Importing GeoJSON with a deeply nested object array

December 18, 2024 · 5 min read

“Importing GeoJSON with a deeply nested object array“

Question

How do I import GeoJSON with a nested object array?

Answer

For this tutorial, we will use open data publicly available here. A copy can be found here.

Download the data in GeoJSON format and rename the file to geojson.json.
Understand the structure.

DESCRIBE TABLE file('geojson.json', 'JSON')
┌─name─────┬─type─────────────────────────────────────────────────────────────────────────────────────────┐
│ type     │ Nullable(String)                                                                             │     
│ name     │ Nullable(String)                                                                             │     
│ crs      │ Tuple( properties Tuple(name Nullable(String)),type Nullable(String))                        │     
│ features │ Array(Tuple(                                                                                 │  
│          │                  geometry Tuple(coordinates Array(Array(Array(Array(Nullable(Float64))))),   │  
│          │                  type Nullable(String)),                                                     │  
│          │                  properties Tuple(   CODIGOINE Nullable(String),                             │  
│          │                                      CODNUT1 Nullable(String),                               │  
│          │                                      CODNUT2 Nullable(String),                               │  
│          │                                      CODNUT3 Nullable(String),                               │  
│          │                                      FID Nullable(Int64),                                    │  
│          │                                      INSPIREID Nullable(String),                             │  
│          │                                      NAMEUNIT Nullable(String),                              │ 
│          │                                      NATCODE Nullable(String),                               │  
│          │                                      SHAPE_Area Nullable(Float64),                           │  
│          │                                      SHAPE_Length Nullable(Float64)                          │  
│          │                                  ),                                                          │  
│          │                  type Nullable(String)                                                       │  
│          │              )                                                                               │  
│          │      )                                                                                       │  
└──────────┴──────────────────────────────────────────────────────────────────────────────────────────────┘ 

Create a table to store the GeoJSON rows.

The requirement here is to generate a row for each object in the features array. The data type inferred for the field geometry suggests that it translates to ClickHouse's MultiPolygon data type.

create table geojson 
(
    type String,
    name String,
    crsType String,
    crsName String,
    featureType String,
    id Int64,
    inspiredId String,
    natCode String,
    nameUnit String,
    codNut1 String,
    codNut2 String,
    codNut3 String,
    codigoIne String,
    shapeLength Float64,
    shapeArea Float64,
    geometryType String,
    geometry MultiPolygon
)
engine = MergeTree
order by id;

Prepare the data.

The main purpose of the query is to verify that we obtain one row for each object in the features array.

Note

The field features.geometry.coordinates is commented to make the result set more readable.

SELECT
    type AS type,
    name AS name,
    crs.type AS crsType,
    crs.properties.name AS crsName,
    features.type AS featureType,
    features.properties.FID AS id,
    features.properties.INSPIREID AS inspiredId,
    features.properties.NATCODE AS natCode,
    features.properties.NAMEUNIT AS nameUnit,
    features.properties.CODNUT1 AS codNut1,
    features.properties.CODNUT2 AS codNut2,
    features.properties.CODNUT3 AS codNut3,
    features.properties.CODIGOINE AS codigoIne,
    features.properties.SHAPE_Length AS shapeLength,
    features.properties.SHAPE_Area AS shapeArea,
    features.geometry.type AS geometryType
    --,features.geometry.coordinates
FROM file('municipios_ign.geojson', 'JSON')
ARRAY JOIN features
LIMIT 5

┌─type──────────────┬─name───────────┬─crsType─┬─crsName───────────────────────┬─featureType─┬─id─┬─inspiredId───────────────┬─natCode─────┬─nameUnit──────────────┬─codNut1─┬─codNut2─┬─codNut3─┬─codigoIne─┬────────shapeLength─┬─────────────shapeArea─┬─geometryType─┐
│ FeatureCollection │ Municipios_IGN │ name    │ urn:ogc:def:crs:OGC:1.3:CRS84 │ Feature     │  1 │ ES.IGN.SIGLIM34081616266 │ 34081616266 │ Villarejo-Periesteban │ ES4     │ ES42    │ ES423   │ 16266     │ 0.2697476997304121 │ 0.0035198414406406673 │ MultiPolygon │
│ FeatureCollection │ Municipios_IGN │ name    │ urn:ogc:def:crs:OGC:1.3:CRS84 │ Feature     │  2 │ ES.IGN.SIGLIM34081616269 │ 34081616269 │ Villares del Saz      │ ES4     │ ES42    │ ES423   │ 16269     │ 0.4476083901269905 │   0.00738179315030249 │ MultiPolygon │
│ FeatureCollection │ Municipios_IGN │ name    │ urn:ogc:def:crs:OGC:1.3:CRS84 │ Feature     │  3 │ ES.IGN.SIGLIM34081616270 │ 34081616270 │ Villarrubio           │ ES4     │ ES42    │ ES423   │ 16270     │ 0.3053942273994179 │ 0.0029777582813496337 │ MultiPolygon │
│ FeatureCollection │ Municipios_IGN │ name    │ urn:ogc:def:crs:OGC:1.3:CRS84 │ Feature     │  4 │ ES.IGN.SIGLIM34081616271 │ 34081616271 │ Villarta              │ ES4     │ ES42    │ ES423   │ 16271     │ 0.2831226979821184 │  0.002680273189024594 │ MultiPolygon │
│ FeatureCollection │ Municipios_IGN │ name    │ urn:ogc:def:crs:OGC:1.3:CRS84 │ Feature     │  5 │ ES.IGN.SIGLIM34081616272 │ 34081616272 │ Villas de la Ventosa  │ ES4     │ ES42    │ ES423   │ 16272     │ 0.5958276749246777 │  0.015354885085133583 │ MultiPolygon │
└───────────────────┴────────────────┴─────────┴───────────────────────────────┴─────────────┴────┴──────────────────────────┴─────────────┴───────────────────────┴─────────┴─────────┴─────────┴───────────┴────────────────────┴───────────────────────┴──────────────┘

