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Merge pull request #29020 from ka1bi4/romanzhukov-DOCSUP-14460-menus-dataset
DOCSUP-14460: [CLICKHOUSEDOCS] Edit and translate to Russian (menus dataset)
This commit is contained in:
Maksim Kita
GitHub
commit
365296eeae
@ -3,7 +3,7 @@ toc_priority: 21
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toc_title: Menus
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---
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# New York Public Library "What's on the Menu?" Dataset
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# New York Public Library "What's on the Menu?" Dataset {#menus-dataset}
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The dataset is created by the New York Public Library. It contains historical data on the menus of hotels, restaurants and cafes with the dishes along with their prices.
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@ -11,34 +11,38 @@ Source: http://menus.nypl.org/data
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The data is in public domain.
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The data is from library's archive and it may be incomplete and difficult for statistical analysis. Nevertheless it is also very yummy.
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The size is just 1.3 million records about dishes in the menus (a very small data volume for ClickHouse, but it's still a good example).
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The size is just 1.3 million records about dishes in the menus — it's a very small data volume for ClickHouse, but it's still a good example.
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## Download the Dataset
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## Download the Dataset {#download-dataset}
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```
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Run the command:
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```bash
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wget https://s3.amazonaws.com/menusdata.nypl.org/gzips/2021_08_01_07_01_17_data.tgz
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```
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Replace the link to the up to date link from http://menus.nypl.org/data if needed.
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Download size is about 35 MB.
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## Unpack the Dataset
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## Unpack the Dataset {#unpack-dataset}
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```
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```bash
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tar xvf 2021_08_01_07_01_17_data.tgz
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```
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Uncompressed size is about 150 MB.
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The data is normalized consisted of four tables:
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- Menu: information about menus: the name of the restaurant, the date when menu was seen, etc;
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- Dish: information about dishes: the name of the dish along with some characteristic;
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- MenuPage: information about the pages in the menus; every page belongs to some menu;
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- MenuItem: an item of the menu - a dish along with its price on some menu page: links to dish and menu page.
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- `Menu` — Information about menus: the name of the restaurant, the date when menu was seen, etc.
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- `Dish` — Information about dishes: the name of the dish along with some characteristic.
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- `MenuPage` — Information about the pages in the menus, because every page belongs to some menu.
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- `MenuItem` — An item of the menu. A dish along with its price on some menu page: links to dish and menu page.
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## Create the Tables
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## Create the Tables {#create-tables}
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```
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We use [Decimal](../../sql-reference/data-types/decimal.md) data type to store prices.
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```sql
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CREATE TABLE dish
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(
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id UInt32,
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@ -101,35 +105,33 @@ CREATE TABLE menu_item
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) ENGINE = MergeTree ORDER BY id;
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```
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We use `Decimal` data type to store prices. Everything else is quite straightforward.
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## Import the Data {#import-data}
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## Import Data
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Upload data into ClickHouse, run:
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Upload data into ClickHouse:
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```
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```bash
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clickhouse-client --format_csv_allow_single_quotes 0 --input_format_null_as_default 0 --query "INSERT INTO dish FORMAT CSVWithNames" < Dish.csv
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clickhouse-client --format_csv_allow_single_quotes 0 --input_format_null_as_default 0 --query "INSERT INTO menu FORMAT CSVWithNames" < Menu.csv
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clickhouse-client --format_csv_allow_single_quotes 0 --input_format_null_as_default 0 --query "INSERT INTO menu_page FORMAT CSVWithNames" < MenuPage.csv
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clickhouse-client --format_csv_allow_single_quotes 0 --input_format_null_as_default 0 --date_time_input_format best_effort --query "INSERT INTO menu_item FORMAT CSVWithNames" < MenuItem.csv
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```
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We use `CSVWithNames` format as the data is represented by CSV with header.
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We use [CSVWithNames](../../interfaces/formats.md#csvwithnames) format as the data is represented by CSV with header.
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We disable `format_csv_allow_single_quotes` as only double quotes are used for data fields and single quotes can be inside the values and should not confuse the CSV parser.
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We disable `input_format_null_as_default` as our data does not have NULLs. Otherwise ClickHouse will try to parse `\N` sequences and can be confused with `\` in data.
