commoncrawl/host-index-testing-v2

Common Crawl Host Index v2

GitHub: https://github.com/commoncrawl/cc-host-index

Each crawl, we generate a Host Index, which aggregates information about each web hosted visited during the crawl. The information is aggregated from the Common Crawl columnar index, web graph, and raw crawler logs.

Quickstart

Using duckdb:

DuckDB can read directly from Huggingface:

$ duckdb
D FROM 'hf://datasets/commoncrawl/host-index-testing-v2/data/crawl=CC-MAIN-2025-18/*.parquet' LIMIT 3;
┌─────────────────────┬─────────────┬────────────────┬────────────┬────────────────┬───┬───────────────────┬─────────────────┬───────────┬───────────────────┬─────────────────┐
│ nutch_unfetched_pct │ fetch_other │ fetch_200_lote │ nutch_gone │ nutch_gone_pct │ … │ fetch_notModified │ nutch_redirPerm │ fetch_200 │ nutch_notModified │      crawl      │
│        int8         │    int64    │     int64      │   int64    │      int8      │   │       int64       │      int64      │   int64   │       int64       │     varchar     │
├─────────────────────┼─────────────┼────────────────┼────────────┼────────────────┼───┼───────────────────┼─────────────────┼───────────┼───────────────────┼─────────────────┤
│                   0 │           0 │              0 │          3 │              7 │ … │                 0 │               0 │        18 │                 0 │ CC-MAIN-2025-18 │
│                   6 │           0 │              8 │          0 │              0 │ … │                 0 │               0 │        10 │                41 │ CC-MAIN-2025-18 │
│                  80 │           0 │              0 │          8 │             17 │ … │                 0 │               0 │         0 │                 0 │ CC-MAIN-2025-18 │
├─────────────────────┴─────────────┴────────────────┴────────────┴────────────────┴───┴───────────────────┴─────────────────┴───────────┴───────────────────┴─────────────────┤
│ 3 rows                                                                                                                                                 43 columns (10 shown) │
└──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘
D SELECT surt_host_name FROM 'hf://datasets/commoncrawl/host-index-testing-v2/data/crawl=CC-MAIN-2025-18/*.parquet' LIMIT 3;
┌──────────────────────┐
│    surt_host_name    │
│       varchar        │
├──────────────────────┤
│ ae,ebury             │
│ be,echappeeduhainaut │
│ au,com,echolaw       │
└──────────────────────┘

Using Hugging Face datasets library:

"Streaming" mode (streaming=True) is recommended (see the datasets docs). This avoids the need to download all of the parquet files up-front.

>>> from datasets import load_dataset
>>> ds = load_dataset("commoncrawl/host-index-testing-v2", data_dir="data/crawl=CC-MAIN-2025-18", split="train", streaming=True)
Resolving data files: 100%|████████████| 30/30 [00:00<00:00, 43614.95it/s]
>>> print(next(iter(ds)))
{'nutch_unfetched_pct': 0, 'fetch_other': 0, 'fetch_200_lote': 0, 'nutch_gone': 3, 'nutch_gone_pct': 7, 'robots_gone': 0, 'robots_5xx': 0, 'hcrank10': 3.446, 'warc_record_length_median': 32867, 'hcrank': 11908759.0, 'nutch_fetched': 42, 'nutch_notModified_pct': 0, 'robots_4xx': 0, 'nutch_redirTemp': 0, 'fetch_redirPerm': 2, 'fetch_4xx': 0, 'url_host_tld': 'ae', 'nutch_fetched_pct': 93, 'robots_notModified': 0, 'robots_other': 0, 'nutch_redirTemp_pct': 0, 'fetch_200_lote_pct': 0, 'nutch_numRecords': 45, 'robots_3xx': 0, 'robots_redirTemp': 0, 'warc_record_length_av': 31769, 'url_host_registered_domain': 'ebury.ae', 'surt_host_name': 'ae,ebury', 'nutch_redirPerm_pct': 0, 'fetch_redirTemp': 0, 'robots_200': 3, 'fetch_3xx': 0, 'fetch_5xx': 0, 'nutch_unfetched': 0, 'prank': 4.73550733982247e-09, 'fetch_gone': 0, 'prank10': 0.0, 'robots_redirPerm': 1, 'fetch_notModified': 0, 'nutch_redirPerm': 0, 'fetch_200': 18, 'nutch_notModified': 0}

Use Cases

Example questions this index can answer

• What's our history of crawling a particular website, or group of websites?
• What popular websites have a lot of non-English content?
• What popular websites seem to have so little content that we might need to execute javascript to crawl them?

