from pyspark.sql import SparkSession from pyspark.sql.functions import from_json from pyspark.sql.types import StructType, StructField, StringType # 创建SparkSession spark = SparkSession.builder.getOrCreate() # 创建示例数据框 data = [("John", '{"age": 30, "city": "New York"}'),...
from pyspark.sql import SparkSession from pyspark.sql.functions import expr # 创建SparkSession spark = SparkSession.builder.getOrCreate() # 创建包含JSON数据的DataFrame data = [ ('{"name": "John", "age": 30, "address": {"city": "New York", "state": "NY", "country": "USA"}}...
首先,我们需要导入必要的PySpark模块。 frompyspark.sqlimportSparkSession 1. 步骤二:创建SparkSession对象 接下来,我们需要创建一个SparkSession对象。SparkSession是与Spark交互的主要入口点,它可以让我们执行各种操作。 spark=SparkSession.builder.appName("ReadJSON").getOrCreate() 1. 这里我们使用builder方法创建一...
from pyspark.sql.types import StructType, StructField, StringType, IntegerType df_children_with_schema = spark.createDataFrame( data = [("Mikhail", 15), ("Zaky", 13), ("Zoya", 8)], schema = StructType([ StructField('name', StringType(), True), StructField('age', IntegerType(), ...
frompyspark.sqlimportSparkSession# 创建SparkSession对象spark=SparkSession.builder \.appName("JSON Parsing")\.getOrCreate() 1. 2. 3. 4. 5. 6. 步骤2:读取JSON文件并创建DataFrame 接下来,我们需要使用SparkSession对象来读取JSON文件并创建一个DataFrame。DataFrame是一个分布式的数据集,它以表格形式组织和...
1. Simple JSON: JSON文件 (Simple.json) 代码 frompyspark.sqlimportSparkSessionspark=SparkSession.builder.config("spark.sql.warehouse.dir","file:///C:/temp").appName("readJSON").getOrCreate()readJSONDF=spark.read.json('Simple.json')readJSONDF.show(truncate=False) ...
PySparkSQL之PySpark解析Json集合数据 数据样本 12341234123412342|asefr-3423|[{"name":"spark","score":"65"},{"name":"airlow","score":"70"},{"name":"flume","score":"55"},{"name":"python","score":"33"},{"name":"scala","score":"44"},{"name":"java","score":"70"},{"name...
from pyspark.sql.types import * ####1、从json文件读取数据,并直接生成DataFrame### path = "20180724141719.json" df = sqlContext.read.json(path) df.printSchema() ### data_dict ={"region":"cn","env":"dev","product":"snap"} schema=StructType([ StructField("region", StringType(),...
最后,JSON 对象的值在它们可以表示的数据类型方面具有更多的灵活性。 JSON 允许以下值类型。 字符串(使用双引号字符 " 作为引号字符); 数字(JavaScript 不区分整数和浮点数); 布尔值(true 或 false,不像 Python 那样大写) null,类似于 Python None;
import pyspark.sql.functions as F from pyspark.sql.types import StructType from util import schema ,meta_date new_schema = StructType.fromJson(json.loads(schema)) with open("largefile.json", "r") as json_file: result_count = len(rapidjson.load(json_file)["data"]["result"]) ...