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We disable [input_format_null_as_default](../../operations/settings/settings.md#settings-input-format-null-as-default) as our data does not have [NULL](../../sql-reference/syntax.md#null-literal). Otherwise ClickHouse will try to parse `\N` sequences and can be confused with `\` in data.
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The setting `--date_time_input_format best_effort` allows to parse `DateTime` fields in wide variety of formats. For example, ISO-8601 without seconds like '2000-01-01 01:02' will be recognized. Without this setting only fixed DateTime format is allowed.
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The setting [date_time_input_format best_effort](../../operations/settings/settings.md#settings-date_time_input_format) allows to parse [DateTime](../../sql-reference/data-types/datetime.md) fields in wide variety of formats. For example, ISO-8601 without seconds like '2000-01-01 01:02' will be recognized. Without this setting only fixed DateTime format is allowed.
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## Denormalize the Data
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## Denormalize the Data {#denormalize-data}
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Data is presented in multiple tables in normalized form. It means you have to perform JOINs if you want to query, e.g. dish names from menu items.
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For typical analytical tasks it is way more efficient to deal with pre-JOINed data to avoid doing JOIN every time. It is called "denormalized" data.
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Data is presented in multiple tables in [normalized form](https://en.wikipedia.org/wiki/Database_normalization#Normal_forms). It means you have to perform [JOIN](../../sql-reference/statements/select/join.md#select-join) if you want to query, e.g. dish names from menu items.
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For typical analytical tasks it is way more efficient to deal with pre-JOINed data to avoid doing `JOIN` every time. It is called "denormalized" data.
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We will create a table that will contain all the data JOINed together:
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We will create a table `menu_item_denorm` where will contain all the data JOINed together:
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```
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```sql
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CREATE TABLE menu_item_denorm
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ENGINE = MergeTree ORDER BY (dish_name, created_at)
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AS SELECT
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@ -171,21 +173,32 @@ AS SELECT
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FROM menu_item
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JOIN dish ON menu_item.dish_id = dish.id
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JOIN menu_page ON menu_item.menu_page_id = menu_page.id
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JOIN menu ON menu_page.menu_id = menu.id
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JOIN menu ON menu_page.menu_id = menu.id;
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```
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## Validate the Data
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## Validate the Data {#validate-data}
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```
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SELECT count() FROM menu_item_denorm
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1329175
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Query:
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```sql
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SELECT count() FROM menu_item_denorm;
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```
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## Run Some Queries
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Averaged historical prices of dishes:
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Result:
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```text
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┌─count()─┐
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│ 1329175 │
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└─────────┘
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```
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## Run Some Queries {#run-queries}
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### Averaged historical prices of dishes {#query-averaged-historical-prices}
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Query:
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```sql
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SELECT
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round(toUInt32OrZero(extract(menu_date, '^\\d{4}')), -1) AS d,
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count(),
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@ -194,8 +207,12 @@ SELECT
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FROM menu_item_denorm
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WHERE (menu_currency = 'Dollars') AND (d > 0) AND (d < 2022)
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GROUP BY d
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ORDER BY d ASC
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ORDER BY d ASC;
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```
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Result:
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```text
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┌────d─┬─count()─┬─round(avg(price), 2)─┬─bar(avg(price), 0, 100, 100)─┐
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│ 1850 │ 618 │ 1.5 │ █▍ │
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│ 1860 │ 1634 │ 1.29 │ █▎ │
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@ -215,15 +232,15 @@ ORDER BY d ASC
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│ 2000 │ 2467 │ 11.85 │ ███████████▋ │
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│ 2010 │ 597 │ 25.66 │ █████████████████████████▋ │
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└──────┴─────────┴──────────────────────┴──────────────────────────────┘
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17 rows in set. Elapsed: 0.044 sec. Processed 1.33 million rows, 54.62 MB (30.00 million rows/s., 1.23 GB/s.)
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```
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Take it with a grain of salt.
