Spark高级操作之json复杂和嵌套数据结构的操作二

Spark学习技巧

发布于 2018-01-30 18:56:26

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发布于 2018-01-30 18:56:26

文章被收录于专栏：Spark学习技巧Spark学习技巧

一，准备阶段

Json格式里面有map结构和嵌套json也是很合理的。本文将举例说明如何用spark解析包含复杂的嵌套数据结构，map。现实中的例子是，一个设备的检测事件，二氧化碳的安全你浓度，高温数据等，需要实时产生数据，然后及时的告警处理。

1，定义schema

import org.apache.spark.sql.types._

val schema = new StructType()
  .add("dc_id", StringType)                               // data center where data was posted to Kafka cluster
 .add("source",                                          // info about the source of alarm
 MapType(                                              // define this as a Map(Key->value)
 StringType,
 new StructType()
        .add("description", StringType)
        .add("ip", StringType)
        .add("id", LongType)
        .add("temp", LongType)
        .add("c02_level", LongType)
        .add("geo",
 new StructType()
            .add("lat", DoubleType)
            .add("long", DoubleType)
        )
    )
  )

2，准备数据

 val dataDS = Seq("""
{
"dc_id": "dc-101",
"source": {
    "sensor-igauge": {
      "id": 10,
      "ip": "68.28.91.22",
      "description": "Sensor attached to the container ceilings",
      "temp":35,
      "c02_level": 1475,
      "geo": {"lat":38.00, "long":97.00}                        
    },
    "sensor-ipad": {
      "id": 13,
      "ip": "67.185.72.1",
      "description": "Sensor ipad attached to carbon cylinders",
      "temp": 34,
      "c02_level": 1370,
      "geo": {"lat":47.41, "long":-122.00}
    },
    "sensor-inest": {
      "id": 8,
      "ip": "208.109.163.218",
      "description": "Sensor attached to the factory ceilings",
      "temp": 40,
      "c02_level": 1346,
      "geo": {"lat":33.61, "long":-111.89}
    },
    "sensor-istick": {
      "id": 5,
      "ip": "204.116.105.67",
      "description": "Sensor embedded in exhaust pipes in the ceilings",
      "temp": 40,
      "c02_level": 1574,
      "geo": {"lat":35.93, "long":-85.46}
    }
  }
}""").toDS()
 // should only be one item
 dataDS.count()

3，准备处理

val df = spark.read.schema(schema).json(dataDS.rdd)

查看schema

df.printSchema

二，如何使用explode()

Explode()方法在spark1.3的时候就已经存在了，在这里展示一下如何抽取嵌套的数据结构。在一些场合，会结合explode，to_json,from_json一起使用。

Explode为给定的map的每一个元素创建一个新的行。比如上面准备的数据，source就是一个map结构。Map中的每一个key/value对都会是一个独立的行。

val explodedDF = df.select($"dc_id", explode($"source"))
explodedDF.printSchema

可以看看操作之后的schema信息

获取内部的数据

case class DeviceAlert(dcId: String, deviceType:String, ip:String, deviceId:Long, temp:Long, c02_level: Long, lat: Double, lon: Double)
val notifydevicesDS = explodedDF.select( $"dc_id" as "dcId",
 $"key" as "deviceType",
 'value.getItem("ip") as 'ip,
 'value.getItem("id") as 'deviceId,
 'value.getItem("c02_level") as 'c02_level,
 'value.getItem("temp") as 'temp,
 'value.getItem("geo").getItem("lat") as 'lat,                //note embedded level requires yet another level of fetching.
 'value.getItem("geo").getItem("long") as 'lon)
  .as[DeviceAlert]  // return as a Dataset

查看schema信息

notifydevicesDS.printSchema

三，再复杂一点

在物联网场景里，通畅物联网设备会将很多json 事件数据发给他的收集器。收集器可以是附近的数据中心，也可以是附近的聚合器，也可以是安装在家里的一个设备，它会有规律的周期的将数据通过加密的互联网发给远程的数据中心。说白一点，数据格式更复杂。

