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@ -3,7 +3,7 @@ toc_priority: 21
|
||||
toc_title: Menus
|
||||
---
|
||||
|
||||
# New York Public Library "What's on the Menu?" Dataset
|
||||
# New York Public Library "What's on the Menu?" Dataset {#menus-dataset}
|
||||
|
||||
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.
|
||||
|
||||
@ -11,34 +11,38 @@ Source: http://menus.nypl.org/data
|
||||
The data is in public domain.
|
||||
|
||||
The data is from library's archive and it may be incomplete and difficult for statistical analysis. Nevertheless it is also very yummy.
|
||||
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).
|
||||
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.
|
||||
|
||||
## Download the Dataset
|
||||
## Download the Dataset {#download-dataset}
|
||||
|
||||
```
|
||||
Run the command:
|
||||
|
||||
```bash
|
||||
wget https://s3.amazonaws.com/menusdata.nypl.org/gzips/2021_08_01_07_01_17_data.tgz
|
||||
```
|
||||
|
||||
Replace the link to the up to date link from http://menus.nypl.org/data if needed.
|
||||
Download size is about 35 MB.
|
||||
|
||||
## Unpack the Dataset
|
||||
## Unpack the Dataset {#unpack-dataset}
|
||||
|
||||
```
|
||||
```bash
|
||||
tar xvf 2021_08_01_07_01_17_data.tgz
|
||||
```
|
||||
|
||||
Uncompressed size is about 150 MB.
|
||||
|
||||
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;
|
||||
- Dish: information about dishes: the name of the dish along with some characteristic;
|
||||
- MenuPage: information about the pages in the menus; every page belongs to some menu;
|
||||
- MenuItem: an item of the menu - a dish along with its price on some menu page: links to dish and menu page.
|
||||
- `Menu` — Information about menus: the name of the restaurant, the date when menu was seen, etc.
|
||||
- `Dish` — Information about dishes: the name of the dish along with some characteristic.
|
||||
- `MenuPage` — Information about the pages in the menus, because every page belongs to some menu.
|
||||
- `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}
|
||||
|
||||
```
|
||||
We use [Decimal](../../sql-reference/data-types/decimal.md) data type to store prices.
|
||||
|
||||
```sql
|
||||
CREATE TABLE dish
|
||||
(
|
||||
id UInt32,
|
||||
@ -101,35 +105,33 @@ CREATE TABLE menu_item
|
||||
) ENGINE = MergeTree ORDER BY id;
|
||||
```
|
||||
|
||||
We use `Decimal` data type to store prices. Everything else is quite straightforward.
|
||||
## Import the Data {#import-data}
|
||||
|
||||
## Import Data
|
||||
Upload data into ClickHouse, run:
|
||||
|
||||
Upload data into ClickHouse:
|
||||
|
||||
```
|
||||
```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
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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
|
||||
```
|
||||
|
||||
We use `CSVWithNames` format as the data is represented by CSV with header.
|
||||
We use [CSVWithNames](../../interfaces/formats.md#csvwithnames) format as the data is represented by CSV with header.
|
||||
|
||||
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.
|
||||
|
||||
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.
|
||||
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.
|
||||
|
||||
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.
|
||||
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.
|
||||
|
||||
## Denormalize the Data
|
||||
## Denormalize the Data {#denormalize-data}
|
||||
|
||||
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.
|
||||
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.
|
||||
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.
|
||||
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.
