python访问mysql将返回的表转化为json

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from __future__ import print_function

import os
import sys

from pyspark import SparkContext
from pyspark.sql import SQLContext
from pyspark.sql.types import Row, StructField, StructType, StringType, IntegerType

if __name__ == "__main__":

sc = SparkContext(appName="PythonSQL")
sqlContext = SQLContext(sc)

# RDD is created from a list of rows
some_rdd = sc.parallelize([Row(name="John", age=19),
Row(name="Smith", age=23),
Row(name="Sarah", age=18)])
# Infer schema from the first row, create a DataFrame and print the schema
some_df = sqlContext.createDataFrame(some_rdd)
some_df.printSchema()

# Another RDD is created from a list of tuples
another_rdd = sc.parallelize([("John", 19), ("Smith", 23), ("Sarah", 18)])
# Schema with two fields - person_name and person_age
schema = StructType([StructField("person_name", StringType(), False),
StructField("person_age", IntegerType(), False)])
# Create a DataFrame by applying the schema to the RDD and print the schema
another_df = sqlContext.createDataFrame(another_rdd, schema)
another_df.printSchema()
# root
#  |-- age: integer (nullable = true)
#  |-- name: string (nullable = true)

# A JSON dataset is pointed to by path.
# The path can be either a single text file or a directory storing text files.
# if len(sys.argv) < 2:
#   path = "file://" + \
#      os.path.join(os.environ['SPARK_HOME'], "examples/src/main/resources/people.json")
# else:
#   path = sys.argv[1]
path="D:\spark-1.6.0-bin-hadoop2.6\data\mllib\people.json";
# Create a DataFrame from the file(s) pointed to by path
people = sqlContext.jsonFile(path)
# root
#  |-- person_name: string (nullable = false)
#  |-- person_age: integer (nullable = false)

# The inferred schema can be visualized using the printSchema() method.
people.printSchema()
# root
#  |-- age: IntegerType
#  |-- name: StringType

# Register this DataFrame as a table.
people.registerAsTable("people")

# SQL statements can be run by using the sql methods provided by sqlContext
teenagers = sqlContext.sql("SELECT name FROM people WHERE age >= 13 AND age <= 19")

for each in teenagers.collect():
print(each[0])
# teenagers.append("namesAndAges.parquet", "parquet");
import json

#teenagers.save("D:\spark-1.6.0-bin-hadoop2.6\data\mllib\peoplenew.json","json","append")
file_object = open("D:\spark-1.6.0-bin-hadoop2.6\data\mllib\peoplenew.json", 'w')
file_object.write("{'name':'222'}")
file_object.close()

#teenagers.rdd.repartition(1).saveAsTextFile("D:\spark-1.6.0-bin-hadoop2.6\data\mllib\peoplenew1.json")
#sc.parallelize(teenagers.collect()).saveAsTextFile("D:\spark-1.6.0-bin-hadoop2.6\data\mllib\peoplenew1.json")
#sc.parallelize(teenagers.collect()).saveAsTextFile("D:\spark-1.6.0-bin-hadoop2.6\data\mllib\peoplenew1.json")
#error teenagers.rdd.repartition(1).saveAsTextFile("D:\spark-1.6.0-bin-hadoop2.6\data\mllib\peoplenew1.json");

import pymysql

conn = pymysql.connect(host='aliyun.ovalcn.com', port=3306, user='root', passwd='oval163', db='pos_wanli_combine', charset='UTF8')
cur = conn.cursor()
cur.execute("SELECT * FROM biz_dms_order limit 2")
results = cur.fetchall()
orders = []
data = {}
# for i in range(len(cur.description)):
#    print("Column {}:".format(i + 1))
#    desc = cur.description[i]
#   print("  column_name = {}".format(desc[0]))

for row in results:

orderDict = {}
for i in range(len(cur.description)):
# print("Column {}:".format(i + 1))
desc = cur.description[i]
#print("  column_name = {}".format(desc[0]))
colName = desc[0]
orderDict.setdefault(colName, str(row[i]))
#print(row[i])
# order[desc[0]] = row[i]
#setattr(orderDict,colName, row[i])

#print(row[i])
#orderDict['id'] =11
#print(row)
#orderDict.setdefault('id', 11)

#i=0
orders.append(orderDict)
#print(json.dumps(orders))
data['code'] = 0
data['orders'] = orders
jsonStr = json.dumps(data)
print(jsonStr)
#print(data)
# for r in cur:
# print("row_number:" + str(cur.rownumber))
#print("id:" + str(r[0]) + "key:" + str(r[1]) + " mean:" + str(r[2]))

# cur.close()

conn.close()

sc.stop()