Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

KB article that shows how to import GeoJSON with a deeply nested object array. #2926

Open
wants to merge 1 commit into
base: main
Choose a base branch
from
Open
Changes from all commits
Commits
File filter

Filter by extension

Filter by extension

Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
222 changes: 222 additions & 0 deletions knowledgebase/importing-geojason-with-nested-object-array.md
Original file line number Diff line number Diff line change
@@ -0,0 +1,222 @@
---
title: Importing GeoJSON with a deeply nested object array
description: “Importing GeoJSON with a deeply nested object array“
date: 2024-12-18
---

# 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](https://opendata.esri.es/datasets/ComunidadSIG::municipios-ign/explore?location=39.536006%2C-0.303882%2C6.57).
Copy link
Member

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

Will this link expire or change? It looks very specific, perhaps we could recommend they go to a specific website rather than this exact location URL.


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

```sql
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) │
│ │ ) │
│ │ ) │
└──────────┴──────────────────────────────────────────────────────────────────────────────────────────────┘
```
3. 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](https://clickhouse.com/docs/en/sql-reference/data-types/geo#multipolygon).

```sql
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;
```

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

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

```sql
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 │
└───────────────────┴────────────────┴─────────┴───────────────────────────────┴─────────────┴────┴──────────────────────────┴─────────────┴───────────────────────┴─────────┴─────────┴─────────┴───────────┴────────────────────┴───────────────────────┴──────────────┘
```

5. Insert the data.

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

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

6. Let's check its data type.

``` sql
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,...)](https://clickhouse.com/docs/en/sql-reference/functions/array-functions#arraymapfunc-arr1-).

```sql
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))))) │
└──────────────────────────────────────────────────────────────────┘
```

7. Insert the data.

```sql
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;
```

```sql
SELECT count()
FROM geojson

┌─count()─┐
│ 8205 │
└─────────┘

SELECT DISTINCT toTypeName(geometry)
FROM geojson

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

Copy link
Member

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

In addition to the count() query, it may be good to demo something complex to show the analytical power of geo data in ClickHouse. Perhaps something that filters on one of the many columns? We also have some functions for measuring distance, but I don't think they can be easily applied to the polygon data

### 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](https://clickhouse.com/docs/en/integrations/data-formats/json).
Loading