# Doordash Email Scraper (`solid-scraper/doordash-email-scraper`) Actor 📧 Doordash Email Scraper quickly extracts verified business emails from DoorDash listings by location and keywords. Perfect for lead gen, sales teams, agencies & recruiters to find targeted prospects fast—save time, boost outreach, and grow faster! 🚀 - **URL**: https://apify.com/solid-scraper/doordash-email-scraper.md - **Developed by:** [SolidScraper](https://apify.com/solid-scraper) (community) - **Categories:** Lead generation, Automation, Developer tools - **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/docs.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python/docs.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/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 ### Doordash Email Scraper 🔍 **Doordash Email Scraper** is an Apify actor that scrapes email addresses related to **Doordash** using the keywords and email-domain filters you provide. It’s designed to help you quickly build outreach-ready lists by extracting emails found in publicly available sources connected to Doordash profiles and posts. Whether you’re a marketer, lead generation specialist, recruiter, researcher, or data analyst, this **DoorDash email scraper** approach helps you move from “we need contacts” to actionable results at scale—saving you hours of manual work with **DoorDash scraper email** and **DoorDash customer email extraction** workflows. --- ### Why choose Doordash Email Scraper? | Feature | Benefit | | --- | --- | | ✅ **All-in-one Doordash email scraping** | Extracts emails in one run using your **keywords**, **customDomains**, and optional **location** filter | | ✅ **Smart proxy support** | Uses built-in proxy support to keep scraping reliable as conditions change | | ✅ **Resilient scraping behavior** | Includes retries and fallbacks for better coverage when a run hits obstacles | | ✅ **Structured, dataset-ready output** | Pushes consistently structured JSON rows (network, keyword, title, description, url, email, etc.) | | ✅ **Scale with limits** | Use **maxEmails** (up to 10,000) to control runtime and cost while collecting unique results | | ✅ **Resume support** | Saves progress incrementally so long runs can continue without starting over from zero | --- ### Key features - 📌 **Keyword-led email discovery**: Uses your provided **keywords** to find relevant publicly available content tied to Doordash contexts (great for **DoorDash lead generation email scraper** use cases). - 🎯 **Email-domain targeting with `customDomains`**: Filters extracted emails to the domains you specify (e.g., `@gmail.com`) to improve relevance for a **DoorDash email finder** workflow. - 🌍 **Optional `location` filtering**: Adds a location filter to narrow results when you’re building **DoorDash emails database** leads by geography. - 🛡️ **Proxy resilience**: Includes proxy support designed for more reliable scraping during longer runs (useful for a **DoorDash merchant email scraper** pipeline). - 🔄 **Retries and fallbacks**: Helps maintain coverage by continuing through common failure scenarios rather than stopping immediately. - 💾 **Real-time saving**: Each discovered email is pushed immediately to the dataset so you don’t lose progress. - 📊 **De-duplicated results**: Tracks unique emails during the run to avoid repeated entries in your export. - ⚙️ **Controlled throughput with `maxEmails`**: Stops once the requested maximum number of emails is reached, balancing coverage and scraping time. --- ### Input Provide input via an `input.json` file. Example structure: ```json { "keywords": ["pizza", "new york"], "location": "", "platform": "Doordash", "customDomains": ["@gmail.com"], "maxEmails": 20, "proxyConfiguration": {} } ```` #### Input Fields | Field | Required | Description | | --- | --- | --- | | `keywords` | ✅ Yes | A list of keywords to search for. The actor uses these keywords to find relevant publicly available Doordash-related sources and extract matching emails. | | `location` | ❌ No | Location to filter search results. Leave empty if you don’t want to apply a location constraint. | | `platform` | ❌ No | Select platform. The schema includes `Doordash` and defaults to `Doordash`. | | `customDomains` | ❌ No | List of custom email domains to target (for example, `@gmail.com`). The scraper uses these domains to focus email extraction. | | `maxEmails` | ❌ No | Maximum number of emails to collect. The scraper stops once this limit is reached (helps control scraping time). Minimum is 1, maximum is 10,000. | | `proxyConfiguration` | ❌ No | Configure proxies for this Actor (Apify proxy configuration). Use this when you want to customize proxy handling for your run. | *** ### Output The actor saves each discovered email as a JSON row in your Apify dataset (pushed incrementally during the run). ```json { "network": "Doordash", "keyword": "pizza", "title": "Some listing or profile title", "description": "Some snippet/description text where contact info is found", "url": "https://example.com/some-page", "email": "contact@example.com", "proxyGroups": ["RESIDENTIAL"] } ``` #### Output Fields | Field | Type | Description | | --- | --- | --- | | `network` | string | The network label for these results (always `Doordash`). | | `keyword` | string | The keyword currently being used when the email was found. | | `title` | string | Title text extracted from the matched source. | | `description` | string | Description/snippet text extracted from the matched source. | | `url` | string | URL associated with the matched source. | | `email` | string | The extracted email address matching your `customDomains` filter. | | `proxyGroups` | array | Proxy group data used for the run. | > Note: The output format above reflects the fields the actor pushes via `Actor.push_data(row)`. *** ### How to use Doordash Email Scraper (via Apify Console) 1. **Open Apify Console** Sign in at https://console.apify.com and open the **Actors** tab. 2. **Find the actor** Search for **Doordash Email Scraper** and open the actor page. 3. **Configure input in the INPUT panel** Paste your `input.json` values into the input form (or upload the JSON).