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 (?)
8 个赞
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)
8 个赞
I thought about TL3 Requirements
Checking which users have the most number of 
Requirements and sort by that
4 个赞
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 个赞
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.
1 个赞
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 个赞
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
8 个赞
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?
3 个赞
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
2 个赞
Yes, tried that: 60, 80, 95, 99 → no effect at all, always the same error message.
2 个赞
这是一个修改后的版本，没有参数：我已将默认的 TL3 要求（高效地？）硬编码到查询中。我稍微扩展了数据集，加入了“点赞数（给出和收到）”，但没有加入“点赞数/独立用户天数”或“点赞数/独立用户”。标志、静默和暂停功能仍然缺失。
这是一个差距报告，它进行减法运算，显示每个用户缺少什么。
在几个地方，它与用户管理页面显示的内容不完全匹配：
- 点赞数（我的计数不知何故更高）
- 过去 100 天的发帖数（我的计数更低）
我对过去 100 天的发帖数进行了大量的猜测。
但以防万一有帮助：
with
t as (
select
CURRENT_TIMESTAMP - ((0 + 100) * (INTERVAL '1 days')) as start,
CURRENT_TIMESTAMP - (0 * (INTERVAL '1 days')) as end
),
-- 过去 100 天的主题数 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
),
-- 过去 100 天的帖子数 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 -- 排除 Discobot 和系统
AND (action_code is null OR action_code != 'assigned')
),
-- 用户
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
),
-- 回复的主题
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
),
-- 查看的所有主题
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
),
-- 查看的主题
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
),
-- 阅读的帖子
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
),
-- 阅读的所有帖子
prat as (
select user_id,
sum(posts_read) as posts_read
from user_visits, t
group by user_id
),
-- 当前信任级别
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 "访问天数差距",
greatest(10-coalesce(trt.topic_id,0), 0) as "回复主题差距",
greatest(tclhd.all_topics-coalesce(tva.topic_id,0),0) AS "查看主题差距",
greatest(200-coalesce(tvat.topic_id,0),0) as "查看主题 (AT) 差距",
greatest(pclhd.all_posts - coalesce(pra.posts_read,0),0) as "阅读帖子差距",
greatest(500-coalesce(prat.posts_read,0),0) as "阅读帖子 (AT) 差距",
greatest(30-likes.likes_given,0) as "点赞给出差距",
greatest(20-likes.likes_received,0) as "点赞收到差距"
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
5 个赞
感谢您的版本！
但是，我仍然收到相同的错误
PG::QueryCanceled: ERROR: canceling statement due to statement timeout
我想我们的安装有问题。
4 个赞
对我来说，这目前运行需要 8,379.4 毫秒，所以我猜这接近极限了。你们的社区肯定更大。
在最后添加 LIMIT 50 可以为我节省 1k 毫秒。也许你可以试试这个，直到你得到一些结果。
5 个赞
这稍微更有效率。如果它仍然无法运行，您可以尝试删除一些列以及它们关联的联接和查询。
编辑 好的，我终于弄清楚了我的联接类型（已经有一段时间了）。这个更新后的查询效率高得多
with
t as (
select
CURRENT_TIMESTAMP - ((0 + 100) * (INTERVAL '1 days')) as start,
CURRENT_TIMESTAMP - (0 * (INTERVAL '1 days')) as end
),
-- 过去 100 天的话题数量 25%
-- 过去 100 天创建的话题的 25% 的较小值
-- 或 500，TL3 的系统默认最大要求
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
),
-- 过去 100 天的帖子数量 25%
-- 过去 100 天创建的帖子的 25% 的较小值
-- 或 20k，TL3 的系统默认最大要求
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 -- 排除 Discobot 和系统
AND (action_code is null OR action_code != 'assigned')
),
-- 信任等级 2 用户
tl AS (
SELECT id as user_id, trust_level
FROM users
WHERE trust_level = 2
),
-- 过去 100 天的用户访问和帖子阅读量
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
),
-- 所有时间帖子阅读量
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
),
-- 回复的话题
