Guide de référence pour le rapport du tableau de bord administrateur

[Discourse]

This is a reference guide for describing how the Admin Dashboard Reports function, the data they’re displaying, the corresponding Data Explorer SQL queries, and where to find the Ruby code for each report.

Required user level: Staff

Discourse contains several built-in admin dashboard Reports that can be useful for exploring stats about a community. To access these reports, you can visit discourse.example.com/admin/dashboard/reports on your site ( or click the Reports link at the top of the dashboard). Note that only staff users will have access to these reports.

Data from all users on a site is included in these reports (including staff activity like visiting admin pages). The only condition that is put on users in the reports is that they are ‘real’ users, which is used to exclude the system user from the reports.

Plugins can also add reports to the dashboard with add_report(name, &block).

Ruby models for most reports are located at: discourse/app/models/concerns/reports/. Some reports also reference: discourse/app/models/report.rb

The dashboard-sql topics contain all of the corresponding SQL queries that can be used to generate reports identical to the Admin Dashboard Reports. These queries can be used within the Data Explorer plugin and for Running Data Explorer queries with the Discourse API

Accepted solutions

Displays daily tally of posts marked as solutions.

Ruby code: discourse-solved/plugin.rb at main · discourse/discourse-solved · GitHub

SQL Query: Dashboard Report - Accepted Solutions

Admin Logins

List of admin login times with locations.

Ruby Code: discourse/app/models/concerns/reports/staff_logins.rb

SQL Query: Dashboard Report - Admin Logins

Anonymous

Number of new pageviews by visitors not logged in to an account.

Ruby Code: discourse/app/models/concerns/reports/consolidated_page_views.rb

SQL Query: Dashboard Report - Anonymous

Bookmarks

Number of new topics and posts bookmarked.

Ruby Code: discourse/app/models/concerns/reports/bookmarks.rb

SQL Query: Dashboard Report - Bookmarks

Consolidated API Requests

API usage statistics by date, tracking both regular API requests and user API requests.

Ruby Code: discourse/app/models/concerns/reports/consolidated_api_requests.rb at main · discourse/discourse · GitHub

SQL Query: Dashboard Report - Consolidated API Requests

Consolidated Pageviews

Pageviews for logged in users, anonymous users and crawlers.

Ruby Code: discourse/app/models/concerns/reports/consolidated_page_views.rb

SQL Query: Dashboard Report - Consolidated Pageviews

Consolidated Pageviews with Browser Detection (Experimental)

Pageviews for logged in users, anonymous users, known crawlers and other. This experimental report ensures logged-in/anon requests are coming from real browsers before counting them.

Ruby Code: discourse/app/models/concerns/reports/consolidated_page_views_browser_detection.rb

SQL Query: Dashboard Report - Consolidated Pageviews with Browser Detection

DAU/MAU

Number of members that logged in in the last day divided by number of members that logged in in the last month – returns a % which indicates community ‘stickiness’. Aim for >20%.

Ruby Code: discourse/app/models/concerns/reports/dau_by_mau.rb

SQL Query: Dashboard Report - DAU/MAU

Daily Engaged Users

Number of users that have liked or posted in the last day.

Ruby Code: discourse/app/models/concerns/reports/daily_engaged_users.rb

SQL Query: Dashboard Report - Daily Engaged Users

Emails Sent

Number of new emails sent.

Ruby Code: discourse/app/models/concerns/reports/emails.rb

SQL Query: Dashboard Report - Emails Sent

Flags

Number of new flags.

Ruby Code: discourse/app/models/concerns/reports/flags.rb

SQL Query: Dashboard Report - Flags

Flags Status

List of flags’ statuses including type of flag, poster, flagger, and time to resolution.

Ruby Code: discourse/app/models/concerns/reports/flags_status.rb

SQL Query: Dashboard Report - Flags Status

Likes

Number of new likes.

Ruby Code: discourse/app/models/concerns/reports/likes.rb

SQL Query: Dashboard Report - Likes

Logged In

Number of new pageviews from logged in users.

Ruby Code: discourse/app/controllers/admin/reports_controller.rb#L5

SQL Query: Dashboard Report - Logged In

Moderator Activity

List of moderator activity including flags reviewed, reading time, topics created, posts created, personal messages created, and revisions.

SQL Query: Dashboard Report - Moderator Activity

Moderator Warning

Number of warnings sent by personal messages from moderators.

Ruby Code: discourse/app/models/concerns/reports/moderator_warning_private_messages.rb

SQL Query: Dashboard Report - Moderator Warnings

New Contributors

Number of users who made their first post during this period.

