Guida di riferimento al rapporto dashboard admin

[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 contiene diversi report integrati nella dashboard di amministrazione che possono essere utili per esplorare le statistiche di una community. Per accedere a questi report, puoi visitare /admin/dashboard/reports sul tuo sito.

Non vedo un link a questo su /admin. Sto sbagliando a leggere? Sembrerebbe che questo dovrebbe essere più facile da trovare. Penso di aver saputo che questi report erano qui ma ho cercato e non sono riuscito a trovarli.

Anche se mi ci sono voluti solo pochi minuti per trovarli, potrebbe essere utile aggiungere qualcosa come

I plugin possono aggiungere report alla dashboard con add_report(name, &block).

[maiki]

@pfaffman

Non vedo un link a questo su /admin.

@pfaffman

Sembrerebbe che questo dovrebbe essere più facile da scoprire. Penso di sapere che questi report fossero qui ma ho cercato e non sono riuscito a trovarli.

Sì, potrebbe essere carino in un PM allo staff menzionarli quando un sito viene creato per la prima volta.

[pfaffman (Jay Pfaffman)]

Mi dispiace. Pensavo sicuramente di averlo già visto da qualche parte.

Non si riesce a far leggere le cose alle persone… . . . A meno che non possa leggere il codice sorgente per scoprire come farlo in un plugin?

Ma forse aggiornare quanto sopra a

@SaraDev

. Per accedere a questi report, puoi visitare /admin/dashboard/reports sul tuo sito o fare clic sul link report nella dashboard.

Penso che questo sia ciò che mi ha davvero confuso. (Ma no, non ho scuse.)

[maiki]

@pfaffman

Ma forse aggiorna quanto sopra a

Ho reso il topic un wiki, vai!

[hello-smile6 (9pfs)]

@SaraDev

Puntare a >30%.

Ciò non corrisponde all’interfaccia utente (l’interfaccia utente utilizza il 20%), quale dovrebbe essere?

[JammyDodger]

Ottima osservazione. È stato recentemente aggiornato al 20%. Apporterò la modifica nell’OP.

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

Principali fonti di traffico

Fonti esterne che hanno collegato più frequentemente a questo sito.

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

Ciao @SaraDev È possibile ottenere l’output di questo report con una query SQL? Puoi condividerla?
Grazie

[SaraDev (Sara Devlaeminck)]

@Yotam_Hagay

Ciao @SaraDev È possibile ottenere l’output di questo report con una query SQL? Puoi condividerla?
Grazie

Sì, puoi usare il seguente report SQL per le principali fonti di traffico:

-- [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

Con questa query, nota che i parametri di data accettano date nel formato giorno/mese/anno.

[Yotam_Hagay]

@SaraDev

Sì, puoi usare il seguente report SQL per le principali origini del traffico:

Ciao @SaraDev, grazie per aver condiviso la query.
Una domanda più generale riguardo a questo report e alla tabella incoming_links in realtà: rappresenta solo il traffico per le pagine dei post e non il traffico verso tutte le pagine del forum, giusto?

Contesto: sto cercando di analizzare le tendenze del traffico generale del forum e speravo di ottenere il traffico generale per origini dal report delle principali origini del traffico.
Ma mentre il traffico generale è di circa 272K nell’ultimo mese (utenti e anonimi), i click totali nel report delle origini del traffico per lo stesso periodo sono solo 59K.
Inoltre, vedo che utilizzi un inner join con le tabelle topics e posts, il che significa che se non c’è un post_id associato al click, non lo conti.

Puoi per favore confermare la mia conclusione e magari spiegare un po’ la logica dietro la tabella incoming_links?

[Yotam_Hagay]

@SaraDev

Post

Nuovi post creati durante questo periodo

Ciao @SaraDev ho eseguito questa query e il risultato non corrisponde esattamente al report Post nella scheda generale.
Ad esempio per il 30 novembre:
Query = 112 post
Report = 120 post
Puoi controllare la differenza?
Grazie

[JammyDodger]

Solo per tua informazione @Yotam_Hagay - anche se Sara è l’OP, la guida è responsabilità di tutti Non c’è bisogno di una @mention su ogni post.

[Yotam_Hagay]

Grazie @JammyDodger per il chiarimento.
C’è qualcun altro che posso taggare o a cui posso rivolgermi per ottenere una risposta?

[Yotam_Hagay]

@SaraDev

Tempo SQL per la prima risposta

--[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

I risultati di questa query sono leggermente diversi dal report Time to first response:
L’8 novembre, ad esempio:
Query: 93 ore
Report: 116 ore
Qualcuno può darmi un consiglio?

[JammyDodger]

Penso che alcune di queste potrebbero richiedere del tempo per essere ricercate. Ci sto dando un’occhiata anch’io per vedere se riesco a capire cosa sta succedendo (anche se il divario tra le mie competenze SQL e le mie competenze Ruby è piuttosto ampio ).

Ma continua a fornire i risultati in quanto sarebbe fantastico consolidare tutte queste informazioni.

[JammyDodger]

@Yotam_Hagay

Ciao @SaraDev ho eseguito questa query e il risultato non corrisponde esattamente al report dei post nella scheda generale.
Ad esempio per il 30 novembre:
Query = 112 post
Report = 120 post
Puoi controllare la differenza?
Grazie

Per quello dei post, penso che il report di magazzino conti anche i post degli argomenti e quelli degli utenti di sistema, ma si rivolge solo a quelli con post_type 1 (cioè, non sussurri, piccoli post di azione o azioni del moderatore).

Penso che l’SQL dovrebbe essere più simile a questo:

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

SELECT
    p.created_at::date AS "Giorno",
    COUNT(p.id) AS "Conteggio"
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

Potresti eseguirlo sul tuo sito e vedere se corrisponde?

[Yotam_Hagay]

Grazie Jammy, controllerò!
Sto attualmente lavorando su un’analisi del tempo di prima risposta, quindi apprezzerei se potessi dare un’occhiata anche a questa.

[JammyDodger]

Guardando la versione SQL, penso che manchi AND p.user_id <> t.user_id per escludere le risposte dall’OP. Se lo aggiungo, ottengo il tempo accurato tra l’OP e la prima risposta di qualcun altro:

--[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

Penso anche che il report di magazzino sia in decimale anziché in ore e minuti come l’SQL. Ci riproverò per farlo corrispondere.

---

Solo un piccolo aggiornamento per includere AVG in modo che sia più simile all’output del report di magazzino:

--[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

Questo sembra corrispondere al report di magazzino, purché si tenga conto che uno è in decimale e l’altro in HH:MM. Sono sicuro che ci sia un modo per convertire il response_time SQL in decimale, ma HH:MM sembra un modo più intuitivo per farlo. (Penso che ci siano anche alcuni criteri aggiuntivi che potrebbero non essere necessari, ma potrebbero anche essere una protezione contro circostanze insolite, quindi alla fine ho lasciato quelle parti così come sono finché non potrò dirlo con certezza )

Potresti eseguirlo e vedere come corrisponde?

[Yotam_Hagay]

Sì, ora corrisponde ai numeri mostrati nel report di magazzino, grazie!
Solo un commento:
Ho scoperto che la funzione AVG sottostante restituisce risultati mancanti nel caso in cui il tempo sia > 24 ore (manca una sezione # di giorni, immagino).

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

[JammyDodger]

Sì, il casting a time è stata una scelta sbagliata. Se rimuovi ::time, tornerà alla versione più accurata (anche se più difficile da leggere).

Modificherò anche quella sopra.