\ Set at least `keywords` (required). Optionally set `location`, `customDomains`, and `maxEmails`. 4. **(Optional) Configure proxies** If you want to customize proxy handling, fill in `proxyConfiguration`. This actor supports proxy configuration and uses it to keep scraping more reliable. 5. **Run the actor** Click **Run**. Watch the logs as the actor discovers emails and pushes results to the dataset incrementally. If a run encounters obstacles, it uses retry and fallback behavior to improve coverage. 6. **Review OUTPUT dataset** After the run completes (or while it runs), open the **OUTPUT** tab and open the dataset created by the actor. 7. **Export to JSON / CSV** Use Apify’s export options to download your results and plug them into your CRM, outreach stack, or analytics pipeline. No coding required—get **DoorDash email scraper** results in minutes. ✅ *** ### Advanced features & SEO optimization - 🚀 **Engineered for “DoorDash email harvesting tool” workflows**: Doordash Email Scraper is built around keyword-driven email discovery plus domain filtering, making it a practical **DoorDash outreach email list** starter. - 🔐 **Targeting with `customDomains`**: When you specify domains (like `@gmail.com`), you’ll get more relevant outreach leads and fewer off-target results for a **DoorDash business email scraper** use case. - 💾 **Incremental dataset pushes**: Each email is saved as it’s found, so large searches don’t risk “all-or-nothing” failures. - 🧭 **Run resilience with retries and fallbacks**: Includes robustness behaviors intended to maintain results even when access conditions change. - 🧹 **De-duplication via in-run tracking**: Avoids pushing duplicate emails during the same run, helping your export stay clean for analysis. *** ### Best use cases - 📈 **Sales teams building DoorDash outreach email lists**: Quickly assemble domain-targeted contact emails for targeted prospecting. - 🧠 **Market research & competitive analysis**: Collect email contacts paired with titles/descriptions/URLs to study who publishes what. - 🎯 **Lead generation for local markets**: Use `location` to focus results when you’re building a regional **DoorDash list email leads** campaign. - 🧾 **Recruiters and staffing research**: Identify business contacts associated with Doordash-relevant profiles for sourcing conversations. - 💼 **B2B data analysts & enrichment pipelines**: Ingest dataset rows into spreadsheets or analytics tools to correlate keywords with email domains. - ✉️ **Email marketing list building**: Export structured email rows and enrich your segmentation logic with keyword and location filters. - 🧑‍💻 **Automation-ready dataset exports**: Use the JSON dataset output as input for downstream tooling (CRM import, BI dashboards, or workflow automation). *** ### Technical specifications - **Supported Input Formats** - ✅ `keywords` (array) — list of search keywords (required) - ✅ `location` (string) — optional location filter - ✅ `platform` (string) — select `Doordash` (defaults to `Doordash`) - ✅ `customDomains` (array) — optional email-domain allowlist - ✅ `maxEmails` (integer) — optional limit (min 1, max 10,000) - ✅ `proxyConfiguration` (object) — optional proxy settings - **Proxy Support** - ✅ Built-in proxy support via `proxyConfiguration` - **Retry Mechanism** - ✅ Includes retries and fallbacks for resilience - **Dataset Structure** - ✅ Each discovered email is pushed as a dataset row with: `network`, `keyword`, `title`, `description`, `url`, `email`, `proxyGroups` - **Limitations** - ❌ This actor collects emails from publicly available sources connected to Doordash contexts; it does not guarantee every email listed will be available in every run. - ❌ Results depend on your selected `keywords`, `customDomains`, and the presence of emails in the matched sources. *** ### FAQ #### What does the Doordash Email Scraper extract? ✅ It extracts email addresses from publicly available sources related to Doordash, using your provided `keywords` and `customDomains` filters. Each email is saved to the dataset along with `network`, `keyword`, `title`, `description`, and the associated `url`. #### Do I need to provide `keywords`? ✅ Yes. `keywords` is required. If you omit it, the actor has no basis for searching, because `keywords` is the only field marked as required in the input schema. #### Can I limit how many emails it collects? ✅ Yes. Use `maxEmails` to set a maximum number of emails to collect. The actor stops once this limit is reached, which helps control scraping time and cost. #### How do `customDomains` affect the results? ✅ `customDomains` defines which email domains are considered valid for output. For example, if you include `@gmail.com`, the actor will focus on emails matching that domain pattern. #### Does it support proxies? ✅ Yes. You can configure proxies using `proxyConfiguration`. The actor uses proxy support to improve reliability during the run. #### Is there a way to resume progress? ✅ Yes. The actor saves progress incrementally, including a cursor and `seen_emails`, so you can continue without losing already collected unique emails. #### Where can I export the results after the run? ✅ In the Apify Console, open the **OUTPUT** tab to access the dataset. From there, you can export your results (commonly as JSON or CSV, depending on your Apify setup). *** ### Support & feature requests Have ideas to improve **Doordash Email Scraper** (for example, better exports, additional fields, or workflow enhancements)? 