `lotSize` | Lot / land area in m² | | `floor` | Floor | | `buildYear` | Year built | | `energyClass` | Energy label (A–G) | | `basementSize` | Basement area in m² | | `daysForSale` | Days on market (active listings) | | `isForeclosure` | Foreclosure flag (active listings) | | `soldDate` | Date the property was sold | | `salesType` | Regular, family or auction sale | | `latitude` / `longitude` | GPS coordinates | | `scrapedAt` | When the record was collected (ISO 8601) | *** ### 💡 Tips for best results - 📍 **Narrow with zip codes.** Targeting `2100,8000` returns exactly the areas you care about and runs much faster than scraping all of Denmark. - 💰 **For valuations, use `sold` + a recent `soldYearMin`.** Recent comparable sales are the gold standard for estimating value. - 🔢 **Always set Max items for sold searches.** Denmark's sold-price archive is enormous; a nationwide sold search without a limit can run for a very long time. - 🎯 **Combine filters** (price + rooms + size + build year) to zero in on exactly the segment you care about. - 🏷️ **Property type codes:** 1 = Villa, 2 = Rækkehus, 3 = Ejerlejlighed, 4 = Fritidshus, 5 = Andelsbolig, 6 = Landejendom, 7 = Helårsgrund, 8 = Fritidsgrund, 9 = Villalejlighed. *** ### ❓ Frequently asked questions #### Does this scraper get sold prices (solgte boliger)? **Yes** — and that's its biggest advantage. Set `listingType` to `sold` to scrape Boliga's full archive of Danish sale prices, including the sale date, sale type and price change. Most other Boliga scrapers only cover active listings. #### Can I scrape the whole of Denmark? Yes. Leave the zip codes field empty to cover the entire country. For sold prices this can be a very large dataset, so set **Max items** or filter by zip code and sale year. #### What's the difference between the sale types? **Almindelig handel** is a regular open-market sale, **Familiehandel** is a sale between family members (often below market value), and **Auktion** is a forced/auction sale. The `salesType` field tells you which one each record is — important when using sold prices for valuation. #### How many properties can I scrape? As many as you need. Use **Max items** to cap a run, and split very large areas by zip code to keep runs fast and focused. #### What formats can I export to? JSON, CSV, Excel, XML, RSS, or programmatically via the Apify API. The data also includes a clean tabular **overview** view in the dataset. #### Can I run this on a schedule? Yes. Use Apify Schedules to run the scraper daily or weekly and keep your dataset fresh — great for monitoring new listings or tracking sold prices over time. #### Can I get notified of new listings? Combine scheduled runs with Apify integrations (webhooks, Make, Zapier, Slack, email) to get alerts whenever new properties match your criteria. *** ### 🔌 Integrations & exporting your data This actor works seamlessly with the Apify ecosystem: - **API** — pull results programmatically with a single HTTP request. - **Webhooks** — trigger downstream workflows when a run finishes. - **Make / Zapier** — connect Boliga data to thousands of apps with no code. - **Google Sheets, Slack, email** — push results where your team works. - **Schedules** — automate recurring runs to keep data fresh. Results can be downloaded as JSON, CSV, Excel, XML, or RSS at any time. *** ### 🆘 Support Found a bug or need a new field or filter? Open an issue on the actor's **Issues** tab. Feedback and feature requests are welcome — this scraper is actively maintained. **Happy scraping! 🏠🇩🇰** # Actor input Schema ## `listingType` (type: `string`): Scrape homes currently FOR SALE (til salg), or historical SOLD prices (solgte boliger / salgspriser). ## `zipCodes` (type: `string`): Comma-separated Danish postal codes to target, e.g. "2100,2200,8000" (Copenhagen Ø, Copenhagen N, Aarhus C). Leave empty to scrape all of Denmark. ## `propertyTypes` (type: `array`): Which dwelling types to include. Empty = all types. ## `priceMin` (type: `integer`): Minimum price in Danish kroner. ## `priceMax` (type: `integer`): Maximum price in Danish kroner. ## `roomsMin` (type: `integer`): Minimum number of rooms. ## `roomsMax` (type: `integer`): Maximum number of rooms. ## `sizeMin` (type: `integer`): Minimum living area in square meters. ## `sizeMax` (type: `integer`): Maximum living area in square meters. ## `buildYearMin` (type: `integer`): Earliest construction year. ## `buildYearMax` (type: `integer`): Latest construction year. ## `soldYearMin` (type: `integer`): Only include homes sold in or after this year. Ignored for homes for sale. ## `soldYearMax` (type: `integer`): Only include homes sold in or before this year. Ignored for homes for sale. ## `startUrls` (type: `array`): Optional. Paste ready-made Boliga API URLs (https://api.boliga.dk/api/v2/search/results?... or .../sold/search/results?...). When provided, these take priority and the filters above are ignored. ## `maxItems` (type: `integer`): Stop after collecting this many properties (0 = no limit). Sold searches can return hundreds of thousands of records, so set a limit unless you really want everything. ## `proxyConfiguration` (type: `object`): Recommended. Boliga rate-limits direct traffic; a Danish (DK) residential proxy gives the most reliable results. ## Actor input object example ```json { "listingType": "forSale", "zipCodes": "2100,8000", "propertyTypes": [], "startUrls": [], "maxItems": 