3. Create DataFrame from Data sources ''' 3. Create DataFrame from Data sources ''' # 从数据源文件(例如CSV,文本,JSON,XML等)创建DataFrame. # 3.1 Creating DataFrame from CSV df2 = spark.read.csv("/src/resources/file.csv") # 3.2. Creating from text (TXT) file df2 = spark.read.text(...
3.1.3 运行Spark SQL 将上一步读取到的数据的DataFrame转换为一个临时视图后,我们就可以用SQL语句愉快的操作Spark程序了 [外链图片转存失败,源站可能有防盗链机制,建议将图片保存下来直接上传(img-UaPKhRQy-1655341289971)(https://upload-images.jianshu.io/upload_images/2638478-6ac4407b7d3daec6.png?imageMogr...
spark.sql("CREATE TABLE IF NOT EXISTS src (key INT, value STRING) USING hive")spark.sql("LOAD DATA LOCAL INPATH 'examples/src/main/resources/kv1.txt' INTO TABLE src") 3.2.3 读写MySQL Spark读写MySQL在实际开发过程中,使用的也表多 image.png 读MySQL数据: df1=spark.read.format("jdbc")....
将dataframe转换为JSON格式。可以使用toJSON函数将dataframe转换为JSON格式的字符串。 代码语言:txt 复制 json_data = df_nested.toJSON().collect() 打印或保存JSON数据。可以使用print函数打印JSON数据,或使用write函数将JSON数据保存到文件中。 代码语言:txt 复制 for json_str in json_data: print(json_str) ...
之后,我直接使用spark对象来查询配置单元表,只需在spark.sql或用于读取配置单元表的任何dataframeapi中指定它们。现在,我使用上面相同的代码测试python与spark的连接性。我已经在本地机器中安装了hdp3.0,并用python测试spark的连接性,如下所示。 from pyspark import SparkConf from pyspark.sql import SparkSession def...
py?#读取parquet文件数据的代码df1=spark.read.load(path=''<存储路径1>/<表名1>'', format=''parquet'',header=True)?#获取表结构_schema=copy.deepcopy(df 1.schema)df2=df1.rdd.zipWithIndex().map(lambdal:list(l[0])+ [l[1]]).toDF(_schema)?#写入空数据集到parquet文件df2.write.parquet...
columns返回一个包含所有列名的列表,我们可以通过计算该列表的长度来获取Dataframe中的列数。 示例代码如下: 代码语言:txt 复制 # 导入必要的模块 from pyspark.sql import SparkSession # 创建SparkSession对象 spark = SparkSession.builder.getOrCreate() # 读取数据并创建Dataframe df = spark.read.csv("data....
df.select("*").write.format('com.databricks.spark.xml').option("rootTag", "Catalog").option("rowTag","book").save('/mnt/raw/booksnew.xml',mode="overwrite") You can then run the following code to read the file and retrieve the results into a dataframe. ...
PySpark is utilized for processing semi-structured data files like the JSON format. We can use the JSON() function of DataFrameReader for reading the JSON files into the DataFrame. Following is the example: df2 = spark.read.json(“/src/resources/file1.json”) ...
Spark SQL− This module allows you to execute SQL queries on DataFrames and RDDs. It provides a programming abstraction called DataFrame and can also act as a distributed SQL query engine. MLlib (Machine Learning Library)− MLlib is Spark's scalable machine learning library, offering various...