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353 lines
18 KiB
Plaintext
---
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slug: /zh/getting-started/example-datasets/menus
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sidebar_label: '纽约公共图书馆“菜单上有什么？”数据集'
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title: '纽约公共图书馆“菜单上有什么？”数据集'
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---
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该数据集由纽约公共图书馆创建。其中含有有关酒店、餐馆和咖啡馆的菜单上的菜肴及其价格的历史数据。
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来源：http://menus.nypl.org/data
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数据为开放数据。
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数据来自于图书馆中的档案，因此可能不完整，以至于难以进行统计分析。尽管如此，该数据集也是非常有意思的。数据集中只有 130 万条关于菜单中的菜肴的记录 - 这对于 ClickHouse 来说是一个非常小的数据量，但这仍是一个很好的例子。
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## 下载数据集 {#download-dataset}
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运行命令：
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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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如果有需要可以使用 http://menus.nypl.org/data 中的最新链接。下载的大小约为 35 MB。
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## 解压数据集 {#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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解压后的的大小约为 150 MB。
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数据集由四个表组成：
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- `Menu` - 有关菜单的信息，其中包含：餐厅名称，看到菜单的日期等
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- `Dish` - 有关菜肴的信息，其中包含：菜肴名称以及一些特征。
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- `MenuPage` - 有关菜单中页面的信息，每个页面都属于某个 `Menu`。
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- `MenuItem` - 菜单项。某个菜单页面上的菜肴及其价格：指向 `Dish` 和 `MenuPage`的链接。
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## 创建表 {#create-tables}
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使用 [Decimal](/docs/zh/sql-reference/data-types/decimal.md) 数据类型来存储价格。
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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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CREATE TABLE menu
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(
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id UInt32,
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name String,
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sponsor String,
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event String,
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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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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,
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uuid UUID
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) ENGINE = MergeTree ORDER BY id;
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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),
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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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## 导入数据 {#import-data}
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执行以下命令将数据导入 ClickHouse：
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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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因为数据由带有标题的 CSV 表示，所以使用 [CSVWithNames](/docs/zh/interfaces/formats.md#csvwithnames) 格式。
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因为只有双引号用于数据字段，单引号可以在值内，所以禁用了 `format_csv_allow_single_quotes` 以避免混淆 CSV 解析器。
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因为数据中没有 [NULL](/docs/zh/sql-reference/syntax.md#null-literal) 值，所以禁用 [input_format_null_as_default](/docs/zh/operations/settings/settings.md#settings-input-format-null-as-default)。不然 ClickHouse 将会尝试解析 `\N` 序列，并可能与数据中的 `\` 混淆。
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设置 [date_time_input_format best_effort](/docs/zh/operations/settings/settings.md#settings-date_time_input_format) 以便解析各种格式的 [DateTime](/docs/zh/sql-reference/data-types/datetime.md)字段。例如，识别像“2000-01-01 01:02”这样没有秒数的 ISO-8601 时间字符串。如果没有此设置，则仅允许使用固定的 DateTime 格式。
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## 非规范化数据 {#denormalize-data}
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数据以 [规范化形式] (https://en.wikipedia.org/wiki/Database_normalization#Normal_forms) 在多个表格中呈现。这意味着如果你想进行如查询菜单项中的菜名这类的查询，则必须执行 [JOIN](/docs/zh/sql-reference/statements/select/join.md#select-join)。在典型的分析任务中，预先处理联接的数据以避免每次都执行“联接”会更有效率。这中操作被称为“非规范化”数据。
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我们将创建一个表“menu_item_denorm”，其中将包含所有联接在一起的数据：
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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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price,
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high_price,
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created_at,
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updated_at,
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xpos,
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ypos,
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dish.id AS dish_id,
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dish.name AS dish_name,
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dish.description AS dish_description,
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dish.menus_appeared AS dish_menus_appeared,
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dish.times_appeared AS dish_times_appeared,
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dish.first_appeared AS dish_first_appeared,
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dish.last_appeared AS dish_last_appeared,
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dish.lowest_price AS dish_lowest_price,
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dish.highest_price AS dish_highest_price,
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menu.id AS menu_id,
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menu.name AS menu_name,
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menu.sponsor AS menu_sponsor,
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menu.event AS menu_event,
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menu.venue AS menu_venue,
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menu.place AS menu_place,
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menu.physical_description AS menu_physical_description,
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menu.occasion AS menu_occasion,
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menu.notes AS menu_notes,
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menu.call_number AS menu_call_number,
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menu.keywords AS menu_keywords,
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menu.language AS menu_language,
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menu.date AS menu_date,
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menu.location AS menu_location,
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menu.location_type AS menu_location_type,
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menu.currency AS menu_currency,
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menu.currency_symbol AS menu_currency_symbol,
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menu.status AS menu_status,
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menu.page_count AS menu_page_count,
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menu.dish_count AS menu_dish_count
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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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```
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## 验证数据 {#validate-data}
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请求：
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```sql
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SELECT count() FROM menu_item_denorm;
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```
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结果：
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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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## 运行一些查询 {#run-queries}
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### 菜品的平均历史价格 {#query-averaged-historical-prices}
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请求：
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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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round(avg(price), 2),
