# Google Autocomplete Actor (`solid-scraper/google-autocomplete-actor`) Actor 🔎 Google Autocomplete Actor scrapes real-time search suggestions from Google to uncover high-intent keywords fast—ideal for SEO teams, content marketers, and agencies to boost rankings, find trends, and optimize campaigns instantly. 🚀 - **URL**: https://apify.com/solid-scraper/google-autocomplete-actor.md - **Developed by:** [SolidScraper](https://apify.com/solid-scraper) (community) - **Categories:** SEO tools, Lead generation, AI - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $2.99 / 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 ### Google Autocomplete Scraper 🔍 **Google Autocomplete Scraper** is an Apify actor that **scrapes autocomplete suggestions for a given query**. If you’re doing **SEO autocomplete keyword research**, building an **autocomplete keyword scraper** workflow, or simply trying to **programmatically get autocomplete suggestions** at scale, this tool helps you turn those suggestion lists into structured data—saving you hours of manual work. Whether you’re a marketer, data analyst, or researcher, **Google autocomplete extraction** and **autosuggest keyword scraper** outputs can feed content planning, keyword clustering, and rankings autocomplete scraping experiments—without switching tools or copy/pasting from your browser. --- ### Why choose Google Autocomplete Scraper? | Feature | Benefit | | --- | --- | | ✅ All-in-one autocomplete keyword research | Lets you scrape suggestions for your main query in one run | | ✅ Reliability-focused scraping | Includes resilient request handling and structured fallbacks when requests fail | | ✅ Structured output for automation | Produces consistent JSON fields so you can import into spreadsheets, BI tools, or pipelines | | ✅ Scale-friendly batch generation | Automatically expands suggestions using your alphabet-based prefix/suffix options | | ✅ Language and country targeting | Helps you tailor suggestions using your selected language and country | | ✅ Designed for repeatable workflows | Built for repeat runs across multiple queries for consistent “google autocomplete list scraper” operations | --- ### Key features - 🔍 **Autocomplete suggestions for your query:** Scrapes suggestions for the provided `query` so you can extract Google autocomplete keywords programmatically. - 🌍 **Language + country targeting:** Uses your selected `language` and `country` inputs to shape the suggestion results you collect. - 🔤 **Prefix options:** With `use_prefix`, the actor can generate and scrape alphabet-prefixed queries to widen your keyword discovery (useful for “google autocomplete list scraper” style expansion). - 🔤 **Suffix options:** With `use_suffix`, it can generate and scrape alphabet-suffixed queries to expand the suggestion pool for “autosuggest keyword scraper” workflows. - 📊 **Consistent field ordering:** Outputs a predictable set of fields like `suggestion_01`, `suggestion_02`, etc., to keep your dataset easy to analyze. - 💾 **Dataset-ready JSON:** Saves results to the default dataset using `await Actor.push_data(all_suggestions)`, ready for export to JSON/CSV downstream. --- ### Input Provide input via an `input.json` file. Example structure: ```json { "query": "apple watch", "language": "English", "country": "United States", "use_prefix": false, "use_suffix": true } ```` #### Input Fields | Field | Required | Description | | --- | --- | --- | | `query` | Yes | The search term you want suggestions for (e.g., “apple watch”). | | `language` | No | Select the language to use for suggestions. Defaults to `English`. | | `country` | No | Select the country to use for suggestions. Defaults to `United States`. | | `use_prefix` | No | Whether to add alphabetic prefixes to the query (generates additional prefix-based queries). Defaults to `false`. | | `use_suffix` | No | Whether to add alphabetic suffixes to the query (generates additional suffix-based queries). Defaults to `false`. | *** ### Output The actor saves your results to the default