Is there a method to get a sorted list of users who might become Trust Level 3?
Maybe using the Data Explorer plugin and a query (?)
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That’s an interesting question. There will be no specific query since each member ‘earns’ TL3 through interaction and engagement, but you could monitor the TL2 users to see who are visiting most regularly and engaging with more content I suppose.
Is there a reason to find this in advance rather than waiting to see who earns it?
Some third party measurement tools create ‘leaderboards’ that might point to members who have been particularly active. Is that what you need?
In the past I have also created queries in excel based on data exported from the Users table and custom Data Explorer queries. I didn’t look at what you are asking, but did create monitoring tools to look at different types of activity, such as reading and posting, to better segment my members.
Let us know what you are trying to achieve and maybe we could come up with cleverer suggestions.
(also, this should probably be moved to community where we can discuss these topics)
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I thought about TL3 Requirements
Checking which users have the most number of 
Requirements and sort by that
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You could do that, write a query to track the key fields and limit it to current TL2 users
There are loads of great query ideas for Data Explorer in another thread, and it seems that you could do a version of the User Directory, with the TL2 limit, to answer your query.
Still interested to know what you are trying to achieve in trying to ‘predict’ TL3 before it happens. Sounds like Minority Report 
8 „Gefällt mir“
Though I have no doubt that some form of query could be put together, I have serious doubt that the amount of work needed would justify the questionable value of the results.
It is one thing to do this per user from a members admin user page, a whole different story for many accounts all at once.
At best, there will be many “moving parts” to take into consideration and arbitrary values to be decided on.
Some criteria could be used to reduce the “haystack”. i.e. only TL2 accounts that are not already TL3, accounts that are activated and not suspended. That might help somewhat.
Because many of the requirements may have been tweaked from their default values, those values would be needed to base a members values against.
Even then, most member values are unpredictable and unstable. eg. Likes could be given / received at any time changing a “0 - requirements not met” to “requirements met” in a heartbeat. Similar with flags given / received.
And what constitutes an “almost TL3” state? How many of the 12 requirements are already met? A percentage? eg.
if (value < requirement)
&& ((value / requirement) > arbitrary_percent) {
The “all time” values should in theory stagnate or increase only. But the “100 days” could be a problem. Should an algorithm somehow “drop” values associated with older days when it is trying to predict values for future days?
Anyway, long story short, if you can put together exact detailed specifications for how such a feature could work it would make it easier for someone to come up with the code needed to meet those specs.
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I have the beginnings of this progress towards level 3 report which allows admins to view the progress of users so far, which I want to then use to email out messages of encouragement to users who are close (We like to promote TL3 user who share our tone of voice to moderators)
Someone kindly sent me the the trust level 3 requirements rb file which has helped a lot. however my limited knowledge and understanding how to convert the fields within the document into sql is limited, Maybe someone can help finish it off?
This is what I have so far.
Data Explorer Query
-- [params]
-- int :from_days_ago = 0
-- int :duration_days = 100
with
t as (
select
CURRENT_TIMESTAMP - ((:from_days_ago + :duration_days) * (INTERVAL '1 days')) as start,
CURRENT_TIMESTAMP - (:from_days_ago * (INTERVAL '1 days')) as end
),
-- Users
pr AS (
SELECT user_id,
count(1) as visits
FROM user_visits, t
WHERE visited_at > t.start
and visited_at < t.end
GROUP BY user_id
ORDER BY visits DESC
),
-- Visits (all time)
vi as (
select user_id,
count(1) as visits
from user_visits, t
group by user_id
),
-- Topics replied to
trt as (
select user_id,
count(distinct topic_id) as topic_id
from posts, t
where created_at > t.start
and created_at < t.end
group by user_id
),
-- Topics Viewed All Time
tva as (
select user_id,
count(topic_id) as topic_id
from posts
group by user_id
),
-- Posts Read
pra as (
select user_id,
sum(posts_read) as posts_read
from user_visits, t
where visited_at > t.start
and visited_at < t.end
group by user_id
),
-- Posts Read All Time
prat as (
select user_id,
sum(posts_read) as posts_read
from user_visits, t
group by user_id
)
SELECT pr.user_id,
coalesce(pr.visits,0) as "Visits",
coalesce(trt.topic_id,0) as "Topic replied to",
coalesce(tva.topic_id,0) as "Topic viewed (AT)",
coalesce(pra.posts_read,0) as "Posts Read",
coalesce(prat.posts_read,0) as "Posts Read (AT)"
FROM pr
left join vi using (user_id)
left join trt using (user_id)
left join tva using (user_id)
left join pra using (user_id)
left join prat using (user_id)
ORDER BY
pr.visits DESC
8 „Gefällt mir“
Great start here, thanks!
