Telegram RegisterThe public register of Telegram

Channel

SQLpedia | Базы данных

@sql_wiki

On this record: Growth · Engagement · Reactions · Advertising · Posts · Citations · Cite this entry

6,006subscribers

-6 since we began measuring on 6 August 2026

Risers and fallers across the register · movement among entries of 3,162–10,000.

Register entry

Telegram ID-1001526752830
TypeChannel
Username@sql_wiki
CreatedBetween 1 August 2021 and 31 January 2023— estimated from Telegram’s id allocation, not measured. How this range is calculated.
First recorded6 August 2026
Last confirmed live12 August 2026
Measurements held4
Confirmed unchanged1 time, most recently 12 August 2026
On Telegramt.me/sql_wiki

Growth

6,0066,0126,0096 August 2026 — 6,012 subscribers6 August 2026 — 6,012 subscribers9 August 2026 — 6,008 subscribers12 August 2026 — 6,006 subscribers6 August 202612 August 2026
4 measurements spanning 6 days, net -6. Dots are measurements; the straight line between them is drawn to join them, not to claim we know the path taken in between — snapshots are recorded only when a count changes, so gaps mean “no change observed”, never “interpolated”. The vertical axis spans 6,005–6,013 and does not start at zero.
Measurement log — every subscribers count we have recorded
Measured (UTC)SubscribersChange
12 Aug 2026, 15:546,006-2
9 Aug 2026, 22:116,008-4
6 Aug 2026, 22:216,012no change
6 Aug 2026, 22:016,012first reading

Engagement

20 posts held, back to 26 February 2026the reader has not yet reached the start of this channel’s public history, so older posts may sit further back, unread. Read across 9 pagesof Telegram’s post history, 20 posts per page.

Nothing published in the last 30 days. ERR and ER are rolling 30-day measures, so there is nothing to compute — we hold 20 posts for this entry, the most recent from 20 April 2026. An engagement rate over an empty window would be a number about nothing.

Reaction mix

35 reactions across 9 posts, in 5 distinct kinds. The most used accounts for 54.3% of them.

Every reaction kind recorded on the sample, most used first
ReactionCountShareShare, drawn
😁1954.3%
1028.6%
👍411.4%
❤‍🔥12.86%
😱12.86%

No sentiment is inferred, and none should be read in. This table is ordered by count and by nothing else. Emoji do not carry stable meaning across languages or communities — 🙏 is thanks in one channel and mourning in another — so we publish which ones were pressed and how often, and pass no judgement on what an audience meant by them.

Precision. Telegram publishes reaction counts per emoji and short-forms each one — 4.34K, 1.2M — so any single kind at or above 1,000 reaches us at three significant figures, and only counts below 1,000 are exact. The shares above are ratios of those figures and carry the same error. This is also why the total here can differ slightly from a reaction total printed elsewhere on the page: both are sums of the same rounded parts, taken over samples with different edges.

Coverage. Reactions were read on 9 of the 20 sampled posts in this sample. Summed by Telegram’s own count on each post — not by adding up the per-emoji breakdown above — those same posts carry 35reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.

Measured over the 20 most recent posts we hold, published 26 February 2026 to 20 April 2026, using the newest reading held for each. Telegram Stars are excluded: they are a payment, not a reaction, and they have their own section.

Advertising

Ad load
5.00%
1 of 20 posts carry an ad marker
Regulatory tokens
1
posts carrying an erid · 1 distinct token
Median views · ads
1,700
over 1 measured post
Median views · rest
1,320
over 19 measured posts

An ad marker, not a judgement about a post. A post is counted here because it carries one of two explicit markings: an erid token, which Russian law has required on paid placements since 2022 and which is issued against a specific advertising contract, or a #реклама / #ad hashtag in the body, which is the channel declaring it itself. The first is documentary; the second is a self-declaration and is weaker. No classifier reads the text and decides — nothing on this site guesses that a post is an advertisement.

This is a floor, and it can only ever be a floor.A channel that runs paid placements without marking them produces no marker for us to count, and an unmarked ad is indistinguishable from an ordinary post on the public surface. The ad load above therefore means “the share of posts that declared themselves”, never “the share of posts that were paid for”. A low figure is not evidence of a channel that runs few ads.

Both figures are medians, and no ratio between them is published. Each is a view reading that actually occurred on a post, picked by percentile_disc rather than averaged, so one viral post cannot move it and no interpolated value is invented between two readings. The sample on one side is under five posts, which is too thin to compare. The two figures are shown side by side with the count behind each, and deliberately not divided into a headline like “ads get x% fewer views” — an arithmetic that is easy to print and, at this sample size, means nothing.