|
||||
|
||||
We will create a table that will contain all the data JOINed together:
|
||||
We will create a table `menu_item_denorm` where will contain all the data JOINed together:
|
||||
|
||||
```
|
||||
```sql
|
||||
CREATE TABLE menu_item_denorm
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||||
ENGINE = MergeTree ORDER BY (dish_name, created_at)
|
||||
AS SELECT
|
||||
@ -171,21 +173,32 @@ AS SELECT
|
||||
FROM menu_item
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||||
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
|
||||
JOIN menu ON menu_page.menu_id = menu.id;
|
||||
```
|
||||
|
||||
## Validate the Data
|
||||
## Validate the Data {#validate-data}
|
||||
|
||||
```
|
||||
SELECT count() FROM menu_item_denorm
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||||
1329175
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||||
Query:
|
||||
|
||||
```sql
|
||||
SELECT count() FROM menu_item_denorm;
|
||||
```
|
||||
|
||||
## Run Some Queries
|
||||
|
||||
Averaged historical prices of dishes:
|
||||
Result:
|
||||
|
||||
```text
|
||||
┌─count()─┐
|
||||
│ 1329175 │
|
||||
└─────────┘
|
||||
```
|
||||
|
||||
## Run Some Queries {#run-queries}
|
||||
|
||||
### Averaged historical prices of dishes {#query-averaged-historical-prices}
|
||||
|
||||
Query:
|
||||
|
||||
```sql
|
||||
SELECT
|
||||
round(toUInt32OrZero(extract(menu_date, '^\\d{4}')), -1) AS d,
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||||
count(),
|
||||
@ -194,8 +207,12 @@ SELECT
|
||||
FROM menu_item_denorm
|
||||
WHERE (menu_currency = 'Dollars') AND (d > 0) AND (d < 2022)
|
||||
GROUP BY d
|
||||
ORDER BY d ASC
|
||||
ORDER BY d ASC;
|
||||
```
|
||||
|
||||
Result:
|
||||
|
||||
```text
|
||||
┌────d─┬─count()─┬─round(avg(price), 2)─┬─bar(avg(price), 0, 100, 100)─┐
|
||||
│ 1850 │ 618 │ 1.5 │ █▍ │
|
||||
│ 1860 │ 1634 │ 1.29 │ █▎ │
|
||||
@ -215,15 +232,15 @@ ORDER BY d ASC
|
||||
│ 2000 │ 2467 │ 11.85 │ ███████████▋ │
|
||||
│ 2010 │ 597 │ 25.66 │ █████████████████████████▋ │
|
||||
└──────┴─────────┴──────────────────────┴──────────────────────────────┘
|
||||
|
||||
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.)
|
||||
```
|
||||
|
||||
Take it with a grain of salt.
|
||||
|
||||
### Burger Prices:
|
||||
### Burger Prices {#query-burger-prices}
|
||||
|
||||
```
|
||||
Query:
|
||||
|
||||
```sql
|
||||
SELECT
|
||||
round(toUInt32OrZero(extract(menu_date, '^\\d{4}')), -1) AS d,
|
||||
count(),
|
||||
@ -232,8 +249,12 @@ SELECT
|
||||
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
|
||||
ORDER BY d ASC;
|
||||
```
|
||||
|
||||
Result:
|
||||
|
||||
```text
|
||||
┌────d─┬─count()─┬─round(avg(price), 2)─┬─bar(avg(price), 0, 50, 100)───────────┐
|
||||
│ 1880 │ 2 │ 0.42 │ ▋ │
|
||||
│ 1890 │ 7 │ 0.85 │ █▋ │
|
||||
@ -250,13 +271,13 @@ ORDER BY d ASC
|
||||
│ 2000 │ 21 │ 7.14 │ ██████████████▎ │
|
||||
│ 2010 │ 6 │ 18.42 │ ████████████████████████████████████▋ │
|
||||
└──────┴─────────┴──────────────────────┴───────────────────────────────────────┘
|
||||
|
||||
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.)
|
||||
```
|
||||
|
||||
### Vodka:
|
||||
### Vodka {#query-vodka}
|
||||
|
||||
```
|
||||
Query:
|
||||
|
||||
```sql
|
||||
SELECT
|
||||
round(toUInt32OrZero(extract(menu_date, '^\\d{4}')), -1) AS d,
|
||||
count(),
|
||||
@ -265,8 +286,12 @@ SELECT
|
||||
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
|
||||
ORDER BY d ASC;
|
||||
```
|
||||
|
||||
Result:
|
||||
|
||||
```text
|
||||
┌────d─┬─count()─┬─round(avg(price), 2)─┬─bar(avg(price), 0, 50, 100)─┐
|
||||
│ 1910 │ 2 │ 0 │ │
|
||||
│ 1920 │ 1 │ 0.3 │ ▌ │
|
||||
@ -282,11 +307,13 @@ ORDER BY d ASC
|
||||
|
||||
To get vodka we have to write `ILIKE '%vodka%'` and this definitely makes a statement.
|
||||
|
||||
### Caviar:
|
||||
### Caviar {#query-caviar}
|
||||
|
||||
Let's print caviar prices. Also let's print a name of any dish with caviar.