I made a few tweaks / fixes:
- Added ‘posts_read > 0’ condition for more accurate user visits calculation
- Removed ‘visits (all time)’ which didn’t seem to be necessary
- Fixed ‘topics viewed’ calculations which was using the wrong table
- Added current trust level (to only get tl2 users)
- Added where clauses for other relevant conditions, set at 50% of current threshold
Also parameterized a bunch of things so you can set your own values for each of the required metrics (since they may vary forum by forum), and also set a threshold percentage to show only users who meet at least that % of ALL the metrics.
So for example the below by default lists only tl2 users who meet 50% or more of all the requirements for visits, topics replied to, topics viewed, posts read…you could set it to 30% or 85% or whatever if it seems to be returning too many or too few results.
I did not add the requirements for likes given/received, or for flags/silences/suspensions. For us at least, the latter are super rare anyway, and I figure likes is one of the easier barriers to get people over if they know about it (some people just barely ever give likes). So this works pretty well for us. But the rest of the requirements could be added if you wanted.
For reference, on our forum we have ~1,000 TL2 users, ~10 TL3 users, and this query identifies ~30 ‘potential/almost TL3’ users with the 50% threshold.
-- [params]
-- int :from_days_ago = 0
-- int :duration_days = 100
-- int :trust_level = 2
-- int :threshold = 50
-- int :visits = 50
-- int :topics_replied_to = 10
-- int :topics_viewed = 76
-- int :topics_viewed_all_time = 200
-- int :posts_read = 755
-- int :posts_read_all_time = 500
-- NOTES
-- trust_level show current TL2 users only
-- threshold show only at users >= this percentage of all above metrics
-- topics_viewed depends on total # of topics (default 25%)
-- posts_read depends on total # of posts (default 25%)
WITH
t AS (
SELECT
CURRENT_TIMESTAMP - ((:from_days_ago + :duration_days) * (INTERVAL '1 days')) AS start,
CURRENT_TIMESTAMP - (:from_days_ago * (INTERVAL '1 days')) AS end
),
-- User Visits
pr AS (
SELECT user_id,
count(1) as visits
FROM user_visits, t
WHERE visited_at > t.start
AND visited_at < t.end
AND posts_read > 0
GROUP BY user_id
ORDER BY visits DESC
),
-- Topics Replied To
trt AS (
SELECT user_id,
COUNT(distinct topic_id) AS topic_id
FROM posts, t
WHERE created_at > t.start
AND created_at < t.end
GROUP BY user_id
),
-- Topics Viewed
tva AS (
SELECT user_id,
COUNT(distinct topic_id) AS topic_id
FROM topic_views, t
WHERE viewed_at > t.start
AND viewed_at < t.end
GROUP BY user_id
),
-- Topics Viewed (All Time)
tvat AS (
SELECT user_id,
COUNT(distinct topic_id) AS topic_id
FROM topic_views
GROUP BY user_id
),
-- Posts Read
pra AS (
SELECT user_id,
SUM(posts_read) AS posts_read
FROM user_visits, t
WHERE visited_at > t.start
AND visited_at < t.end
GROUP BY user_id
),
-- Posts Read (All Time)
prat AS (
SELECT user_id,
SUM(posts_read) AS posts_read
FROM user_visits, t
GROUP BY user_id
),
-- Current Trust Level
tl AS (
SELECT id,
trust_level