dataset in JSON format (it pushes `all_suggestions`, where each item contains the original `query` plus labeled suggestions fields like `suggestion_01`). Example output: ```json [ { "query": "apple watch", "suggestion_01": "apple watch series", "suggestion_02": "apple watch bands", "suggestion_03": "apple watch waterproof" } ] ``` > Note: Each output object uses the same schema style: `query` plus `suggestion_XX` keys (zero-padded ordering like `suggestion_01`, `suggestion_02`, etc.). #### Output Fields | Field | Type | Description | | --- | --- | --- | | `query` | string | The query string that was used to fetch suggestions. | | `suggestion_01` | string | The first suggestion returned for that query. | | `suggestion_02` | string | The second suggestion returned for that query. | | `suggestion_03` | string | The third suggestion returned for that query. | | `suggestion_04` | string | The fourth suggestion returned for that query. | | `suggestion_05` | string | The fifth suggestion returned for that query. | | `suggestion_06` | string | The sixth suggestion returned for that query. | | `suggestion_07` | string | The seventh suggestion returned for that query. | | `suggestion_08` | string | The eighth suggestion returned for that query. | | `suggestion_09` | string | The ninth suggestion returned for that query. | | `suggestion_10` | string | The tenth suggestion returned for that query. | Because suggestions are indexed and labeled in a consistent way, this output is ideal when you’re scraping google autocomplete keywords for analysis or when you’re comparing autocomplete keyword research sets across languages/countries. *** ### How to use Google Autocomplete Scraper (via Apify Console) 1. **Open Apify Console** 🔐\ Log in at https://console.apify.com and go to the **Actors** section. 2. **Find the actor** 🔎\ Search for **Google Autocomplete Scraper** and open the actor page. 3. **Enter your INPUT** 🧩\ In the **INPUT** field, paste JSON matching the schema, including at least `query`.\ You can also set `language`, `country`, `use_prefix`, and `use_suffix`. 4. **Run the actor** ▶️\ Click **Run** to start scraping autocomplete keyword suggestions. 5. **Watch the logs** 🧾\ During the run, you’ll see log messages indicating the scraping process and progress. 6. **Open the OUTPUT dataset** 📦\ After completion, open the **OUTPUT** tab to view the dataset with your structured suggestion objects. 7. **Export results** 💾\ Export the dataset from Apify in formats that work for your workflow (commonly JSON/CSV in downstream tools). No coding required—get accurate autocomplete extraction results in minutes. *** ### Advanced features & SEO optimization - ⚡ **Engineered for autocomplete expansion:** With `use_prefix` and `use_suffix`, Google autosuggest scraper workflows become easier by generating more query variations automatically. - 🌐 **Targeted research by language and location:** Set `language` and `country` to support more relevant “scrape google autocomplete keywords” and “google suggest scraper” experiments. - 📌 **Reliable, structured “keyword list” outputs:** The actor formats suggestions into consistent fields (`suggestion_01`, `suggestion_02`, …), making it straightforward to compare “google autocomplete extraction” results across runs. - 🧠 **Practical for SEO autocomplete keyword research:** Use it for “google autocomplete keyword research” and “extract google autocomplete suggestions” tasks where speed and repeatability matter. *** ### Best use cases - 📈 **SEO keyword research for content planning:** Quickly expand seed topics into suggestion lists for autocomplete keyword research and ideation. - 🧠 **Keyword clustering and grouping:** Build structured datasets to analyze patterns in autosuggest keyword scraper outputs. - 🔍 **Internationalized search exploration:** Compare suggestion sets across languages and countries using the `language` and `country` inputs. - 🛠️ **Marketing ops automation:** Feed programmatically scraped suggestions into spreadsheets, dashboards, or reporting