# Workday Jobs Crawler (`jupri/workday`) Actor
💫 Scrape Workday Jobs Websites
- **URL**: https://apify.com/jupri/workday.md
- **Developed by:** [cat](https://apify.com/jupri) (community)
- **Categories:** Jobs
- **Stats:** 75 total users, 19 monthly users, 85.6% runs succeeded, 3 bookmarks
- **User rating**: No ratings yet
## Pricing
$5.00 / 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.
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
## 💫 Welcome To Workday Jobs Scraper
### 🍃 About Workday.com
**Workday, Inc.**, is an American on‑demand ([cloud](https://en.wikipedia.org/wiki/Cloud_computing "Cloud computing")-based) [financial management](https://en.wikipedia.org/wiki/Managerial_finance "Managerial finance"), [human capital management](https://en.wikipedia.org/wiki/Human_resource_management_systems "Human resource management systems"), and [student information system](https://en.wikipedia.org/wiki/Student_information_system "Student information system") software vendor. Workday was founded by [David Duffield](https://en.wikipedia.org/wiki/David_Duffield "David Duffield"), founder and former CEO of [ERP](https://en.wikipedia.org/wiki/Enterprise_resource_planning "Enterprise resource planning") company [PeopleSoft](https://en.wikipedia.org/wiki/PeopleSoft "PeopleSoft"), along with former PeopleSoft chief strategist [Aneel Bhusri](https://en.wikipedia.org/wiki/Aneel_Bhusri "Aneel Bhusri"), following [Oracle](https://en.wikipedia.org/wiki/Oracle_Corporation "Oracle Corporation")'s acquisition of PeopleSoft in 2005.[[2]](https://en.wikipedia.org/wiki/Workday,_Inc.#cite_note-2)
In October 2012, Workday launched a successful [initial public offering](https://en.wikipedia.org/wiki/Initial_public_offering "Initial public offering") that valued the company at $9.5 billion.[[3]](https://en.wikipedia.org/wiki/Workday,_Inc.#cite_note-wsj-3) Competitors of Workday include [SAP Successfactors](https://en.wikipedia.org/wiki/SuccessFactors "SuccessFactors"), [Dayforce](https://en.wikipedia.org/wiki/Dayforce "Dayforce"), [UKG](https://en.wikipedia.org/wiki/UKG "UKG"), and [Oracle](https://en.wikipedia.org/wiki/Oracle_Corporation "Oracle Corporation").[[4]](https://en.wikipedia.org/wiki/Workday,_Inc.#cite_note-4)
In 2020, _[Fortune](https://en.wikipedia.org/wiki/Fortune_(magazine) "Fortune (magazine)")_ magazine ranked Workday Inc. at number five on their Fortune List of the Top 100 Companies to Work For in 2020 based on an employee satisfaction survey.[[5]](https://en.wikipedia.org/wiki/Workday,_Inc.#cite_note-5)
### 🍃 Output Samples
```yaml
{
"applyUrl": "https://workday.wd5.myworkdayjobs.com/Workday/job/USA-CA-Pleasanton/Machine-Learning-Engineer_JR-0097159/apply",
"canApply": true,
"country": {
"descriptor": "United States of America",
"id": "bc33aa3152ec42d4995f4791a106ed09"
},
"description": "****Your work days are brighter here.****\n\nAt Workday, it all began with a conversation over breakfast. When our founders\nmet at a sunny California diner, they came up with an idea to revolutionize\nthe enterprise software market. And when we began to rise, one thing that\nreally set us apart was our culture. A culture which was driven by our value\nof putting our people first. And ever since, the happiness, development, and\ncontribution of every Workmate is central to who we are. Our Workmates believe\na healthy employee-centric, collaborative culture is the essential mix of\ningredients for success in business. That’s why we look after our people,\ncommunities and the planet while still being profitable. Feel encouraged to\nshine, however that manifests: you don’t need to hide who you are. You can\nfeel the energy and the passion, it's what makes us unique. Inspired to make a\nbrighter work day for all and transform with us to the next stage of our\ngrowth journey? Bring your brightest version of you and have a brighter work\nday here.\n\nAt Workday, we value our candidates’ privacy and data security. Workday will\nnever ask candidates to apply to jobs through websites that are not Workday\nCareers.\n\nPlease be aware of sites that may ask for you to input your data in connection\nwith a job posting that appears to be from Workday but is not.\n\nIn addition, Workday will never ask candidates to pay a recruiting fee, or pay\nfor consulting or coaching services, in order to apply for a job at Workday.