FROM users
)
SELECT pr.user_id,
-- tl.trust_level AS "Trust Level",
coalesce(pr.visits,0) AS "Visits",
coalesce(trt.topic_id,0) AS "Topic Replied To",
coalesce(tva.topic_id,0) AS "Topics Viewed",
coalesce(tvat.topic_id,0) AS "Topics Viewed (AT)",
coalesce(pra.posts_read,0) AS "Posts Read",
coalesce(prat.posts_read,0) AS "Posts Read (AT)"
FROM pr
LEFT JOIN trt USING (user_id)
LEFT JOIN tva USING (user_id)
LEFT JOIN tvat USING (user_id)
LEFT JOIN pra USING (user_id)
LEFT JOIN prat USING (user_id)
LEFT JOIN tl ON (tl.id = pr.user_id)
WHERE
tl.trust_level = :trust_level
AND pr.visits >= :visits * :threshold / 100
AND trt.topic_id >= :topics_replied_to * :threshold / 100
AND tva.topic_id >= :topics_viewed * :threshold / 100
AND tvat.topic_id >= :topics_viewed_all_time * :threshold / 100
AND pra.posts_read >= :posts_read * :threshold / 100
AND prat.posts_read >= :posts_read_all_time * :threshold / 100
ORDER BY
pr.visits DESC
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This seems to be exactly what I am searching for, however, I get the following error when executing the query:
PG::QueryCanceled: ERROR: canceling statement due to statement timeout
Any ideas how to make this work?
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Hello,
This report returns
- Total topics
- Total topics in AT
but the requirements for TL3 use
- Total topics excluding private messages.
- Total topics in AT excluding private messages.
Anyone knows how to adapt the query to exclude the private messages?
Thanks in advance
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Yes, tried that: 60, 80, 95, 99 → no effect at all, always the same error message.
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Hier ist eine modifizierte Version ohne Parameter: Ich habe die Standardanforderungen für TL3 faul (effizient?) fest in die Abfrage einprogrammiert. Ich habe den Datensatz leicht erweitert, um gegebene und erhaltene Likes einzuschließen, jedoch keine Likes/eindeutige Tage oder Likes/eindeutige Benutzer. Flags, Silences und Suspendierungen fehlen ebenfalls noch.
Dies ist ein Lückenbericht, daher führt er die Subtraktion durch, um Ihnen zu zeigen, was jedem Benutzer fehlt.
An einigen Stellen stimmt er nicht genau mit dem überein, was auf der Benutzeradministrationsseite angezeigt wird:
- Gegebene Likes (meine Zählung ist irgendwie höher)
- Beiträge in den letzten 100 Tagen (meine Zählung ist niedriger)
Bei meiner Zählung der Beiträge der letzten 100 Tage gibt es eine ganze Menge Rätselraten.
Aber falls es hilfreich ist:
with
t as (
select
CURRENT_TIMESTAMP - ((0 + 100) * (INTERVAL '1 days')) as start,
CURRENT_TIMESTAMP - (0 * (INTERVAL '1 days')) as end
),
-- Themenanzahl letzte 100 Tage 25%
tclhd AS (
SELECT LEAST(floor(count(id)*.25)::REAL,500) as all_topics
FROM topics, t
WHERE created_at > t.start
AND archetype = 'regular'
AND deleted_at is null
),
-- Beitragsanzahl letzte 100 Tage 25%
pclhd AS (
SELECT LEAST(FLOOR(count(id)*.25)::REAL,20000) AS all_posts
FROM t, posts
WHERE posts.created_at > start
AND posts.deleted_at is null
