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crawlora-net
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dead-web-index

Tags:
dead-web
domain-reachability
web-health
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The dataset viewer is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
domain: string
tld: string
rank: int64
mode: string
outcome: string
reason: string
first_status: int64
final_status: int64
final_url: string
scheme: string
hops: int64
parked: bool
park_vendor: string
run_id: string
probed_at: timestamp[s]
chunks: list<item: struct<file: string, arm: string, rows: int64, rank_min: int64, rank_max: int64, bytes: i (... 22 chars omitted)
  child 0, item: struct<file: string, arm: string, rows: int64, rank_min: int64, rank_max: int64, bytes: int64, sha25 (... 10 chars omitted)
      child 0, file: string
      child 1, arm: string
      child 2, rows: int64
      child 3, rank_min: int64
      child 4, rank_max: int64
      child 5, bytes: int64
      child 6, sha256: string
span: int64
codec: string
to
{'run_id': Value('string'), 'codec': Value('string'), 'span': Value('int64'), 'chunks': List({'file': Value('string'), 'arm': Value('string'), 'rows': Value('int64'), 'rank_min': Value('int64'), 'rank_max': Value('int64'), 'bytes': Value('int64'), 'sha256': Value('string')})}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 149, in get_rows_or_raise
                  return get_rows(
                      dataset=dataset,
                  ...<4 lines>...
                      column_names=column_names,
                  )
                File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
                  return func(*args, **kwargs)
                File "/src/services/worker/src/worker/utils.py", line 129, in get_rows
                  rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
                File "/src/services/worker/src/worker/utils.py", line 489, in safe_iter
                  yield from ds.decode(False) if ds.features else ds
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2818, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2355, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2380, in _iter_arrow
                  for key, pa_table in self.ex_iterable._iter_arrow():
                                       ~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 536, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 419, in _iter_arrow
                  for key, pa_table in self.generate_tables_fn(**gen_kwags):
                                       ~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 343, in _generate_tables
                  self._cast_table(pa_table, json_field_paths=json_field_paths),
                  ~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
                  pa_table = table_cast(pa_table, self.info.features.arrow_schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2369, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2297, in cast_table_to_schema
                  raise CastError(
                  ...<3 lines>...
                  )
              datasets.table.CastError: Couldn't cast
              domain: string
              tld: string
              rank: int64
              mode: string
              outcome: string
              reason: string
              first_status: int64
              final_status: int64
              final_url: string
              scheme: string
              hops: int64
              parked: bool
              park_vendor: string
              run_id: string
              probed_at: timestamp[s]
              chunks: list<item: struct<file: string, arm: string, rows: int64, rank_min: int64, rank_max: int64, bytes: i (... 22 chars omitted)
                child 0, item: struct<file: string, arm: string, rows: int64, rank_min: int64, rank_max: int64, bytes: int64, sha25 (... 10 chars omitted)
                    child 0, file: string
                    child 1, arm: string
                    child 2, rows: int64
                    child 3, rank_min: int64
                    child 4, rank_max: int64
                    child 5, bytes: int64
                    child 6, sha256: string
              span: int64
              codec: string
              to
              {'run_id': Value('string'), 'codec': Value('string'), 'span': Value('int64'), 'chunks': List({'file': Value('string'), 'arm': Value('string'), 'rows': Value('int64'), 'rank_min': Value('int64'), 'rank_max': Value('int64'), 'bytes': Value('int64'), 'sha256': Value('string')})}
              because column names don't match

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Dead-Web Index — data

A reachability census of the most popular domains on the web. Every domain in the DomCop top-10M popularity list (this release: the full top 10 million) is probed and labelled alive / redirect / blocked / dead — once by an honest polite bot and once by a browser-like reachability client.

It is the dataset behind the Crawlora Dead-Web Index explorer.

What "dead" means

A domain is dead only if it is genuinely unreachable — no DNS resolution, a refused/reset connection, or nothing accepting a TCP connection. A server that answers any HTTP status (including 404, 5xx, or a Cloudflare 522) or merely accepts a TCP connection is alive or blocked, not dead: the host is up, it just may not serve a homepage or may refuse a bot. Many entries in a popularity list are asset/CDN/API hosts (e.g. static.cloudflareinsights.com, fonts.googleapis.com) that 404 or tarpit a bare GET / yet are very much alive.

  • alive — a usable HTTP response (2xx, or a 4xx/5xx the server answered).
  • redirect — ended on an unresolved redirect.
  • blocked — host up but would not serve us: anti-bot / auth / rate-limit, or it accepts a TCP connection yet will not complete HTTP (tarpit/timeout, strict TLS).
  • dead — no DNS, connection refused/reset, or nothing listening.

The two modes are the point: a polite plain-HTTP bot vs a reachability client with a real Chrome TLS/JA3 fingerprint. The gap — a domain dead/blocked to the bot but alive to the browser — is the slice of the web that is reachable with better tooling.