|
||||
|
||||
```
|
||||
Query:
|
||||
|
||||
```sql
|
||||
SELECT
|
||||
round(toUInt32OrZero(extract(menu_date, '^\\d{4}')), -1) AS d,
|
||||
count(),
|
||||
@ -296,8 +323,12 @@ SELECT
|
||||
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
|
||||
ORDER BY d ASC;
|
||||
```
|
||||
|
||||
Result:
|
||||
|
||||
```text
|
||||
┌────d─┬─count()─┬─round(avg(price), 2)─┬─bar(avg(price), 0, 50, 100)──────┬─any(dish_name)──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┐
|
||||
│ 1090 │ 1 │ 0 │ │ Caviar │
|
||||
│ 1880 │ 3 │ 0 │ │ Caviar │
|
||||
@ -319,6 +350,6 @@ ORDER BY d ASC
|
||||
|
||||
At least they have caviar with vodka. Very nice.
|
||||
|
||||
### Test it in Playground
|
||||
## Online Playground {#playground}
|
||||
|
||||
The data is uploaded to ClickHouse Playground, [example](https://gh-api.clickhouse.com/play?user=play#U0VMRUNUCiAgICByb3VuZCh0b1VJbnQzMk9yWmVybyhleHRyYWN0KG1lbnVfZGF0ZSwgJ15cXGR7NH0nKSksIC0xKSBBUyBkLAogICAgY291bnQoKSwKICAgIHJvdW5kKGF2ZyhwcmljZSksIDIpLAogICAgYmFyKGF2ZyhwcmljZSksIDAsIDUwLCAxMDApLAogICAgYW55KGRpc2hfbmFtZSkKRlJPTSBtZW51X2l0ZW1fZGVub3JtCldIRVJFIChtZW51X2N1cnJlbmN5IElOICgnRG9sbGFycycsICcnKSkgQU5EIChkID4gMCkgQU5EIChkIDwgMjAyMikgQU5EIChkaXNoX25hbWUgSUxJS0UgJyVjYXZpYXIlJykKR1JPVVAgQlkgZApPUkRFUiBCWSBkIEFTQw==).
|
||||
|
||||
@ -386,7 +386,7 @@ The CSV format supports the output of totals and extremes the same way as `TabSe
|
||||
|
||||
## CSVWithNames {#csvwithnames}
|
||||
|
||||
Also prints the header row, similar to `TabSeparatedWithNames`.
|
||||
Also prints the header row, similar to [TabSeparatedWithNames](#tabseparatedwithnames).
|
||||
|
||||
## CustomSeparated {#format-customseparated}
|
||||
|
||||
|
||||
@ -45,7 +45,7 @@ Configuration template:
|
||||
- `min_part_size` – The minimum size of a data part.
|
||||
- `min_part_size_ratio` – The ratio of the data part size to the table size.
|
||||
- `method` – Compression method. Acceptable values: `lz4`, `lz4hc`, `zstd`.
|
||||
- `level` – Compression level. See [Codecs](../../sql-reference/statements/create/table/#create-query-general-purpose-codecs).
|
||||
- `level` – Compression level. See [Codecs](../../sql-reference/statements/create/table.md#create-query-general-purpose-codecs).
|
||||
|
||||
You can configure multiple `<case>` sections.
|
||||
|
||||
|
||||
@ -7,7 +7,8 @@ toc_title: DateTime64
|
||||
|
||||
Allows to store an instant in time, that can be expressed as a calendar date and a time of a day, with defined sub-second precision
|
||||
|
||||
Tick size (precision): 10<sup>-precision</sup> seconds
|
||||
Tick size (precision): 10<sup>-precision</sup> seconds. Valid range: [ 0 : 9 ].
|
||||
Typically are used - 3 (milliseconds), 6 (microseconds), 9 (nanoseconds).
|
||||
|
||||
**Syntax:**
|
||||
|
||||
|
||||
386
docs/en/sql-reference/functions/geo/s2.md
Normal file
386
docs/en/sql-reference/functions/geo/s2.md
Normal file
@ -0,0 +1,386 @@
|
||||
---
|
||||
toc_title: S2 Geometry
|
||||
---
|
||||
|
||||
# Functions for Working with S2 Index {#s2Index}
|
||||
|
||||
[S2](https://s2geometry.io/) is a geographical indexing system where all geographical data is represented on a three-dimensional sphere (similar to a globe).