100, "proxyConfiguration": { "useApifyProxy": true, "apifyProxyGroups": [ "RESIDENTIAL" ], "apifyProxyCountry": "DK" } } ``` # Actor output Schema ## `dataset` (type: `string`): Active listings and sold prices collected from Boliga. # 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 = { "maxItems": 100, "proxyConfiguration": { "useApifyProxy": true, "apifyProxyGroups": [ "RESIDENTIAL" ], "apifyProxyCountry": "DK" } }; // Run the Actor and wait for it to finish const run = await client.actor("haketa/boliga-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 = { "maxItems": 100, "proxyConfiguration": { "useApifyProxy": True, "apifyProxyGroups": ["RESIDENTIAL"], "apifyProxyCountry": "DK", }, } # Run the Actor and wait for it to finish run = client.actor("haketa/boliga-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 '{ "maxItems": 100, "proxyConfiguration": { "useApifyProxy": true, "apifyProxyGroups": [ "RESIDENTIAL" ], "apifyProxyCountry": "DK" } }' | apify call haketa/boliga-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=haketa/boliga-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Boliga Scraper – Danish Real Estate Listings & Sold Prices", "description": "Extract active listings AND sold prices (solgte boliger) from Boliga.dk, Denmark's #1 property portal. Price, m² price, rooms, size, address, energy class, coordinates & full sale history.", "version": "0.1", "x-build-id": "v2i6cA29Shk1IT4Nu" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/haketa~boliga-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-haketa-boliga-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/haketa~boliga-scraper/runs": { "post": { "operationId": "runs-sync-haketa-boliga-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/haketa~boliga-scraper/run-sync": { "post": { "operationId": "run-sync-haketa-boliga-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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", "properties": { "listingType": { "title": "Listing type", "enum": [ "forSale", "sold" ], "type": "string", "description": "Scrape homes currently FOR SALE (til salg), or historical SOLD prices (solgte boliger / salgspriser).", "default": "forSale" }, "zipCodes": { "title": "Zip codes", "type": "string", "description": "Comma-separated Danish postal codes to target, e.g. \"2100,2200,8000\" (Copenhagen Ø, Copenhagen N, Aarhus C). Leave empty to scrape all of Denmark." }, "propertyTypes": { "title": "Property types", "type": "array", "description": "Which dwelling types to include. Empty = all types.", "items": { "type": "string", "enum": [ "1", "2", "3", "4", "5", "6", "7", "8", "9" ], "enumTitles": [ "Villa", "Townhouse (rækkehus)", "Apartment (ejerlejlighed)", "Vacation home (fritidshus)", "Cooperative (andelsbolig)", "Country property (landejendom)", "Plot, year-round (helårsgrund)", "Plot, vacation (fritidsgrund)", "Villa apartment (villalejlighed)" ] }, "default": [] }, "priceMin": { "title": "Min price (DKK)", "minimum": 0, "type": "integer", "description": "Minimum price in Danish kroner." }, "priceMax": { "title": "Max price (DKK)", "minimum": 0, "type": "integer", "description": "Maximum price in Danish kroner." }, "roomsMin": { "title": "Min rooms", "minimum": 0, "type": "integer", "description": "Minimum number of rooms." }, "roomsMax": { "title": "Max rooms", "minimum": 0, "type": "integer", "description": "Maximum number of rooms." }, "sizeMin": { "title": "Min living area (m²)", "minimum": 0, "type": "integer", "description": "Minimum living area in square meters." }, "sizeMax": { "title": "Max living area (m²)", "minimum": 0, "type": "integer", "description": "Maximum living area in square meters." }, "buildYearMin": { "title": "Min build year", "type": "integer", "description": "Earliest construction year." }, "buildYearMax": { "title": "Max build year", "type": "integer", "description": "Latest construction year." }, "soldYearMin": { "title": "Sold from year (only for Sold prices)", "type": "integer", "description": "Only include homes sold in or after this year. Ignored for homes for sale." }, "soldYearMax": { "title": "Sold to year (only for Sold prices)", "type": "integer", "description": "Only include homes sold in or before this year. Ignored for homes for sale." }, "startUrls": { "title": "Start URLs (advanced)", "type": "array", "description": "Optional. Paste ready-made Boliga API URLs (https://api.boliga.dk/api/v2/search/results?... or .../sold/search/results?...). When provided, these take priority and the filters above are ignored.", "default": [], "items": { "type": "object", "required": [ "url" ], "properties": { "url": { "type": "string", "title": "URL of a web page", "format": "uri" } } } }, "maxItems": { "title": "Max items", "minimum": 0, "type": "integer", "description": "Stop after collecting this many properties (0 = no limit). Sold searches can return hundreds of thousands of records, so set a limit unless you really want everything.", "default": 0 }, "proxyConfiguration": { "title": "Proxy configuration", "type": "object", "description": "Recommended. Boliga rate-limits direct traffic; a Danish (DK) residential proxy gives the most reliable results.", "default": { "useApifyProxy": true, "apifyProxyGroups": [ "RESIDENTIAL" ], "apifyProxyCountry": "DK" } } } }, "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 } } } } } } } } } } ```