\n\n**About the Team**\n\nWe're working on making machine learning core to Workday's products by\nbuilding features and capabilities that can be scaled out to hundreds of use\ncases within Workday. Illuminate: The next generation of Workday AI is\nunlocking a whole new level of productivity and human potential by\naccelerating manual tasks, assisting every employee, and ultimately\ntransforming entire business processes. With more than 70 million users under\ncontract generating more than 800 billion transactions a year on our platform,\nIlluminate leverages the world’s largest and cleanest HR and Finance dataset.\nThe combination of this data—with Illuminate’s ability to understand the\ncontext behind it—enables Workday to unlock value in a way no competitor can.\nJoin us as we change the way millions of people work.\n\n**About the Role**\n\nWe are developing ML-powered Information Retrieval, Recommendation and Agentic\nservices and platforms to modernize and simplify user interactions with\nWorkday. As a machine learning engineer, you will help develop tailored user\nexperiences using advanced Agentic AI, LLMs, Knowledge Graphs,\npersonalization, and predictive analysis. You will collaborate with other\nengineers to deliver ML solutions across Workday’s product ecosystem and\nutilize current software and data engineering stacks to enable training,\ndeployment, and lifecycle management of a variety of ML models; supervised and\nunsupervised, and agentic AI powered by LLMs. Additionally, you will develop\nand deploy new APIs/microservices using docker/kubernetes at scale and\nleverage Workday’s vast computing resources on rich datasets to deliver\ntransformative value to our customers. Sound like your kind of challenge?\n\n**About You**\n\nIn addition to contributing to feature and service development, you must have\nan approach of continuous improvement, passion for quality, scale, and\nsecurity. You must be curious and prepared to question or challenge choices\nand practices where they don't make sense to you or could be improved. You\nalso should have a product approach and strong intuition around how ML can\ndrive a better customer experience. Lastly, a strong sense of ownership and\nteamwork are essential to succeed in this role.\n\n**Key Responsibilities:**\n\n * Own exploration, design and execution of advanced ML models, algorithms and frameworks that deliver value to our users.\n\n * Apply machine learning techniques including LLMs, knowledge graphs, deep learning including generative models, natural language understanding, topic modeling, GNNs and named entity recognition to analyze large sets of HR and Finance-related text data, and design and launch pioneering cloud based machine learning architectures.\n\n * Own the performance, scalability, metric based deployed evaluation, and ongoing data driven enhancements of your products.\n\n * Keep abreast of the latest advancements in NLP research, techniques, and tools and apply this knowledge onto ML Features.\n\n**Basic Qualifications:**\n\n * Bachelor’s (Master’s or PhD preferred) degree in engineering, computer science, physics, math or equivalent\n\n * 3+ years of professional experience in building information retrieval systems and/or graph-based recommendation systems. \n\n * 3+ years of hands-on professional experience in developing text-based or graph-based machine learning models for production, including data processing, model fine-tuning, model deployment and model evaluation\n\n * 2+ years of professional experience in building services to host machine learning models in production at scale \n\n * 2+ years of professional experience working with large language models (LLMs), text generation models, and/or graph neural network models for real-world use cases\n\n * 2+ years of professional experience in machine learning and deep learning frameworks & toolkits such as Pytorch, TensorFlow, and Sklearn\n\n * 2+ years of professional experience with data engineering and data wrangling using e.g. Pandas and PySpark and other industry tools used to build scalable machine learning systems, such as Kubernetes and Docker\n\n * 2+ years of professional experience with cloud computing platforms (e.g. AWS, GCP, etc.)