|
||||
|
||||
In the S2 library points are represented as unit length vectors called S2 point indices (points on the surface of a three dimensional unit sphere) as opposed to traditional (latitude, longitude) pairs.
|
||||
|
||||
## geoToS2 {#geoToS2}
|
||||
|
||||
Returns [S2](#s2index) point index corresponding to the provided coordinates `(longitude, latitude)`.
|
||||
|
||||
**Syntax**
|
||||
|
||||
``` sql
|
||||
geoToS2(lon, lat)
|
||||
```
|
||||
|
||||
**Arguments**
|
||||
|
||||
- `lon` — Longitude. [Float64](../../../sql-reference/data-types/float.md).
|
||||
- `lat` — Latitude. [Float64](../../../sql-reference/data-types/float.md).
|
||||
|
||||
**Returned values**
|
||||
|
||||
- S2 point index.
|
||||
|
||||
Type: [UInt64](../../../sql-reference/data-types/int-uint.md).
|
||||
|
||||
**Example**
|
||||
|
||||
Query:
|
||||
|
||||
``` sql
|
||||
SELECT geoToS2(37.79506683, 55.71290588) as s2Index;
|
||||
```
|
||||
|
||||
Result:
|
||||
|
||||
``` text
|
||||
┌─────────────s2Index─┐
|
||||
│ 4704772434919038107 │
|
||||
└─────────────────────┘
|
||||
```
|
||||
|
||||
## s2ToGeo {#s2ToGeo}
|
||||
|
||||
Returns geo coordinates `(longitude, latitude)` corresponding to the provided [S2](#s2index) point index.
|
||||
|
||||
**Syntax**
|
||||
|
||||
``` sql
|
||||
s2ToGeo(s2index)
|
||||
```
|
||||
|
||||
**Arguments**
|
||||
|
||||
- `s2Index` — S2 Index. [UInt64](../../../sql-reference/data-types/int-uint.md).
|
||||
|
||||
**Returned values**
|
||||
|
||||
- A tuple consisting of two values: `tuple(lon,lat)`.
|
||||
|
||||
Type: `lon` - [Float64](../../../sql-reference/data-types/float.md). `lat` — [Float64](../../../sql-reference/data-types/float.md).
|
||||
|
||||
**Example**
|
||||
|
||||
Query:
|
||||
|
||||
``` sql
|
||||
SELECT s2ToGeo(4704772434919038107) as s2Coodrinates;
|
||||
```
|
||||
|
||||
Result:
|
||||
|
||||
``` text
|
||||
┌─s2Coodrinates────────────────────────┐
|
||||
│ (37.79506681471008,55.7129059052841) │
|
||||
└──────────────────────────────────────┘
|
||||
```
|
||||
|
||||
## s2GetNeighbors {#s2GetNeighbors}
|
||||
|
||||
Returns S2 neighbor indices corresponding to the provided [S2](#s2index)). Each cell in the S2 system is a quadrilateral bounded by four geodesics. So, each cell has 4 neighbors.
|
||||
|
||||
**Syntax**
|
||||
|
||||
``` sql
|
||||
s2GetNeighbors(s2index)
|
||||
```
|
||||
|
||||
**Arguments**
|
||||
|
||||
- `s2index` — S2 Index. [UInt64](../../../sql-reference/data-types/int-uint.md).
|
||||
|
||||
**Returned values**
|
||||
|
||||
- An array consisting of the 4 neighbor indices: `array[s2index1, s2index3, s2index2, s2index4]`.
|
||||
|
||||
Type: Each S2 index is [UInt64](../../../sql-reference/data-types/int-uint.md).
|
||||
|
||||
**Example**
|
||||
|
||||
Query:
|
||||
|
||||
``` sql
|
||||
select s2GetNeighbors(5074766849661468672) AS s2Neighbors;
|
||||
```
|
||||
|
||||
Result:
|
||||
|
||||
``` text
|
||||
┌─s2Neighbors───────────────────────────────────────────────────────────────────────┐
|
||||
│ [5074766987100422144,5074766712222515200,5074767536856236032,5074767261978329088] │
|
||||
└───────────────────────────────────────────────────────────────────────────────────┘
|
||||
```
|
||||
|
||||
## s2CellsIntersect {#s2CellsIntersect}
|
||||
|
||||
Determines if the two provided [S2](#s2index)) cell indices intersect or not.