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### Burger Prices:
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### Burger Prices {#query-burger-prices}
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```
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Query:
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```sql
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SELECT
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round(toUInt32OrZero(extract(menu_date, '^\\d{4}')), -1) AS d,
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count(),
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@ -232,8 +249,12 @@ SELECT
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FROM menu_item_denorm
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WHERE (menu_currency = 'Dollars') AND (d > 0) AND (d < 2022) AND (dish_name ILIKE '%burger%')
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GROUP BY d
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ORDER BY d ASC
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ORDER BY d ASC;
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```
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Result:
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```text
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┌────d─┬─count()─┬─round(avg(price), 2)─┬─bar(avg(price), 0, 50, 100)───────────┐
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│ 1880 │ 2 │ 0.42 │ ▋ │
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│ 1890 │ 7 │ 0.85 │ █▋ │
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@ -250,13 +271,13 @@ ORDER BY d ASC
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│ 2000 │ 21 │ 7.14 │ ██████████████▎ │
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│ 2010 │ 6 │ 18.42 │ ████████████████████████████████████▋ │
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└──────┴─────────┴──────────────────────┴───────────────────────────────────────┘
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14 rows in set. Elapsed: 0.052 sec. Processed 1.33 million rows, 94.15 MB (25.48 million rows/s., 1.80 GB/s.)
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```
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### Vodka:
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### Vodka {#query-vodka}
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```
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Query:
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```sql
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SELECT
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round(toUInt32OrZero(extract(menu_date, '^\\d{4}')), -1) AS d,
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count(),
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@ -265,8 +286,12 @@ SELECT
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FROM menu_item_denorm
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WHERE (menu_currency IN ('Dollars', '')) AND (d > 0) AND (d < 2022) AND (dish_name ILIKE '%vodka%')
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GROUP BY d
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ORDER BY d ASC
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ORDER BY d ASC;
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```
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Result:
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```text
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┌────d─┬─count()─┬─round(avg(price), 2)─┬─bar(avg(price), 0, 50, 100)─┐
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│ 1910 │ 2 │ 0 │ │
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│ 1920 │ 1 │ 0.3 │ ▌ │
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@ -282,11 +307,13 @@ ORDER BY d ASC
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To get vodka we have to write `ILIKE '%vodka%'` and this definitely makes a statement.
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### Caviar:
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### Caviar {#query-caviar}
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Let's print caviar prices. Also let's print a name of any dish with caviar.
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```
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Query:
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```sql
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SELECT
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round(toUInt32OrZero(extract(menu_date, '^\\d{4}')), -1) AS d,
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count(),
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@ -296,8 +323,12 @@ SELECT
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FROM menu_item_denorm
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WHERE (menu_currency IN ('Dollars', '')) AND (d > 0) AND (d < 2022) AND (dish_name ILIKE '%caviar%')
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GROUP BY d
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ORDER BY d ASC
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ORDER BY d ASC;
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```
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Result:
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```text
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┌────d─┬─count()─┬─round(avg(price), 2)─┬─bar(avg(price), 0, 50, 100)──────┬─any(dish_name)──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┐
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│ 1090 │ 1 │ 0 │ │ Caviar │
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│ 1880 │ 3 │ 0 │ │ Caviar │
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@ -319,6 +350,6 @@ ORDER BY d ASC
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At least they have caviar with vodka. Very nice.
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### Test it in Playground
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## Online Playground {#playground}
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The data is uploaded to ClickHouse Playground, [example](https://gh-api.clickhouse.tech/play?user=play#U0VMRUNUCiAgICByb3VuZCh0b1VJbnQzMk9yWmVybyhleHRyYWN0KG1lbnVfZGF0ZSwgJ15cXGR7NH0nKSksIC0xKSBBUyBkLAogICAgY291bnQoKSwKICAgIHJvdW5kKGF2ZyhwcmljZSksIDIpLAogICAgYmFyKGF2ZyhwcmljZSksIDAsIDUwLCAxMDApLAogICAgYW55KGRpc2hfbmFtZSkKRlJPTSBtZW51X2l0ZW1fZGVub3JtCldIRVJFIChtZW51X2N1cnJlbmN5IElOICgnRG9sbGFycycsICcnKSkgQU5EIChkID4gMCkgQU5EIChkIDwgMjAyMikgQU5EIChkaXNoX25hbWUgSUxJS0UgJyVjYXZpYXIlJykKR1JPVVAgQlkgZApPUkRFUiBCWSBkIEFTQw==).
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@ -386,7 +386,7 @@ The CSV format supports the output of totals and extremes the same way as `TabSe
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## CSVWithNames {#csvwithnames}
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Also prints the header row, similar to `TabSeparatedWithNames`.
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Also prints the header row, similar to [TabSeparatedWithNames](#tabseparatedwithnames).