Insert the data.

INSERT INTO geojson
SELECT
    type AS type,
    name AS name,
    crs.type AS crsType,
    crs.properties.name AS crsName,
    features.type AS featureType,
    features.properties.FID AS id,
    features.properties.INSPIREID AS inspiredId,
    features.properties.NATCODE AS natCode,
    features.properties.NAMEUNIT AS nameUnit,
    features.properties.CODNUT1 AS codNut1,
    features.properties.CODNUT2 AS codNut2,
    features.properties.CODNUT3 AS codNut3,
    features.properties.CODIGOINE AS codigoIne,
    features.properties.SHAPE_Length AS shapeLength,
    features.properties.SHAPE_Area AS shapeArea,
    features.geometry.type AS geometryType,
    features.geometry.coordinates as geometry
FROM file('municipios_ign.geojson', 'JSON')
ARRAY JOIN features

Here, we get the following error:

Code: 53. DB::Exception: Received from localhost:9000. DB::Exception: ARRAY JOIN requires array or map argument. (TYPE_MISMATCH)
Received exception from server (version 24.1.2):

This is caused by the parsing of features.geometry.coordinates.

Let's check its data type.

SELECT DISTINCT toTypeName(features.geometry.coordinates) AS geometry
FROM file('municipios_ign.geojson', 'JSON')
ARRAY JOIN features

┌─geometry──────────────────────────────────────┐
│ Array(Array(Array(Array(Nullable(Float64))))) │
└───────────────────────────────────────────────┘

It can be fixed by casting multipolygon.properties.coordinates to Array(Array(Array(Tuple(Float64,Float64)))). To do so, we can use the function arrayMap(func,arr1,...).

SELECT distinct
    toTypeName(
        arrayMap(features.geometry.coordinates->
                    arrayMap(features.geometry.coordinates-> 
                            arrayMap(features.geometry.coordinates-> (features.geometry.coordinates[1],features.geometry.coordinates[2]) 
                    ,features.geometry.coordinates),
                features.geometry.coordinates),
        features.geometry.coordinates)
    ) as toTypeName
FROM file('municipios_ign.geojson', 'JSON')
ARRAY JOIN features;

┌─toTypeName───────────────────────────────────────────────────────┐
│ Array(Array(Array(Tuple(Nullable(Float64), Nullable(Float64))))) │
└──────────────────────────────────────────────────────────────────┘

Insert the data.

INSERT INTO geojson
SELECT
    type as type,
    name as name,
    crs.type as crsType,
    crs.properties.name as crsName,
    features.type as featureType,
    features.properties.FID id,
    features.properties.INSPIREID inspiredId,
    features.properties.NATCODE natCode,
    features.properties.NAMEUNIT nameUnit,
    features.properties.CODNUT1 codNut1,
    features.properties.CODNUT2 codNut2,
    features.properties.CODNUT3 codNut3,
    features.properties.CODIGOINE codigoIne,
    features.properties.SHAPE_Length shapeLength,
    features.properties.SHAPE_Area shapeArea,
    features.geometry.type geometryType,
    arrayMap(features.geometry.coordinates->
                arrayMap(features.geometry.coordinates-> 
                        arrayMap(features.geometry.coordinates-> (features.geometry.coordinates[1],features.geometry.coordinates[2]),features.geometry.coordinates)
                ,features.geometry.coordinates)
    ,features.geometry.coordinates) geometry
FROM file('municipios_ign.geojson', 'JSON')
ARRAY JOIN features;

SELECT count()
FROM geojson

┌─count()─┐
│    8205 │
└─────────┘

SELECT DISTINCT toTypeName(geometry)
FROM geojson

┌─toTypeName(geometry)─┐
│ MultiPolygon         │
└──────────────────────┘

Conclusion

Handling JSON can result in a complex task. This tutorial addressed a scenario where a nested object array could make this task even more difficult.
For any other JSON-related requirements, please refer to our documentation.

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