1)准备数据源文件。 Spark安装目录中自带了一个people.json文件,位于“examples/src/main/resources/”目录下。其内容如下: {"name":"Michael"} {"name":"Andy", "age":30} {"name":"Justin", "age":19} 我们将这个people.json文件,拷贝到/home/hduser/data/spark/resources/目录下。 2)创建一个Jupyte...
Notes --- This method should only be used if the resulting array is expected to be small, as all the data is loaded into the driver's memory. Examples --- >>> rdd = sc.parallelize(range(0, 10)) >>> len(rdd.takeSample(True, 20, 1)) 20 >>> len(rdd.takeSample(False, 5,...
# spark is an existing SparkSessiondf = spark.read.json("examples/src/main/resources/people.json")# Displays the content of the DataFrame to stdoutdf.show()#+---+---+#| age| name|#+---+---+#+null|Jackson|#| 30| Martin|#| 19| Melvin|#+---|---| 与pandas 或 R 一样,read...
df = spark.read.load("examples/src/main/resources/people.json", format="json") #format: Default to ‘parquet’ ## read.csv df_csv = spark.read.csv("examples/src/main/resources/people.csv",sep=';', header= True) ## read.text df_txt = spark.read.text("examples/src/main/resources/...
("examples/src/main/resources/users.parquet") ## orc df_orc = spark.read.orc("examples/src/main/resources/users.orc") ## rdd sc = spark.sparkContext rdd = sc.textFile('examples/src/main/resources/people.json') df_rdd1 = spark.read.json(rdd) # createDataFrame: rdd, list, pandas....
1 Transformation 转换操作具有懒惰执行的特性,它只指定新的RDD和其父RDD的依赖关系,只有当Action操作触发到该依赖的时候,它才被计算。 2 map 操作对每个元素进行一个映射转换 3 filter 应用过滤条件过滤掉一些数据 4 flatMap 操作执行将每个元素生成一个Array后压平 ...
在PySpark中,RDD(Resilient Distributed Dataset)是一个不可变的分布式数据集,它可以在集群中的多个节点上进行并行操作。重新排列RDD通常指的是改变其分区布局,以便...
PySpark Join Types Explained with Examples PySpark Union and UnionAll Explained PySpark UDF (User Defined Function) PySpark flatMap() Transformation PySpark map Transformation PySpark SQL Functions PySpark Aggregate Functions with Examples PySpark Window Functions PySpark Datasources PySpark Read CSV file int...
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t require shuffling. Examples includemap(),filter(), andunion. On the contrary, wide transformations are necessary for operations where each input partition may contribute to multiple output partitions and require data shuffling, joins, or aggregations. Examples includegroupBy(),join(), andsortBy()...