ClickHouse/docs/en/getting-started/example-datasets/cell-towers.md
2022-11-07 09:26:50 -05:00

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/en/getting-started/example-datasets/cell-towers Cell Towers 3 Cell Towers

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This dataset is from OpenCellid - The world's largest Open Database of Cell Towers.

As of 2021, it contains more than 40 million records about cell towers (GSM, LTE, UMTS, etc.) around the world with their geographical coordinates and metadata (country code, network, etc).

OpenCelliD Project is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License, and we redistribute a snapshot of this dataset under the terms of the same license. The up-to-date version of the dataset is available to download after sign in.

Get the Dataset

ClickHouse Cloud provides an easy-button for uploading this dataset from S3. Log in to your ClickHouse Cloud organization, or create a free trial at ClickHouse.cloud.

Choose the Cell Towers dataset from the Sample data tab, and Load data:

Load cell towers dataset

Examine the schema of the cell_towers table:

DESCRIBE TABLE cell_towers
  1. Download the snapshot of the dataset from February 2021: cell_towers.csv.xz (729 MB).

  2. Validate the integrity (optional step):

md5sum cell_towers.csv.xz
8cf986f4a0d9f12c6f384a0e9192c908  cell_towers.csv.xz
  1. Decompress it with the following command:
xz -d cell_towers.csv.xz
  1. Create a table:
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);
  1. Insert the dataset:
clickhouse-client --query "INSERT INTO cell_towers FORMAT CSVWithNames" < cell_towers.csv

Example queries

  1. A number of cell towers by type:
SELECT radio, count() AS c FROM cell_towers GROUP BY radio ORDER BY c DESC
┌─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.)
  1. Cell towers by mobile country code (MCC):
SELECT mcc, count() FROM cell_towers GROUP BY mcc ORDER BY count() DESC LIMIT 10
┌─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.)

So, the top countries are: the USA, Germany, and Russia.

You may want to create a Dictionary in ClickHouse to decode these values.

Use case: Incorporate geo data

Using pointInPolygon function.

  1. Create a table where we will store polygons:
CREATE TABLE moscow (polygon Array(Tuple(Float64, Float64)))
ORDER BY polygon;
CREATE TEMPORARY TABLE
moscow (polygon Array(Tuple(Float64, Float64)));
  1. This is a rough shape of Moscow (without "new Moscow"):
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),
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(37.37890062088197, 55.73167906388319), (37.37750451918789, 55.734703664681774), (37.375610832015965, 55.734851959522246), (37.3723813571472, 55.74105626086403), (37.37014935714723, 55.746115620904355), (37.36944173016362, 55.750883999993725),
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(37.83632178569025, 55.81276696067908), (37.838623105812026, 55.811486181656385), (37.83912198147584, 55.807329380532785), (37.839079078033414, 55.80510270463816), (37.83965844708251, 55.79940712529036), (37.840581150787344, 55.79131399999368),
(37.84172564285271, 55.78000432402266)]);
  1. Check how many cell towers are in Moscow:
SELECT count() FROM cell_towers
WHERE pointInPolygon((lon, lat), (SELECT * FROM moscow))
┌─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.)

The data is also available for interactive queries in the Playground, example.

Although you cannot create temporary tables there.