apache-sedona-learning: apache sedona(塞多纳)是一个用于处理大规模空间数据的集群计算系统。Sedona扩展了现有的集群计算系统，如Apache Spark和Apache Flink，使用一组开箱即用的分布式空间数据集和空间SQL，可以有效地加载、处理和分析跨机器的大规模空间数据，docs下的文档持续翻译更新中

# GIS大数据处理框架sedona(塞多纳)编程入门指导
## 简介
[Apache Sedona™](https://sedona.apache.org/latest-snapshot)是一个用于处理大规模空间数据的集群计算系统。Sedona扩展了现有的集群计算系统，如Apache Spark和Apache Flink，使用一组开箱即用的分布式空间数据集和空间SQL，可以有效地加载、处理和分析跨机器的大规模空间数据。[码云镜像](https://gitee.com/shengshifeiyang/sedona)
# 代码结构
![代码结构图](docs/assets/code_1.png)
1. **common** java核心包,对底层JTS、geotools坐标系转换等操作方法的接口包装,并提供了circle(扩展JTS功能),距离计算方法:Haversine方式,Spheroid椭球;WKT,GeoJSON等格式转换;索引支持QUADTREE,RTREE;geohash计算;供spark、flink等上层应用调用使用
2. **core** 与spark适配核心包,封装提供基础对象SpatialRDD,PointRDD,LineStingRDD,CircleRDD,PolygonRDD;几何链接操作joinJudgement(通过几何拓扑关系),knnJudgement(几何距离),rangeJudgement(treeIndex索引范围查询);数据读取转换formatMapper:cvs,wkt,geoJson,shapefile,netcdf;spatialPartitioning分区器:QuadtreePartitioning,KDBTreePartitioner等
3. **flink** flink适配,调用common下的functions里面提供的函数方法
4. **python-adapter** python适配,调用common下的functions里面提供的函数方法
5. **sql** spark-sql适配,调用common下的functions里面提供的函数方法
# 使用说明
在spark下面的使用说明
## 1.安装
[具体参看](https://sedona.apache.org/latest-snapshot/setup/overview/)
```maven
<dependency>
  <groupId>org.apache.sedona</groupId>
  <artifactId>sedona-spark-shaded-3.0_2.12</artifactId>
  <version>1.4.0</version>
</dependency>
<dependency>
  <groupId>org.apache.sedona</groupId>
  <artifactId>sedona-viz-3.0_2.12</artifactId>
  <version>1.4.0</version>
</dependency>

<dependency>
    <groupId>org.datasyslab</groupId>
    <artifactId>geotools-wrapper</artifactId>
    <version>1.4.0-28.2</version>
</dependency>

