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#
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#
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from typing import List
from pyspark import RDD
from pyspark.sql import DataFrame, SparkSession
from sedona.spark.core.enums.spatial import SpatialType
from sedona.spark.core.spatialOperator.rdd import SedonaPairRDD, SedonaRDD
from sedona.spark.core.SpatialRDD.spatial_rdd import SpatialRDD
from sedona.spark.utils.meta import MultipleMeta
[docs]
class Adapter(metaclass=MultipleMeta):
"""
Class which allow to convert between Spark DataFrame and SpatialRDD and reverse.
This class is used to convert between PySpark DataFrame and SpatialRDD. Schema
is lost during the conversion. This should be used if your data starts as a
SpatialRDD and you want to convert it to a DataFrame for further processing.
"""
@staticmethod
def _create_dataframe(jdf, sparkSession: SparkSession) -> DataFrame:
return DataFrame(jdf, sparkSession)
[docs]
@classmethod
def toRdd(cls, dataFrame: DataFrame) -> "JvmSpatialRDD":
from sedona.spark.core.SpatialRDD.spatial_rdd import JvmSpatialRDD
sc = dataFrame._sc
jvm = sc._jvm
srdd = jvm.Adapter.toRdd(dataFrame._jdf)
return JvmSpatialRDD(srdd.toJavaRDD(), sc, SpatialType.SPATIAL)
@classmethod
def toSpatialRdd(cls, dataFrame: DataFrame, geometryFieldName: str) -> SpatialRDD:
"""
:param dataFrame:
:param geometryFieldName:
:return:
"""
sc = dataFrame._sc
jvm = sc._jvm
srdd = jvm.Adapter.toSpatialRdd(dataFrame._jdf, geometryFieldName)
spatial_rdd = SpatialRDD(sc)
spatial_rdd.set_srdd(srdd)
return spatial_rdd
@classmethod
def toSpatialRdd(
cls, dataFrame: DataFrame, geometryFieldName: str, fieldNames: List
) -> SpatialRDD:
"""
:param dataFrame:
:param geometryFieldName:
:param fieldNames:
:return:
"""
sc = dataFrame._sc
jvm = sc._jvm
srdd = jvm.PythonAdapterWrapper.toSpatialRdd(
dataFrame._jdf, geometryFieldName, fieldNames
)
spatial_rdd = SpatialRDD(sc)
spatial_rdd.set_srdd(srdd)
return spatial_rdd
@classmethod
def toDf(
cls, spatialRDD: SpatialRDD, fieldNames: List, sparkSession: SparkSession
) -> DataFrame:
"""
:param spatialRDD:
:param fieldNames:
:param sparkSession:
:return:
"""
sc = spatialRDD._sc
jvm = sc._jvm
jdf = jvm.PythonAdapterWrapper.toDf(
spatialRDD._srdd, fieldNames, sparkSession._jsparkSession
)
df = Adapter._create_dataframe(jdf, sparkSession)
return df
@classmethod
def toDf(cls, spatialRDD: SpatialRDD, sparkSession: SparkSession) -> DataFrame:
"""
:param spatialRDD:
:param sparkSession:
:return:
"""
sc = spatialRDD._sc
jvm = sc._jvm
jdf = jvm.Adapter.toDf(spatialRDD._srdd, sparkSession._jsparkSession)
df = Adapter._create_dataframe(jdf, sparkSession)
return df
@classmethod
def toDf(cls, spatialPairRDD: RDD, sparkSession: SparkSession):
"""
:param spatialPairRDD:
:param sparkSession:
:return:
"""
spatial_pair_rdd_mapped = spatialPairRDD.map(
lambda x: [
x[0].geom,
*x[0].getUserData().split("\t"),
x[1].geom,
*x[1].getUserData().split("\t"),
]
)
df = sparkSession.createDataFrame(spatial_pair_rdd_mapped)
return df
@classmethod
def toDf(
cls,
spatialPairRDD: RDD,
leftFieldnames: List,
rightFieldNames: List,
sparkSession: SparkSession,
):
"""
:param spatialPairRDD:
:param leftFieldnames:
:param rightFieldNames:
:param sparkSession:
:return:
"""
df = Adapter.toDf(spatialPairRDD, sparkSession)
columns_length = df.columns.__len__()
combined_columns = ["geom_1", *leftFieldnames, "geom_2", *rightFieldNames]
if columns_length == combined_columns.__len__():
return df.toDF(*combined_columns)
else:
raise TypeError("Column length does not match")
@classmethod
def toDf(cls, rawPairRDD: SedonaPairRDD, sparkSession: SparkSession):
jvm = sparkSession._jvm
jdf = jvm.Adapter.toDf(rawPairRDD.jsrdd, sparkSession._jsparkSession)
df = Adapter._create_dataframe(jdf, sparkSession)
return df
@classmethod
def toDf(
cls,
rawPairRDD: SedonaPairRDD,
leftFieldnames: List,
rightFieldNames: List,
sparkSession: SparkSession,
):
jvm = sparkSession._jvm
jdf = jvm.PythonAdapterWrapper.toDf(
rawPairRDD.jsrdd,
leftFieldnames,
rightFieldNames,
sparkSession._jsparkSession,
)
df = Adapter._create_dataframe(jdf, sparkSession)
return df
@classmethod
def toDf(
cls, spatialRDD: SedonaRDD, spark: SparkSession, fieldNames: List = None
) -> DataFrame:
srdd = SpatialRDD(spatialRDD.sc)
srdd.setRawSpatialRDD(spatialRDD.jsrdd)
if fieldNames:
return Adapter.toDf(srdd, fieldNames, spark)
else:
return Adapter.toDf(srdd, spark)