Ruby Code: discourse/app/models/concerns/reports/new_contributors.rb

SQL Query: Dashboard Report - New Contributors

Notify Moderators

Number of times moderators have been privately notified by a flag.

Ruby Code: discourse/app/models/concerns/reports/notify_moderators_private_messages.rb

SQL Query: Dashboard Report - Notify Moderators

Notify User

Number of times users have been privately notified by a flag.

Ruby Code: discourse/app/models/concerns/reports/notify_user_private_messages.rb

SQL Query: Dashboard Report - Notify User

Overall Sentiment

Number of posts classified either positive or negative with the “Sentiment” AI, over a specified period.

Ruby Code: discourse-ai/lib/sentiment/entry_point.rb at main · discourse/discourse-ai · GitHub

SQL Query: Dashboard Report - Overall Sentiment

Pageviews

Number of new pageviews from all visitors. Same as the total for Consolidated Pageviews.

Discourse uses the follow query to determine total pageviews:

SQL Query: Dashboard Report - Consolidated Pageviews

Post Edits

Number of new post edits.

Ruby Code: discourse/app/models/concerns/reports/post_edits.rb

SQL Query: Dashboard Report - Post Edits

Posts

New posts created during the selected time period

Ruby Code: discourse/app/models/concerns/reports/posts.rb

SQL Query: Dashboard Report - Posts

Post Emotion

Number of posts classified by AI with one of following emotions: Sadness, Surprise, Fear, Anger, Joy, Disgust - group by poster trust level, over a specified period.

Ruby Code: discourse-ai/lib/sentiment/entry_point.rb at main · discourse/discourse-ai · GitHub

SQL Query: Dashboard Report - Post Emotion

Reactions

List most recent reactions.

Ruby code: discourse-reactions/plugin.rb at main · discourse/discourse-reactions · GitHub

SQL Query: Dashboard Report - Reactions

Signups

New account registrations for this period.

Ruby Code: discourse/app/models/concerns/reports/signups.rb

SQL Query: Dashboard Report - Signups

Site Traffic

Ruby Code: discourse/app/models/concerns/reports/site_traffic.rb

SQL Query: Dashboard Report - Site Traffic

Suspicious Logins

Details of new logins that differ suspiciously from previous logins.

Ruby Code: discourse/app/models/concerns/reports/suspicious_logins.rb

SQL Query: Dashboard Report - Suspicious Logins

System

Number of personal messages sent automatically by the System.

Ruby Code: discourse/app/models/concerns/reports/system_private_messages.rb

SQL Query: Dashboard Report - System

Time to first response

Average time (in hours) of the first response to new topics.

Ruby Code: discourse/app/models/concerns/reports/time_to_first_response.rb + discourse/discourse/blob/main/app/models/topic.rb#L1799-L1844

SQL Query: Dashboard Report - Time to First Response

Top Ignored / Muted Users

Users who have been muted and/or ignored by many other users.

Ruby Code: discourse/app/models/concerns/reports/top_ignored_users.rb

SQL Query: Dashboard Report - Top Ignored / Muted Users

Top Referred Topics

Topics that have received the most clicks from external sources.

Ruby Code: discourse/app/models/concerns/reports/top_referred_topics.rb

SQL Query: Dashboard Report - Top Referred Topics

Top Referrers

Users listed by number of clicks on links they have shared.

Ruby Code: discourse/app/models/concerns/reports/top_referrers.rb

SQL Query: Dashboard Report - Top Referrers

Top Traffic Sources

External sources that have linked to this site the most.

Ruby Code: discourse/app/models/concerns/reports/top_traffic_sources.rb

SQL Query: Dashboard Report - Top Traffic Sources

Top Uploads

List all uploads by extension, filesize and author.

Ruby Code: discourse/app/models/concerns/reports/top_uploads.rb

SQL Query: Dashboard Report - Top Uploads

Top Users by likes received

Top 10 users who have received likes.

Ruby Code: discourse/app/models/concerns/reports/top_users_by_likes_received.rb

SQL Query: Dashboard Report - Top Users by Likes Received

Top Users by likes received from a user with a lower trust level

Top 10 users in a higher trust level being liked by people in a lower trust level.

Ruby Code: discourse/app/models/concerns/reports/top_users_by_likes_received_from_inferior_trust_level.rb

SQL Query: Dashboard Report - Top Users by Likes Received from a User with a Lower Trust Level

Top Users by likes received from a variety of people

Top 10 users who have had likes from a wide range of people.