💡 - 💡 **Feature Requests**: Share what you need for your **DoorDash email scraper** workflow—your feedback helps shape the roadmap. - 📧 **Contact**: Email us at Thanks for helping make **Doordash Email Scraper** more useful for your email lead generation and data enrichment workflows. ✅ *** ### Closing CTA / Final thoughts *If you’re looking for an SEO-optimized way to build a **Doordash Email Scraper** dataset for outreach, this actor gives you structured results fast—without manual copy-paste.* *** ### Disclaimer **This tool only accesses publicly available sources.** It does not access private profiles, authenticated data, or password-protected content. You are responsible for complying with applicable laws (including GDPR and CCPA where relevant), spam regulations, and the target platform’s terms of service. For data-removal requests, contact . Please use **Doordash Email Scraper** responsibly, ethically, and only for legitimate purposes. # Actor input Schema ## `keywords` (type: `array`): A list of keywords to search for. ## `location` (type: `string`): Location to filter search results. ## `platform` (type: `string`): Select platform. ## `customDomains` (type: `array`): List of custom email domains ## `maxEmails` (type: `integer`): Maximum number of emails to collect. The scraper will stop once this limit is reached. Setting a higher limit allows for more potential results but doesn't guarantee reaching that number. This helps save costs by controlling scraping time. ## `proxyConfiguration` (type: `object`): Configure proxies for this Actor. ## Actor input object example ```json { "keywords": [ "pizza", "new york" ], "location": "", "platform": "Doordash", "customDomains": [ "@gmail.com" ], "maxEmails": 20 } ``` # 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": [ "pizza", "new york" ], "location": "", "customDomains": [ "@gmail.com" ] }; // Run the Actor and wait for it to finish const run = await client.actor("solid-scraper/doordash-email-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": [ "pizza", "new york", ], "location": "", "customDomains": ["@gmail.com"], } # Run the Actor and wait for it to finish run = client.actor("solid-scraper/doordash-email-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": [ "pizza", "new york" ], "location": "", "customDomains": [ "@gmail.com" ] }' | apify call solid-scraper/doordash-email-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=solid-scraper/doordash-email-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Doordash Email Scraper", "description": "📧 Doordash Email Scraper quickly extracts verified business emails from DoorDash listings by location and keywords. Perfect for lead gen, sales teams, agencies & recruiters to find targeted prospects fast—save time, boost outreach, and grow faster! 🚀", "version": "0.0", "x-build-id": "sVuTVSNVnAQ0CZYJ8" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/solid-scraper~doordash-email-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-solid-scraper-doordash-email-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/solid-scraper~doordash-email-scraper/runs": { "post": { "operationId": "runs-sync-solid-scraper-doordash-email-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/solid-scraper~doordash-email-scraper/run-sync": { "post": { "operationId": "run-sync-solid-scraper-doordash-email-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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", "type": "array", "description": "A list of keywords to search for.", "default": [ "pizza", "new york" ], "items": { "type": "string" } }, "location": { "title": "Location", "type": "string", "description": "Location to filter search results.", "default": "" }, "platform": { "title": "Platform", "enum": [ "Doordash" ], "type": "string", "description": "Select platform.", "default": "Doordash" }, "customDomains": { "title": "Custom Email Domains", "type": "array", "description": "List of custom email domains", "default": [ "@gmail.com" ], "items": { "type": "string" } }, "maxEmails": { "title": "Max Emails", "minimum": 1, "maximum": 10000, "type": "integer", "description": "Maximum number of emails to collect. The scraper will stop once this limit is reached. Setting a higher limit allows for more potential results but doesn't guarantee reaching that number. This helps save costs by controlling scraping time.", "default": 20 }, "proxyConfiguration": { "title": "Proxy Configuration", "type": "object", "description": "Configure proxies for this Actor." } } }, "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 } } } } } } } } } } ```