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## CustomSeparated {#format-customseparated}
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@ -45,7 +45,7 @@ Configuration template:
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- `min_part_size` – The minimum size of a data part.
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- `min_part_size_ratio` – The ratio of the data part size to the table size.
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- `method` – Compression method. Acceptable values: `lz4`, `lz4hc`, `zstd`.
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- `level` – Compression level. See [Codecs](../../sql-reference/statements/create/table/#create-query-general-purpose-codecs).
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- `level` – Compression level. See [Codecs](../../sql-reference/statements/create/table.md#create-query-general-purpose-codecs).
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You can configure multiple `<case>` sections.
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@ -1 +0,0 @@
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../../../en/getting-started/example-datasets/menus.md
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360
docs/ru/getting-started/example-datasets/menus.md
Normal file
360
docs/ru/getting-started/example-datasets/menus.md
Normal file
@ -0,0 +1,360 @@
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---
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toc_priority: 21
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toc_title: Меню
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---
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# Набор данных публичной библиотеки Нью-Йорка "Что в меню?" {#menus-dataset}
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Набор данных создан Нью-Йоркской публичной библиотекой. Он содержит исторические данные о меню отелей, ресторанов и кафе с блюдами, а также их ценами.
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Источник: http://menus.nypl.org/data
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Эти данные находятся в открытом доступе.
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Данные взяты из архива библиотеки, и они могут быть неполными и сложными для статистического анализа. Тем не менее, это тоже очень интересно.
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В наборе всего 1,3 миллиона записей о блюдах в меню — очень небольшой объем данных для ClickHouse, но это все равно хороший пример.
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## Загрузите набор данных {#download-dataset}
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Выполните команду:
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```bash
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wget https://s3.amazonaws.com/menusdata.nypl.org/gzips/2021_08_01_07_01_17_data.tgz
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```
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При необходимости замените ссылку на актуальную ссылку с http://menus.nypl.org/data.
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Размер архива составляет около 35 МБ.
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## Распакуйте набор данных {#unpack-dataset}
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```bash
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tar xvf 2021_08_01_07_01_17_data.tgz
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```
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Размер распакованных данных составляет около 150 МБ.
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Данные нормализованы и состоят из четырех таблиц:
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- `Menu` — информация о меню: название ресторана, дата, когда было просмотрено меню, и т.д.
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- `Dish` — информация о блюдах: название блюда вместе с некоторыми характеристиками.
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- `MenuPage` — информация о страницах в меню, потому что каждая страница принадлежит какому-либо меню.
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- `MenuItem` — один из пунктов меню. Блюдо вместе с его ценой на какой-либо странице меню: ссылки на блюдо и страницу меню.
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## Создайте таблицы {#create-tables}
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Для хранения цен используется тип данных [Decimal](../../sql-reference/data-types/decimal.md).
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||||
```sql
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CREATE TABLE dish
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(
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id UInt32,
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name String,
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description String,
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menus_appeared UInt32,
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times_appeared Int32,
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first_appeared UInt16,
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last_appeared UInt16,
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lowest_price Decimal64(3),
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highest_price Decimal64(3)
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) ENGINE = MergeTree ORDER BY id;
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||||
|
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CREATE TABLE menu
|
||||
(
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id UInt32,
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name String,
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sponsor String,
|
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event String,
|
||||
venue String,
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||||
place String,
|
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physical_description String,
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occasion String,
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notes String,
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call_number String,
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keywords String,
|
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language String,
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date String,
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location String,
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location_type String,
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currency String,
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currency_symbol String,
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status String,
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page_count UInt16,
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dish_count UInt16
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) ENGINE = MergeTree ORDER BY id;
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||||
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CREATE TABLE menu_page
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||||
(
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id UInt32,
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menu_id UInt32,
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page_number UInt16,
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image_id String,
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||||
full_height UInt16,
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full_width UInt16,
|
||||
uuid UUID
|
||||
) ENGINE = MergeTree ORDER BY id;
|
||||
|
||||
CREATE TABLE menu_item
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||||
(
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||||
id UInt32,
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menu_page_id UInt32,
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||||
price Decimal64(3),
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||||
high_price Decimal64(3),
|
||||
dish_id UInt32,
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||||
created_at DateTime,
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updated_at DateTime,
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xpos Float64,
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||||
ypos Float64
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||||
) ENGINE = MergeTree ORDER BY id;
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||||
```
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||||
|
||||
## Импортируйте данные {#import-data}
|
||||
|
||||
Импортируйте данные в ClickHouse, выполните команды:
|
||||
|
||||
```bash
|
||||
clickhouse-client --format_csv_allow_single_quotes 0 --input_format_null_as_default 0 --query "INSERT INTO dish FORMAT CSVWithNames" < Dish.csv
|
||||
clickhouse-client --format_csv_allow_single_quotes 0 --input_format_null_as_default 0 --query "INSERT INTO menu FORMAT CSVWithNames" < Menu.csv
|
||||
clickhouse-client --format_csv_allow_single_quotes 0 --input_format_null_as_default 0 --query "INSERT INTO menu_page FORMAT CSVWithNames" < MenuPage.csv
|
||||
clickhouse-client --format_csv_allow_single_quotes 0 --input_format_null_as_default 0 --date_time_input_format best_effort --query "INSERT INTO menu_item FORMAT CSVWithNames" < MenuItem.csv
|
||||
```
|
||||
|
||||
Поскольку данные представлены в формате CSV с заголовком, используется формат [CSVWithNames](../../interfaces/formats.md#csvwithnames).
