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英国房产价格数据集

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这些数据包含英格兰和威尔士房地产的成交价格。该数据自 1995 年起即可获取，未压缩大小约为 4 GiB (在 ClickHouse 中仅占约 278 MiB) 。

创建表

CREATE DATABASE uk;

CREATE TABLE uk.uk_price_paid
(
    price UInt32,
    date Date,
    postcode1 LowCardinality(String),
    postcode2 LowCardinality(String),
    type Enum8('terraced' = 1, 'semi-detached' = 2, 'detached' = 3, 'flat' = 4, 'other' = 0),
    is_new UInt8,
    duration Enum8('freehold' = 1, 'leasehold' = 2, 'unknown' = 0),
    addr1 String,
    addr2 String,
    street LowCardinality(String),
    locality LowCardinality(String),
    town LowCardinality(String),
    district LowCardinality(String),
    county LowCardinality(String)
)
ENGINE = MergeTree
ORDER BY (postcode1, postcode2, addr1, addr2);

预处理并插入数据

我们将使用 url 函数将数据流式导入 ClickHouse。首先需要对部分传入的数据进行预处理，包括：

  • 将 postcode 拆分为两个不同的列——postcode1 和 postcode2，这样更利于存储和查询
  • 将 time 字段转换为日期，因为它只包含 00:00 时间
  • 忽略 UUid 字段，因为分析时不需要它
  • 使用 transform 函数将 type 和 duration 转换为更易读的 Enum 字段
  • 将 is_new 字段从单字符字符串 (Y/N) 转换为取值为 0 或 1 的 UInt8 字段
  • 删除最后两列，因为它们的值都相同 (均为 0)

url 函数会将数据从 Web 服务器流式导入你的 ClickHouse 表。以下命令会向 uk_price_paid 表插入 500 万行：

INSERT INTO uk.uk_price_paid
SELECT
    toUInt32(price_string) AS price,
    parseDateTimeBestEffortUS(time) AS date,
    splitByChar(' ', postcode)[1] AS postcode1,
    splitByChar(' ', postcode)[2] AS postcode2,
    transform(a, ['T', 'S', 'D', 'F', 'O'], ['terraced', 'semi-detached', 'detached', 'flat', 'other']) AS type,
    b = 'Y' AS is_new,
    transform(c, ['F', 'L', 'U'], ['freehold', 'leasehold', 'unknown']) AS duration,
    addr1,
    addr2,
    street,
    locality,
    town,
    district,
    county
FROM url(
    'http://prod1.publicdata.landregistry.gov.uk.s3-website-eu-west-1.amazonaws.com/pp-complete.csv',
    'CSV',
    'uuid_string String,
    price_string String,
    time String,
    postcode String,
    a String,
    b String,
    c String,
    addr1 String,
    addr2 String,
    street String,
    locality String,
    town String,
    district String,
    county String,
    d String,
    e String'
) SETTINGS max_http_get_redirects=10;

等待数据完成插入——根据网络速度，这可能需要一到两分钟。

验证数据

我们可以通过查看插入了多少行来确认是否成功：

SELECT count()
FROM uk.uk_price_paid

执行此查询时，该数据集有 27,450,499 行。再来看看 ClickHouse 中这张表占用了多少存储空间：

SELECT formatReadableSize(total_bytes)
FROM system.tables
WHERE name = 'uk_price_paid'

可以看到，这张表的大小仅为 221.43 MiB！

执行一些查询

执行一些查询来分析数据：

查询 1：每年的平均价格

SELECT
   toYear(date) AS year,
   round(avg(price)) AS price,
   bar(price, 0, 1000000, 80
)
FROM uk.uk_price_paid
GROUP BY year
ORDER BY year

查询 2：伦敦每年的平均房价

SELECT
   toYear(date) AS year,
   round(avg(price)) AS price,
   bar(price, 0, 2000000, 100
)
FROM uk.uk_price_paid
WHERE town = 'LONDON'
GROUP BY year
ORDER BY year

2020 年的房价似乎发生了一些变化！不过这大概也不让人意外……

查询 3. 房价最高的街区

SELECT
    town,
    district,
    count() AS c,
    round(avg(price)) AS price,
    bar(price, 0, 5000000, 100)
FROM uk.uk_price_paid
WHERE date >= '2020-01-01'
GROUP BY
    town,
    district
HAVING c >= 100
ORDER BY price DESC
LIMIT 100

使用投影加速查询

我们可以用投影来加速这些查询。有关此数据集的示例，请参阅"投影"。

在 Playground 中试用

该数据集也可在在线 Playground中使用。

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