python访问mysql将返回的表转化为json

#
# Licensed to the Apache Software Foundation (ASF) under one or more
# contributor license agreements.  See the NOTICE file distributed with
# this work for additional information regarding copyright ownership.
# The ASF licenses this file to You under the Apache License, Version 2.0
# (the "License"); you may not use this file except in compliance with
# the License.  You may obtain a copy of the License at
#
#    http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#

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-hadoop2.6datamllibpeople.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-hadoop2.6datamllibpeoplenew.json","json","append")
    file_object = open("D:spark-1.6.0-bin-hadoop2.6datamllibpeoplenew.json", 'w')
    file_object.write("{'name':'222'}")
    file_object.close()

    #teenagers.rdd.repartition(1).saveAsTextFile("D:spark-1.6.0-bin-hadoop2.6datamllibpeoplenew1.json")
    #sc.parallelize(teenagers.collect()).saveAsTextFile("D:spark-1.6.0-bin-hadoop2.6datamllibpeoplenew1.json")
    #sc.parallelize(teenagers.collect()).saveAsTextFile("D:spark-1.6.0-bin-hadoop2.6datamllibpeoplenew1.json")
    #error teenagers.rdd.repartition(1).saveAsTextFile("D:spark-1.6.0-bin-hadoop2.6datamllibpeoplenew1.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()