|
||||
|
||||
Отключите `format_csv_allow_single_quotes`, так как для данных используются только двойные кавычки, а одинарные кавычки могут находиться внутри значений и не должны сбивать с толку CSV-парсер.
|
||||
|
||||
Отключите [input_format_null_as_default](../../operations/settings/settings.md#settings-input-format-null-as-default), поскольку в данных нет значений [NULL](../../sql-reference/syntax.md#null-literal).
|
||||
|
||||
В противном случае ClickHouse попытается проанализировать последовательности `\N` и может перепутать с `\` в данных.
|
||||
|
||||
Настройка [date_time_input_format best_effort](../../operations/settings/settings.md#settings-date_time_input_format) позволяет анализировать поля [DateTime](../../sql-reference/data-types/datetime.md) в самых разных форматах. К примеру, будет распознан ISO-8601 без секунд: '2000-01-01 01:02'. Без этой настройки допускается только фиксированный формат даты и времени.
|
||||
|
||||
## Денормализуйте данные {#denormalize-data}
|
||||
|
||||
Данные представлены в нескольких таблицах в [нормализованном виде](https://ru.wikipedia.org/wiki/%D0%9D%D0%BE%D1%80%D0%BC%D0%B0%D0%BB%D1%8C%D0%BD%D0%B0%D1%8F_%D1%84%D0%BE%D1%80%D0%BC%D0%B0).
|
||||
|
||||
Это означает, что вам нужно использовать условие объединения [JOIN](../../sql-reference/statements/select/join.md#select-join), если вы хотите получить, например, названия блюд из пунктов меню.
|
||||
|
||||
Для типовых аналитических задач гораздо эффективнее работать с предварительно объединенными данными, чтобы не использовать `JOIN` каждый раз. Такие данные называются денормализованными.