pipelines. - 💻 **Developer workflows for keyword discovery:** Programmatically get autocomplete keyword data for testing, research, and experimentation. - 📊 **Competitive keyword gap research:** Use repeatable runs to measure how autocomplete suggestion coverage changes across queries. *** ### Technical specifications - **Supported Input Formats** - ✅ `query` as a string (required) - ✅ `language` as a string from the provided enum - ✅ `country` as a string from the provided enum - ✅ `use_prefix` as boolean - ✅ `use_suffix` as boolean - **Proxy Support** - ❗ Not specified in the provided actor input schema. - The actor includes resilient request handling, but proxy configuration fields are not part of the input schema you provided. - **Retry Mechanism** - ❗ Not specified in the provided actor input schema. - The actor handles request failures by catching request exceptions and returning an empty list for that request. - **Dataset Structure** - ✅ A JSON array is pushed to the default dataset. - ✅ Each array item contains `query` plus `suggestion_XX` fields (zero-padded). - **Rate Limits & Performance** - ❗ Exact limits are not specified in the provided source data. - Performance will depend on how many prefix/suffix requests are generated by `use_prefix` / `use_suffix`. - **Limitations** - ❌ No additional fields beyond `query` and `suggestion_XX` are produced in the output schema shown in the provided code. - ❌ The actor does not include an explicit per-row `success` or `error_message` field in the dataset output. *** ### FAQ #### What does Google Autocomplete Scraper return? ✅ It returns a JSON array where each item includes the original `query` and labeled suggestion fields such as `suggestion_01`, `suggestion_02`, and so on, generated from the autocomplete suggestions. #### Do I need to provide a country and language? ❌ No. You only must provide `query`. If you don’t set `language` or `country`, the actor uses defaults defined in the input handling logic (defaults to `English` for language and `United States` for country). #### What is `use_prefix` and when should I enable it? ✅ `use_prefix` controls whether the actor generates additional queries by adding alphabetic prefixes to your `query`. This is useful when you want broader keyword discovery—an approach common in autocomplete keyword scraper workflows. #### What is `use_suffix` and when should I enable it? ✅ `use_suffix` controls whether the actor generates additional queries by adding alphabetic suffixes to your `query`. Enabling it helps you extract more suggestion variations for autosuggest keyword scraper-style research. #### Can I use this for SEO autocomplete keyword research? ✅ Yes. The actor is directly set up to help you scrape autocomplete suggestions for a given topic, making it practical for google autocomplete keyword research and extract google autocomplete suggestions projects. #### Does it require any login or special setup? ❌ No special authentication is described in the provided input schema. You configure parameters in Apify Console and run the actor. #### Can I integrate the output into my pipeline? 💻 Yes. The actor saves structured JSON objects to the dataset, which you can export and load into spreadsheets, BI tools, or automation scripts. It’s designed to support “google autocomplete data scraper” style workflows. *** ### Support & feature requests If you’re using Google Autocomplete Scraper for google autocomplete scraper and google autocomplete extraction workflows, tell us what would make your runs smoother. - 💡 **Feature Requests:** For example, adding alternative output shapes (like flat rows for each suggestion), or improving how expansions are controlled for prefix/suffix use cases (ideal for google autocomplete list scraper projects). - 📧 **Contact:** Reach out via . Your feedback helps shape the roadmap for the Google Autocomplete Scraper. *** ### *Google Autocomplete Scraper* — Final thoughts *If you’re building an SEO autocomplete keyword research workflow, Google Autocomplete Scraper gives you structured autocomplete suggestion data you can reuse immediately.