AND posts.hidden_at is null
AND posts.last_editor_id >0 -- Discobot & System auslassen
AND (action_code is null OR action_code != 'assigned')
),
-- Benutzer
pr AS (
SELECT user_id,
count(1) as visits
FROM user_visits, t
WHERE visited_at > t.start
AND visited_at < t.end
GROUP BY user_id
ORDER BY visits DESC
),
-- Antworten auf Themen
trt as (
select user_id,
count(distinct topic_id) as topic_id
from posts, t
where created_at > t.start
and created_at < t.end
group by user_id
),
-- Themen alle Zeiten angesehen
tvat as (
select tv.user_id,
COUNT(distinct tv.topic_id) AS topic_id
FROM topic_views tv
LEFT JOIN topics t on tv.topic_id=t.id
WHERE
t.archetype = 'regular'
AND t.deleted_at is null
group by tv.user_id
),
-- Themen angesehen
tva AS (
SELECT tv.user_id,
COUNT(distinct tv.topic_id) AS topic_id
FROM t, topic_views tv
LEFT JOIN topics on topic_id=topics.id
WHERE
topics.archetype = 'regular'
AND topics.deleted_at is null
AND viewed_at > t.start
AND viewed_at < t.end
GROUP BY tv.user_id
),
-- Gelesene Beiträge
pra as (
select user_id,
sum(posts_read) as posts_read
from user_visits, t
where visited_at > t.start
and visited_at < t.end
group by user_id
),
-- Gelesene Beiträge alle Zeiten
prat as (
select user_id,
sum(posts_read) as posts_read
from user_visits, t
group by user_id
),
-- Aktueller Vertrauenslevel
tl AS (
SELECT id,
trust_level
FROM users
),
likes AS (
SELECT user_id,
likes_given, likes_received
from user_stats
)
SELECT pr.user_id,
greatest(50-coalesce(pr.visits,0),0) as "Tage besucht Lücke",
greatest(10-coalesce(trt.topic_id,0), 0) as "Themenantwort Lücke",
greatest(tclhd.all_topics-coalesce(tva.topic_id,0),0) AS "Themen angesehen Lücke",
greatest(200-coalesce(tvat.topic_id,0),0) as "Themen angesehen (AT) Lücke",
greatest(pclhd.all_posts - coalesce(pra.posts_read,0),0) as "Gelesene Beiträge Lücke",
greatest(500-coalesce(prat.posts_read,0),0) as "Gelesene Beiträge (AT) Lücke",
greatest(30-likes.likes_given,0) as "Gegebene Likes Lücke",
greatest(20-likes.likes_received,0) as "Erhaltene Likes Lücke"
FROM pclhd, tclhd, pr
left join trt using (user_id)
LEFT JOIN tva USING (user_id)
left join tvat using (user_id)
left join pra using (user_id)
left join prat using (user_id)
LEFT JOIN likes using (user_id)
LEFT JOIN tl ON (tl.id = pr.user_id)
WHERE tl.trust_level = 2
ORDER BY
pr.visits DESC
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Danke für deine Version!
Ich erhalte jedoch weiterhin denselben Fehler
PG::QueryCanceled: ERROR: canceling statement due to statement timeout
Ich vermute, wir haben dann ein Problem mit unserer Installation.
4 „Gefällt mir“
Für mich läuft das derzeit in 8.379,4 ms, also nahe am Limit, nehme ich an. Sie müssen eine größere Community haben.
Das Hinzufügen von LIMIT 50 ganz am Ende spart mir 1.000 ms. Vielleicht könnten Sie damit herumspielen, bis Sie etwas zurückbekommen.
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Dies ist etwas effizienter. Wenn es bei Ihnen immer noch nicht funktioniert, können Sie versuchen, einige der Spalten zusammen mit den zugehörigen Joins und Abfragen zu entfernen.