|
||||
|
||||
Создайте таблицу `menu_item_denorm`, которая будет содержать все данные, объединенные вместе:
|
||||
|
||||
```sql
|
||||
CREATE TABLE menu_item_denorm
|
||||
ENGINE = MergeTree ORDER BY (dish_name, created_at)
|
||||
AS SELECT
|
||||
price,
|
||||
high_price,
|
||||
created_at,
|
||||
updated_at,
|
||||
xpos,
|
||||
ypos,
|
||||
dish.id AS dish_id,
|
||||
dish.name AS dish_name,
|
||||
dish.description AS dish_description,
|
||||
dish.menus_appeared AS dish_menus_appeared,
|
||||
dish.times_appeared AS dish_times_appeared,
|
||||
dish.first_appeared AS dish_first_appeared,
|
||||
dish.last_appeared AS dish_last_appeared,
|
||||
dish.lowest_price AS dish_lowest_price,
|
||||
dish.highest_price AS dish_highest_price,
|
||||
menu.id AS menu_id,
|
||||
menu.name AS menu_name,
|
||||
menu.sponsor AS menu_sponsor,
|
||||
menu.event AS menu_event,
|
||||
menu.venue AS menu_venue,
|
||||
menu.place AS menu_place,
|
||||
menu.physical_description AS menu_physical_description,
|
||||
menu.occasion AS menu_occasion,
|
||||
menu.notes AS menu_notes,
|
||||
menu.call_number AS menu_call_number,
|
||||
menu.keywords AS menu_keywords,
|
||||
menu.language AS menu_language,
|
||||
menu.date AS menu_date,
|
||||
menu.location AS menu_location,
|
||||
menu.location_type AS menu_location_type,
|
||||
menu.currency AS menu_currency,
|
||||
menu.currency_symbol AS menu_currency_symbol,
|
||||
menu.status AS menu_status,
|
||||
menu.page_count AS menu_page_count,
|
||||
menu.dish_count AS menu_dish_count
|
||||
FROM menu_item
|
||||
JOIN dish ON menu_item.dish_id = dish.id
|
||||
JOIN menu_page ON menu_item.menu_page_id = menu_page.id
|
||||
JOIN menu ON menu_page.menu_id = menu.id;
|
||||
```
|
||||
|
||||
## Проверьте загруженные данные {#validate-data}
|
||||
|
||||
Запрос:
|
||||
|
||||
```sql
|
||||
SELECT count() FROM menu_item_denorm;
|
||||
```
|
||||
|
||||
Результат:
|
||||
|
||||
```text
|
||||
┌─count()─┐
|
||||
│ 1329175 │
|
||||
└─────────┘
|
||||
```
|
||||
|
||||
## Примеры запросов {#run-queries}
|
||||
|
||||
### Усредненные исторические цены на блюда {#query-averaged-historical-prices}
|
||||
|
||||
Запрос:
|
||||
|
||||
```sql
|
||||
SELECT
|
||||
round(toUInt32OrZero(extract(menu_date, '^\\d{4}')), -1) AS d,
|
||||
count(),
|
||||
round(avg(price), 2),
|
||||
bar(avg(price), 0, 100, 100)
|
||||
FROM menu_item_denorm
|
||||
WHERE (menu_currency = 'Dollars') AND (d > 0) AND (d < 2022)
|
||||
GROUP BY d
|
||||
ORDER BY d ASC;
|
||||
```
|
||||
|
||||
Результат:
|
||||
|
||||
```text
|
||||
┌────d─┬─count()─┬─round(avg(price), 2)─┬─bar(avg(price), 0, 100, 100)─┐
|
||||
│ 1850 │ 618 │ 1.5 │ █▍ │
|
||||
│ 1860 │ 1634 │ 1.29 │ █▎ │
|
||||
│ 1870 │ 2215 │ 1.36 │ █▎ │
|
||||
│ 1880 │ 3909 │ 1.01 │ █ │
|
||||
│ 1890 │ 8837 │ 1.4 │ █▍ │
|
||||
│ 1900 │ 176292 │ 0.68 │ ▋ │
|
||||
│ 1910 │ 212196 │ 0.88 │ ▊ │
|
||||
│ 1920 │ 179590 │ 0.74 │ ▋ │
|
||||
│ 1930 │ 73707 │ 0.6 │ ▌ │
|
||||
│ 1940 │ 58795 │ 0.57 │ ▌ │
|
||||
│ 1950 │ 41407 │ 0.95 │ ▊ │
|
||||
│ 1960 │ 51179 │ 1.32 │ █▎ │
|
||||
│ 1970 │ 12914 │ 1.86 │ █▋ │
|
||||
│ 1980 │ 7268 │ 4.35 │ ████▎ │
|
||||
│ 1990 │ 11055 │ 6.03 │ ██████ │
|
||||
│ 2000 │ 2467 │ 11.85 │ ███████████▋ │
|
||||
│ 2010 │ 597 │ 25.66 │ █████████████████████████▋ │
|
||||
└──────┴─────────┴──────────────────────┴──────────────────────────────┘
|
||||
```
|
||||
|
||||
Просто не принимайте это всерьез.
