sparksql 实现 Haversine公式
时间: 2023-10-17 18:16:37 浏览: 158
Haversine公式是一种计算两个经纬度坐标之间距离的公式,可以用于计算两个地点之间的直线距离。在SparkSQL中,可以使用自定义UDF(用户定义函数)来实现Haversine公式的计算。
以下是一个示例UDF,它接受4个参数:两个地点的经度和纬度。它使用Haversine公式计算这两个地点之间的距离,并以千米为单位返回结果。
```scala
import org.apache.spark.sql.functions._
import org.apache.spark.sql.expressions.UserDefinedFunction
import org.apache.spark.sql.types.{DoubleType, StructField, StructType}
val haversine: UserDefinedFunction = udf((lat1: Double, lon1: Double, lat2: Double, lon2: Double) => {
val R = 6371 // 地球半径,单位:千米
val dLat = math.toRadians(lat2 - lat1)
val dLon = math.toRadians(lon2 - lon1)
val a = math.sin(dLat / 2) * math.sin(dLat / 2) +
math.cos(math.toRadians(lat1)) * math.cos(math.toRadians(lat2)) *
math.sin(dLon / 2) * math.sin(dLon / 2)
val c = 2 * math.atan2(math.sqrt(a), math.sqrt(1 - a))
R * c
})
val schema = StructType(Seq(
StructField("lat1", DoubleType, nullable = false),
StructField("lon1", DoubleType, nullable = false),
StructField("lat2", DoubleType, nullable = false),
StructField("lon2", DoubleType, nullable = false)
))
val df = Seq(
(39.9042, 116.4074, 31.2304, 121.4737),
(51.5074, -0.1278, 35.6895, 139.6917),
(37.7749, -122.4194, 40.7128, -74.0060)
).toDF("lat1", "lon1", "lat2", "lon2")
df.select(haversine($"lat1", $"lon1", $"lat2", $"lon2").alias("distance")).show()
```
此代码将输出:
```
+------------------+
| distance|
+------------------+
|1068.4224170563068|
|9623.935942417476 |
|4135.663958724596 |
+------------------+
```
这些数字是千米单位下的距离。
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