|
||||
|
||||
**Syntax**
|
||||
|
||||
``` sql
|
||||
s2CellsIntersect(s2index1, s2index2)
|
||||
```
|
||||
|
||||
**Arguments**
|
||||
|
||||
- `siIndex1`, `s2index2` — S2 Index. [UInt64](../../../sql-reference/data-types/int-uint.md).
|
||||
|
||||
**Returned values**
|
||||
|
||||
- 1 — If the S2 cell indices intersect.
|
||||
- 0 — If the S2 cell indices don't intersect.
|
||||
|
||||
Type: [UInt8](../../../sql-reference/data-types/int-uint.md).
|
||||
|
||||
**Example**
|
||||
|
||||
Query:
|
||||
|
||||
``` sql
|
||||
select s2CellsIntersect(9926595209846587392, 9926594385212866560) as intersect;
|
||||
```
|
||||
|
||||
Result:
|
||||
|
||||
``` text
|
||||
┌─intersect─┐
|
||||
│ 1 │
|
||||
└───────────┘
|
||||
```
|
||||
|
||||
## s2CapContains {#s2CapContains}
|
||||
|
||||
A cap represents a portion of the sphere that has been cut off by a plane. It is defined by a point on a sphere and a radius in degrees.
|
||||
|
||||
Determines if a cap contains a s2 point index.
|
||||
|
||||
**Syntax**
|
||||
|
||||
``` sql
|
||||
s2CapContains(center, degrees, point)
|
||||
```
|
||||
|
||||
**Arguments**
|
||||
|
||||
- `center` - S2 point index corresponding to the cap. [UInt64](../../../sql-reference/data-types/int-uint.md).
|
||||
- `degrees` - Radius of the cap in degrees. [Float64](../../../sql-reference/data-types/float.md).
|
||||
- `point` - S2 point index. [UInt64](../../../sql-reference/data-types/int-uint.md).
|
||||
|
||||
**Returned values**
|
||||
|
||||
- 1 — If the cap contains the S2 point index.
|
||||
- 0 — If the cap doesn't contain the S2 point index.
|
||||
|
||||
Type: [UInt8](../../../sql-reference/data-types/int-uint.md).
|
||||
|
||||
**Example**
|
||||
|
||||
Query:
|
||||
|
||||
``` sql
|
||||
select s2CapContains(1157339245694594829, 1.0, 1157347770437378819) as capContains;
|
||||
```
|
||||
|
||||
Result:
|
||||
|
||||
``` text
|
||||
┌─capContains─┐
|
||||
│ 1 │
|
||||
└─────────────┘
|
||||
```
|
||||
|
||||
## s2CapUnion {#s2CapUnion}
|
||||
|
||||
A cap represents a portion of the sphere that has been cut off by a plane. It is defined by a point on a sphere and a radius in degrees.
|
||||
|
||||
Determines the smallest cap that contains the given two input caps.
|
||||
|
||||
**Syntax**
|
||||
|
||||
``` sql
|
||||
s2CapUnion(center1, radius1, center2, radius2)
|
||||
```
|
||||
|
||||
**Arguments**
|
||||
|
||||
- `center1`, `center2` - S2 point indices corresponding to the two input caps. [UInt64](../../../sql-reference/data-types/int-uint.md).
|
||||
- `radius1`, `radius2` - Radii of the two input caps in degrees. [Float64](../../../sql-reference/data-types/float.md).
|
||||
|
||||
**Returned values**
|
||||
|
||||
- `center` - S2 point index corresponding the center of the smallest cap containing the two input caps. Type: [UInt64](../../../sql-reference/data-types/int-uint.md).
|
||||
- `radius` - Radius of the smallest cap containing the two input caps. Type: [Float64](../../../sql-reference/data-types/float.md).
|
||||
|
||||
**Example**
|
||||
|
||||
Query:
|
||||
|
||||
``` sql
|
||||
SELECT s2CapUnion(3814912406305146967, 1.0, 1157347770437378819, 1.0) AS capUnion;
|
||||
```
|
||||
|
||||
Result:
|
||||
|
||||
``` text
|
||||
┌─capUnion───────────────────────────────┐
|
||||
│ (4534655147792050737,60.2088283994957) │
|
||||
└────────────────────────────────────────┘
|
||||
```
|
||||
|
||||
## s2RectAdd{#s2RectAdd}
|
||||
|
||||
In the S2 system, a rectangle is represented by a type of S2Region called a S2LatLngRect that represents a rectangle in latitude-longitude space.