EDIT Okay, ich habe endlich meine Join-Typen richtig hinbekommen (es ist eine Weile her). Diese aktualisierte Abfrage ist viel effizienter
with
t as (
select
CURRENT_TIMESTAMP - ((0 + 100) * (INTERVAL '1 days')) as start,
CURRENT_TIMESTAMP - (0 * (INTERVAL '1 days')) as end
),
-- Themaanzahl letzte 100 Tage 25%
-- kleiner von 25% Themen, die in den letzten 100 Tagen erstellt wurden
-- ODER 500, die standardmäßige maximale Systemanforderung für TL3
tclhd AS (
SELECT LEAST(floor(count(id)*.25)::REAL,500) as all_topics
FROM topics, t
WHERE created_at > t.start
AND archetype = 'regular'
AND deleted_at is null
),
-- Beitraganzahl letzte 100 Tage 25%
-- kleiner von 25% Beiträgen, die in den letzten 100 Tagen erstellt wurden
-- ODER 20k, die standardmäßige maximale Systemanforderung für TL3
pclhd AS (
SELECT LEAST(FLOOR(count(id)*.25)::REAL,20000) AS all_posts
FROM t, posts
WHERE posts.created_at > start
AND posts.deleted_at is null
AND posts.hidden_at is null
AND posts.last_editor_id >0 -- Discobot & System weglassen
AND (action_code is null OR action_code != 'assigned')
),
-- Vertrauensstufe 2 Benutzer
tl AS (
SELECT id as user_id, trust_level
FROM users
WHERE trust_level = 2
),
-- Benutzer, Besuche & gelesene Beiträge letzte 100 Tage
pr AS (
SELECT user_id,
count(1) as visits,
sum(posts_read) as posts_read
FROM t, user_visits
INNER JOIN tl using (user_id)
WHERE visited_at > t.start
AND visited_at < t.end
GROUP BY user_id
ORDER BY visits DESC
),
-- Gelesene Beiträge aller Zeiten
prat as (
select user_id,
sum(posts_read) as posts_read
from t, user_visits
INNER JOIN tl using (user_id)
group by user_id
),
-- Antworten auf Themen
trt as (
select user_id,
count(distinct topic_id) as topic_id
from t, posts
INNER JOIN tl using (user_id)
where posts.created_at > t.start
and posts.created_at < t.end
group by user_id
),
-- Angesehene Themen aller Zeiten
tvat as (
select tv.user_id,
COUNT(distinct tv.topic_id) AS topic_id
FROM topic_views tv
LEFT JOIN topics t on tv.topic_id=t.id
INNER JOIN tl on tv.user_id=tl.user_id
WHERE
t.archetype = 'regular'
AND t.deleted_at is null
group by tv.user_id
),
-- Angesehene Themen
tva AS (
SELECT tv.user_id,
COUNT(distinct tv.topic_id) AS topic_id
FROM t, topic_views tv
LEFT JOIN topics on topic_id=topics.id
INNER JOIN tl on tv.user_id=tl.user_id
WHERE
topics.archetype = 'regular'
AND topics.deleted_at is null
AND viewed_at > t.start
AND viewed_at < t.end
GROUP BY tv.user_id
),
likes AS (
SELECT user_id,
likes_given, likes_received
from user_stats
INNER JOIN tl using (user_id)
)
SELECT pr.user_id,
greatest(50-coalesce(pr.visits,0),0) as "Tage Lücken Besuche",
greatest(10-coalesce(trt.topic_id,0), 0) as "Themen Antwort Lücke",
greatest(tclhd.all_topics-coalesce(tva.topic_id,0),0) AS "Angesehene Themen Lücke",
greatest(200-coalesce(tvat.topic_id,0),0) as "Angesehene Themen (AT) Lücke",
greatest(pclhd.all_posts - coalesce(pr.posts_read,0),0) as "Gelesene Beiträge Lücke",
greatest(500-coalesce(prat.posts_read,0),0) as "Gelesene Beiträge (AT) Lücke",
greatest(30-likes.likes_given,0) as "Gegebene Likes Lücke",
greatest(20-likes.likes_received,0) as "Erhaltene Likes Lücke"
FROM pclhd, tclhd, pr
left join trt using (user_id)
LEFT JOIN tva USING (user_id)
left join tvat using (user_id)
left join prat using (user_id)
LEFT JOIN likes using (user_id)