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
),
-- 所有时间查看的话题
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
),
-- 查看的话题
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 "访问天数差距",
greatest(10-coalesce(trt.topic_id,0), 0) as "回复话题差距",
greatest(tclhd.all_topics-coalesce(tva.topic_id,0),0) AS "查看话题差距",
greatest(200-coalesce(tvat.topic_id,0),0) as "查看话题 (AT) 差距",
greatest(pclhd.all_posts - coalesce(pr.posts_read,0),0) as "阅读帖子差距",
greatest(500-coalesce(prat.posts_read,0),0) as "阅读帖子 (AT) 差距",
greatest(30-likes.likes_given,0) as "点赞数差距",
greatest(20-likes.likes_received,0) as "获赞数差距"
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 个赞
Aaand… here’s the TL2 gap report:
with
-- Trust Level 1 users
tl AS (
SELECT id as user_id, trust_level, last_seen_at
FROM users
WHERE trust_level = 1
),
-- Users seen in last 3mo + visits, posts read, reading time
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
),
-- Topics replied to
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
),
-- Topics Viewed All Time
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 "最后访问时间",
-- days visited: 15
greatest(15-coalesce(pr.visits,0),0) as "访问天数差距",
-- topic replies: 3
greatest(3-coalesce(trt.replied_count,0), 0) as "回复帖子差距",
-- topics entered: 20
greatest(20-coalesce(tvat.topic_id,0),0) as "浏览帖子差距",
-- posts read: 100
greatest(100-coalesce(pr.posts_read,0),0) as "阅读帖子差距",
-- time spent reading posts: 60min
greatest(60-pr.minutes_reading_time,0) as "阅读时间差距",
-- likes given: 1
greatest(1-likes.likes_given,0) as "点赞差距",
-- likes received: 1
greatest(1-likes.likes_received,0) as "被点赞差距"
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 个赞
再一次！
我才刚意识到 TL3 的点赞是过去 100 天的！
已为此进行更正：
WITH
t as (
select
CURRENT_TIMESTAMP - ((0 + 100) * (INTERVAL '1 days')) as start,
CURRENT_TIMESTAMP - (0 * (INTERVAL '1 days')) as end
),
-- 过去 100 天主题数量的 25%
-- 过去 100 天创建主题数量的 25%
-- 或 500，系统默认的 TL3 最大要求
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
),
-- 过去 100 天帖子数量的 25%
-- 过去 100 天创建帖子数量的 25%
-- 或 20k，系统默认的 TL3 最大要求
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 -- 排除 Discobot 和系统
AND (action_code is null OR action_code != 'assigned')
),
-- 信任等级 2 用户
tl AS (
SELECT id as user_id
FROM users
WHERE trust_level = 2
),
-- 用户 + 过去 100 天的访问和帖子阅读数
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
),
-- 所有时间的帖子阅读数
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
),
-- 回复的主题
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
),
-- 所有时间的查看主题数
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
),
-- 查看的主题
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 "过去 100 天访问天数差距",
greatest(10-coalesce(trt.replied_count,0), 0) as "主题回复差距",
greatest(tclhd.all_topics-coalesce(tva.topic_id,0),0) AS "过去 100 天查看主题差距（150）",
greatest(200-coalesce(tvat.topic_id,0),0) as "查看主题（所有时间）差距",
greatest(pclhd.all_posts - coalesce(pr.posts_read,0),0) as "过去 100 天阅读帖子差距（250）",
greatest(500-coalesce(prat.posts_read,0),0) as "阅读帖子（所有时间）差距",
GREATEST(30-COALESCE(likes_given_lhd,0),0) as "过去 100 天点赞数差距",
GREATEST(20-COALESCE(likes_received_lhd,0),0) as "过去 100 天收到的点赞数差距"
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 个赞
@alefattorini 这是一个差距报告。当列为空时，表示用户在该要求上没有差距。因此，您的第一个用户几乎符合 TL3 的资格，他唯一的差距是给出 25 个赞和收到 15 个赞。
有意义吗？
5 个赞
是的！谢谢。我正在调整我的 tl3 参数
2 个赞