|
||||
|
||||
Increases the size of the bounding rectangle to include the given S2 point index.
|
||||
|
||||
**Syntax**
|
||||
|
||||
``` sql
|
||||
s2RectAdd(s2pointLow, s2pointHigh, s2Point)
|
||||
```
|
||||
|
||||
**Arguments**
|
||||
|
||||
- `s2PointLow` - Low S2 point index corresponding to the rectangle. [UInt64](../../../sql-reference/data-types/int-uint.md).
|
||||
- `s2PointHigh` - High S2 point index corresponding to the rectangle. [UInt64](../../../sql-reference/data-types/int-uint.md).
|
||||
- `s2Point` - Target S2 point index that the bound rectangle should be grown to include. [UInt64](../../../sql-reference/data-types/int-uint.md).
|
||||
|
||||
**Returned values**
|
||||
|
||||
- `s2PointLow` - Low S2 cell id corresponding to the grown rectangle. Type: [UInt64](../../../sql-reference/data-types/int-uint.md).
|
||||
- `s2PointHigh` - Hight S2 cell id corresponding to the grown rectangle. Type: [UInt64](../../../sql-reference/data-types/float.md).
|
||||
|
||||
**Example**
|
||||
|
||||
Query:
|
||||
|
||||
``` sql
|
||||
SELECT s2RectAdd(5178914411069187297, 5177056748191934217, 5179056748191934217) as rectAdd;
|
||||
```
|
||||
|
||||
Result:
|
||||
|
||||
``` text
|
||||
┌─rectAdd───────────────────────────────────┐
|
||||
│ (5179062030687166815,5177056748191934217) │
|
||||
└───────────────────────────────────────────┘
|
||||
```
|
||||
|
||||
## s2RectContains{#s2RectContains}
|
||||
|
||||
In the S2 system, a rectangle is represented by a type of S2Region called a S2LatLngRect that represents a rectangle in latitude-longitude space.
|
||||
|
||||
Determines if a given rectangle contains a S2 point index.
|
||||
|
||||
**Syntax**
|
||||
|
||||
``` sql
|
||||
s2RectContains(s2PointLow, s2PointHi, s2Point)
|
||||
```
|
||||
|
||||
**Arguments**
|
||||
|
||||
- `s2PointLow` - Low S2 point index corresponding to the rectangle. [UInt64](../../../sql-reference/data-types/int-uint.md).
|
||||
- `s2PointHigh` - High S2 point index corresponding to the rectangle. [UInt64](../../../sql-reference/data-types/int-uint.md).
|
||||
- `s2Point` - Target S2 point index. [UInt64](../../../sql-reference/data-types/int-uint.md).
|
||||
|
||||
**Returned values**
|
||||
|
||||
- 1 — If the rectangle contains the given S2 point.
|
||||
- 0 — If the rectangle doesn't contain the given S2 point.
|
||||
|
||||
**Example**
|
||||
|
||||
Query:
|
||||
|
||||
``` sql
|
||||
SELECT s2RectContains(5179062030687166815, 5177056748191934217, 5177914411069187297) AS rectContains
|
||||
```
|
||||
|
||||
Result:
|
||||
|
||||
``` text
|
||||
┌─rectContains─┐
|
||||
│ 0 │
|
||||
└──────────────┘
|
||||
```
|
||||
|
||||
## s2RectUinion{#s2RectUnion}
|
||||
|
||||
In the S2 system, a rectangle is represented by a type of S2Region called a S2LatLngRect that represents a rectangle in latitude-longitude space.
|
||||
|
||||
Returns the smallest rectangle containing the union of this rectangle and the given rectangle.
|
||||
|
||||
**Syntax**
|
||||
|
||||
``` sql
|
||||
s2RectUnion(s2Rect1PointLow, s2Rect1PointHi, s2Rect2PointLow, s2Rect2PointHi)
|
||||
```
|
||||
|
||||
**Arguments**
|
||||
|
||||
- `s2Rect1PointLow`, `s2Rect1PointHi` - Low and High S2 point indices corresponding to the first rectangle. [UInt64](../../../sql-reference/data-types/int-uint.md).