Advertising tokens recorded on this entry
eridPostsFirst seenLast seen
2W5zFHxuPV316 March 20266 March 2026

A token repeated across several posts is one advertising contract placed more than once, which is what the identifier is for. The strings are reproduced exactly as they appeared in the post or in its click-through URL and are not validated against any registry — we record the marker a channel published, and whether it resolves to a real contract is a question for the register that issued it.

Measured over the 20 most recent posts we hold, published 26 February 2026 to 20 April 2026. Views are the latest single reading held for each post, and any reading at or above 1,000 is rounded by Telegram to three significant figures.

Recent posts

20 Apr 2026, 06:30 UTC≈1,190 views4 reactionsread 12 August 2026

​​Как читать BUFFERS в EXPLAIN ANALYZE и находить I/O-узкие места в PostgreSQL PostgreSQL показывает использование буферов для каждого узла плана, и когда вы научитесь читать эти числа, то сможете точно понять, где ваш запрос тратил время на ожидание операций ввода-вывода (I/O), а где этого не происходило. Перейти к статье | SQLpedia

👍3❤‍🔥1

15 Apr 2026, 06:03 UTC≈1,480 views1 reactionsread 12 August 2026

​​Как мы строим OLTP-ядро: от API-контрактов до eBPF-проб В предыдущих статьях я подробно разбирал фундамент нашей новой OLTP-СУБД: принцип Restrictive by Default (fail-closed вместо деградации), отказ от VACUUM в пользу UNDO-лога и концепцию Unified Storage. Перейти к статье | SQLpedia

1

13 Apr 2026, 06:13 UTC≈1,190 views1 reactionsread 12 August 2026

​​Как мы строим OLTP-ядро: от API-контрактов до eBPF-проб В предыдущих статьях я подробно разбирал фундамент нашей новой OLTP-СУБД: принцип Restrictive by Default (fail-closed вместо деградации), отказ от VACUUM в пользу UNDO-лога и концепцию Unified Storage. Перейти к статье | SQLpedia

1

6 Apr 2026, 06:30 UTC≈1,510 viewsread 12 August 2026

​​Мониторинг SQL Server Always On в Zabbix Если у вас в инфраструктуре стоит Always On Availability Groups, вы наверняка бывали в такой ситуации: в SSMS всё зелёное, дашборд показывает «Synchronized», пользователи звонят с жалобами на тормоза. Перейти к статье | SQLpedia

3 Apr 2026, 06:12 UTC≈1,300 views4 reactionsread 12 August 2026
Forwarded from @ba_wiki

​​Гайд системного аналитика по корректировкам витрин Данный материал подходит для тех сотрудников, которые не имеют опыта работы или недавно пришли на проект, связанный с хранилищами данных. Перейти к статье | BApedia

3😱1

1 Apr 2026, 06:31 UTC≈1,140 views1 reactionsread 12 August 2026

​​Temporal Tables в MS SQL Server: история изменений Temporal tables позволяют следить за историями изменений уровне движка. SQL Server сам хранит полную историю изменений каждой строки — без триггеров, без дополнительного кода и без самописного аудита. Перейти к статье | SQLpedia

1

30 Mar 2026, 07:01 UTC≈1,100 views1 reactionsread 12 August 2026

​​CPU 80%. Как найти проблемный запрос в ClickHouse? CPU 80%, память на пределе, диск нагружен. Запросы тормозят. Расчёты не завершаются. Сервер на грани. Что же делать? Перейти к статье | SQLpedia

👍1

26 Mar 2026, 06:30 UTC≈1,400 viewsread 12 August 2026

​​PostgreSQL: транзакции, блокировки и почему Serializable падает Если вы когда-либо сталкивались с тем, что запросы внезапно «висят», транзакции ведут себя странно, а UPDATE из двух сессий приводит к неожиданным результатам — эта статья поможет понять, что происходит внутри PostgreSQL. Перейти к статье | SQLpedia

24 Mar 2026, 14:13 UTC≈1,200 viewsread 12 August 2026

​​Как отчисление одного студента может закрыть всю кафедру. Нормализуем БД и избавляемся от аномалий В прошлой статье мы детально разобрали функциональные зависимости. Возможно, после нее у вас, как и у многих, остался закономерный вопрос: зачем нам вообще так париться, выискивая эти зависимости? Как это применяется в проектировании баз данных? Перейти к статье | SQLpedia

