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[DOCS] add shapefiles documentation page (#1837)
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# Shapefiles with Apache Sedona and Spark | ||
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This post explains how to read Shapefiles with Apache Sedona and Spark. | ||
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A Shapefile is “an Esri vector data storage format for storing the location, shape, and attributes of geographic features.” The Shapefile format is proprietary, but [the spec is open](https://www.esri.com/content/dam/esrisites/sitecore-archive/Files/Pdfs/library/whitepapers/pdfs/shapefile.pdf). | ||
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Shapefiles have many limitations but are extensively used, so it’s beneficial that they are readable by Sedona. | ||
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Let’s look at how to read Shapefiles with Sedona and Spark. | ||
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## Read Shapefiles with Sedona and Spark | ||
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Let’s start by creating a Shapefile with GeoPandas and Shapely: | ||
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```python | ||
import geopandas as gpd | ||
from shapely.geometry import Point | ||
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point1 = Point(0, 0) | ||
point2 = Point(1, 1) | ||
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data = { | ||
'name': ['Point A', 'Point B'], | ||
'value': [10, 20], | ||
'geometry': [point1, point2] | ||
} | ||
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gdf = gpd.GeoDataFrame(data, geometry='geometry') | ||
gdf.to_file("/tmp/my_geodata.shp") | ||
``` | ||
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Here are the files that are output: | ||
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``` | ||
/tmp/ | ||
my_geodata.cpg | ||
my_geodata.dbf | ||
my_geodata.shp | ||
my_geodata.shx | ||
``` | ||
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Shapefiles are not stored in a single file. They contain data in many different files. | ||
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Here’s how to read a Shapefile into a Sedona DataFrame powered by Spark: | ||
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```python | ||
df = sedona.read.format("shapefile").load("/tmp/my_geodata.shp") | ||
df.show() | ||
``` | ||
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``` | ||
+-----------+-------+-----+ | ||
| geometry| name|value| | ||
+-----------+-------+-----+ | ||
|POINT (0 0)|Point A| 10| | ||
|POINT (1 1)|Point B| 20| | ||
+-----------+-------+-----+ | ||
``` | ||
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You can also see the unique record number for each row in the Shapefile as follows: | ||
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```python | ||
df = ( | ||
sedona.read.format("shapefile") | ||
.option("key.name", "FID") | ||
.load("/tmp/my_geodata.shp") | ||
) | ||
``` | ||
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``` | ||
+-----------+---+-------+-----+ | ||
| geometry|FID| name|value| | ||
+-----------+---+-------+-----+ | ||
|POINT (0 0)| 1|Point A| 10| | ||
|POINT (1 1)| 2|Point B| 20| | ||
+-----------+---+-------+-----+ | ||
``` | ||
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The name of the geometry column is geometry by default. You can change the name of the geometry column using the `geometry.name` option. Suppose one of the non-spatial attributes is named "geometry", `geometry.name` must be configured to avoid conflict. | ||
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```python | ||
df = sedona.read.format("shapefile").option("geometry.name", "geom").load("/path/to/shapefile") | ||
``` | ||
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The character encoding of string attributes are inferred from the `.cpg` file. If you see garbled values in string fields, you can manually specify the correct charset using the `charset` option. For example: | ||
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=== "Scala/Java" | ||
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```scala | ||
val df = sedona.read.format("shapefile").option("charset", "UTF-8").load("/path/to/shapefile") | ||
``` | ||
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=== "Java" | ||
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```java | ||
Dataset<Row> df = sedona.read().format("shapefile").option("charset", "UTF-8").load("/path/to/shapefile") | ||
``` | ||
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=== "Python" | ||
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```python | ||
df = sedona.read.format("shapefile").option("charset", "UTF-8").load("/path/to/shapefile") | ||
``` | ||
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Let’s see how to load many Shapefiles into a Sedona DataFrame. | ||
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## Load many Shapefiles with Sedona | ||
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Suppose you have a directory with many Shapefiles as follows: | ||
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``` | ||
/tmp/shapefiles/ | ||
file1.cpg | ||
file1.dbf | ||
file1.shp | ||
file1.shx | ||
file2.cpg | ||
file2.dbf | ||
file2.shp | ||
file2.shx | ||
``` | ||
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The directory contains two `.shp` files and other supporting files. | ||
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Here’s how to load many Shapefiles into a Sedona DataFrame: | ||
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```python | ||
df = sedona.read.format("shapefile").load("/tmp/shapefiles") | ||
df.show() | ||
``` | ||
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``` | ||
+-----------+-------+-----+ | ||
| geometry| name|value| | ||
+-----------+-------+-----+ | ||
|POINT (0 0)|Point A| 10| | ||
|POINT (1 1)|Point B| 20| | ||
|POINT (2 2)|Point C| 10| | ||
|POINT (3 3)|Point D| 20| | ||
+-----------+-------+-----+ | ||
``` | ||
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You can just pass the directory where the Shapefiles are stored, and the Sedona reader will pick them up. | ||
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The input path can be a directory containing one or multiple Shapefiles or a path to a `.shp` file. | ||
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* All shapefiles directly under the directory will be loaded when the input path is a directory. If you want to load all shapefiles in subdirectories, please specify `.option("recursiveFileLookup", "true")`. | ||
* The shapefile will be loaded when the input path is a .shp file. Sedona will look for sibling files (.dbf, .shx, etc.) with the same main file name and load them automatically. | ||
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## Advantages of Shapefiles | ||
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Shapefiles are deeply integrated into the Esri ecosystem and extensively used in many services. | ||
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You can output a Shapefile from Esri and then read it with another engine like Sedona. | ||
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However, Esri created the Shapefile format in the early 1990s, so it has many limitations. | ||
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## Limitations of Shapefiles | ||
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Here are some of the disadvantages of Shapefiles: | ||
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* Don’t support complex geometries | ||
* They don’t support NULL values | ||
* They round numbers | ||
* Bad Unicode support | ||
* Don’t allow for long field names | ||
* 2GB file size limit | ||
* Spatial indexes are slower compared to alternatives | ||
* Unable to store datetimes | ||
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See this page for more information on [the limitations of Shapefiles](http://switchfromshapefile.org/). | ||
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Due to these limitations, other options are worth investigating. | ||
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## Shapefile alternatives | ||
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There are a variety of other file formats that are good for geometric data: | ||
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* Iceberg | ||
* [GeoParquet](../geoparquet-sedona-spark) | ||
* FlatGeoBuf | ||
* [GeoPackage](../geopackage-sedona-spark) | ||
* [GeoJSON](../geojson-sedona-spark) | ||
* [CSV](../csv-geometry-sedona-spark) | ||
* GeoTIFF | ||
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## Why Sedona does not support Shapefile writes | ||
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Sedona does not write Shapefiles for two main reasons: | ||
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1. Each Shapefile is a collection of files, which is hard for distributed systems to write. | ||
2. A Shapefile has a hard 2 GB size limit, which isn’t large enough for some spatial data. | ||
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## Conclusion | ||
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Shapefiles are a legacy file format still used in many production applications. However, they have many limitations and aren’t the best option in a modern data pipeline unless you need compatibility with legacy systems. |
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