Ruby Code: discourse/app/models/concerns/reports/top_users_by_likes_received_from_a_variety_of_people.rb

SQL Query: Dashboard Report - Top Users by Likes Received From a Variety of People

Topics

New topics created during this period.

Ruby Code: discourse/app/models/concerns/reports/topics.rb

SQL Query: Dashboard Report - Topics

Topics with no response

Number of new topics created that did not receive a response.

Ruby Code: discourse/app/models/concerns/reports/topics_with_no_response.rb

SQL Query: Dashboard Report - Topics with No Response

Topic View Stats

Ruby Code: discourse/app/models/concerns/reports/topic_view_stats.rb

SQL Query: Dashboard Report - Topic View Stats

Trending Search Terms

Most popular search terms with their click-through rates.

Ruby Code: discourse/app/models/concerns/reports/trending_search.rb

SQL Query: Dashboard Report - Trending Search Terms

Trust Level growth

Number of users who increased their Trust Level during this period.

The Trust Level Growth report is pulling data from the user_histories table in the Discourse database. Specifically, this report is counting the number of times a user_histories.action is recorded for an increase in a user trust level.

Ruby Code: discourse/app/models/concerns/reports/trust_level_growth.rb

SQL Query: Dashboard Report - Trust Level Growth

Unaccepted policies

This dashboard report identifies topics with policies that have not been accepted by certain users.

Ruby code: discourse-policy/plugin.rb at main · discourse/discourse-policy · GitHub

SQL Query: Dashboard Report - Unaccepted Policies

User Flagging Ratio

List of users ordered by ratio of staff response to their flags (disagreed to agreed).

Ruby Code: discourse/app/models/concerns/reports/user_flagging_ratio.rb

SQL Query: Dashboard Report - User Flagging Ratio

User notes

List most recent user notes.

Ruby code: discourse-user-notes/plugin.rb at main · discourse/discourse-user-notes · GitHub

SQL Query: Dashboard Report - User Notes

User Profile Views

Total new views of user profiles.

Ruby Code: discourse/app/models/concerns/reports/profile_views.rb

SQL Query: Dashboard Report - User Profile Views

User Visits

The total number of logged-in user visits in the forum for the selected time period (today, yesterday, last 7 days, etc).

A User Visit is counted anytime a unique logged in user visits the site, up to once per day. For example, if a user visited a site every day within a week, Discourse would count that as 7 user visits.

Ruby Code: discourse/app/models/concerns/reports/visits.rb

SQL Query: Dashboard Report - User Visits

User Visits (mobile)

Number of unique logged-in users who visited using a mobile device.

Ruby Code: discourse/app/models/concerns/reports/mobile_visits.rb

SQL Query: Dashboard Report - User Visits

User-to-User (excluding replies)

Number of newly initiated personal messages.

Ruby Code: discourse/app/models/concerns/reports/user_to_user_private_messages.rb

SQL Query: Dashboard Report - User-to-User

User-to-User (with replies)

Number of all new personal messages and responses.

Ruby Code: discourse/app/models/concerns/reports/user_to_user_private_messages_with_replies.rb

SQL Query: Dashboard Report - User-to-User

Users per Trust Level

Number of users grouped by trust level.

Ruby Code: discourse/app/models/concerns/reports/users_by_trust_level.rb

SQL Query: Dashboard Report - Users Per Trust Level

Users per Type

Number of users grouped by admin, moderator, suspended, and silenced.

Ruby Code: discourse/app/models/concerns/reports/users_by_type.rb

SQL Query: Dashboard Report - Users Per Type

Web Crawler Pageviews

Total pageviews from web crawlers over time.

Ruby Code: discourse/app/models/report.rb

SQL Query: Dashboard Report - Web Crawler Pageviews

Web Crawler User Agents

List of web crawler user agents, sorted by pageviews.

Ruby Code: discourse/app/models/concerns/reports/web_crawlers.rb

SQL Query: Dashboard Report - Web Crawler User Agents

Last edited by @SaraDev 2025-10-15T20:49:23Z

Last checked by @SaraDev 2025-01-28T21:42:37Z

Check document

Perform check on document:

[pfaffman (Jay Pfaffman)]

@SaraDev

Discourse contient plusieurs rapports intégrés au tableau de bord d’administration qui peuvent être utiles pour explorer les statistiques d’une communauté. Pour accéder à ces rapports, vous pouvez visiter /admin/dashboard/reports sur votre site.

