Crawler Inspector

1. Shard Calculation

Query:

Response:

Calculated Shard: 77 (from laksa050)

2. Crawled Status Check

Query:

curl -X POST \
  'http://laksa077.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 = 31 AND (src_root_hash, src_unparsed) IN ((getAhrefsRootHashFromUnparsed(getAhrefsUnparsedNoserviceFromURL(\'https://medium.com/@ai_academy/time-series-forecasting-lesson-1-exponential-smoothing-eb55ecbb679d\')), getAhrefsUnparsedNoserviceFromURL(\'https://medium.com/@ai_academy/time-series-forecasting-lesson-1-exponential-smoothing-eb55ecbb679d\'))) FORMAT JSONEachRow'

Response:

{"found_url":"https:\/\/medium.com\/@ai_academy\/time-series-forecasting-lesson-1-exponential-smoothing-eb55ecbb679d","crawl_time":1778899710,"first_indexed_time":0,"http_code":200,"src_unparsed":"com,medium!\/@ai_academy\/time-series-forecasting-lesson-1-exponential-smoothing-eb55ecbb679d s443","src_root_hash":"13179037029838926277","history_drop_reason":null,"meta_title":"Time Series Forecasting Lesson 1: Exponential Smoothing | by Debasish Dutta | Medium","meta_descriptions":["Time Series Forecasting Lesson 1: Exponential Smoothing Time Series Forecasting: Time Series is a time stamped data. Time-series forecasting assumes that a time series is a combination of a pattern …"],"meta_canonical":null,"ml_categories_json":"","ml_types_json":"","ml_intent_types_json":"","meta_language":"en","attrs_author":"Debasish Dutta","attrs_publish_time":1759916853,"attrs_original_publish_time":0,"attrs_is_republished":1,"attrs_nr_words":"2046","attrs_boilerpipe_nr_words":"1946","body_ext_links_number":2,"body_int_links_number":27,"meta_nofollow":0,"meta_noarchive":0,"props_was_rendered":0,"src_redirect":"","download_time_msec":501,"download_ttfb_msec":498,"download_size":39734}

Filter	Status	Condition	Details
HTTP status	PASS	`download_http_code = 200`	HTTP 200
Age cutoff	PASS	`download_stamp > now() - 6 MONTH`	0.6 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://medium.com/@ai_academy/time-series-forecasting-lesson-1-exponential-smoothing-eb55ecbb679d
Last Crawled	2026-05-16 02:48:30 (18 days ago)
First Indexed	not set
HTTP Status Code	200
Content
Meta Title	Time Series Forecasting Lesson 1: Exponential Smoothing \| by Debasish Dutta \| Medium
Meta Description	Time Series Forecasting Lesson 1: Exponential Smoothing Time Series Forecasting: Time Series is a time stamped data. Time-series forecasting assumes that a time series is a combination of a pattern …
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
Author	Debasish Dutta
Publish Time	2025-10-08 09:47:33 (7 months ago)
Original Publish Time	not set
Republished	Yes
Word Count (Total)	2,046
Word Count (Content)	1,946
Links
External Links	2
Internal Links	27
Technical SEO
Meta Nofollow	No
Meta Noarchive	No
JS Rendered	No
Redirect Target	null
Performance
Download Time (ms)	501
TTFB (ms)	498
Download Size (bytes)	39,734
Location
Host ID	77 (laksa077)
Partition ID	31
Root Hash	13179037029838926277
Unparsed URL	com,medium!/@ai_academy/time-series-forecasting-lesson-1-exponential-smoothing-eb55ecbb679d s443

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1. Shard Calculation

Query:

Response:

2. Crawled Status Check

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Response:

3. Robots.txt Check

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Response:

4. Spam/Ban Check

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Response:

5. Seen Status Check

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