```
## 2.初始化SparkSession
```
SparkSession sparkSession = SparkSession.builder()
.master("local[*]") // Delete this if run in cluster mode
.appName("readTestScala") // Change this to a proper name
// Enable Sedona custom Kryo serializer
.config("spark.serializer", KryoSerializer.class.getName) // org.apache.spark.serializer.KryoSerializer
.config("spark.kryo.registrator", SedonaKryoRegistrator.class.getName)
.getOrCreate() // org.apache.sedona.core.serde.SedonaKryoRegistrator
```
## 3.安装函数
SedonaSQLRegistrator.registerAll(sparkSession)
## 4.使用例子
### 4.1 dataFrame方式加载数据
#### 4.1.1 从文件加载数据
假设有一个WKT数据格式的tsv文件,存储位置/Download/usa-county.tsv
```
POLYGON (..., ...)  Cuming County   
POLYGON (..., ...)  Wahkiakum County
POLYGON (..., ...)  De Baca County
POLYGON (..., ...)  Lancaster County
```
加载
```
Dataset<Row> rawDf = sparkSession.read.format("csv").option("delimiter", "\t").option("header", "false").load("/Download/usa-county.tsv")
rawDf.createOrReplaceTempView("rawdf")
rawDf.show()
```
结果展示
```
|                 _c0|_c1|_c2|     _c3|  _c4|        _c5|                 _c6|_c7|_c8|  _c9|_c10| _c11|_c12|_c13|      _c14|    _c15|       _c16|        _c17|
+--------------------+---+---+--------+-----+-----------+--------------------+---+---+-----+----+-----+----+----+----------+--------+-----------+------------+
|POLYGON ((-97.019...| 31|039|00835841|31039|     Cuming|       Cuming County| 06| H1|G4020|null| null|null|   A|1477895811|10447360|+41.9158651|-096.7885168|
|POLYGON ((-123.43...| 53|069|01513275|53069|  Wahkiakum|    Wahkiakum County| 06| H1|G4020|null| null|null|   A| 682138871|61658258|+46.2946377|-123.4244583|
|POLYGON ((-104.56...| 35|011|00933054|35011|    De Baca|      De Baca County| 06| H1|G4020|null| null|null|   A|6015539696|29159492|+34.3592729|-104.3686961|
|POLYGON ((-96.910...| 31|109|00835876|31109|  Lancaster|    Lancaster County| 06| H1|G4020| 339|30700|null|   A|2169240202|22877180|+40.7835474|-096.6886584|
```
#### 4.1.1 通过ST_函数
```
SELECT ST_GeomFromWKT(_c0) AS countyshape, _c1, _c2
```
#### 4.1.3 从GeoJSON文件读取
```
String schema = "type string, crs string, totalFeatures long, features array<struct<type string, geometry string, properties map<string, string>>>";
sparkSession.read.schema(schema).json(geojson_path)
    .selectExpr("explode(features) as features") // Explode the envelope to get one feature per row.
    .select("features.*") // Unpack the features struct.
    .withColumn("geometry", expr("ST_GeomFromGeoJSON(geometry)")) // Convert the geometry string.
    .printSchema();
```
#### 4.1.4 从数据库读取
```
// For any JDBC data source, inluding Postgis.
Dataset<Row> df = sparkSession.read().format("jdbc")
    // Other options.
    .option("query", "SELECT id, ST_AsBinary(geom) as geom FROM my_table")
    .load()
    .withColumn("geom", expr("ST_GeomFromWKB(geom)"))

// This is a simplified version that works for Postgis.
Dataset<Row> df = sparkSession.read().format("jdbc")
    // Other options.
    .option("dbtable", "my_table")
    .load()
    .withColumn("geom", expr("ST_GeomFromWKB(geom)"))

```
### 4.2 CRS（坐标系）转换
```
SELECT ST_Transform(countyshape, "epsg:4326", "epsg:3857") AS newcountyshape, _c1, _c2, _c3, _c4, _c5, _c6, _c7
FROM spatialdf
```
### 4.3 地理空间查询
#### 4.3.1 范围查询
ST_Contains, ST_Intersects, ST_Within
```
SELECT *
FROM spatialdf
WHERE ST_Contains (ST_PolygonFromEnvelope(1.0,100.0,1000.0,1100.0), newcountyshape)
```
#### 4.3.2 距离查询
ST_Distance
```
SELECT countyname, ST_Distance(ST_PolygonFromEnvelope(1.0,100.0,1000.0,1100.0), newcountyshape) AS distance
FROM spatialdf
ORDER BY distance DESC
LIMIT 5
```
#### 4.3.3 关联查询
```
SELECT *
FROM polygondf, pointdf
WHERE ST_Contains(polygondf.polygonshape,pointdf.pointshape)

SELECT *
FROM polygondf, pointdf
WHERE ST_Intersects(polygondf.polygonshape,pointdf.pointshape)

SELECT *
FROM pointdf, polygondf
WHERE ST_Within(pointdf.pointshape, polygondf.polygonshape)

SELECT *
FROM pointdf1, pointdf2
WHERE ST_Distance(pointdf1.pointshape1,pointdf2.pointshape2) < 2

```
### 5 存储
已入postgis为例
```
my_postgis_db# create table my_table (id int8, geom geometry);

df.withColumn("geom", expr("ST_AsEWKB(geom)")
    .write.format("jdbc")
    .option("truncate","true") // Don't let Spark recreate the table.
    // Other options.
    .save()

// If you didn't create the table before writing you can change the type afterward.
my_postgis_db# alter table my_table alter column geom type geometry;

```
### 6 SpatialRDD与DataFrame相好转换
#### 6.1 SpatialRDD转DataFrame
```
Dataset<Row> spatialDf = Adapter.toDf(spatialRDD, sparkSession)
```
#### 6.2 DataFrame转SpatialRDD
```
val schema = StructType(Array(
  StructField("county", GeometryUDT, nullable = true),
  StructField("name", StringType, nullable = true),
  StructField("price", DoubleType, nullable = true),
  StructField("age", IntegerType, nullable = true)
))
val spatialDf = Adapter.toDf(spatialRDD, schema, sparkSession)
```

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