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task up to arrays

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dr0zd
2026-01-29 04:50:55 +07:00
parent 97d55ac267
commit 0e385bd6d0
6 changed files with 420 additions and 0 deletions
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mdrozdov/clickhouse/array_map_nested.sql
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--task 1
select
user_id,
indexOf(tags, 'promo') as promo_index,
arrayJoin(tags)
from events
where has(tags, 'promo')
-- task 2
select
user_id,
scores['math'] as math_score,
mapValues(scores) as all_values
from metrics
-- task 3
select
name,
contact.type as contact_type,
contact.value as contact_value
from users_nested
array join contacts as contact
where contact.type = 'email'
mdrozdov/clickhouse/data_types.sql
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-- task 1
CREATE TABLE raw_data (
user_id String,
balance String,
created_at String
) ENGINE = MergeTree ORDER BY tuple();
select
user_id,
toDecimal64OrNull(balance, 2) as balance,
toTimeDate64OrNull(created_at, 3) as created_at
from raw_data
where
toDecimal64OrNull(balance, 2) is not null and
toTimeDate64OrNull(created_at, 3) is not null
-- task 2 (failed)
CREATE TABLE events (
event_id String,
event_date String, -- форматы: "2023-01-01", "01.02.2023", "invalid"
event_time String -- форматы: "12:30:45", "25:99:99"
);
select
event_id,
toStartOfHour(toDateTimeOrNull(concatWithSeparator(' ', events_date, events_time)))
from events
where
toDateOrNull(event_date) is not null and toTimeOrNull(event_time) is not null
-- task 3
CREATE TABLE user_activity (
user_id UInt32,
actions String -- JSON: ["login", "purchase", "logout"]
);
SELECT
user_id,
actions_arr,
length(actions_arr) AS actions_count
FROM
(
SELECT
user_id,
JSONExtract(actions, 'Array(String)') AS actions_arr
FROM user_activity
)
WHERE has(actions_arr, 'purchase');
-- task 4
CREATE TABLE sensor_data (
sensor_id UInt32,
value Float64,
status Enum('ok' = 1, 'error' = 2)
);
SELECT
toString(status) as status,
avg(value) as avg_value
from sensor_data
where
toDecimal64OrNull(value, 2) is not null
group by status
mdrozdov/clickhouse/joins.sql
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--task 1
create table mdrozdov.products (
product_id Int,
name String
)
engine = MergeTree
order by product_id
insert into mdrozdov.products values
(101, 'iPhone 14'),
(102, 'Galaxy S22'),
(103, 'Pixel 7')
create table mdrozdov.prices (
product_id Int,
price Int,
region String
)
engine = MergeTree
order by product_id
insert into mdrozdov.prices values
(101, 999, 'US'),
(101, 1049, 'EU'),
(102, 850, 'US'),
(102, 899, 'CA'),
(103, 799, 'JP')
-- 1)
select
product_id,
name,
price,
region
from mdrozdov.products
any left join mdrozdov.prices
on mdrozdov.products.product_id = mdrozdov.prices.product_id
-- 2)
create table mdrozdov.ticks
ON CLUSTER clickhouse_cluster (
time DateTime,
symbol String,
price Decimal(10, 2)
)
engine = ReplicatedMergeTree(
'/clickhouse/tables/{shard}/mdrozdov/ticks',
'{replica}'
)
order by (time, symbol)
insert into mdrozdov.ticks values
('2025-07-17 10:00:00', 'AAPL', 190.5),
('2025-07-17 10:01:00', 'AAPL', 191.2),
('2025-07-17 10:02:30', 'AAPL', 192.0),
('2025-07-17 10:05:00', 'GOOG', 2850.0)
create table mdrozdov.orders
ON CLUSTER clickhouse_cluster (
