ClickHouse/docs/en/getting-started/example-datasets/cell-towers.md
2022-12-05 08:07:52 -05:00

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

import ConnectionDetails from '@site/docs/en/_snippets/_gather_your_details_http.mdx';

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'; import SupersetDocker from '@site/docs/en/_snippets/_add_superset_detail.md';

Goal

In this guide you will learn how to:

  • Load the OpenCelliD data in ClickHouse
  • Connect Apache Superset to ClickHouse
  • Build a dashboard based on data available in the dataset

Here is a preview of the dashboard created in this guide:

Dashboard of cell towers by radio type in mcc 204

Get the Dataset

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.

Load the sample data

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

This is the output of DESCRIBE. Down further in this guide the field type choices will be described.

┌─name──────────┬─type──────────────────────────────────────────────────────────────────┬
│ 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                                                                 │
└───────────────┴───────────────────────────────────────────────────────────────────────┴
  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

Run some 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.)

Based on the above query and the MCC list, the countries with the most cell towers 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 the 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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(37.827697554878185, 55.68951421135665), (37.82447346292115, 55.68965045405069), (37.83136543914793, 55.68322046195302), (37.833554015869154, 55.67814012759211), (37.83544184655761, 55.67295011628339), (37.837480388885474, 55.6672498719639),
(37.838960677246064, 55.66316274139358), (37.83926093121332, 55.66046999999383), (37.839025050262435, 55.65869897264431), (37.83670784390257, 55.65794084879904), (37.835656529083245, 55.65694309303843), (37.83704060449217, 55.65689306460552),
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(37.80100123544311, 55.62873670012244), (37.79598013491824, 55.62554336109055), (37.78634567724606, 55.62033499605651), (37.78334147619623, 55.618768681480326), (37.77746201055901, 55.619855533402706), (37.77527329626457, 55.61909966711279),
(37.77801986242668, 55.618770300976294), (37.778212973541216, 55.617257701952106), (37.77784818518065, 55.61574504433011), (37.77016867724609, 55.61148576294007), (37.760191219573976, 55.60599579539028), (37.75338926983641, 55.60227892751446),
(37.746329965606634, 55.59920577639331), (37.73939925396728, 55.59631430313617), (37.73273665739439, 55.5935318803559), (37.7299954450912, 55.59350760316188), (37.7268679946899, 55.59469840523759), (37.72626726983634, 55.59229549697373),
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(37.578692203704804, 55.57982895000019), (37.57327546607398, 55.58116294118248), (37.57385012109279, 55.581550362779), (37.57399562266922, 55.5820107079112), (37.5735356072979, 55.58226289171689), (37.57290393054962, 55.582393529795155),
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(37.41922139651101, 55.68235377885389), (37.419218771842885, 55.68359335082235), (37.417196501327446, 55.684375235224735), (37.41607020370478, 55.68540557585352), (37.415640857147146, 55.68686637150793), (37.414632153442334, 55.68903015131686),
(37.413344899475064, 55.690896881757396), (37.41171432275391, 55.69264232162232), (37.40948282275393, 55.69455101638112), (37.40703674603271, 55.69638690385348), (37.39607169577025, 55.70451821283731), (37.38952706878662, 55.70942491932811),
(37.387778313491815, 55.71149057784176), (37.39049275399779, 55.71419814298992), (37.385557272491454, 55.7155489617061), (37.38388335714726, 55.71849856042102), (37.378368238098155, 55.7292763261685), (37.37763597123337, 55.730845879211614),
(37.37890062088197, 55.73167906388319), (37.37750451918789, 55.734703664681774), (37.375610832015965, 55.734851959522246), (37.3723813571472, 55.74105626086403), (37.37014935714723, 55.746115620904355), (37.36944173016362, 55.750883999993725),
(37.36975304365541, 55.76335905525834), (37.37244070571134, 55.76432079697595), (37.3724259757175, 55.76636979670426), (37.369922155757884, 55.76735417953104), (37.369892695770275, 55.76823419316575), (37.370214730163575, 55.782312184391266),
