Crawler Inspector

1. Shard Calculation

Query:

Response:

Calculated Shard: 46 (from laksa001)

2. Crawled Status Check

Query:

curl -X POST \
  'http://laksa046.int.ahrefs:8124/' \
  -H 'Content-Type: text/plain' \
  -H 'X-ClickHouse-Database: crawler3' \
  -H 'Authorization: Basic YXBpOg==' \
  -d 'SELECT getAhrefsURLFromUnparsed(src_unparsed) AS found_url, ifNull(toUnixTimestamp(download_stamp), 0) AS crawl_time, ifNull(toUnixTimestamp(props_url_first_seen), 0) AS first_indexed_time, download_http_code AS http_code, src_unparsed AS src_unparsed, src_root_hash AS src_root_hash, history_drop_reason AS history_drop_reason, meta_title AS meta_title, meta_descriptions AS meta_descriptions, meta_canonical AS meta_canonical, ml_categories_json AS ml_categories_json, ml_types_json AS ml_types_json, ml_intent_types_json AS ml_intent_types_json, meta_language AS meta_language, attrs_author AS attrs_author, ifNull(toUnixTimestamp(attrs_publish_time), 0) AS attrs_publish_time, ifNull(toUnixTimestamp(attrs_original_publish_time), 0) AS attrs_original_publish_time, ifNull(attrs_is_republished, 0) AS attrs_is_republished, ifNull(attrs_nr_words, 0) AS attrs_nr_words, ifNull(attrs_boilerpipe_nr_words, 0) AS attrs_boilerpipe_nr_words, ifNull(body_ext_links_number, 0) AS body_ext_links_number, ifNull(body_int_links_number, 0) AS body_int_links_number, ifNull(meta_nofollow, 0) AS meta_nofollow, ifNull(meta_noarchive, 0) AS meta_noarchive, ifNull(props_was_rendered, 0) AS props_was_rendered, ifNull(src_redirect, \'\') AS src_redirect, ifNull(download_time_msec, 0) AS download_time_msec, ifNull(download_ttfb_msec, 0) AS download_ttfb_msec, ifNull(download_size, 0) AS download_size FROM crawler3.page_info_local FINAL PREWHERE int_partition_id = 81 AND (src_root_hash, src_unparsed) IN ((getAhrefsRootHashFromUnparsed(getAhrefsUnparsedNoserviceFromURL(\'https://sparkbyexamples.com/pandas/pandas-drop-rows-with-nan-values-in-dataframe/\')), getAhrefsUnparsedNoserviceFromURL(\'https://sparkbyexamples.com/pandas/pandas-drop-rows-with-nan-values-in-dataframe/\'))) FORMAT JSONEachRow'

Response:

{"found_url":"https:\/\/sparkbyexamples.com\/pandas\/pandas-drop-rows-with-nan-values-in-dataframe\/","crawl_time":1778676010,"first_indexed_time":1632324901,"http_code":200,"src_unparsed":"com,sparkbyexamples!\/pandas\/pandas-drop-rows-with-nan-values-in-dataframe\/ s443","src_root_hash":"13197168827745396246","history_drop_reason":null,"meta_title":"Pandas Drop Rows with NaN Values in DataFrame - Spark By {Examples}","meta_descriptions":["To drop rows with NaN (null) values in a Pandas DataFrame, you can use the dropna() function. Python doesn't support Null hence any missing data is"],"meta_canonical":null,"ml_categories_json":"","ml_types_json":"","ml_intent_types_json":"","meta_language":"en-us","attrs_author":"Naveen Nelamali","attrs_publish_time":1625513409,"attrs_original_publish_time":1625513409,"attrs_is_republished":0,"attrs_nr_words":"371","attrs_boilerpipe_nr_words":"239","body_ext_links_number":13,"body_int_links_number":46,"meta_nofollow":0,"meta_noarchive":0,"props_was_rendered":0,"src_redirect":"","download_time_msec":919,"download_ttfb_msec":652,"download_size":53219}

Filter	Status	Condition	Details
HTTP status	PASS	`download_http_code = 200`	HTTP 200
Age cutoff	PASS	`download_stamp > now() - 6 MONTH`	0.7 months ago
History drop	PASS	`isNull(history_drop_reason)`	No drop reason
Spam/ban	PASS	`fh_dont_index != 1 AND ml_spam_score = 0`	ml_spam_score=0
Canonical	PASS	`meta_canonical IS NULL OR = '' OR = src_unparsed`	Not set

Property	Value
URL	https://sparkbyexamples.com/pandas/pandas-drop-rows-with-nan-values-in-dataframe/
Last Crawled	2026-05-13 12:40:10 (20 days ago)
First Indexed	2021-09-22 15:35:01 (4 years ago)
HTTP Status Code	200
Content
Meta Title	Pandas Drop Rows with NaN Values in DataFrame - Spark By {Examples}
Meta Description	To drop rows with NaN (null) values in a Pandas DataFrame, you can use the dropna() function. Python doesn't support Null hence any missing data is
Meta Canonical	null
Boilerpipe Text	heavy column, fetched on demand
Markdown	heavy column, fetched on demand
Readable Markdown	heavy column, fetched on demand
ML Classification
ML Categories	null
ML Page Types	null
ML Intent Types	null
Content Metadata
Language	en-us
Author	Naveen Nelamali
Publish Time	2021-07-05 19:30:09 (4 years ago)
Original Publish Time	2021-07-05 19:30:09 (4 years ago)
Republished	No
Word Count (Total)	371
Word Count (Content)	239
Links
External Links	13
Internal Links	46
Technical SEO
Meta Nofollow	No
Meta Noarchive	No
JS Rendered	No
Redirect Target	null
Performance
Download Time (ms)	919
TTFB (ms)	652
Download Size (bytes)	53,219
Location
Host ID	46 (laksa046)
Partition ID	81
Root Hash	13197168827745396246
Unparsed URL	com,sparkbyexamples!/pandas/pandas-drop-rows-with-nan-values-in-dataframe/ s443

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