Je ne vois pas de lien vers cela sur /admin. Est-ce que je ne lis pas bien ? Cela devrait être plus facile à trouver. Je pense que je savais que ces rapports existaient mais je n’ai pas réussi à les trouver.

Bien que cela m’ait pris seulement quelques minutes pour les trouver, il serait peut-être agréable d’ajouter quelque chose comme

Les plugins peuvent ajouter des rapports au tableau de bord avec add_report(name, &block).

[maiki]

@pfaffman

Je ne vois pas de lien vers ceci sur /admin.

@pfaffman

Il semblerait que cela devrait être plus facile à découvrir. Je pense que je savais que ces rapports étaient ici mais j’ai cherché et je ne les ai pas trouvés.

Ouais, ce serait bien dans un MP au personnel pour les mentionner lorsqu’un site est créé.

[pfaffman (Jay Pfaffman)]

Désolé. Je pensais vraiment l’avoir déjà vu quelque part.

On n’arrive pas à faire lire les choses aux gens. . . . Sauf que je pouvais lire le code source pour savoir comment le faire dans un plugin ?

Mais peut-être mettre à jour ce qui précède avec

@SaraDev

. Pour accéder à ces rapports, vous pouvez visiter /admin/dashboard/reports sur votre site ou cliquer sur le lien reports sur le tableau de bord.

Je pense que c’est ce qui m’a vraiment dérouté. (Mais non, je n’ai pas d’excuse.)

[maiki]

@pfaffman

Mais peut-être mettre à jour ce qui précède pour

J’ai fait du sujet un wiki, allez-y !

[hello-smile6 (9pfs)]

@SaraDev

Visez \u003e30 %.

Cela ne correspond pas à l’interface utilisateur (l’interface utilisateur utilise 20 %), laquelle devrait-elle être ?

[JammyDodger]

Bien vu. Il a été récemment mis à jour à 20 %. Je vais apporter la modification dans le message d’origine.

Is there a difference in target retention rate between a support-first forum and discussion-first forum?

Here’s a PR with that change: Longer term, I think a more nuanced approach could be powerful. I’d love for communities to be able to set their own goals (with some guidance like what you shared above about why they might choose one vs. the other) and use the dashboard or other related features to help them track their results vs. what they are aiming for.

[Yotam_Hagay]

@SaraDev

Principales sources de trafic

Sources externes qui ont le plus lié à ce site.

Code Ruby : discourse/app/models/concerns/reports/top_traffic_sources.rb

Salut @SaraDev Ce résultat de rapport peut-il être obtenu avec une requête SQL ? Pouvez-vous la partager ?
Merci

[SaraDev (Sara Devlaeminck)]

@Yotam_Hagay

Bonjour @SaraDev Est-il possible d’obtenir le résultat de ce rapport avec une requête SQL ? Pouvez-vous la partager ?
Merci

Oui, vous pouvez utiliser le rapport SQL suivant pour les principales sources de trafic :

-- [params]
-- date :start_date = 01/05/2023
-- date :end_date = 03/06/2023

WITH count_links AS (

SELECT COUNT(*) AS clicks,
       ind.name AS domain
FROM incoming_links il
  INNER JOIN posts p ON p.deleted_at ISNULL AND p.id = il.post_id
  INNER JOIN topics t ON t.deleted_at ISNULL AND t.id = p.topic_id
  INNER JOIN incoming_referers ir ON ir.id = il.incoming_referer_id
  INNER JOIN incoming_domains ind ON ind.id = ir.incoming_domain_id
WHERE t.archetype = 'regular'
  AND il.created_at::date BETWEEN :start_date AND :end_date
GROUP BY ind.name
ORDER BY clicks DESC
),

count_topics AS (

SELECT COUNT(DISTINCT p.topic_id) AS topics,
       ind.name AS domain
FROM incoming_links il
INNER JOIN posts p ON p.deleted_at ISNULL AND p.id = il.post_id
INNER JOIN topics t ON t.deleted_at ISNULL AND t.id = p.topic_id
INNER JOIN incoming_referers ir ON ir.id = il.incoming_referer_id
INNER JOIN incoming_domains ind ON ind.id = ir.incoming_domain_id
WHERE t.archetype = 'regular'
  AND il.created_at > (CURRENT_TIMESTAMP - INTERVAL '30 DAYS')
GROUP BY ind.name
)

SELECT cl.domain,
       cl.clicks AS "Clicks",
       ct.topics AS "Topics"
FROM count_links cl
JOIN count_topics ct ON cl.domain = ct.domain
LIMIT 10

Avec cette requête, notez que les paramètres de date acceptent les dates au format jour/mois/année.