|
||||
|
||||
### Цены на бургеры {#query-burger-prices}
|
||||
|
||||
Запрос:
|
||||
|
||||
```sql
|
||||
SELECT
|
||||
round(toUInt32OrZero(extract(menu_date, '^\\d{4}')), -1) AS d,
|
||||
count(),
|
||||
round(avg(price), 2),
|
||||
bar(avg(price), 0, 50, 100)
|
||||
FROM menu_item_denorm
|
||||
WHERE (menu_currency = 'Dollars') AND (d > 0) AND (d < 2022) AND (dish_name ILIKE '%burger%')
|
||||
GROUP BY d
|
||||
ORDER BY d ASC;
|
||||
```
|
||||
|
||||
Результат:
|
||||
|
||||
```text
|
||||
┌────d─┬─count()─┬─round(avg(price), 2)─┬─bar(avg(price), 0, 50, 100)───────────┐
|
||||
│ 1880 │ 2 │ 0.42 │ ▋ │
|
||||
│ 1890 │ 7 │ 0.85 │ █▋ │
|
||||
│ 1900 │ 399 │ 0.49 │ ▊ │
|
||||
│ 1910 │ 589 │ 0.68 │ █▎ │
|
||||
│ 1920 │ 280 │ 0.56 │ █ │
|
||||
│ 1930 │ 74 │ 0.42 │ ▋ │
|
||||
│ 1940 │ 119 │ 0.59 │ █▏ │
|
||||
│ 1950 │ 134 │ 1.09 │ ██▏ │
|
||||
│ 1960 │ 272 │ 0.92 │ █▋ │
|
||||
│ 1970 │ 108 │ 1.18 │ ██▎ │
|
||||
│ 1980 │ 88 │ 2.82 │ █████▋ │
|
||||
│ 1990 │ 184 │ 3.68 │ ███████▎ │
|
||||
│ 2000 │ 21 │ 7.14 │ ██████████████▎ │
|
||||
│ 2010 │ 6 │ 18.42 │ ████████████████████████████████████▋ │
|
||||
└──────┴─────────┴──────────────────────┴───────────────────────────────────────┘
|
||||
```
|
||||
|
||||
### Водка {#query-vodka}
|
||||
|
||||
Запрос:
|
||||
|
||||
```sql
|
||||
SELECT
|
||||
round(toUInt32OrZero(extract(menu_date, '^\\d{4}')), -1) AS d,
|
||||
count(),
|
||||
round(avg(price), 2),
|
||||
bar(avg(price), 0, 50, 100)
|
||||
FROM menu_item_denorm
|
||||
WHERE (menu_currency IN ('Dollars', '')) AND (d > 0) AND (d < 2022) AND (dish_name ILIKE '%vodka%')
|
||||
GROUP BY d
|
||||
ORDER BY d ASC;
|
||||
```
|
||||
|
||||
Результат:
|
||||
|
||||
```text
|
||||
┌────d─┬─count()─┬─round(avg(price), 2)─┬─bar(avg(price), 0, 50, 100)─┐
|
||||
│ 1910 │ 2 │ 0 │ │
|
||||
│ 1920 │ 1 │ 0.3 │ ▌ │
|
||||
│ 1940 │ 21 │ 0.42 │ ▋ │
|
||||
│ 1950 │ 14 │ 0.59 │ █▏ │
|
||||
│ 1960 │ 113 │ 2.17 │ ████▎ │
|
||||
│ 1970 │ 37 │ 0.68 │ █▎ │
|
||||
│ 1980 │ 19 │ 2.55 │ █████ │
|
||||
│ 1990 │ 86 │ 3.6 │ ███████▏ │
|
||||
│ 2000 │ 2 │ 3.98 │ ███████▊ │
|
||||
└──────┴─────────┴──────────────────────┴─────────────────────────────┘
|
||||
```
|
||||
|
||||
Чтобы получить водку, мы должны написать `ILIKE '%vodka%'`, и это хорошая идея.
|
||||
|
||||
### Икра {#query-caviar}
|
||||
|
||||
Посмотрите цены на икру. Получите название любого блюда с икрой.