*\ *Get results faster with this SEO-optimized google autocomplete scraper approach for your next keyword discovery sprint.* *** ### Disclaimer **This tool accesses publicly accessible sources** to scrape autocomplete suggestion data. It does not access private profiles, authenticated data, or password-protected content. You are responsible for ensuring your use complies with applicable laws and regulations (for example, GDPR/CCPA where relevant), as well as platform terms and rules. Please use this actor responsibly, ethically, and only for legitimate purposes. For data-removal requests, contact . # Actor input Schema ## `query` (type: `string`): The search term to get suggestions for. ## `language` (type: `string`): Select your language ## `country` (type: `string`): Select your country ## `use_prefix` (type: `boolean`): Whether to add alphabetic prefixes to the query. ## `use_suffix` (type: `boolean`): Whether to add alphabetic suffixes to the query. ## Actor input object example ```json { "query": "apple watch", "language": "English", "country": "United States", "use_prefix": false, "use_suffix": 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 = {}; // Run the Actor and wait for it to finish const run = await client.actor("solid-scraper/google-autocomplete-actor").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("solid-scraper/google-autocomplete-actor").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 solid-scraper/google-autocomplete-actor --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=solid-scraper/google-autocomplete-actor", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Google Autocomplete Actor", "description": "🔎 Google Autocomplete Actor scrapes real-time search suggestions from Google to uncover high-intent keywords fast—ideal for SEO teams, content marketers, and agencies to boost rankings, find trends, and optimize campaigns instantly. 🚀", "version": "1.0", "x-build-id": "4aPDfCG7kcM9Uwz3a" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/solid-scraper~google-autocomplete-actor/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-solid-scraper-google-autocomplete-actor", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/solid-scraper~google-autocomplete-actor/runs": { "post": { "operationId": "runs-sync-solid-scraper-google-autocomplete-actor", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/solid-scraper~google-autocomplete-actor/run-sync": { "post": { "operationId": "run-sync-solid-scraper-google-autocomplete-actor", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "query" ], "properties": { "query": { "title": "Query", "type": "string", "description": "The search term to get suggestions for.", "default": "apple watch" }, "language": { "title": "Language", "enum": [ "Afrikaans", "Akan", "Albanian", "Amharic", "Arabic", "Armenian", "Azerbaijani", "Basque", "Belarusian", "Bengali", "Bihari", "Bosnian", "Breton", "Bulgarian", "Catalan", "Croatian", "Czech", "Danish", "Dutch", "English", "Esperanto", "Estonian", "Finnish", "French", "Frisian", "Galician", "Georgian", "German", "Greek", "Guarani", "Gujarati", "Haitian Creole", "Hausa", "Hebrew", "Hindi", "Hungarian", "Icelandic", "Indonesian", "Irish", "Italian", "Japanese", "Javanese", "Kannada", "Kazakh", "Kinyarwanda", "Korean", "Kurdish", "Kyrgyz", "Latin", "Latvian", "Lithuanian", "Macedonian", "Malagasy", "Malay", "Malayalam", "Maltese", "Maori", "Marathi", "Mongolian", "Nepali", "Norwegian", "Oriya", "Pashto", "Persian", "Polish", "Portuguese", "Punjabi", "Quechua", "Romanian", "Russian", "Scottish Gaelic", "Serbian", "Serbo-Croatian", "Sesotho", "Shona", "Sindhi", "Sinhalese", "Slovak", "Slovenian", "Somali", "Spanish", "Swahili", "Swedish", "Tajik", "Tamil", "Tatar", "Telugu", "Thai", "Tigrinya", "Turkish", "Turkmen", "Ukrainian", "Urdu", "Uzbek", "Vietnamese", "Welsh", "Xhosa", "Yiddish", "Yoruba", "Zulu" ], "type": "string", "description": "Select your