[Yotam_Hagay]

@SaraDev

Oui, vous pouvez utiliser le rapport SQL suivant pour les principales sources de trafic :

Bonjour @SaraDev, merci d’avoir partagé la requête.
Une question plus générale concernant ce rapport, et la table incoming_links en fait : elle ne représente que le trafic des pages de publication et non le trafic de toutes les pages du forum, n’est-ce pas ?

Contexte : Je cherche à analyser les tendances du trafic global du forum et j’espérais obtenir le trafic global par sources à partir du rapport des principales sources de trafic.
Mais alors que le trafic global est d’environ 272 K le mois dernier (utilisateurs et anonymes), le total des clics dans le rapport des sources de trafic pour la même période n’est que de 59 K.
De plus, je vois que vous utilisez une jointure interne avec les tables topics et posts, ce qui signifie que si aucun post_id n’est associé au clic, vous ne le comptez pas.

Pouvez-vous confirmer ma conclusion et peut-être expliquer un peu la logique derrière la table incoming_links ?

[Yotam_Hagay]

@SaraDev

Posts

Nouveaux messages créés pendant cette période

Salut @SaraDev J’ai exécuté cette requête et le résultat ne correspond pas exactement au rapport Posts dans l’onglet général.
Par exemple pour le 30 novembre :
Requête = 112 messages
Rapport = 120 messages
Pouvez-vous s’il vous plaît vérifier l’écart ?
Merci

[JammyDodger]

Juste pour information @Yotam_Hagay - même si Sara est l’auteure originale, le guide est la responsabilité de tout le monde Il n’est pas nécessaire de mentionner @ tout le monde sur chaque publication.

[Yotam_Hagay]

Merci @JammyDodger pour ces précisions.
Y a-t-il quelqu’un d’autre que je puisse mentionner ou vers qui me tourner pour obtenir une réponse ?

[Yotam_Hagay]

@SaraDev

Temps de réponse SQL

--[params]
-- date :date_start
-- date :date_end

WITH first_reply AS (
    SELECT 
        p.topic_id, 
        MIN(post_number) post_number, 
        t.created_at
    FROM posts p
    INNER JOIN topics t ON (p.topic_id = t.id)
    WHERE p.deleted_at IS NULL
        AND p.post_number != 1
        AND p.post_type = 1
        AND p.user_id > 0
        AND t.user_id > 0
        AND t.deleted_at IS NULL
        AND t.archetype = 'regular'
        AND t.created_at::date BETWEEN :date_start AND :date_end
    GROUP BY p.topic_id, t.created_at
    ORDER BY 2 DESC
)

SELECT 
    p.topic_id, 
    fr.created_at::date dt_topic_created,
    (p.created_at - fr.created_at) response_time
FROM posts p
INNER JOIN first_reply fr 
    ON fr.topic_id = p.topic_id 
    AND fr.post_number = p.post_number
    AND p.created_at > fr.created_at
ORDER BY response_time

Les résultats de cette requête sont légèrement différents de ceux du rapport « Temps de réponse » :
Le 8 novembre, par exemple :
Requête : 93 heures
Rapport : 116 heures
Quelqu’un peut-il me conseiller ?

[JammyDodger]

Je pense que certaines de ces recherches prendront du temps. Je regarde moi-même pour voir si je peux comprendre ce qui se passe (bien que l’écart entre mes compétences en SQL et mes compétences en Ruby soit assez grand ).

Mais continuez à partager vos découvertes, car ce serait formidable de consolider toutes ces informations.

[JammyDodger]

@Yotam_Hagay

Salut @SaraDev J’ai exécuté cette requête et le résultat ne correspond pas exactement au rapport Posts dans l’onglet général.
Par exemple, pour le 30 novembre :
Requête = 112 messages
Rapport = 120 messages
Pouvez-vous vérifier l’écart ?
Merci

Pour celui des Posts, je pense que le rapport stock compte également les messages de sujet ainsi que ceux des utilisateurs système, mais cible uniquement ceux avec post_type 1 (c’est-à-dire pas les murmures, les petits messages d’action ou les actions de modérateur).