time DateTime,
symbol String,
size Int
)
engine = ReplicatedMergeTree(
'/clickhouse/tables/{shard}/mdrozdov/orders',
'{replica}'
)
order by size
insert into mdrozdov.orders values
('2025-07-17 10:00:30', 'AAPL', 50),
('2025-07-17 10:02:00', 'AAPL', 75),
('2025-07-17 10:03:00', 'AAPL', 20),
('2025-07-17 10:04:00', 'GOOG', 15)
select
o.time,
o.symbol,
t.price as last_price
from mdrozdov.orders o
asof join mdrozdov.ticks t
on o.symbol = t.symbol and o.time >= t.time
-- task 3
select
user_id,
name
from users
where user_id in (
select user_id
from payments
where amount > 1000
)
-- task 4
select
bl.user_id,
u.name,
bl.event_time,
bl.event_type,
from big_logs bl
any left join users u
using(user_id)
mdrozdov/clickhouse/merge_tree_family.sql
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--page_views table
create table page_views (
user_id UInt64,
url String,
view_time DateTime
)
Engine = MergeTree
Order by (user_id, view_time)
Partition by toYYYYMM(view_time)
-- users table
create table users(
id UInt64,
name String,
updated_at DateTime
)
ENGINE = ReplacingMergeTree(updated_at)
Order by id;
-- region_sales table
create table region_sales(
region String,
date Date,
sales UInt64
)
ENGINE = SummingMergeTree
Partition by toYYYYMM(date)
Order by (region, date)
insert into mdrozdov.region_sales (region, date, sales)
values ('Russia', '2025-01-14', 30), ('Russia', '2025-01-16', 22), ('USA', '2025-01-14', 1400)
OPTIMIZE TABLE mdrozdov.region_sales FINAL
-- user_metrics table
create table mdrozdov.user_metrics (
region String,
avg_age_state AggregateFunction(avg, UInt8),
active_users_state AggregateFunction(uniq, UInt64)
)
Engine = AggregatingMergeTree
Order by region
INSERT INTO mdrozdov.user_metrics
SELECT
'Moscow' AS region,
avgState(toUInt8(25)) AS avg_age_state,
uniqState(toUInt64(100)) AS active_users_state
UNION ALL
SELECT
'Moscow',
avgState(toUInt8(30)),
uniqState(toUInt64(50))
UNION ALL
SELECT
'SPB',
avgState(toUInt8(20)),
uniqState(toUInt64(10));
SELECT
region,
avgMerge(avg_age_state) AS avg_age,
uniqMerge(active_users_state) AS active_users
FROM mdrozdov.user_metrics
GROUP BY region
mdrozdov/clickhouse/queries.sql
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CREATE TABLE users (
user_id Int,
name String,
age Int,
country String,
is_active bool
)
engine = MergeTree
order by user_id
insert into mdrozdov.users values
(1, 'Ivan' ,30, 'RU', 1),
(2, 'Anna' ,25, 'RU', 1),
(3, 'Petr' ,45, 'RU', 0),
(4, 'Olga' ,35, 'RU', 1),
(5, 'John' ,55, 'US', 1),
(6, 'Alice' ,42, 'US', 0),
(7, 'Tom' ,38, 'US', 1),
(8, 'Lucas' ,28, 'BR', 1),
(9, 'Maria' ,60, 'BR', 1),
(10, 'João' ,27, 'BR', 0),
(11, 'Kenji' ,33, 'JP', 1),
(12, 'Yuki' ,29, 'JP', 0),
(13, 'Chen' ,40, 'CN', 1),
(14, 'Li' ,48, 'CN', 1),
(15, 'Fatima',31, 'AE', 1),
(16, 'Ahmed' ,36, 'AE', 1),
(17, 'George',24, 'US', 1),
(18, 'Nina' ,34, 'RU', 1),
(19, 'Max' ,50, 'DE', 1),
(20, 'Sophie',22, 'DE', 0)
--1. Получи 10 самых старших **активных** пользователей.
select
user_id,
name,
age,
country,
is_active
from mdrozdov.users
where is_active
order by age desc
limit 10
--2. Выведи пользователей из `country = 'RU'`, отсортированных по имени.
select
user_id,
name,
age,
country,
is_active
from mdrozdov.users
where country = 'RU'
order by name asc
--3. Что произойдёт, если написать `LIMIT 10` **без `ORDER BY`**?
