Docker Hub Images Scraper

Docker Hub Images Scraper — Repository Metadata, Pull Counts, and Image Tags

Overview

Docker Hub Images Scraper collects publicly available Docker image and repository information for research, inventory management, and development workflows.

Choose a collection mode to search repositories, retrieve repository details, or collect image tags. Configure the maximum number of records and export structured results in JSON, CSV, Excel, or other supported dataset formats.

Features

• Repository Search: Find Docker repositories matching a search query.
• Repository Details: Collect metadata for specified repositories.
• Tag Collection: Retrieve available tags for selected images.
• Pull Statistics: Capture repository pull counts when available.
• Popularity Metrics: Collect repository star counts.
• Official Image Identification: Identify official images when this information is available.
• Configurable Collection: Set record limits to control dataset size.
• Structured Exports: Prepare repository information for spreadsheets and data analysis.

Collection Modes

1. Search Repositories

Search for repositories using a keyword or phrase. Results can include repository names, descriptions, popularity metrics, and available ownership information.

2. Repository Details

Collect detailed metadata for specified repositories, such as library/python.

3. Image Tags

Retrieve available tags for selected repositories. Each tag record identifies the repository and its associated tag.

Use Cases

• Image Inventory: Maintain a structured inventory of Docker images and their metadata.
• Popularity Analysis: Compare repository star counts and pull statistics.
• Official Image Research: Distinguish official images from community-maintained repositories when the relevant metadata is available.
• Tag Audits: Review available image tags for development and deployment workflows.
• Repository Discovery: Find images relevant to Python, databases, web servers, and other technologies.
• Dataset Monitoring: Collect recurring snapshots to compare repository metadata over time.

Input Parameters

Parameter	Type	Default	Description
mode	String	search	Selects search, details, or tags collection mode.
query	String	Empty	Search term used to find repositories.
repositories	Array	[]	Repository names to inspect, such as library/python.
maxItems	Integer	100	Maximum number of records to save, up to the actor's configured limit.
requestDelaySecs	Number	1	Configured delay between requests, where applicable.
timeoutSecs	Integer	300	Overall run time limit in seconds.
requestTimeoutSecs	Integer	30	Maximum waiting time for an individual request, in seconds.

Example Input

{
  "mode": "search",
  "query": "python",
  "repositories": ["library/python"],
  "maxItems": 50,
  "requestDelaySecs": 1,
  "timeoutSecs": 300,
  "requestTimeoutSecs": 30
}

This configuration searches for Python-related repositories and saves up to 50 matching records.

What Data Does This Actor Extract?

The actor collects repository metadata and, depending on the selected mode, repository details or image tag information.

Extracted Data Fields

Field	Description
repo_name	Repository name.
short_description	Short description of the repository.
star_count	Number of stars, when available.
pull_count	Total pull count, when available.
repo_owner	Repository owner or namespace, when available.
is_automated	Indicates whether the repository is marked as automated, when available.
is_official	Indicates whether the repository is identified as official, when available.
namespace	Namespace associated with the repository, when available.
name	Repository or image name, depending on the collection mode.
tag	Image tag, when collecting tags.
scraped_at	Date and time when the record was collected.

The available fields depend on the collection mode and the metadata returned for each repository or tag.

Example Output

The following is an illustrative repository search record.

{
  "repo_name": "python",
  "short_description": "Python is an interpreted, interactive, object-oriented, open-source programming language.",
  "star_count": 10535,
  "pull_count": 9373865365,
  "repo_owner": "",
  "is_automated": false,
  "is_official": true,
  "scraped_at": "2026-10-26T00:00:00+00:00"
}

Actual values and available fields may vary depending on the repository and collection mode.

Working with Your Dataset

After collection, review the dataset in Apify and export the results in a suitable format.

• JSON: Use structured repository records in scripts and data-processing applications.
• CSV: Compare repository names, pull counts, and star counts in spreadsheet software.
• Excel: Create image inventories, repository comparisons, and tag audit reports.
• Other supported formats: Choose from the dataset export formats available in Apify.

Important Notes

• Select the collection mode that matches your needs: repository search, repository details, or image tags.
• Repository names should identify the intended namespace and image where required.
• The maxItems parameter controls the maximum number of saved records.
• Some metadata fields may be absent for particular repositories or collection modes.
• Pull counts, star counts, and available tags can change over time.
• Collected information represents the metadata available during the run.

FAQ

Who is this actor for?

It is designed for developers, DevOps engineers, security researchers, and analysts who need structured Docker image and repository metadata.

Can I search for repositories by keyword?

Yes. Use the search mode and provide a search term through query.

Can I collect details for specific repositories?

Yes. Provide repository names through repositories and select the details mode.

Can I collect image tags?

Yes. Select the tags mode and provide the repositories you want to inspect.

What export formats are supported?

You can export collected records as JSON, CSV, Excel, and other formats supported by Apify datasets.

Can I schedule recurring collections?

Yes. Configure an Apify schedule to refresh repository metadata or image tags at your preferred interval.

Disclaimer

This actor is an independent tool and is not affiliated with, endorsed by, sponsored by, or officially associated with Docker Hub, Docker, Inc., or their owners. The collected information is provided for research and informational purposes. Users are responsible for ensuring that their collection and use of data comply with applicable laws and relevant terms.

Is Web Scraping Legal?

Collecting information that is already public is generally legal. There is no single law that bans all forms of web scraping. The limits are the usual ones: do not break into accounts, do not take private or confidential data, and be careful with personal data and copyrighted works. A site's terms can also restrict how you access it. Facts such as a public name, price, or rating are not the same thing as republishing someone's article or photo.

This actor collects information from public pages. It does not log in and it does not bypass access controls.

For a longer explanation, see Apify's article Is web scraping legal?.