Je pense que le SQL ressemblerait plutôt à ceci :

--[params]
-- date :start_date
-- date :end_date

SELECT 
    p.created_at::date AS "Jour",
    COUNT(p.id) AS "Nombre"
FROM posts p
INNER JOIN topics t ON t.id = p.topic_id AND t.deleted_at ISNULL
WHERE p.created_at::date BETWEEN :start_date AND :end_date
    AND p.deleted_at ISNULL
    AND t.archetype = 'regular'
    AND p.post_type = 1
GROUP BY p.created_at::date
ORDER BY 1

Pourriez-vous exécuter cela sur votre site et voir si cela correspond ?

[Yotam_Hagay]

Merci Jammy, je vais vérifier !
Je travaille actuellement sur une analyse du délai de première réponse, donc j’apprécierais si vous pouviez jeter un œil à celle-ci également.

[JammyDodger]

En examinant la version SQL, je pense qu’il manque le AND p.user_id <> t.user_id pour exclure les réponses de l’OP. Si je l’ajoute, cela me donne le temps précis entre l’OP et la première réponse de quelqu’un d’autre :

--[params]
-- date :date_start
-- date :date_end

WITH first_reply AS (
    SELECT 
        p.topic_id, 
        MIN(post_number) post_number, 
        t.created_at
    FROM posts p
    INNER JOIN topics t ON (p.topic_id = t.id)
    WHERE p.deleted_at IS NULL
        AND p.user_id <> t.user_id
        AND p.post_number != 1
        AND p.post_type = 1
        AND p.user_id > 0
        AND t.user_id > 0
        AND t.deleted_at IS NULL
        AND t.archetype = 'regular'
        AND t.created_at::date BETWEEN :date_start AND :date_end
    GROUP BY p.topic_id, t.created_at
    ORDER BY 2 DESC
)

SELECT 
    p.topic_id, 
    fr.created_at::date dt_topic_created,
    (p.created_at - fr.created_at) response_time
FROM posts p
INNER JOIN first_reply fr 
    ON fr.topic_id = p.topic_id 
    AND fr.post_number = p.post_number
    AND p.created_at > fr.created_at
ORDER BY response_time

Je pense aussi que le rapport des stocks est en décimal plutôt qu’en heures et minutes comme le SQL. Je vais essayer de nouveau pour qu’il corresponde.

---

Juste une petite mise à jour pour inclure la AVG afin qu’elle soit plus similaire à la sortie du rapport des stocks :

--[params]
-- date :date_start
-- date :date_end

WITH first_reply AS (
    SELECT 
        p.topic_id, 
        MIN(post_number) post_number, 
        t.created_at
    FROM posts p
    INNER JOIN topics t ON p.topic_id = t.id
    WHERE p.deleted_at IS NULL
        AND p.user_id <> t.user_id
        AND p.post_type = 1
        AND p.user_id > 0
        AND t.user_id > 0
        AND t.deleted_at IS NULL
        AND t.archetype = 'regular'
        AND t.created_at::date BETWEEN :date_start AND :date_end
    GROUP BY p.topic_id, t.created_at
)

SELECT 
    fr.created_at::date dt_topic_created,
    AVG(p.created_at - fr.created_at) response_time
FROM posts p
INNER JOIN first_reply fr 
    ON fr.topic_id = p.topic_id 
    AND fr.post_number = p.post_number
    AND p.created_at > fr.created_at
GROUP BY fr.created_at::date
ORDER BY response_time

Cela semble correspondre au rapport des stocks, à condition de tenir compte du fait que l’un est en décimal et l’autre en HH:MM. Je suis sûr qu’il existe un moyen de convertir le response_time SQL en décimal, mais le HH:MM semble être une manière plus intuitive de le faire. (Je pense qu’il y a aussi des critères supplémentaires qui ne sont peut-être pas nécessaires, mais qui pourraient aussi servir de garde-fou contre des circonstances inhabituelles, j’ai donc finalement laissé ces parties telles quelles jusqu’à ce que je puisse le dire avec certitude )

Pourriez-vous exécuter celui-ci et voir comment il correspond ?

[Yotam_Hagay]

Oui, cela correspond maintenant aux chiffres indiqués dans le rapport de stock, merci !
Juste un commentaire -
J’ai constaté que la fonction AVG ci-dessous renvoie des résultats manquants si le temps est > 24h (une section de # de jours est manquante, je suppose).

AVG(p.created_at - fr.created_at)::time response_time

[JammyDodger]

Ah oui, caster en time était un mauvais choix. Si vous supprimez le ::time, cela reviendra à la version plus précise (bien que plus difficile à lire).

Je vais également modifier celui ci-dessus.