How to Scrape Hashnode Tech Blog Posts (2026)

Hashnode’s public API makes scraping tech blog posts more straightforward than most platforms, but there are enough quirks in rate limiting, pagination, and publication routing that a naive implementation will fail in production. Here’s how to extract Hashnode articles, author data, and publication stats reliably in 2026.

Why Hashnode Is Worth Scraping

Hashnode hosts tens of thousands of developer blogs on custom subdomains (e.g., yourname.hashnode.dev) plus mapped custom domains. Each publication runs on a shared GraphQL API at gql.hashnode.com, which means one endpoint covers the entire platform. Unlike scraping Quora questions and answers programmatically where you’re fighting JavaScript-heavy renders and login walls, Hashnode’s API is publicly accessible and returns clean JSON.

Use cases worth building for:

Trend analysis across developer topics (Rust, AI, DevOps)
Author discovery for outreach or competitive research
Content aggregation for niche newsletters
Dataset construction for LLM fine-tuning on technical writing

The GraphQL API: Your Primary Entry Point

Hashnode exposes everything through https://gql.hashnode.com. No API key required for read-only public data. The main queries you’ll use are publication (to get posts by a specific blog) and searchPostsOfPublication for filtered access.

A basic query to fetch recent posts from a publication:

import httpx

ENDPOINT = "https://gql.hashnode.com"

QUERY = """
query GetPosts($host: String!, $first: Int!, $after: String) {
  publication(host: $host) {
    title
    posts(first: $first, after: $after) {
      edges {
        node {
          title
          slug
          publishedAt
          readTimeInMinutes
          tags { name }
          author { name username }
          views
          reactionCount
        }
        cursor
      }
      pageInfo { hasNextPage endCursor }
    }
  }
}
"""

def fetch_posts(host: str, first: int = 20, after: str = None):
    variables = {"host": host, "first": first, "after": after}
    resp = httpx.post(ENDPOINT, json={"query": QUERY, "variables": variables}, timeout=15)
    resp.raise_for_status()
    return resp.json()["data"]["publication"]["posts"]

# Example: fetch from a publication
result = fetch_posts("engineering.hashnode.com")

Pagination is cursor-based. Pull pageInfo.endCursor and pass it as after on the next call. For full crawls, loop until hasNextPage is false.

Handling Rate Limits and Scale

Hashnode doesn’t publish hard rate limit numbers, but in practice you’ll hit 429s around 60–80 requests per minute from a single IP. For small publications (under 500 posts), this is fine with a 1-second sleep between pages. For large-scale crawls across hundreds of publications, you need rotating proxies.

Residential proxies are overkill here since Hashnode’s API doesn’t do sophisticated bot detection. Datacenter proxies work fine. If you’re already running infrastructure for scraping Pinterest pin and board data at scale, the same proxy pool applies.

Approach	RPM limit	Cost	Works on Hashnode
Direct IP	~60 RPM	Free	Yes, small jobs
Datacenter proxy	~300 RPM pooled	$2-5/GB	Yes, recommended
Residential proxy	~500 RPM pooled	$8-15/GB	Yes, overkill
Scraping API (e.g. Apify)	Varies	$0.25-1/1k	Yes, no setup

Discovering Publications at Scale

If you want to crawl Hashnode broadly rather than targeting specific blogs, you have two options:

Hashnode’s search API — use the searchPosts query with keyword filters. This surfaces posts across all publications but is limited to ~1000 results per search term.
Sitemap crawl — Hashnode generates sitemaps at https://{host}/sitemap.xml. Scraping sitemaps is faster and more complete than paginating the API for large publications.
Tag-based discovery — query posts by tag using tag(slug: "python") { posts { ... } }. Good for vertical topic coverage.
GitHub user discovery — many developers link their Hashnode blog in GitHub profiles. Cross-reference GitHub’s API to build a seed list.

This tag-based approach is similar to how you’d approach scraping Dev.to public articles at scale, where tag feeds give you a structured entry point into the full content graph.

Enriching Post Data

The base API response gives you views and reaction counts, but Hashnode also exposes comment threads and series (multi-part post collections). Add these to your query if you’re building engagement datasets:

ENRICHED_POST_FIELDS = """
  title
  slug
  content { text }
  comments(first: 10) {
    edges {
      node {
        content { text }
        author { username }
        dateAdded
      }
    }
  }
  series { name slug }
  coverImage { url }
  seo { title description }
"""

Note that content { text } returns plain text, not HTML. Use content { html } if you need markup preserved. For author pages, the user(username: "...") query gives you follower count, total post count, and social links.

If you’re building cross-platform content datasets, Hashnode pairs well with Medium article scraping for broader developer writing coverage since both platforms serve the technical blogging niche but have very different author demographics.

Error Handling and Edge Cases

A few things that will break naive scrapers:

Deleted or private posts: The API returns null for deleted posts in cursor results without raising an error. Always null-check before processing.
Custom domains: Publications on custom domains (not .hashnode.dev) use the same API but you need the exact host string. If the custom domain redirects, follow the redirect to find the canonical host.
Draft posts: The API only exposes published posts. No workaround without authentication.
GraphQL complexity limits: Deeply nested queries (posts + comments + author + series all at once) hit a complexity cap around 15,000 points. Break into separate queries.
views field access: As of early 2026, the views field on posts requires the requester’s blog to be the publication owner. Querying another publication’s posts returns null for views. Plan your schema accordingly.

For feeds that update frequently, checkpoint with publishedAt rather than relying on stable cursor positions. Cursors can shift if posts are deleted between crawl sessions. This is the same pagination hygiene you’d apply when using the Bluesky AT Protocol for post scraping where cursor stability is equally unreliable across session boundaries.

Key retry logic to implement:

Retry 429s with exponential backoff starting at 2 seconds
Retry 502/503 immediately once, then backoff
Log and skip on null node results rather than crashing
Validate pageInfo.hasNextPage before each next-page call

Bottom Line

Hashnode is one of the cleaner platforms to scrape: public GraphQL API, no authentication for read access, cursor-based pagination, and predictable response shapes. The main gotchas are the views field restriction, GraphQL complexity limits on deep queries, and the need for proxy rotation above ~100 publications. Start with the sitemap for full-publication crawls and fall back to tag queries for cross-platform topic coverage. DRT covers scraping infrastructure like this across the full stack, from data extraction through proxy management to structured storage.