(37.370493611114505, 55.78436801120489), (37.37120164550783, 55.78596427165359), (37.37284851456452, 55.7874378183096), (37.37608325135799, 55.7886695054807), (37.3764587460632, 55.78947647305964), (37.37530000265506, 55.79146512926804),
(37.38235915344241, 55.79899647809345), (37.384344043655396, 55.80113596939471), (37.38594269577028, 55.80322699999366), (37.38711208598329, 55.804919036911976), (37.3880239841309, 55.806610999993666), (37.38928977249147, 55.81001864976979),
(37.39038389947512, 55.81348641242801), (37.39235781481933, 55.81983538336746), (37.393709457672124, 55.82417822811877), (37.394685720901464, 55.82792275755836), (37.39557615344238, 55.830447148154136), (37.39844478226658, 55.83167107969975),
(37.40019761214057, 55.83151823557964), (37.400398790382326, 55.83264967594742), (37.39659544313046, 55.83322180909622), (37.39667059524539, 55.83402792148566), (37.39682089947515, 55.83638877400216), (37.39643489154053, 55.83861656112751),
(37.3955338994751, 55.84072348043264), (37.392680272491454, 55.84502158126453), (37.39241188227847, 55.84659117913199), (37.392529730163616, 55.84816071336481), (37.39486835714723, 55.85288092980303), (37.39873052645878, 55.859893456073635),
(37.40272161111449, 55.86441833633205), (37.40697072750854, 55.867579567544375), (37.410007082016016, 55.868369880337), (37.4120992989502, 55.86920843741314), (37.412668021163924, 55.87055369615854), (37.41482461111453, 55.87170587948249),
(37.41862266137694, 55.873183961039565), (37.42413732540892, 55.874879126654704), (37.4312182698669, 55.875614937236705), (37.43111093783558, 55.8762723478417), (37.43332105622856, 55.87706546369396), (37.43385747619623, 55.87790681284802),
(37.441303050262405, 55.88027084462084), (37.44747234260555, 55.87942070143253), (37.44716141796871, 55.88072960917233), (37.44769797085568, 55.88121221323979), (37.45204320500181, 55.882080694420715), (37.45673176190186, 55.882346110794586),
(37.463383999999984, 55.88252729504517), (37.46682797486874, 55.88294937719063), (37.470014457672086, 55.88361266759345), (37.47751410450743, 55.88546991372396), (37.47860317658232, 55.88534929207307), (37.48165826025772, 55.882563306475106),
(37.48316434442331, 55.8815803226785), (37.483831555817645, 55.882427612793315), (37.483182967125686, 55.88372791409729), (37.483092277908824, 55.88495581062434), (37.4855716508179, 55.8875561994203), (37.486440636245746, 55.887827444039566),
(37.49014203439328, 55.88897899871799), (37.493210285705544, 55.890208937135604), (37.497512451065035, 55.891342397444696), (37.49780744510645, 55.89174030252967), (37.49940333499519, 55.89239745507079), (37.50018383334346, 55.89339220941865),
(37.52421672750851, 55.903869074155224), (37.52977457672118, 55.90564076517974), (37.53503220370484, 55.90661661218259), (37.54042858064267, 55.90714113744566), (37.54320461007303, 55.905645048442985), (37.545686966066306, 55.906608607018505),
(37.54743976120755, 55.90788552162358), (37.55796999999999, 55.90901557907218), (37.572711542327866, 55.91059395704873), (37.57942799999998, 55.91073854155573), (37.58502865872187, 55.91009969268444), (37.58739968913264, 55.90794809960554),
(37.59131567193598, 55.908713267595054), (37.612687423278814, 55.902866854295375), (37.62348079629517, 55.90041967242986), (37.635797880950896, 55.898141151686396), (37.649487626983664, 55.89639275532968), (37.65619302513125, 55.89572360207488),
(37.66294133862307, 55.895295577183965), (37.66874564418033, 55.89505457604897), (37.67375601586915, 55.89254677027454), (37.67744661901856, 55.8947775867987), (37.688347, 55.89450045676125), (37.69480554232789, 55.89422926332761),
(37.70107096560668, 55.89322256101114), (37.705962965606716, 55.891763491662616), (37.711885134918205, 55.889110234998974), (37.71682005026245, 55.886577568759876), (37.7199315476074, 55.88458159806678), (37.72234560316464, 55.882281005794134),
(37.72364385977171, 55.8809452036196), (37.725371142837474, 55.8809722706006), (37.727870902099546, 55.88037213862385), (37.73394330422971, 55.877941504088696), (37.745339592590376, 55.87208120378722), (37.75525267724611, 55.86703807949492),
(37.76919976190188, 55.859821640197474), (37.827835219574, 55.82962968399116), (37.83341438888553, 55.82575289922351), (37.83652584655761, 55.82188784027888), (37.83809213491821, 55.81612575504693), (37.83605359521481, 55.81460347077685),
(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.)