Example questions that we'll use to improve our crawl

• What's the full list of websites where more than half of the webpages are primarily not English?
• What popular websites end our crawls with most of their crawl budget left uncrawled?

Example questions that future versions of this host index can answer

• What websites have a lot of content in particular languages?
• What websites have a lot of content with particular Unicode scripts?

Examples

US Federal government websites in the *.gov domain (about 1,400 domains, y-axis scale is millions):

See all graphs from this dataset

commoncrawl.org fetch. You can see that we revamped our website in CC-2023-14, which caused a lot of permanent redirects to be crawled in the next few crawls:

See all graphs from this dataset

Python code examples

More examples of how to use this dataset with Python can be found at the Common Crawl Host Index GitHub repo.

Highlights of the Schema

• There is a Hive-style partition on crawl, which is a crawl name like CC-MAIN-2025-13.
• There is one row for every webhost in the web graph, even if we didn't crawl that website in that particular crawl
• The primary key is surt_host_name, which stores the URL in Sort-friendly URI Reordering Transform (SURT) format. For example, the URI commoncrawl.org -> becomes org,commoncrawl).
• There is also url_host_tld, which we recommend that you use whenever possible (eg org for commoncrawl.org) - it typically improves performance, significantly.
• There are counts of what we stored in our archive (warc, crawldiagnostics, robots)
• fetch_200, fetch_3xx, fetch_4xx, fetch_5xx, fetch_gone, fetch_notModified, fetch_other, fetch_redirPerm, fetch_redirTemp
• robots_200, robots_3xx, robots_4xx, robots_5xx, robots_gone, robots_notModified, robots_other, robots_redirPerm, robots_redirTemp
• There is ranking information from the web graph: harmonic centrality, page rank, and both normalized to a 0-10 scale
  • hcrank, prank, hcrank10, prank10
• There is a language summary (for now, just the count of languages other than English (LOTE))
  • fetch_200_lote, fetch_200_lote_pct
• For a subset of the numbers, there is a foo_pct (eg fetch3xx_pct) which can help you avoid doing math in SQL. It is an integer 0-100.
• There are raw numbers from our crawler logs, which also reveal the crawl budget and if we exhausted it
  • nutch_fetched, nutch_gone, nutch_notModified, nutch_numRecords, nutch_redirPerm, nutch_redirTemp, nutch_unfetched
  • nutch_fetched_pct, nutch_gone_pct, nutch_notModified_pct, nutch_redirPerm_pct, nutch_redirTemp_pct, nutch_unfetched_pct
• There is a size summary (average and median size (compressed))
  • warc_record_length_median, warc_record_length_av (will be renamed to _avg in v3)

The full schema is available here: athena_schema.v2.sql.

Known bugs

• Some of the partitions have a different schema from others, so you will get errors for some of the columns in some of the crawls. We recommend that you avoid using those crawls, and only use the columns you need.
• When the S3 bucket is under heavy use, AWS Athena will sometimes throw 503 errors. We have yet to figure out how to increase the retry limit.
• Hint: https://status.commoncrawl.org/ has graphs of S3 performance for the last day, week, and month.
• The sort order is a bit messed up, so database queries take more time than they should.

Expected changes in test v3

• warc_record_length_av will be renamed to _avg (that was a typo)
• more _pct columns
• count truncations: length, time, disconnect, unspecified
• addition of indegree and outdegree from the web graph
• improve language details to be more than only LOTE and LOTE_pct
  • content_language_top, content_language_top_pct
• add unicode block information, similar to languages
• prank10 needs its power law touched up (hcrank10 might change too)
• there's a sort problem that .com shards have a smattering of not-.com hosts. This hurts performance.
• add domain prank/hcrank
• CI running against S3
• robots_digest_count_distinct
• robots_digest (if there is exactly 1 robots digest)
• Summarize fetch_redirect: same surt, same surt host, other.

Contributing

We'd love to get testing and code contributions! Here are some clues:

• We'd love to hear if you tried it out, and what your comments are
• We'd love to have python examples using Athena, similar to duckdb
• We'd love to have more python examples
• Please use pyarrow whenever possible

Terms of Use

https://commoncrawl.org/terms-of-use