Files

The full per-arm data ships as release assets on the latest release, split into 10 chunks per arm partitioned by DomCop rank (1,000,000 domains each) and compressed with zstd. Every chunk is a self-contained .jsonl.zst (20 MB) — independently decodable, so you can grab just the popularity band you need, resume a download, or fetch in parallel. The repo carries a small preview, the run-level aggregates, and a checksummed manifest.

file rows where description
polite.part-01.jsonl.zst … part-10 9,992,781 total release assets polite arm, 10 rank-partitioned chunks (part-01 = ranks 1–1,000,000 … part-10 = 9,000,001–10,000,000)
reachability.part-01.jsonl.zst … part-10 9,997,315 total release assets reachability (browser) arm, same 10-chunk rank partition
data/manifest.json — in repo per-chunk rows, rank range, byte size, and sha256
data/sample.jsonl 1,000 in repo uncompressed preview (both arms)
data/summary.json — in repo run-level aggregates (outcome split, by-reason, by-TLD)

Getting the data 

base=https://github.com/Crawlora-org/dead-web-index-data/releases/download/v1.0.0
# grab one arm's 10 chunks, then decode + concatenate into a single JSONL stream:
for i in $(seq -w 1 10); do curl -fL -O "$base/polite.part-$i.jsonl.zst"; done
zstd -dc polite.part-*.jsonl.zst > polite.jsonl        # needs `zstd` (apt/brew install zstd)

zstd-compressed JSONL is read directly by DuckDB (read_json_auto('polite.part-*.jsonl.zst')) and Polars; pandas needs the zstandard package (or just decode with zstd -d first). Verify each download against the sha256 in data/manifest.json.

Each line is one JSON record:

{"domain":"example.com","tld":"com","rank":12345,"mode":"polite","outcome":"alive","reason":"ok","first_status":200,"final_status":200,"final_url":"https://example.com/","scheme":"https","hops":0,"parked":false,"run_id":"top10m-20260615","probed_at":"2026-06-15T..."}
field meaning
domain the probed host
tld last dot-label (coarse TLD)
rank DomCop popularity rank
mode polite or reachability
outcome alive / redirect / blocked / dead
reason finer reason (ok, dns_failed, timeout, not_found, forbidden, rate_limited, server_error, tls_error, connection_refused, …)
first_status HTTP status of the first hop (0 if none)
final_status HTTP status after redirects (0 if no response)
final_url URL of the final response
scheme https or http
hops redirects followed
parked / park_vendor parked/for-sale domain + vendor
run_id scan run (this release: top10m-20260615)
probed_at RFC3339 timestamp

Method (brief)

HTTPS-first with HTTP fallback; cross-resolver DNS retry (8 public resolvers); redirects followed (max 10); a retry on transient/timeout failures with an escalating deadline; a raw TCP-connect check to separate "up but unresponsive" (blocked) from "gone" (dead); parking-page detection. Homepage-level, from a datacenter vantage — a lower bound on "alive" (deep pages and residential vantages reach more). The classification logic — DNS / TCP / HTTP probing and the alive / redirect / blocked / dead rules — is open-sourced as a CLI you can run on your own domains: crawlora-deadweb (go install github.com/Crawlora-org/crawlora-deadweb@latest).

This release

  • run_id: top10m-20260615 · the full top 10,000,000 domains (DomCop), June 2026
  • polite: 14.1% dead · 8.9% blocked · 76.6% alive · 0.3% redirect (9,992,781 domains)
  • reachability: 14.1% dead · 8.2% blocked · 77.5% alive · 0.2% redirect (9,997,315 domains)
  • Genuinely-dead (14.1%) is identical across both arms — no-DNS / no-connect does not depend on the client fingerprint. The browser-fingerprint arm clears more anti-bot walls, so its blocked rate is lower (8.9% → 8.2%): a real Chrome TLS/JA3 fingerprint reaches ~72,000 sites the polite bot is shut out of.

How to cite

This repository ships a CITATION.cff, so GitHub shows a “Cite this repository” button that generates BibTeX/APA for you. Plain text:

Crawlora (2026). Crawlora Dead-Web Index: a reachability census of the top 10 million domains (v1.0.0) [Data set]. https://github.com/Crawlora-org/dead-web-index-data

Data collection & ethics

  • Public infrastructure only. Each record is the result of an unauthenticated HTTP GET / (plus DNS/TCP checks) to a domain's public homepage — the same request any browser makes. No authentication was bypassed, no login-only or robots.txt-disallowed paths were fetched, and no page content is republished. The dataset records only the reachability state of a public endpoint.
  • No personal data. Records contain domain names and their observed HTTP/DNS status — no WHOIS registrant data, no emails, no personal identifiers. Domain names in a popularity ranking are public records of (overwhelmingly) organisations, not individuals.
  • Point-in-time. This is a single snapshot (see run_id); a domain dead today may resolve tomorrow and vice-versa. Treat it as a census, not a live status feed.
  • A lower bound on “alive.” Probing is homepage-level from a datacenter vantage. Deep pages, residential vantages, and per-geo content reach more than a bare GET / does, so the true “alive” share is somewhat higher than reported.

License

Data is licensed CC BY 4.0 — free to use, share and adapt with attribution to Crawlora (https://crawlora.net). See LICENSE.
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