language", "default": "English" }, "country": { "title": "Country", "enum": [ "Afghanistan", "Albania", "Algeria", "American Samoa", "Andorra", "Angola", "Anguilla", "Antarctica", "Antigua and Barbuda", "Argentina", "Armenia", "Aruba", "Australia", "Austria", "Azerbaijan", "Bahamas", "Bahrain", "Bangladesh", "Barbados", "Belarus", "Belgium", "Belize", "Benin", "Bermuda", "Bhutan", "Bolivia", "Bosnia and Herzegovina", "Botswana", "Bouvet Island", "Brazil", "British Indian Ocean Territory", "Brunei Darussalam", "Bulgaria", "Burkina Faso", "Burundi", "Cambodia", "Cameroon", "Canada", "Cape Verde", "Cayman Islands", "Central African Republic", "Chad", "Chile", "China", "Christmas Island", "Cocos (Keeling) Islands", "Colombia", "Comoros", "Congo", "Congo, The Democratic Republic of the", "Cook Islands", "Costa Rica", "Côte d'Ivoire", "Croatia", "Cuba", "Cyprus", "Czech Republic", "Denmark", "Djibouti", "Dominica", "Dominican Republic", "Ecuador", "Egypt", "El Salvador", "Equatorial Guinea", "Eritrea", "Estonia", "Ethiopia", "Falkland Islands (Malvinas)", "Faroe Islands", "Fiji", "Finland", "France", "French Guiana", "French Polynesia", "French Southern Territories", "Gabon", "Gambia", "Georgia", "Germany", "Ghana", "Gibraltar", "Greece", "Greenland", "Grenada", "Guadeloupe", "Guam", "Guatemala", "Guinea", "Guinea-Bissau", "Guyana", "Haiti", "Heard Island and Mcdonald Islands", "Holy See (Vatican City State)", "Honduras", "Hong Kong", "Hungary", "Iceland", "India", "Indonesia", "Iran, Islamic Republic of", "Iraq", "Ireland", "Israel", "Italy", "Jamaica", "Japan", "Jordan", "Kazakhstan", "Kenya", "Kiribati", "Korea, Democratic People's Republic of", "Korea, Republic of", "Kuwait", "Kyrgyzstan", "Lao People's Democratic Republic", "Latvia", "Lebanon", "Lesotho", "Liberia", "Libya", "Liechtenstein", "Lithuania", "Luxembourg", "Macao", "Macedonia, The Former Yugoslav Republic of", "Madagascar", "Malawi", "Malaysia", "Maldives", "Mali", "Malta", "Marshall Islands", "Martinique", "Mauritania", "Mauritius", "Mayotte", "Mexico", "Micronesia, Federated States of", "Moldova, Republic of", "Monaco", "Mongolia", "Montserrat", "Morocco", "Mozambique", "Myanmar", "Namibia", "Nauru", "Nepal", "Netherlands", "Netherlands Antilles", "New Caledonia", "New Zealand", "Nicaragua", "Niger", "Nigeria", "Niue", "Norfolk Island", "Northern Mariana Islands", "Norway", "Oman", "Pakistan", "Palau", "Palestinian Territory, Occupied", "Panama", "Papua New Guinea", "Paraguay", "Peru", "Philippines", "Pitcairn", "Poland", "Portugal", "Puerto Rico", "Qatar", "Réunion", "Romania", "Russian Federation", "Rwanda", "Saint Helena", "Saint Kitts and Nevis", "Saint Lucia", "Saint Pierre and Miquelon", "Saint Vincent and The Grenadines", "Samoa", "San Marino", "Sao Tome and Principe", "Saudi Arabia", "Senegal", "Serbia", "Seychelles", "Sierra Leone", "Singapore", "Slovakia", "Slovenia", "Solomon Islands", "Somalia", "South Africa", "South Georgia and The South Sandwich Islands", "Spain", "Sri Lanka", "Sudan", "Suriname", "Svalbard and Jan Mayen", "Swaziland", "Sweden", "Switzerland", "Syrian Arab Republic", "Taiwan, Province of China", "Tajikistan", "Tanzania, United Republic of", "Thailand", "Timor-Leste", "Togo", "Tokelau", "Tonga", "Trinidad and Tobago", "Tunisia", "Turkey", "Turkmenistan", "Turks and Caicos Islands", "Tuvalu", "Uganda", "Ukraine", "United Arab Emirates", "United Kingdom", "United States", "United States Minor Outlying Islands", "Uruguay", "Uzbekistan", "Vanuatu", "Venezuela", "Viet Nam", "Virgin Islands, British", "Virgin Islands, U.S.", "Wallis and Futuna", "Western Sahara", "Yemen", "Zambia", "Zimbabwe" ], "type": "string", "description": "Select your country", "default": "United States" }, "use_prefix": { "title": "Use Prefix", "type": "boolean", "description": "Whether to add alphabetic prefixes to the query.", "default": false }, "use_suffix": { "title": "Use Suffix", "type": "boolean", "description": "Whether to add alphabetic suffixes to the query.", "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 } } } } } } } } } } ```