\n\n * Deep understanding of statistical analysis, unsupervised and supervised machine learning algorithms, and natural language processing for information retrieval and/or recommendation system use cases\n\n**Other Qualifications:**\n\n * Exposure to advanced techniques such as reinforcement learning and graph neural networks\n\n * Standout colleague, strong communication skills, with experience working across functions and teams. Ability to teach, mentor and lead through influence\n\n * Bonus points for relevant PhD and/or machine learning related research publications\n\n \n**Workday Pay Transparency Statement**\n\nThe annualized base salary ranges for the primary location and any additional\nlocations are listed below. Workday pay ranges vary based on work location. As\na part of the total compensation package, this role may be eligible for the\nWorkday Bonus Plan or a role-specific commission/bonus, as well as annual\nrefresh stock grants. Recruiters can share more detail during the hiring\nprocess. Each candidate’s compensation offer will be based on multiple factors\nincluding, but not limited to, geography, experience, skills, job duties, and\nbusiness need, among other things. For more information regarding Workday’s\ncomprehensive benefits, please [click\nhere](http://workdaybenefits.com/us/welcome-to-workday-benefits/prospective-\nworkmates).\n\nPrimary Location: USA.CA.Pleasanton\n\n \n\nPrimary Location Base Pay Range: $165,600 USD - $248,400 USD\n\n \n\nAdditional US Location(s) Base Pay Range: $139,800 USD - $248,400 USD\n\n \n \n**Our Approach to Flexible Work** \n\nWith Flex Work, we’re combining the best of both worlds: in-person time and\nremote. Our approach enables our teams to deepen connections, maintain a\nstrong community, and do their best work. We know that flexibility can take\nshape in many ways, so rather than a number of required days in-office each\nweek, we simply **spend at least half (50%) of our time each quarter in the\noffice or in the field** with our customers, prospects, and partners\n(depending on role). This means you'll have the freedom to create a flexible\nschedule that caters to your business, team, and personal needs, while being\nintentional to make the most of time spent together. Those in our remote \"home\noffice\" roles also have the opportunity to come together in our offices for\nimportant moments that matter.\n\nPursuant to applicable Fair Chance law, Workday will consider for employment\nqualified applicants with arrest and conviction records.\n\nWorkday is an Equal Opportunity Employer including individuals with\ndisabilities and protected veterans.\n\n**Are you being referred to one of our roles? If so, ask your connection at\nWorkday about our Employee Referral process!**\n\n",
"hiringOrganization": {
"name": "Workday, Inc.",
"url": "https://www.workday.com/en-us/company/careers/overview.html"
},
"id": "5c141a9e2bff1000da08726a21960000",
"includeResumeParsing": true,
"jobPostingId": "Machine-Learning-Engineer_JR-0097159",
"jobPostingSiteId": "Workday",
"jobReqId": "JR-0097159",
"jobRequisitionLocation": {
"country": {
"alpha2Code": "US",
"descriptor": "United States of America",
"id": "bc33aa3152ec42d4995f4791a106ed09"
},
"descriptor": "USA, CA, Pleasanton"
},
"location": "USA, CA, Pleasanton",
"posted": true,
"postedOn": "Posted Today",
"questionnaireId": "15cb5c09d59b10194f4f392d4e9e0000",
"remoteType": "Flex",
"secondaryQuestionnaireId": "17b317d5f803100e867834a8420d0000",
"startDate": "2025-05-28",
"timeType": "Full Time",
"title": "Machine Learning Engineer",
"url": "https://workday.wd5.myworkdayjobs.com/Workday/job/USA-CA-Pleasanton/Machine-Learning-Engineer_JR-0097159"
}
````
### 💌 Support
Feel free to [reach out](https://console.apify.com/actors/jL1YWWuJn3QGc6myN/issues) to the developer for any issues or suggestions for improvement.
# Actor input Schema
## `url` (type: `array`):
💡 Workday Jobs URL(s)
## `limit` (type: `integer`):
💡 Number of results (per-query)
## `dev_proxy_config` (type: `object`):
💡 Supported protocol:
HTTP(S), SOCKS5
{http|socks5}://{user:pass}@{hostname|ip-address}:port
Example: socks5://example.com:9000
## `dev_custom_headers` (type: `array`):
💡 Additional HTTP Headers
## `dev_custom_cookies` (type: `array`):
💡 Additional HTTP Cookies
## `dev_transform_fields` (type: `array`):
💡 Transform the resulting output. Select only needed fields.
For nested object use DOT. For example: address.streetAddress
For nested array use NUMBER (index of array element starting from index=0). For example: images.0.url
## `dev_dataset_name` (type: `string`):
💡 Save results into custom named Dataset, use mask to customize dataset name
{ACTOR} = actor name
{DATE} = date (YYYYMMDD)
{TIME} = time (HHMMSS)
This masks can be used to autogenerate Dataset Name.
example: data-{DATE}
Depending on today date the dataset name will be: data-20230603
default: data-{ACTOR}-{DATE}-{TIME}
## `dev_dataset_clear` (type: `boolean`):
Clear Dataset before insert/update.