ORDER BY
pr.visits DESC
LIMIT 50
8 „Gefällt mir“
Und hier ist der TL2-Lückenbericht:
with
-- Trust Level 1 Benutzer
tl AS (
SELECT id as user_id, trust_level, last_seen_at
FROM users
WHERE trust_level = 1
),
-- Benutzer, die in den letzten 3 Monaten gesehen wurden + Besuche, gelesene Beiträge, Lesezeit
pr AS (
SELECT user_id,
count(1) as visits,
sum(posts_read) as posts_read,
SUM(time_read)/60 as minutes_reading_time,
DATE(last_seen_at) AS last_seen
FROM user_visits
INNER JOIN tl using (user_id)
WHERE DATE(last_seen_at) >= CURRENT_DATE - INTERVAL '3 month'
GROUP BY user_id, last_seen
ORDER BY visits, last_seen DESC
),
-- Themen, auf die geantwortet wurde
trt AS (
select posts.user_id,
count(distinct topic_id) as replied_count
from posts
INNER JOIN tl using (user_id)
INNER JOIN topics ON topics.id = posts.topic_id
WHERE topics.user_id <> posts.user_id
AND posts.deleted_at IS NULL AND topics.deleted_at IS NULL
-- AND topics.archetype <> 'private_message'
AND archetype = 'regular'
GROUP BY posts.user_id
ORDER BY replied_count DESC
),
-- Themen, die aller Zeiten angesehen wurden
tvat AS (
select tv.user_id,
COUNT(distinct tv.topic_id) AS topic_id
FROM topic_views tv
LEFT JOIN topics t on tv.topic_id=t.id
INNER JOIN tl on tv.user_id=tl.user_id
WHERE
t.archetype = 'regular'
AND t.deleted_at is null
group by tv.user_id
),
likes AS (
SELECT user_id,
likes_given, likes_received
from user_stats
INNER JOIN tl using (user_id)
)
SELECT pr.user_id,
pr.last_seen as "Zuletzt gesehen",
-- Tage besucht: 15
greatest(15-coalesce(pr.visits,0),0) as "Tage besucht Lücke",
-- Themenantworten: 3
greatest(3-coalesce(trt.replied_count,0), 0) as "Themenantwort Lücke",
-- Themen eingegeben: 20
greatest(20-coalesce(tvat.topic_id,0),0) as "Themen angesehen Lücke",
-- Beiträge gelesen: 100
greatest(100-coalesce(pr.posts_read,0),0) as "Beiträge gelesen Lücke",
-- Zeit mit Lesen von Beiträgen verbracht: 60min
greatest(60-pr.minutes_reading_time,0) as "Lesezeit Lücke",
-- Likes gegeben: 1
greatest(1-likes.likes_given,0) as "Likes gegeben Lücke",
-- Likes erhalten: 1
greatest(1-likes.likes_received,0) as "Likes erhalten Lücke"
FROM pr
left join trt using (user_id)
left join tvat using (user_id)
LEFT JOIN likes using (user_id)
ORDER BY
pr.visits DESC
LIMIT 500
3 „Gefällt mir“
Nochmal!
Ich habe gerade erst festgestellt, dass die Likes für TL3 die letzten 100 Tage sind! 
Korrigiert dafür:
WITH
t as (
select
CURRENT_TIMESTAMP - ((0 + 100) * (INTERVAL '1 days')) as start,
CURRENT_TIMESTAMP - (0 * (INTERVAL '1 days')) as end
),
-- Themaanzahl letzte 100 Tage 25%
-- das kleinere von 25% der in den letzten 100 Tagen erstellten Themen
-- ODER 500, die systemseitige Standardanforderung für TL3
tclhd AS (
SELECT LEAST(floor(count(id)*.25)::REAL,500) as all_topics
FROM topics, t
WHERE created_at > t.start
AND archetype = 'regular'
AND deleted_at is null
),
-- Beitraganzahl letzte 100 Tage 25%
-- das kleinere von 25% der in den letzten 100 Tagen erstellten Beiträge
-- ODER 20k, die systemseitige Standardanforderung für TL3
pclhd AS (
SELECT LEAST(FLOOR(count(id)*.25)::REAL,20000) AS all_posts
FROM t, posts
WHERE posts.created_at > start
AND posts.deleted_at is null
AND posts.hidden_at is null
AND posts.last_editor_id >0 -- Discobot & System ausschließen
