Crawler Inspector

1. Shard Calculation

Query:

Response:

Calculated Shard: 129 (from laksa172)

2. Crawled Status Check

Query:

curl -X POST \
  'http://laksa129.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 = 82 AND (src_root_hash, src_unparsed) IN ((getAhrefsRootHashFromUnparsed(getAhrefsUnparsedNoserviceFromURL(\'https://forecastegy.com/posts/catboost-feature-importance-python/\')), getAhrefsUnparsedNoserviceFromURL(\'https://forecastegy.com/posts/catboost-feature-importance-python/\'))) FORMAT JSONEachRow'

Response:

{"found_url":"https:\/\/forecastegy.com\/posts\/catboost-feature-importance-python\/","crawl_time":1778948631,"first_indexed_time":1694242398,"http_code":200,"src_unparsed":"com,forecastegy!\/posts\/catboost-feature-importance-python\/ s443","src_root_hash":"1095914986031676529","history_drop_reason":null,"meta_title":"How to Get Feature Importance in CatBoost in Python | Forecastegy","meta_descriptions":["If you’ve ever used CatBoost for machine learning, you know it’s a powerful tool. But did you know it has several ways of calculating feature importances? Understanding how these methods work can help you get more out of your models. However, these methods can get a bit complex, and it’s not always clear when to use each one. It’s like trying to choose the right tool from a toolbox when you don’t know what each tool does."],"meta_canonical":null,"ml_categories_json":"","ml_types_json":"","ml_intent_types_json":"","meta_language":"en","attrs_author":"Mario Filho","attrs_publish_time":1694131200,"attrs_original_publish_time":1694131200,"attrs_is_republished":0,"attrs_nr_words":"1735","attrs_boilerpipe_nr_words":"1605","body_ext_links_number":10,"body_int_links_number":14,"meta_nofollow":0,"meta_noarchive":0,"props_was_rendered":0,"src_redirect":"","download_time_msec":645,"download_ttfb_msec":546,"download_size":11319}

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://forecastegy.com/posts/catboost-feature-importance-python/
Last Crawled	2026-05-16 16:23:51 (18 days ago)
First Indexed	2023-09-09 06:53:18 (2 years ago)
HTTP Status Code	200
Content
Meta Title	How to Get Feature Importance in CatBoost in Python \| Forecastegy
Meta Description	If you’ve ever used CatBoost for machine learning, you know it’s a powerful tool. But did you know it has several ways of calculating feature importances? Understanding how these methods work can help you get more out of your models. However, these methods can get a bit complex, and it’s not always clear when to use each one. It’s like trying to choose the right tool from a toolbox when you don’t know what each tool does.
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	Mario Filho
Publish Time	2023-09-08 00:00:00 (2 years ago)
Original Publish Time	2023-09-08 00:00:00 (2 years ago)
Republished	No
Word Count (Total)	1,735
Word Count (Content)	1,605
Links
External Links	10
Internal Links	14
Technical SEO
Meta Nofollow	No
Meta Noarchive	No
JS Rendered	No
Redirect Target	null
Performance
Download Time (ms)	645
TTFB (ms)	546
Download Size (bytes)	11,319
Location
Host ID	129 (laksa129)
Partition ID	82
Root Hash	1095914986031676529
Unparsed URL	com,forecastegy!/posts/catboost-feature-importance-python/ s443

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