--- slug: /zh/getting-started/example-datasets/cell-towers sidebar_label: 蜂窝信号塔 sidebar_position: 3 title: "蜂窝信号塔" --- import Tabs from '@theme/Tabs'; import TabItem from '@theme/TabItem'; import CodeBlock from '@theme/CodeBlock'; import ActionsMenu from '@site/docs/en/_snippets/_service_actions_menu.md'; import SQLConsoleDetail from '@site/docs/en/_snippets/_launch_sql_console.md'; 该数据集来自 [OpenCellid](https://www.opencellid.org/) - 世界上最大的蜂窝信号塔的开放数据库。 截至 2021 年，它拥有超过 4000 万条关于全球蜂窝信号塔（GSM、LTE、UMTS 等）的记录及其地理坐标和元数据（国家代码、网络等）。 OpenCelliD 项目在 `Creative Commons Attribution-ShareAlike 4.0 International License` 协议下许可使用，我们根据相同许可条款重新分发此数据集的快照。登录后即可下载最新版本的数据集。 ## 获取数据集 {#get-the-dataset} 在 ClickHouse Cloud 上可以通过一个按钮实现通过 S3 上传此数据集。登录你的 ClickHouse Cloud 组织，或通过 [ClickHouse.cloud](https://clickhouse.cloud) 创建免费试用版。 从 **Sample data** 选项卡中选择 **Cell Towers** 数据集，然后选择 **Load data**： ![加载数据集](@site/docs/en/_snippets/images/cloud-load-data-sample.png) 检查 cell_towers 的表结构： ```sql DESCRIBE TABLE cell_towers ``` 1. 下载 2021 年 2 月以来的数据集快照：[cell_towers.csv.xz](https://datasets.clickhouse.com/cell_towers.csv.xz) (729 MB)。 2. 验证完整性（可选步骤）： ```bash md5sum cell_towers.csv.xz ``` ```response 8cf986f4a0d9f12c6f384a0e9192c908 cell_towers.csv.xz ``` 3. 使用以下命令解压： ```bash xz -d cell_towers.csv.xz ``` 4. 创建表： ```sql CREATE TABLE cell_towers ( radio Enum8('' = 0, 'CDMA' = 1, 'GSM' = 2, 'LTE' = 3, 'NR' = 4, 'UMTS' = 5), mcc UInt16, net UInt16, area UInt16, cell UInt64, unit Int16, lon Float64, lat Float64, range UInt32, samples UInt32, changeable UInt8, created DateTime, updated DateTime, averageSignal UInt8 ) ENGINE = MergeTree ORDER BY (radio, mcc, net, created); ``` 5. 插入数据集： ```bash clickhouse-client --query "INSERT INTO cell_towers FORMAT CSVWithNames" < cell_towers.csv ``` ## 查询示例 {#examples} 1. 按类型划分的基站数量： ```sql SELECT radio, count() AS c FROM cell_towers GROUP BY radio ORDER BY c DESC ``` ```response ┌─radio─┬────────c─┐ │ UMTS │ 20686487 │ │ LTE │ 12101148 │ │ GSM │ 9931312 │ │ CDMA │ 556344 │ │ NR │ 867 │ └───────┴──────────┘ 5 rows in set. Elapsed: 0.011 sec. Processed 43.28 million rows, 43.28 MB (3.83 billion rows/s., 3.83 GB/s.) ``` 2. 各个[移动国家代码（MCC）](https://en.wikipedia.org/wiki/Mobile_country_code)对应的蜂窝信号塔数量： ```sql SELECT mcc, count() FROM cell_towers GROUP BY mcc ORDER BY count() DESC LIMIT 10 ``` ```response ┌─mcc─┬─count()─┐ │ 310 │ 5024650 │ │ 262 │ 2622423 │ │ 250 │ 1953176 │ │ 208 │ 1891187 │ │ 724 │ 1836150 │ │ 404 │ 1729151 │ │ 234 │ 1618924 │ │ 510 │ 1353998 │ │ 440 │ 1343355 │ │ 311 │ 1332798 │ └─────┴─────────┘ 10 rows in set. Elapsed: 0.019 sec. Processed 43.28 million rows, 86.55 MB (2.33 billion rows/s., 4.65 GB/s.) ``` 排名靠前的国家是：美国、德国和俄罗斯。 你可以通过在 ClickHouse 中创建一个 [External Dictionary](../../sql-reference/dictionaries/external-dictionaries/external-dicts.md) 来解码这些值。 ## 用例：合并地理数据 {#use-case} 使用 `pointInPolygon` 函数。 1. 创建一个用于存储多边形的表： ```sql CREATE TABLE moscow (polygon Array(Tuple(Float64, Float64))) ORDER BY polygon; ``` ```sql CREATE TEMPORARY TABLE moscow (polygon Array(Tuple(Float64, Float64))); ``` 2. 以下点大致上构造了莫斯科的地理围栏（除“新莫斯科”外）： ```sql INSERT INTO moscow VALUES ([(37.84172564285271, 55.78000432402266), (37.8381207618713, 55.775874525970494), (37.83979446823122, 55.775626746008065), (37.84243326983639, 55.77446586811748), (37.84262672750849, 55.771974101091104), (37.84153238623039, 55.77114545193181), (37.841124690460184, 55.76722010265554), (37.84239076983644, 55.76654891107098), (37.842283558197025, 55.76258709833121), (37.8421759312134, 55.758073999993734), (37.84198330422974, 55.75381499999371), (37.8416827275085, 55.749277102484484), (37.84157576190186, 55.74794544108413), (37.83897929098507, 55.74525257875241), (37.83739676451868, 55.74404373042019), (37.838732481460525, 55.74298009816793), (37.841183997352545, 55.743060321833575), (37.84097476190185, 55.73938799999373), (37.84048155819702, 55.73570799999372), (37.840095812164286, 55.73228210777237), (37.83983814285274, 55.73080491981639), (37.83846476321406, 55.729799917464675), (37.83835745269769, 55.72919751082619), (37.838636380279524, 55.72859509486539), (37.8395161005249, 55.727705075632784), (37.83897964285276, 55.722727886185154), (37.83862557539366, 55.72034817326636), (37.83559735744853, 55.71944437307499), (37.835370708803126, 55.71831419154461), (37.83738169402022, 55.71765218986692), (37.83823396494291, 55.71691750159089), (37.838056931213345, 55.71547311301385), (37.836812846557606, 55.71221445615604), (37.83522525396725, 55.709331054395555), (37.83269301586908, 55.70953687463627), (37.829667367706236, 55.70903403789297), 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55.79940712529036), (37.840581150787344, 55.79131399999368), (37.84172564285271, 55.78000432402266)]); ``` 3. 检查莫斯科有多少个蜂窝信号塔： ```sql SELECT count() FROM cell_towers WHERE pointInPolygon((lon, lat), (SELECT * FROM moscow)) ``` ```response ┌─count()─┐ │ 310463 │ └─────────┘ 1 rows in set. Elapsed: 0.067 sec. Processed 43.28 million rows, 692.42 MB (645.83 million rows/s., 10.33 GB/s.) ``` 虽然不能创建临时表，但此数据集仍可在 [Playground](https://play.clickhouse.com/play?user=play) 中进行交互式的请求, [example](https://play.clickhouse.com/play?user=play#U0VMRUNUIG1jYywgY291bnQoKSBGUk9NIGNlbGxfdG93ZXJzIEdST1VQIEJZIG1jYyBPUkRFUiBCWSBjb3VudCgpIERFU0M=).