Pyspark 从cloudant IBM Bluemix NoSQL数据库中提取值

如何从以JSON格式存储的Cloudant IBM Bluemix NoSQL数据库中提取值

我试过这个密码

def readDataFrameFromCloudant(host,user,pw,database):
   cloudantdata=spark.read.format("com.cloudant.spark"). \
      option("cloudant.host",host). \
      option("cloudant.username", user). \
      option("cloudant.password", pw). \
      load(database)

cloudantdata.createOrReplaceTempView("washing")
spark.sql("SELECT * from washing").show()
return cloudantdata

hostname = ""
user = ""
pw = ""
database = "database"
cloudantdata=readDataFrameFromCloudant(hostname, user, pw, database)

它以这种格式存储

{
  "_id": "31c24a382f3e4d333421fc89ada5361e",
  "_rev": "1-8ba1be454fed5b48fa493e9fe97bedae",
  "d": {
    "count": 9,
    "hardness": 72,
    "temperature": 85,
    "flowrate": 11,
    "fluidlevel": "acceptable",
    "ts": 1502677759234
  }
}

我想要这个结果

期望

实际结果

---

创建一个虚拟数据集以再现问题：

cloudantdata = spark.read.json(sc.parallelize(["""
{
  "_id": "31c24a382f3e4d333421fc89ada5361e",
  "_rev": "1-8ba1be454fed5b48fa493e9fe97bedae",
  "d": {
    "count": 9,
    "hardness": 72,
    "temperature": 85,
    "flowrate": 11,
    "fluidlevel": "acceptable",
    "ts": 1502677759234
  }
}
"""]))
cloudantdata.take(1)

返回：

[Row(_id='31c24a382f3e4d333421fc89ada5361e', _rev='1-8ba1be454fed5b48fa493e9fe97bedae', d=Row(count=9, flowrate=11, fluidlevel='acceptable', hardness=72, temperature=85, ts=1502677759234))]

[Row(_id='31c24a382f3e4d333421fc89ada5361e', _rev='1-8ba1be454fed5b48fa493e9fe97bedae', count=9, flowrate=11, fluidlevel='acceptable', hardness=72, temperature=85, ts=1502677759234)]

现在展平：

flat_df = cloudantdata.select("_id", "_rev", "d.*")
flat_df.take(1)

返回：

[Row(_id='31c24a382f3e4d333421fc89ada5361e', _rev='1-8ba1be454fed5b48fa493e9fe97bedae', d=Row(count=9, flowrate=11, fluidlevel='acceptable', hardness=72, temperature=85, ts=1502677759234))]

[Row(_id='31c24a382f3e4d333421fc89ada5361e', _rev='1-8ba1be454fed5b48fa493e9fe97bedae', count=9, flowrate=11, fluidlevel='acceptable', hardness=72, temperature=85, ts=1502677759234)]

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我使用IBM Data Science Experience笔记本，使用Python3.5（实验版）和Spark 2.0测试了这段代码

这个答案基于：