|
||||
|
||||
Запрос:
|
||||
|
||||
```sql
|
||||
SELECT
|
||||
round(toUInt32OrZero(extract(menu_date, '^\\d{4}')), -1) AS d,
|
||||
count(),
|
||||
round(avg(price), 2),
|
||||
bar(avg(price), 0, 50, 100),
|
||||
any(dish_name)
|
||||
FROM menu_item_denorm
|
||||
WHERE (menu_currency IN ('Dollars', '')) AND (d > 0) AND (d < 2022) AND (dish_name ILIKE '%caviar%')
|
||||
GROUP BY d
|
||||
ORDER BY d ASC;
|
||||
```
|
||||
|
||||
Результат:
|
||||
|
||||
```text
|
||||
┌────d─┬─count()─┬─round(avg(price), 2)─┬─bar(avg(price), 0, 50, 100)──────┬─any(dish_name)──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┐
|
||||
│ 1090 │ 1 │ 0 │ │ Caviar │
|
||||
│ 1880 │ 3 │ 0 │ │ Caviar │
|
||||
│ 1890 │ 39 │ 0.59 │ █▏ │ Butter and caviar │
|
||||
│ 1900 │ 1014 │ 0.34 │ ▋ │ Anchovy Caviar on Toast │
|
||||
│ 1910 │ 1588 │ 1.35 │ ██▋ │ 1/1 Brötchen Caviar │
|
||||
│ 1920 │ 927 │ 1.37 │ ██▋ │ ASTRAKAN CAVIAR │
|
||||
│ 1930 │ 289 │ 1.91 │ ███▋ │ Astrachan caviar │
|
||||
│ 1940 │ 201 │ 0.83 │ █▋ │ (SPECIAL) Domestic Caviar Sandwich │
|
||||
│ 1950 │ 81 │ 2.27 │ ████▌ │ Beluga Caviar │
|
||||
│ 1960 │ 126 │ 2.21 │ ████▍ │ Beluga Caviar │
|
||||
│ 1970 │ 105 │ 0.95 │ █▊ │ BELUGA MALOSSOL CAVIAR AMERICAN DRESSING │
|
||||
│ 1980 │ 12 │ 7.22 │ ██████████████▍ │ Authentic Iranian Beluga Caviar the world's finest black caviar presented in ice garni and a sampling of chilled 100° Russian vodka │
|
||||
│ 1990 │ 74 │ 14.42 │ ████████████████████████████▋ │ Avocado Salad, Fresh cut avocado with caviare │
|
||||
│ 2000 │ 3 │ 7.82 │ ███████████████▋ │ Aufgeschlagenes Kartoffelsueppchen mit Forellencaviar │
|
||||
│ 2010 │ 6 │ 15.58 │ ███████████████████████████████▏ │ "OYSTERS AND PEARLS" "Sabayon" of Pearl Tapioca with Island Creek Oysters and Russian Sevruga Caviar │
|
||||
└──────┴─────────┴──────────────────────┴──────────────────────────────────┴─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘
|
||||
```
|
||||
|
||||
По крайней мере, есть икра с водкой. Очень мило.
|
||||
|
||||
## Online Playground {#playground}
|
||||
|
||||
Этот набор данных доступен в интерактивном ресурсе [Online Playground](https://gh-api.clickhouse.tech/play?user=play#U0VMRUNUCiAgICByb3VuZCh0b1VJbnQzMk9yWmVybyhleHRyYWN0KG1lbnVfZGF0ZSwgJ15cXGR7NH0nKSksIC0xKSBBUyBkLAogICAgY291bnQoKSwKICAgIHJvdW5kKGF2ZyhwcmljZSksIDIpLAogICAgYmFyKGF2ZyhwcmljZSksIDAsIDUwLCAxMDApLAogICAgYW55KGRpc2hfbmFtZSkKRlJPTSBtZW51X2l0ZW1fZGVub3JtCldIRVJFIChtZW51X2N1cnJlbmN5IElOICgnRG9sbGFycycsICcnKSkgQU5EIChkID4gMCkgQU5EIChkIDwgMjAyMikgQU5EIChkaXNoX25hbWUgSUxJS0UgJyVjYXZpYXIlJykKR1JPVVAgQlkgZApPUkRFUiBCWSBkIEFTQw==).
|
||||
@ -364,7 +364,7 @@ $ clickhouse-client --format_csv_delimiter="|" --query="INSERT INTO test.csv FOR
|
||||
|
||||
## CSVWithNames {#csvwithnames}
|
||||
|
||||
Выводит также заголовок, аналогично `TabSeparatedWithNames`.
|
||||
Выводит также заголовок, аналогично [TabSeparatedWithNames](#tabseparatedwithnames).
|
||||
|
||||
## CustomSeparated {#format-customseparated}
|
||||
|
||||
|
||||
Loading…
Reference in New Issue
Block a user