23 Mar 2026, 08:36 UTC≈1,110 viewsread 12 August 2026
Photo

Планы на последнюю пятницу марта: заглянуть к коллегам на Avito Database meetup Мы узнали, что 27-го ребята собираются, чтобы разобрать пару архитектурных болей. Отличный повод посмотреть на чужой прод и сравнить со своими решениями. В программе: — чек-лист по защите данных в DBaaS; — FoundationDB против Cassandra 5 в реальных условиях; — нетипичный путь масштабирования S3. Можно дойти до их офиса, а можно просто

19 Mar 2026, 06:25 UTC≈1,170 viewsread 12 August 2026

​​Определение фактического профиля нагрузки в PostgreSQL и динамические состояния БД Когда вы знакомитесь с документацией по какой-то системе в части базы данных, то обычно характер нагрузки определяется исходно в архитектуре проекта. Но если система определена архитектором как OLTP, но в действительности может вести себя в некоторые периоды времени как OLAP. Нормально ли такое поведение, и каким образом мы можем оп

16 Mar 2026, 05:40 UTC≈1,380 views3 reactionsread 12 August 2026

​​Почему `SUM() OVER (ORDER BY ...)` иногда считает «неправильно»: разбираем оконные фреймы в SQL Оконные функции в SQL полезны тем, что позволяют делать аналитику по строкам без GROUP BY: считать ранги, накопительные итоги, скользящие средние, доли, сравнения с соседними строками и агрегаты по группе, при этом не теряя детализацию исходных данных. Перейти к статье | SQLpedia

3

Showing the 12 most recent of 20 posts we hold for @sql_wiki. View and reaction counts are the latest single reading for each post, not a live figure, and a recent post is still accumulating both. A view count marked was rounded by Telegram before we ever saw it — t.me prints views in full below 1,000 and to three significant figures above, so ≈1,200,000 means somewhere between 1,150,000 and 1,249,999. Unmarked counts are exact. Text is reproduced from the public post preview and truncated for length.

Citation-graph rank

Citation-graph rank — 853,954 of 1,151,006entries in the measured graph. A weighted position computed from the forward and mention edges below — republished posts weigh more than named mentions — and recomputed periodically, over the whole graph. Published only as this ordinal position, never as a score: a position is a fact, and a score printed beside one channel’s name would read as a verdict this register does not make. The two counts beneath stay separate for the same reason mentions are never summed with forwards anywhere else on this page — a named-by count costs nothing to manufacture. The top 100 by this measure, or how it is computed.

Forward network

Republished by

Channels on the register that have forwarded this channel's posts into their own feed.

Republishes

Channels on the register whose posts this channel has forwarded.

Built only from forwarded posts we have actually read, on both sides. Coverage is early and deliberately incomplete: a missing link means we have not read the post that would prove it, never that the relationship does not exist. Counts are distinct forwarded posts observed, so they only ever go up as we read more.

Mentions

Named by 2 registered channels — every channel on the register whose own posts have named this one, by its current username or any other username it currently holds, merged from two separately captured readings of the same fact so a namer caught by only one of them is not missed and a namer both caught is not counted twice. A username this channel has since dropped is not matched — that handle may belong to someone else now, and crediting today’s namer to yesterday’s owner would misattribute it.

Named by

Channels on the register whose posts name this channel's handle.

Names

Channels on the register whose handles appear in this channel's posts.

A mention is a weaker signal than a forward and is counted separately for that reason — naming a channel is not republishing it, and a handle in a post body is easy to place deliberately. The post counts beside each row below are distinct posts in which the handle appeared, from posts we have read on both sides — the “Named by N registered channels” figure above is a different count, of distinct NAMING CHANNELS rather than posts, and is not the sum of the rows under it.

Cite this entry

A live page changes as we take new readings, so a citation should name the measurement it is based on, not just the URL. The line below cites the subscriber count as measured 12 August 2026 — this entry's latest reading, not the date you are reading this.

“SQLpedia | Базы данных” (@sql_wiki), 6,006 subscribers as measured 12 August 2026. Telegram Register, tgregister.com/channel/sql_wiki.

Full measurement history, CC BY 4.0. Every reading this register holds for this entry, not just the latest one, as a dated, downloadable record: CSV · JSON. Free to use with attribution to tgregister.com. Each file carries its own generation timestamp, which is the figure to cite for exactly when the data was retrieved.