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Add anchors to all the headings.
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romanzhukov
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@ -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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@ -13,7 +13,7 @@ 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 — 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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Run the command:
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@ -24,7 +24,7 @@ wget https://s3.amazonaws.com/menusdata.nypl.org/gzips/2021_08_01_07_01_17_data.
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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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```bash
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tar xvf 2021_08_01_07_01_17_data.tgz
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@ -38,7 +38,7 @@ The data is normalized consisted of four tables:
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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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```sql
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CREATE TABLE dish
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@ -105,7 +105,7 @@ CREATE TABLE menu_item
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We use [Decimal](../../sql-reference/data-types/decimal.md) data type to store prices. Everything else is quite straightforward.
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## Import Data
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## Import the Data {#import-data}
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Upload data into ClickHouse:
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@ -120,13 +120,13 @@ We use [CSVWithNames](../../interfaces/formats.md#csvwithnames) format as the da
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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.mdsettings-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](../../sql-reference/data-types/datetime.md) 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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Data is presented in multiple tables in normalized form. 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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@ -176,7 +176,7 @@ FROM menu_item
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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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Query:
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@ -192,9 +192,9 @@ Result:
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└─────────┘
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```
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## Run Some Queries
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## Run Some Queries {#run-queries}
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### Averaged historical prices of dishes
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### Averaged historical prices of dishes {#query-averaged-historical-prices}
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Query:
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@ -236,7 +236,7 @@ Result:
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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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Query:
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@ -273,7 +273,7 @@ Result:
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└──────┴─────────┴──────────────────────┴───────────────────────────────────────┘
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```
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### Vodka
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### Vodka {#query-vodka}
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Query:
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@ -307,7 +307,7 @@ Result:
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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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@ -350,6 +350,6 @@ Result:
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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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### Test it in 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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@ -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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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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@ -13,7 +13,7 @@ toc_title: Menus
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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
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## Загрузите набор данных {#download-dataset}
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Выполните команду:
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@ -24,7 +24,7 @@ wget https://s3.amazonaws.com/menusdata.nypl.org/gzips/2021_08_01_07_01_17_data.
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Замените ссылку на актуальную ссылку с http://menus.nypl.org/data, если это необходимо.
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Размер загрузки составляет около 35 МБ.
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## Распакуйте набор данных
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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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@ -38,7 +38,7 @@ The data is normalized consisted of four tables:
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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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## Создайте таблицы
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## Создайте таблицы {#create-tables}
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```sql
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CREATE TABLE dish
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@ -105,7 +105,7 @@ CREATE TABLE menu_item
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We use [Decimal](../../sql-reference/data-types/decimal.md) data type to store prices. Everything else is quite straightforward.
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## Import Data
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## Import Data {#import-data}
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Upload data into ClickHouse:
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@ -125,7 +125,7 @@ We disable `input_format_null_as_default` as our data does not have NULLs. Other
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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](../../sql-reference/data-types/datetime.md) 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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@ -177,8 +177,7 @@ FROM menu_item
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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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Запрос:
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@ -194,9 +193,9 @@ SELECT count() FROM menu_item_denorm;
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└─────────┘
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```
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## Примеры
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## Примеры запросов {#run-queries}
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### Усредненные исторические цены на блюда
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### Усредненные исторические цены на блюда {#query-averaged-historical-prices}
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Запрос:
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@ -238,7 +237,7 @@ ORDER BY d ASC;
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Просто не принимайте это всерьез.
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### Цены на бургеры
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### Цены на бургеры {#query-burger-prices}
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Запрос:
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@ -275,7 +274,7 @@ ORDER BY d ASC;
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└──────┴─────────┴──────────────────────┴───────────────────────────────────────┘
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```
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### Водка
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### Водка {#query-vodka}
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Запрос:
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@ -309,7 +308,7 @@ 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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### Икра
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### Икра {#query-caviar}
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Давайте выведем цены на икру. Также давайте выведем название любого блюда с икрой.
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@ -352,6 +351,6 @@ ORDER BY d ASC;
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По крайней мере, есть икра с водкой. Очень мило.
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### Test it in Playground
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### Test it in 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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