## `dev_no_strip` (type: `boolean`):
💡 Keep/Save empty values (NULL, FALSE, empty ARRAY, empty OBJECT, empty STRING)
## Actor input object example
```json
{
"url": [
"https://workday.wd5.myworkdayjobs.com/Workday"
],
"limit": 5
}
```
# 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 = {
"url": [
"https://workday.wd5.myworkdayjobs.com/Workday"
],
"limit": 5
};
// Run the Actor and wait for it to finish
const run = await client.actor("jupri/workday").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 = {
"url": ["https://workday.wd5.myworkdayjobs.com/Workday"],
"limit": 5,
}
# Run the Actor and wait for it to finish
run = client.actor("jupri/workday").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 '{
"url": [
"https://workday.wd5.myworkdayjobs.com/Workday"
],
"limit": 5
}' |
apify call jupri/workday --silent --output-dataset
```
## MCP server setup
```json
{
"mcpServers": {
"apify": {
"command": "npx",
"args": [
"mcp-remote",
"https://mcp.apify.com/?tools=jupri/workday",
"--header",
"Authorization: Bearer "
]
}
}
}
```
## OpenAPI specification
```json
{
"openapi": "3.0.1",
"info": {
"title": "Workday Jobs Crawler",
"description": "💫 Scrape Workday Jobs Websites",
"version": "0.0",
"x-build-id": "a4iPA0uv3y3Mn5le6"
},
"servers": [
{
"url": "https://api.apify.com/v2"
}
],
"paths": {
"/acts/jupri~workday/run-sync-get-dataset-items": {
"post": {
"operationId": "run-sync-get-dataset-items-jupri-workday",
"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": {
"$ref": "#/components/schemas/inputSchema"
}
}
}
},
"parameters": [
{
"name": "token",
"in": "query",
"required": true,
"schema": {
"type": "string"
},
"description": "Enter your Apify token here"
}
],
"responses": {
"200": {
"description": "OK"
}
}
}
},
"/acts/jupri~workday/runs": {
"post": {
"operationId": "runs-sync-jupri-workday",
"x-openai-isConsequential": false,
"summary": "Executes an Actor and returns information about the initiated run in response.",
"tags": [
"Run Actor"
],
"requestBody": {
"required": true,
"content": {
"application/json": {
"schema": {
"$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": {
"$ref": "#/components/schemas/runsResponseSchema"
}
}
}
}
}
}
},
"/acts/jupri~workday/run-sync": {
"post": {
"operationId": "run-sync-jupri-workday",
"x-openai-isConsequential": false,
"summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.",
"tags": [
"Run Actor"
],
"requestBody": {
"required": true,
"content": {
"application/json": {
"schema": {
"$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": {
"url": {
"title": "❓ URL",
"type": "array",
"description": "💡 Workday Jobs URL(s)",
"items": {
"type": "string"
}
},
"limit": {
"title": "♾️ Limit",
"type": "integer",
"description": "💡 Number of results (per-query)"
},
"dev_proxy_config": {
"title": "🌐 PROXY NETWORKING",
"type": "object",
"description": "💡 Supported protocol:
HTTP(S), SOCKS5
{http|socks5}://{user:pass}@{hostname|ip-address}:port
Example: socks5://example.com:9000"
},
"dev_custom_headers": {
"title": "📜 HTTP HEADERS",
"type": "array",
"description": "💡 Additional HTTP Headers",
"items": {
"type": "object",
"required": [
"key",
"value"
],
"properties": {
"key": {
"type": "string",
"title": "Key"
},
"value": {
"type": "string",
"title": "Value"
}
}
}
},
"dev_custom_cookies": {
"title": "🍰 HTTP COOKIES",
"type": "array",
"description": "💡 Additional HTTP Cookies",
"items": {
"type": "object",
"required": [
"key",
"value"
],
"properties": {
"key": {
"type": "string",
"title": "Key"
},
"value": {
"type": "string",
"title": "Value"
}
}
}
},
"dev_transform_fields": {
"title": "♻️ CUSTOM FIELD",
"type": "array",
"description": "💡 Transform the resulting output. Select only needed fields.
For nested object use DOT. For example: address.streetAddress
For nested array use NUMBER (index of array element starting from index=0). For example: images.0.url
",
"items": {
"type": "object",
"required": [
"key",
"value"
],
"properties": {
"key": {
"type": "string",
"title": "Key"
},
"value": {
"type": "string",
"title": "Value"
}
}
}
},
"dev_dataset_name": {
"title": "📁 CUSTOM STORAGE",
"type": "string",
"description": "💡 Save results into custom named Dataset, use mask to customize dataset name
{ACTOR} = actor name
{DATE} = date (YYYYMMDD)
{TIME} = time (HHMMSS)
This masks can be used to autogenerate Dataset Name.
example: data-{DATE}
Depending on today date the dataset name will be: data-20230603
default: data-{ACTOR}-{DATE}-{TIME}"
},
"dev_dataset_clear": {
"title": "Clear Storage",
"type": "boolean",
"description": "Clear Dataset before insert/update."
},
"dev_no_strip": {
"title": "Disable data cleansing",
"type": "boolean",
"description": "💡 Keep/Save empty values (NULL, FALSE, empty ARRAY, empty OBJECT, empty STRING)"
}
}
},
"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
}
}
}
}
}
}
}
}
}
}
```