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":"hdfs","score":"66"},{"name":"hbase","score":"77"},{"name":"qq","score":"70"},{"name":"sun","score":"88"},{"name":"mysql","score":"96"},{"name":"php","score":"88"},{"name":"hive","score":"97"},{"name":"oozie","score":"45"},{"name":"meizu","score":"70"},{"name":"hw","score":"32"},{"name":"sql","score":"75"},{"name":"r","score":"64"},{"name":"mr","score":"83"},{"name":"kafka","score":"64"},{"name":"mo","score":"75"},{"name":"apple","score":"70"},{"name":"jquery","score":"86"},{"name":"js","score":"95"},{"name":"pig","score":"70"}]
正菜:
#-*- coding:utf-8 –*- from __future__ import print_function from pyspark import SparkContext from pyspark.sql import SQLContext from pyspark.sql.types import Row, StructField, StructType, StringType, IntegerType import sys reload(sys) import json if __name__ == "__main__": sc = SparkContext(appName="PythonSQL") sqlContext = SQLContext(sc) fileName = sys.argv[1] lines = sc.textFile(fileName) sc.setLogLevel("WARN") def parse_line(line): fields=line.split("|",-1) keyword=fields[2] return keyword def parse_json(keyword): return keyword.replace("[","").replace("]","").replace("},{","}|{") keywordRDD = lines.map(parse_line) #print(keywordRDD.take(1)) #print("---------------") jsonlistRDD = keywordRDD.map(parse_json) #print(jsonlistRDD.take(1)) jsonRDD = jsonlistRDD.flatMap(lambda jsonlist:jsonlist.split("|")) schema = StructType([StructField("name", StringType()),StructField("score", IntegerType())]) df = sqlContext.read.schema(schema).json(jsonRDD) # df.printSchema() # df.show() df.registerTempTable("json") df_result = sqlContext.sql("SELECT name,score FROM json WHERE score > 70") df_result.coalesce(1).write.json(sys.argv[2]) sc.stop()
提交作业
spark-submit .demo2.py "C:\Users\txdyl\Desktop\test.txt" "c:\users\txdyl\Desktop\output"
数据结果