-- ответ будет недетерминирован
--4. Посчитай количество пользователей по странам.
select
country,
count(user_id) as total_users
from mdrozdov.users
group by country
--5. Для каждой страны выведи минимальный и максимальный возраст.
select
country,
min(age) as min_user_age,
max(age) as max_user_age
from mdrozdov.users
group by country
--6. Отсортируй страны по убыванию количества пользователей.
select
country,
count(user_id) as total_users
from mdrozdov.users
group by country
order by total_users desc
-- task 2
CREATE TABLE mdrozdov.events (
user_id Int,
event_time DateTime,
event_type String --mb better enum but idc
)
engine = MergeTree
order by user_id
insert into events values
(1, '2025-07-17 09:00:00', 'click'),
(1, '2025-07-17 09:05:00', 'view'),
(1, '2025-07-17 09:10:00', 'purchase'),
(2, '2025-07-17 10:00:00', 'click'),
(2, '2025-07-17 10:05:00', 'click'),
(2, '2025-07-17 11:00:00', 'view'),
(3, '2025-07-16 12:00:00', 'view'),
(3, '2025-07-16 12:30:00', 'click'),
(3, '2025-07-17 08:00:00', 'purchase'),
(4, '2025-07-17 13:00:00', 'click'),
(4, '2025-07-17 14:00:00', 'view'),
(4, '2025-07-17 15:00:00', 'view'),
(5, '2025-07-16 20:00:00', 'view'),
(5, '2025-07-17 01:00:00', 'click'),
(5, '2025-07-17 06:00:00', 'purchase'),
(6, '2025-07-17 07:30:00', 'view'),
(6, '2025-07-17 08:45:00', 'click'),
(7, '2025-07-16 23:59:59', 'click'),
(7, '2025-07-17 00:01:00', 'purchase'),
(7, '2025-07-17 10:10:00', 'view')
--1-2) Добавь колонку с кумулятивным количеством событий по user_id (используй count() OVER ...)
select
user_id,
event_time,
event_type,
count(*) over (partition by user_id order by event_time) as total_click_exact_moment,
row_number() over (partition by user_id order by event_time) as row_number
from mdrozdov.events
--3) да, поддерживается, но не обязательно для коммулятивного суммирования строк
--4-6) Сделай WITH recent_events AS (...) — все события за последние 24 часа.
with recent_events AS (
select *
from mdrozdov.events
where event_time >= '2025-07-18'::DATE - interval '1 day'
), category_list as (
select 'click' as event_type, 'interaction' as category
union all
select 'view', 'interaction'
union all
select 'purchase', 'conversion'
)
select
user_id,
count(*) as total_events_last_24h,
count(distinct cl.category) as uniq_categories
from recent_events re
left join category_list cl
using(event_type)
group by user_id
order by total_events_last_24h desc, user_id asc
limit 5
mdrozdov/clickhouse/view_distribute.sql
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-- task 1
CREATE TABLE dist_table
ENGINE = Distributed(
'cluster_name', -- нет доступа к конфигу infra clickhouse
'mdrozdov',
'logs',
rand()
);
--rand() это параметр по которому мы распределяем данные при insert на разные ноды, в данном
--случае нам это нужно для равномерности распределения, потому что таблица не требует особых связей или тп
-- task 2
--Создай VIEW top_active_users, который показывает топ-100 пользователей по числу событий
CREATE VIEW top_active_users AS
select user_id, count(*) as total_events
from events
group by user_id
order by total_events desc
limit 100
-- не подходит для дашборда с миллионами строк, потому что вычисляется каждый раз при вызове
-- для дашбордов лучше подойдет materialized view, тк хранит данные запроса и обновляется автоматически
-- при вставке новых данных
--task 3
CREATE MATERIALIZED VIEW
ENGINE = SummingMergeTree
order by date
mv_clicks_per_day AS
select
toDate(event_time) as date,
count(*) as total_clicks
from raw_events
group date