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bar(avg(price), 0, 100, 100)
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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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```
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结果：
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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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│ 1870 │ 2215 │ 1.36 │ █▎ │
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│ 1880 │ 3909 │ 1.01 │ █ │
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│ 1890 │ 8837 │ 1.4 │ █▍ │
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│ 1900 │ 176292 │ 0.68 │ ▋ │
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│ 1910 │ 212196 │ 0.88 │ ▊ │
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│ 1920 │ 179590 │ 0.74 │ ▋ │
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│ 1930 │ 73707 │ 0.6 │ ▌ │
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│ 1940 │ 58795 │ 0.57 │ ▌ │
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│ 1950 │ 41407 │ 0.95 │ ▊ │
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│ 1960 │ 51179 │ 1.32 │ █▎ │
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│ 1970 │ 12914 │ 1.86 │ █▋ │
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│ 1980 │ 7268 │ 4.35 │ ████▎ │
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│ 1990 │ 11055 │ 6.03 │ ██████ │
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│ 2000 │ 2467 │ 11.85 │ ███████████▋ │
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│ 2010 │ 597 │ 25.66 │ █████████████████████████▋ │
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└──────┴─────────┴──────────────────────┴──────────────────────────────┘
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```
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带上一粒盐。
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### 汉堡价格 {#query-burger-prices}
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请求：
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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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round(avg(price), 2),
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bar(avg(price), 0, 50, 100)
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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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```
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结果：
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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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│ 1900 │ 399 │ 0.49 │ ▊ │
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│ 1910 │ 589 │ 0.68 │ █▎ │
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│ 1920 │ 280 │ 0.56 │ █ │
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│ 1930 │ 74 │ 0.42 │ ▋ │
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│ 1940 │ 119 │ 0.59 │ █▏ │
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│ 1950 │ 134 │ 1.09 │ ██▏ │
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│ 1960 │ 272 │ 0.92 │ █▋ │
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│ 1970 │ 108 │ 1.18 │ ██▎ │
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│ 1980 │ 88 │ 2.82 │ █████▋ │
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│ 1990 │ 184 │ 3.68 │ ███████▎ │
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│ 2000 │ 21 │ 7.14 │ ██████████████▎ │
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│ 2010 │ 6 │ 18.42 │ ████████████████████████████████████▋ │
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└──────┴─────────┴──────────────────────┴───────────────────────────────────────┘
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```
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###伏特加{#query-vodka}
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请求：
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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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round(avg(price), 2),
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bar(avg(price), 0, 50, 100)
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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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```
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结果：
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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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│ 1940 │ 21 │ 0.42 │ ▋ │
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│ 1950 │ 14 │ 0.59 │ █▏ │
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│ 1960 │ 113 │ 2.17 │ ████▎ │
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│ 1970 │ 37 │ 0.68 │ █▎ │
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│ 1980 │ 19 │ 2.55 │ █████ │
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│ 1990 │ 86 │ 3.6 │ ███████▏ │
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│ 2000 │ 2 │ 3.98 │ ███████▊ │
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└──────┴─────────┴──────────────────────┴─────────────────────────────┘
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```
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要查询 `Vodka`，必须声明通过 `ILIKE '%vodka%'` 进行查询。
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### 鱼子酱 {#query-caviar}
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列出鱼子酱的价格。另外，列出任何带有鱼子酱的菜肴的名称。
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请求：
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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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round(avg(price), 2),
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bar(avg(price), 0, 50, 100),
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any(dish_name)
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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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```
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结果：
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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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│ 1890 │ 39 │ 0.59 │ █▏ │ Butter and caviar │
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│ 1900 │ 1014 │ 0.34 │ ▋ │ Anchovy Caviar on Toast │
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│ 1910 │ 1588 │ 1.35 │ ██▋ │ 1/1 Brötchen Caviar │
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│ 1920 │ 927 │ 1.37 │ ██▋ │ ASTRAKAN CAVIAR │
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│ 1930 │ 289 │ 1.91 │ ███▋ │ Astrachan caviar │
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│ 1940 │ 201 │ 0.83 │ █▋ │ (SPECIAL) Domestic Caviar Sandwich │
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│ 1950 │ 81 │ 2.27 │ ████▌ │ Beluga Caviar │
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│ 1960 │ 126 │ 2.21 │ ████▍ │ Beluga Caviar │
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│ 1970 │ 105 │ 0.95 │ █▊ │ BELUGA MALOSSOL CAVIAR AMERICAN DRESSING │
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│ 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 │
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│ 1990 │ 74 │ 14.42 │ ████████████████████████████▋ │ Avocado Salad, Fresh cut avocado with caviare │
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│ 2000 │ 3 │ 7.82 │ ███████████████▋ │ Aufgeschlagenes Kartoffelsueppchen mit Forellencaviar │
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│ 2010 │ 6 │ 15.58 │ ███████████████████████████████▏ │ "OYSTERS AND PEARLS" "Sabayon" of Pearl Tapioca with Island Creek Oysters and Russian Sevruga Caviar │
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└──────┴─────────┴──────────────────────┴──────────────────────────────────┴─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘
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```
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至少他们有伏特加配鱼子酱。真棒。
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## 在线 Playground{#playground}
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此数据集已经上传到了 ClickHouse Playground 中，[example](https://play.clickhouse.com/play?user=play#U0VMRUNUCiAgICByb3VuZCh0b1VJbnQzMk9yWmVybyhleHRyYWN0KG1lbnVfZGF0ZSwgJ15cXGR7NH0nKSksIC0xKSBBUyBkLAogICAgY291bnQoKSwKICAgIHJvdW5kKGF2ZyhwcmljZSksIDIpLAogICAgYmFyKGF2ZyhwcmljZSksIDAsIDUwLCAxMDApLAogICAgYW55KGRpc2hfbmFtZSkKRlJPTSBtZW51X2l0ZW1fZGVub3JtCldIRVJFIChtZW51X2N1cnJlbmN5IElOICgnRG9sbGFycycsICcnKSkgQU5EIChkID4gMCkgQU5EIChkIDwgMjAyMikgQU5EIChkaXNoX25hbWUgSUxJS0UgJyVjYXZpYXIlJykKR1JPVVAgQlkgZApPUkRFUiBCWSBkIEFTQw==)。
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