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One developer says a crawl project that began on a 2 GB VPS grew to 38.5 million known domains. That figure describes discovered domains—not 38.5 million fully indexed websites. In a September 27, 2026 account, the project author, writing as cl0q, reported 24.8 million indexed pages, with 24.4 million still live. The numbers are self-reported, not independently audited, but the engineering story is a useful look at what makes a small search crawler difficult: keeping discovery broad, storing what it finds, and refusing to index pages that would make results worse.
What does “38.5 million domains” actually mean?
In a first-person account published September 27, 2026, cl0q reported 38.5 million known domains in the project database. The same snapshot listed 24.8 million indexed pages, of which 24.4 million were live. The author also said the crawler had attempted 97% of known domains and that 92% resolved in DNS. These are the author’s project figures, not an independent audit or a measure of complete website coverage. Read the account on DEV Community.
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Outbyte PC Repair FREERepair Windows errors before they cause bigger problemsFix Now →Outbyte Driver Updater FREEFix the driver behind crashes, sound loss and screen glitchesFind Drivers →A domain discovered by a crawler is not necessarily a site with many pages in the index. The project’s approach emphasized breadth, including fetching homepages, with deeper crawling described as a future direction. Its count therefore says more about the size of the discovery set than how much content it had collected from every site.
The author reported 1,027 represented top-level domains and a crawl rate of roughly 250,000 to 300,000 pages per day. Those figures are also attributed to cl0q’s 2026 database snapshot; they should not be read as a current live count.
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How did the crawl find so many domains?
The project began crawling in 2025, and its first recorded crawl dates to April of that year. Discovery accelerated in June 2026, when cl0q says it found 8.5 million domains in a month. A key change was moving away from following links in a way that let one corner of the web dominate the crawl.
Why the first approach became skewed
Following links naively led the crawler into about a million Blogspot subdomains associated with one domain, according to the author. That can inflate the apparent reach of a crawl without expanding its coverage proportionally across the wider web. The problem was not simply finding links; it was deciding which parts of the frontier to revisit and which new paths to explore.
How a rotating frontier helped
Cl0q says the crawler shifted to a rotating frontier: repeatedly sample known domains and mine their homepages for outbound links. This was intended to balance revisiting the known set with discovering new domains, rather than allowing one dense cluster of linked subdomains to consume attention.
Certificate Transparency logs provided another discovery source. At peak, the author says, the project surfaced around 1,700 new domains per minute through this route. That is a reported peak discovery rate, not a sustained crawl or indexing rate: newly surfaced names still have to be resolved, fetched, screened, and stored.
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What did the system look like?
The author describes four small, incremental stages: discovery, page crawl, enrichment, and serving. The stages could be restarted rather than requiring every failure to trigger a full rebuild. This is a practical design for a resource-constrained project: work can accumulate in pieces, and interrupted jobs need not erase all earlier progress.
- Discovery: gather candidate domains from links and Certificate Transparency logs.
- Page crawl: fetch pages from selected domains; the project’s emphasis at the time was on homepages and breadth.
- Enrichment: process fetched material into additional signals, including favicon hashes.
- Serving: make the resulting index searchable.
These are the stages cl0q reports using; the account does not establish that they are a universal best-practice architecture or provide a benchmark against other designs.
Why was 2 GB not the whole infrastructure story?
The 2 GB VPS was an early constraint, not evidence that a mature search engine could run indefinitely on that amount of memory and storage. The author identifies disk use and competition for database time as the major infrastructure problems. In particular, search and crawling shared a single-core database and table, so background work and target selection could slow searches.
Cl0q says storage was moved to a remote host connected over Tailscale, but storage remained a constraint. Moving data off the original VPS changed where the pressure sat; it did not make the growing crawl cheap or remove the need to manage data volume.
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- Disk capacity: a broad crawl continually adds records and creates pressure to decide what to retain.
- Database contention: crawler jobs competing with search requests can impair responsiveness when they share constrained resources.
- Recovery: small, restartable pipeline stages limit the cost of interruption, but they do not eliminate storage or throughput limits.
Cl0q’s practical warning was: “If there’s one thing I’d tell anyone starting a crawl project: budget ten times the disk you think you need, then double it.” That is advice from this project author, not a measured capacity formula.
Why did the crawler reject some pages?
A crawler can fetch a page successfully and still make a poor choice by indexing it. The project filters anti-bot interstitials instead of treating challenge text as ordinary site content. Without that step, search results can contain boilerplate asking a visitor to prove they are human rather than useful information from the site.
The author frames this as a quality decision: “A search engine is only as good as what it refuses to index.” In this account, rejecting challenge pages is one example of how crawl policy shapes the usefulness of an index, not merely its size.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What can favicon data reveal?
Cl0q reports that 16.3 million sites had favicons in the project data, which corresponded to about 7 million distinct icons after hashing with SHA-256. Sites sharing an icon can offer an OSINT pivot: an investigator might look for other domains associated with the same asset. That is an observed investigative lead, not proof that sites with the same favicon have the same owner or are otherwise related.
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What does the account say about privacy and openness?
Cl0q says the service sets no cookies and stores no searches for logged-out visitors. Accounts were described as optional and passkey-based, with search history available only when enabled. These are claims in the author’s September 2026 account, not an independent privacy audit or confirmation of current behavior.
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What was still a goal rather than a result?
The stated target was 100 million domains. The author also mentioned a free beta API and anticipated paid tiers. These were goals or plans described in the September 27, 2026 post, not confirmed present-day outcomes. The post said recrawls would start October 9, 2026; it does not establish whether they began as planned.
The central achievement in the account is therefore not proof of a finished, comprehensive alternative to established search engines. It is a self-reported snapshot of a small project prioritizing domain discovery and incremental growth while confronting skewed link paths, limited storage, database contention, and crawl-quality choices.
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