从多个源创建单个Spark Dataframe？

from pyspark.sql import SparkSession

spark = SparkSession.builder.getOrCreate()

# 从文件系统读取数据
df1 = spark.read.format("csv").option("header", "true").load("hdfs://path/to/file1.csv")
df2 = spark.read.format("csv").option("header", "true").load("hdfs://path/to/file2.csv")

# 从关系型数据库读取数据
df3 = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost/db").option("dbtable", "table1").load()

# 从NoSQL数据库读取数据
df4 = spark.read.format("org.apache.spark.sql.cassandra").option("keyspace", "ks").option("table", "table2").load()

from pyspark.sql import SparkSession

spark = SparkSession.builder.getOrCreate()

# 创建两个DataFrame
df1 = spark.createDataFrame([(1, "Alice"), (2, "Bob")], ["id", "name"])
df2 = spark.createDataFrame([(3, "Charlie"), (4, "David")], ["id", "name"])

# 使用union函数合并DataFrame
df_combined = df1.union(df2)

from pyspark.sql import SparkSession

spark = SparkSession.builder.getOrCreate()

# 创建两个DataFrame
df1 = spark.createDataFrame([(1, "Alice"), (2, "Bob")], ["id", "name"])
df2 = spark.createDataFrame([(3, "Charlie"), (4, "David")], ["id", "name"])

# 注册DataFrame为临时表
df1.createOrReplaceTempView("table1")
df2.createOrReplaceTempView("table2")

# 使用SQL语句合并DataFrame
df_combined = spark.sql("SELECT * FROM table1 UNION SELECT * FROM table2")