|
||||
- `s2Rect2PointLow`, `s2Rect2PointHi` - Low and High S2 point indices corresponding to the second rectangle. [UInt64](../../../sql-reference/data-types/int-uint.md).
|
||||
|
||||
**Returned values**
|
||||
|
||||
- `s2UnionRect2PointLow` - Low S2 cell id corresponding to the union rectangle. Type: [UInt64](../../../sql-reference/data-types/int-uint.md).
|
||||
- `s2UnionRect2PointHi` - High S2 cell id corresponding to the union rectangle. Type: [UInt64](../../../sql-reference/data-types/int-uint.md).
|
||||
|
||||
**Example**
|
||||
|
||||
Query:
|
||||
|
||||
``` sql
|
||||
SELECT s2RectUnion(5178914411069187297, 5177056748191934217, 5179062030687166815, 5177056748191934217) AS rectUnion
|
||||
```
|
||||
|
||||
Result:
|
||||
|
||||
``` text
|
||||
┌─rectUnion─────────────────────────────────┐
|
||||
│ (5179062030687166815,5177056748191934217) │
|
||||
└───────────────────────────────────────────┘
|
||||
```
|
||||
|
||||
## s2RectIntersection{#s2RectIntersection}
|
||||
|
||||
Returns the smallest Rectangle containing the intersection of this rectangle and the given rectangle.
|
||||
|
||||
**Syntax**
|
||||
|
||||
``` sql
|
||||
s2RectIntersection(s2Rect1PointLow, s2Rect1PointHi, s2Rect2PointLow, s2Rect2PointHi)
|
||||
```
|
||||
|
||||
**Arguments**
|
||||
|
||||
- `s2Rect1PointLow`, `s2Rect1PointHi` - Low and High S2 point indices corresponding to the first rectangle. [UInt64](../../../sql-reference/data-types/int-uint.md).
|
||||
- `s2Rect2PointLow`, `s2Rect2PointHi` - Low and High S2 point indices corresponding to the second rectangle. [UInt64](../../../sql-reference/data-types/int-uint.md).
|
||||
|
||||
**Returned values**
|
||||
|
||||
- `s2UnionRect2PointLow` - Low S2 cell id corresponding to the rectangle containing the intersection of the given rectangles. Type: [UInt64](../../../sql-reference/data-types/int-uint.md).
|
||||
- `s2UnionRect2PointHi` - Hi S2 cell id corresponding to the rectangle containing the intersection of the given rectangles. Type: [UInt64](../../../sql-reference/data-types/int-uint.md).
|
||||
|
||||
**Example**
|
||||
|
||||
Query:
|
||||
|
||||
``` sql
|
||||
SELECT s2RectIntersection(5178914411069187297, 5177056748191934217, 5179062030687166815, 5177056748191934217) AS rectIntersection
|
||||
```
|
||||
|
||||
Result:
|
||||
|
||||
``` text
|
||||
┌─rectIntersection──────────────────────────┐
|
||||
│ (5178914411069187297,5177056748191934217) │
|
||||
└───────────────────────────────────────────┘
|
||||
```
|
||||
@ -1 +0,0 @@
|
||||
../../../en/getting-started/example-datasets/menus.md
|
||||
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 @@
|
||||
---
|
||||
toc_priority: 21
|
||||
toc_title: Меню
|
||||
---
|
||||
|
||||
# Набор данных публичной библиотеки Нью-Йорка "Что в меню?" {#menus-dataset}
|
||||
|
||||
Набор данных создан Нью-Йоркской публичной библиотекой. Он содержит исторические данные о меню отелей, ресторанов и кафе с блюдами, а также их ценами.
|
||||
|
||||
Источник: http://menus.nypl.org/data
|
||||
Эти данные находятся в открытом доступе.
|
||||
|
||||
Данные взяты из архива библиотеки, и они могут быть неполными и сложными для статистического анализа. Тем не менее, это тоже очень интересно.
|
||||
В наборе всего 1,3 миллиона записей о блюдах в меню — очень небольшой объем данных для ClickHouse, но это все равно хороший пример.
