dataframe和dataset分别能通过哪些方式创建，编程举例

DataFrame和Dataset都可以通过以下方式创建：

1. 从本地文件读取数据
2. 从数据库中读取数据
3. 从RDD转换而来

举例：

1. 从本地文件读取数据创建DataFrame：

from pyspark.sql import SparkSession

spark = SparkSession.builder.appName("create_dataframe").getOrCreate()

df = spark.read.csv("path/to/file.csv", header=True, inferSchema=True)

2. 从本地文件读取数据创建Dataset：

from pyspark.sql import SparkSession
from pyspark.sql.types import StructType, StructField, StringType, IntegerType

spark = SparkSession.builder.appName("create_dataset").getOrCreate()

schema = StructType([
    StructField("name", StringType(), True),
    StructField("age", IntegerType(), True),
    StructField("gender", StringType(), True)
])

df = spark.read.csv("path/to/file.csv", header=True, schema=schema)

ds = df.as[Person]

3. 从RDD转换而来创建DataFrame：

from pyspark.sql import SparkSession
from pyspark.sql.types import StructType, StructField, StringType, IntegerType

spark = SparkSession.builder.appName("create_dataframe_from_rdd").getOrCreate()

rdd = spark.sparkContext.parallelize([(1, "John"), (2, "Jane"), (3, "Bob")])

schema = StructType([
    StructField("id", IntegerType(), True),
    StructField("name", StringType(), True)
])

df = spark.createDataFrame(rdd, schema)

4. 从RDD转换而来创建Dataset：

from pyspark.sql import SparkSession
from pyspark.sql.types import StructType, StructField, StringType, IntegerType

spark = SparkSession.builder.appName("create_dataset_from_rdd").getOrCreate()

rdd = spark.sparkContext.parallelize([(1, "John"), (2, "Jane"), (3, "Bob")])

schema = StructType([
    StructField("id", IntegerType(), True),
    StructField("name", StringType(), True)
])

df = spark.createDataFrame(rdd, schema)

ds = df.as[Person]