AND (action_code is null OR action_code != 'assigned')
),
-- Vertrauensstufe 2 Benutzer
tl AS (
SELECT id as user_id
FROM users
WHERE trust_level = 2
),
-- Benutzer + Besuche & gelesene Beiträge letzte 100 Tage
pr AS (
SELECT user_id,
count(1) as visits,
sum(posts_read) as posts_read
FROM t, user_visits
INNER JOIN tl using (user_id)
WHERE visited_at > t.start
AND visited_at < t.end
GROUP BY user_id
ORDER BY visits DESC
),
-- Gelesene Beiträge aller Zeiten
prat as (
select user_id,
sum(posts_read) as posts_read
from t, user_visits
INNER JOIN tl using (user_id)
group by user_id
),
-- Antworten auf Themen
trt as (
select posts.user_id,
count(distinct topic_id) as replied_count
from t, posts
INNER JOIN tl using (user_id)
INNER JOIN topics ON topics.id = posts.topic_id
WHERE posts.created_at > t.start
AND posts.created_at < t.end
AND topics.user_id <> posts.user_id
AND posts.deleted_at IS NULL AND topics.deleted_at IS NULL
AND archetype = 'regular'
group by posts.user_id
),
-- Themen angesehen aller Zeiten
tvat as (
select tv.user_id,
COUNT(distinct tv.topic_id) AS topic_id
FROM topic_views tv
LEFT JOIN topics t on tv.topic_id=t.id
INNER JOIN tl on tv.user_id=tl.user_id
WHERE t.archetype = 'regular'
AND t.deleted_at is null
group by tv.user_id
),
-- Themen angesehen
tva AS (
SELECT tv.user_id,
COUNT(distinct tv.topic_id) AS topic_id
FROM t, topic_views tv
LEFT JOIN topics on topic_id=topics.id
INNER JOIN tl on tv.user_id=tl.user_id
WHERE
topics.archetype = 'regular'
AND topics.deleted_at is null
AND viewed_at > t.start
AND viewed_at < t.end
GROUP BY tv.user_id
),
likes_received_lhd AS (
SELECT ua.user_id
, count(*) as likes_received_lhd
FROM t, user_actions ua
JOIN posts p on p.id=ua.target_post_id
JOIN tl on ua.user_id=tl.user_id
WHERE ua.action_type=1
AND ua.created_at > t.start
AND ua.created_at < t.end
GROUP BY ua.user_id
),
likes_given_lhd AS (
SELECT user_id, count(*) as likes_given_lhd
FROM t, given_daily_likes
INNER JOIN tl using (user_id)
WHERE given_date > t.start
AND given_date < t.end
GROUP BY user_id
)
SELECT pr.user_id,
greatest(50-coalesce(pr.visits,0),0) as "Tage besucht LHD Lücke",
greatest(10-coalesce(trt.replied_count,0), 0) as "Themenantwort Lücke",
greatest(tclhd.all_topics-coalesce(tva.topic_id,0),0) AS "Themen angesehen LHD Lücke von 150",
greatest(200-coalesce(tvat.topic_id,0),0) as "Themen angesehen (AT) Lücke",
greatest(pclhd.all_posts - coalesce(pr.posts_read,0),0) as "Gelesene Beiträge LHD Lücke von 250",
greatest(500-coalesce(prat.posts_read,0),0) as "Gelesene Beiträge (AT) Lücke",
GREATEST(30-COALESCE(likes_given_lhd,0),0) as "Likes gegeben LHD Lücke",
GREATEST(20-COALESCE(likes_received_lhd,0),0) as "Likes erhalten LHD Lücke"
FROM pclhd, tclhd, pr
LEFT JOIN trt using (user_id)
LEFT JOIN tva USING (user_id)
LEFT JOIN tvat using (user_id)
LEFT JOIN prat using (user_id)
LEFT JOIN likes_received_lhd using (user_id)
LEFT JOIN likes_given_lhd using (user_id)
ORDER BY pr.visits DESC
LIMIT 25
6 „Gefällt mir“
@alefattorini Es ist ein Lückenbericht. Wenn die Spalten leer sind, hat der Benutzer keine Lücke für diese Anforderung. Ihr erster Benutzer ist also fast für TL3 qualifiziert und seine einzige Lücke besteht darin, 25 Likes zu geben und 15 zu erhalten.
Ist das sinnvoll?
5 „Gefällt mir“
Ja! Danke. Ich passe meine tl3-Parameter an.
2 „Gefällt mir“