|
||||
|
||||
## Загрузите набор данных {#download-dataset}
|
||||
|
||||
Выполните команду:
|
||||
|
||||
```bash
|
||||
wget https://s3.amazonaws.com/menusdata.nypl.org/gzips/2021_08_01_07_01_17_data.tgz
|
||||
```
|
||||
|
||||
При необходимости замените ссылку на актуальную ссылку с http://menus.nypl.org/data.
|
||||
Размер архива составляет около 35 МБ.
|
||||
|
||||
## Распакуйте набор данных {#unpack-dataset}
|
||||
|
||||
```bash
|
||||
tar xvf 2021_08_01_07_01_17_data.tgz
|
||||
```
|
||||
|
||||
Размер распакованных данных составляет около 150 МБ.
|
||||
|
||||
Данные нормализованы и состоят из четырех таблиц:
|
||||
- `Menu` — информация о меню: название ресторана, дата, когда было просмотрено меню, и т.д.
|
||||
- `Dish` — информация о блюдах: название блюда вместе с некоторыми характеристиками.
|
||||
- `MenuPage` — информация о страницах в меню, потому что каждая страница принадлежит какому-либо меню.
|
||||
- `MenuItem` — один из пунктов меню. Блюдо вместе с его ценой на какой-либо странице меню: ссылки на блюдо и страницу меню.
|
||||
|
||||
## Создайте таблицы {#create-tables}
|
||||
|
||||
Для хранения цен используется тип данных [Decimal](../../sql-reference/data-types/decimal.md).
|
||||
|
||||
```sql
|
||||
CREATE TABLE dish
|
||||
(
|
||||
id UInt32,
|
||||
name String,
|
||||
description String,
|
||||
menus_appeared UInt32,
|
||||
times_appeared Int32,
|
||||
first_appeared UInt16,
|
||||
last_appeared UInt16,
|
||||
lowest_price Decimal64(3),
|
||||
highest_price Decimal64(3)
|
||||
) ENGINE = MergeTree ORDER BY id;
|
||||
|
||||
CREATE TABLE menu
|
||||
(
|
||||
id UInt32,
|
||||
name String,
|
||||
sponsor String,
|
||||
event String,
|
||||
venue String,
|
||||
place String,
|
||||
physical_description String,
|
||||
occasion String,
|
||||
notes String,
|
||||
call_number String,
|
||||
keywords String,
|
||||
language String,
|
||||
date String,
|
||||
location String,
|
||||
location_type String,
|
||||
currency String,
|
||||
currency_symbol String,
|
||||
status String,
|
||||
page_count UInt16,
|
||||
dish_count UInt16
|
||||
) ENGINE = MergeTree ORDER BY id;
|
||||
|
||||
CREATE TABLE menu_page
|
||||
(
|
||||
id UInt32,
|
||||
menu_id UInt32,
|
||||
page_number UInt16,
|
||||
image_id String,
|
||||
full_height UInt16,
|
||||
full_width UInt16,
|
||||
uuid UUID
|
||||
) ENGINE = MergeTree ORDER BY id;
|
||||
|
||||
CREATE TABLE menu_item
|
||||
(
|
||||
id UInt32,
|
||||
menu_page_id UInt32,
|
||||
price Decimal64(3),
|
||||
high_price Decimal64(3),
|
||||
dish_id UInt32,
|
||||
created_at DateTime,
|
||||
updated_at DateTime,
|
||||
xpos Float64,
|
||||
ypos Float64
|
||||
) ENGINE = MergeTree ORDER BY id;
|
||||
```
|
||||
|
||||
## 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}
|
||||
|
||||
|
||||
@ -7,7 +7,8 @@ toc_title: DateTime64
|
||||
|
||||
Позволяет хранить момент времени, который может быть представлен как календарная дата и время, с заданной суб-секундной точностью.
|
||||
|
||||
Размер тика (точность, precision): 10<sup>-precision</sup> секунд, где precision - целочисленный параметр.
|
||||
Размер тика (точность, precision): 10<sup>-precision</sup> секунд, где precision - целочисленный параметр. Возможные значения: [ 0 : 9 ].
|
||||
Обычно используются - 3 (миллисекунды), 6 (микросекунды), 9 (наносекунды).
|
||||
|
||||
**Синтаксис:**
|
||||
|
||||
|
||||
Loading…
Reference in New Issue
Block a user