Review of the schema

Before building visualizations in Superset have a look at the columns that you will use. This dataset primarily provides the location (Longitude and Latitude) and radio types at mobile cellular towers worldwide. The column descriptions can be found in the community forum. The columns used in the visualizations that will be built are described below

Here is a description of the columns taken from the OpenCelliD forum:

Column Description
radio Technology generation: CDMA, GSM, UMTS, 5G NR
mcc Mobile Country Code: 204 is The Netherlands
lon Longitude: With Latitude, approximate tower location
lat Latitude: With Longitude, approximate tower location

:::tip mcc To find your MCC check Mobile network codes, and use the three digits in the Mobile country code column. :::

The schema for this table was designed for compact storage on disk and query speed.

  • The radio data is stored as an Enum8 (UInt8) rather than a string.
  • mcc or Mobile country code, is stored as a UInt16 as we know the range is 1 - 999.
  • lon and lat are Float64.

None of the other fields are used in the queries or visualizations in this guide, but they are described in the forum linked above if you are interested.

Build visualizations with Apache Superset

Superset is easy to run from Docker. If you already have Superset running, all you need to do is add ClickHouse Connect with pip install clickhouse-connect. If you need to install Superset open the Launch Apache Superset in Docker directly below.

To build a Superset dashboard using the OpenCelliD dataset you should:

  • Add your ClickHouse service as a Superset database
  • Add the table cell_towers as a Superset dataset
  • Create some charts
  • Add the charts to a dashboard

Add your ClickHouse service as a Superset database

In Superset a database can be added by choosing the database type, and then providing the connection details. Open Superset and look for the +, it has a menu with Data and then Connect database options.

Add a database

Choose ClickHouse Connect from the list:

Choose clickhouse connect as database type

:::note If ClickHouse Connect is not one of your options, then you will need to install it. The comand is pip install clickhouse-connect, and more info is available here. :::

Add your connection details:

:::tip Make sure that you set SSL on when connecting to ClickHouse Cloud or other ClickHouse systems that enforce the use of SSL. :::

Add ClickHouse as a Superset datasource

Add the table cell_towers as a Superset dataset

In Superset a dataset maps to a table within a database. Click on add a dataset and choose your ClickHouse service, the database containing your table (default), and choose the cell_towers table:

Add cell_towers table as a dataset

Create some charts

When you choose to add a chart in Superset you have to specify the dataset (cell_towers) and the chart type. Since the OpenCelliD dataset provides longitude and latitude coordinates for cell towers we will create a Map chart. The deck.gL Scatterplot type is suited to this dataset as it works well with dense data points on a map.

Create a map in Superset

Specify the query used for the map

A deck.gl Scatterplot requires a longitude and latitude, and one or more filters can also be applied to the query. In this example two filters are applied, one for cell towers with UMTS radios, and one for the Mobile country code assigned to The Netherlands.

The fields lon and lat contain the longitude and latitude:

Specify longitude and latitude fields

Add a filter with mcc = 204 (or substitute any other mcc value):

Filter on MCC 204

Add a filter with radio = 'UMTS' (or substitute any other radio value, you can see the choices in the output of DESCRIBE TABLE cell_towers):

Filter on radio = UMTS

This is the full configuration for the chart that filters on radio = 'UMTS' and mcc = 204:

Chart for UMTS radios in MCC 204

Click on UPDATE CHART to render the visualization.

Add the charts to a dashboard

This screenshot shows cell tower locations with LTE, UMTS, and GSM radios. The charts are all created in the same way and they are added to a dashboard.

Dashboard of cell towers by radio type in mcc 204

:::tip The data is also available for interactive queries in the Playground.

This example will populate the username and even the query for you.

Although you cannot create tables in the Playground, you can run all of the queries and even use Superset (adjust the hostname and port number). :::