在學(xué)習(xí)轉(zhuǎn)換之前先了解以下它們的基本概念

• RDD：彈性分布式數(shù)據(jù)集，是一個(gè)只讀分區(qū)集合
• DataFrame：以命名列方式組織的分布式數(shù)據(jù)集，概念上和關(guān)系型數(shù)據(jù)庫(kù)的一張表一樣
• DataSet：分布式數(shù)據(jù)集合， Python暫時(shí)不支持

了解了基本的概念之后，接下來(lái)我們通過(guò)代碼編寫三種數(shù)據(jù)集的形成

RDD的形成

            
              from pyspark.sql import SparkSession

if __name__ == '__main__':
    spark = SparkSession \
        .builder \
        .appName("rddData") \
        .master("local[*]") \
        .getOrCreate()
    # 方式一：
    data = [1, 2, 3, 4, 5]
    rdd1 = spark.sparkContext.parallelize(data)
    print(rdd1.collect())
    # [1, 2, 3, 4, 5]
    # 方式二：
    rdd2 = spark.sparkContext.textFile("/home/llh/data/people.txt")
    print(rdd2.collect())
    # ['Jack 27', 'Rose 24', 'Andy 32']
    spark.stop()
            
          

DataFrame的形成

            
              from pyspark.sql import SparkSession

if __name__ == '__main__':
    spark = SparkSession \
        .builder \
        .appName("rddDataFrame") \
        .master("local[*]") \
        .getOrCreate()
    df = spark.read.text("/home/llh/data/people.txt")
    df.show()
    # +---+----+
    # |age|name|
    # +---+----+
    # | 27|Jack|
    # | 24|Rose|
    # | 32|Andy|
    # +---+----+
    spark.stop()
            
          

RDD轉(zhuǎn)成DataFrame

            
              from pyspark.sql import SparkSession
from pyspark.sql import Row

if __name__ == '__main__':
    spark = SparkSession \
        .builder \
        .appName("rddRDD") \
        .master("local[*]") \
        .getOrCreate()
    data = [1, 2, 3]
    rdd1 = spark.sparkContext.parallelize(data)
    print(rdd1.collect())
    # [1, 2, 3]
    # rdd ->  dataframe
    rdd2 = rdd1.map(lambda x: Row(x))
    df = spark.createDataFrame(rdd2, schema=['num'])
    df.show()
    # +---+
    # |num|
    # +---+
    # | 1 |
    # | 2 |
    # | 3 |
    # +---+
    spark.stop()
            
          

DataFrame轉(zhuǎn)成RDD

            
              from pyspark.sql import SparkSession

if __name__ == '__main__':
    spark = SparkSession \
        .builder \
        .appName("rddDataFrame") \
        .master("local[*]") \
        .getOrCreate()
    df = spark.read.text("/home/llh/data/people.txt")
    rdd = df.rdd
    print(rdd.collect())
    # [Row(value='Jack 27'), Row(value='Rose 24'), Row(value='Andy 32')]
    spark.stop()
            
          

以上就是RDD與DataFrame形成與相互轉(zhuǎn)換

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Spark學(xué)習(xí)目錄：

• Spark學(xué)習(xí)實(shí)例1(Python)：?jiǎn)卧~統(tǒng)計(jì) Word Count
• Spark學(xué)習(xí)實(shí)例2(Python)：加載數(shù)據(jù)源Load Data Source
• Spark學(xué)習(xí)實(shí)例3(Python)：保存數(shù)據(jù)Save Data
• Spark學(xué)習(xí)實(shí)例4(Python)：RDD轉(zhuǎn)換 Transformations
• Spark學(xué)習(xí)實(shí)例5(Python)：RDD執(zhí)行 Actions
• Spark學(xué)習(xí)實(shí)例6(Python)：共享變量Shared Variables
• Spark學(xué)習(xí)實(shí)例7(Python)：RDD、DataFrame、DataSet相互轉(zhuǎn)換
• Spark學(xué)習(xí)實(shí)例8(Python)：輸入源實(shí)時(shí)處理 Input Sources Streaming
• Spark學(xué)習(xí)實(shí)例9(Python)：窗口操作 Window Operations

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