我们下面会有三个map的数据格式：恒温计，摄像机，烟雾报警器。

import org.apache.spark.sql.types._

// a bit longish, nested, and convuloted JSON schema :)
val nestSchema2 = new StructType()
  .add("devices",
 new StructType()
      .add("thermostats", MapType(StringType,
 new StructType()
        .add("device_id", StringType)
        .add("locale", StringType)
        .add("software_version", StringType)
        .add("structure_id", StringType)
        .add("where_name", StringType)
        .add("last_connection", StringType)
        .add("is_online", BooleanType)
        .add("can_cool", BooleanType)
        .add("can_heat", BooleanType)
        .add("is_using_emergency_heat", BooleanType)
        .add("has_fan", BooleanType)
        .add("fan_timer_active", BooleanType)
        .add("fan_timer_timeout", StringType)
        .add("temperature_scale", StringType)
        .add("target_temperature_f", DoubleType)
        .add("target_temperature_high_f", DoubleType)
        .add("target_temperature_low_f", DoubleType)
        .add("eco_temperature_high_f", DoubleType)
        .add("eco_temperature_low_f", DoubleType)
        .add("away_temperature_high_f", DoubleType)
        .add("away_temperature_low_f", DoubleType)
        .add("hvac_mode", StringType)
        .add("humidity", LongType)
        .add("hvac_state", StringType)
        .add("is_locked", StringType)
        .add("locked_temp_min_f", DoubleType)
        .add("locked_temp_max_f", DoubleType)))
      .add("smoke_co_alarms", MapType(StringType,
 new StructType()
        .add("device_id", StringType)
        .add("locale", StringType)
        .add("software_version", StringType)
        .add("structure_id", StringType)
        .add("where_name", StringType)
        .add("last_connection", StringType)
        .add("is_online", BooleanType)
        .add("battery_health", StringType)
        .add("co_alarm_state", StringType)
        .add("smoke_alarm_state", StringType)
        .add("is_manual_test_active", BooleanType)
        .add("last_manual_test_time", StringType)
        .add("ui_color_state", StringType)))
      .add("cameras", MapType(StringType,
 new StructType()
        .add("device_id", StringType)
        .add("software_version", StringType)
        .add("structure_id", StringType)
        .add("where_name", StringType)
        .add("is_online", BooleanType)
        .add("is_streaming", BooleanType)
        .add("is_audio_input_enabled", BooleanType)
        .add("last_is_online_change", StringType)
        .add("is_video_history_enabled", BooleanType)
        .add("web_url", StringType)
        .add("app_url", StringType)
        .add("is_public_share_enabled", BooleanType)
        .add("activity_zones",
 new StructType()
            .add("name", StringType)
            .add("id", LongType))
        .add("last_event", StringType))))

对应的数据

val nestDataDS2 = Seq("""{
  "devices": {
     "thermostats": {
        "peyiJNo0IldT2YlIVtYaGQ": {
          "device_id": "peyiJNo0IldT2YlIVtYaGQ",
          "locale": "en-US",
          "software_version": "4.0",
          "structure_id": "VqFabWH21nwVyd4RWgJgNb292wa7hG_dUwo2i2SG7j3-BOLY0BA4sw",
          "where_name": "Hallway Upstairs",
          "last_connection": "2016-10-31T23:59:59.000Z",
          "is_online": true,
          "can_cool": true,
          "can_heat": true,
          "is_using_emergency_heat": true,
          "has_fan": true,
          "fan_timer_active": true,
          "fan_timer_timeout": "2016-10-31T23:59:59.000Z",
          "temperature_scale": "F",
          "target_temperature_f": 72,
          "target_temperature_high_f": 80,
          "target_temperature_low_f": 65,
          "eco_temperature_high_f": 80,
          "eco_temperature_low_f": 65,
          "away_temperature_high_f": 80,
          "away_temperature_low_f": 65,
          "hvac_mode": "heat",
          "humidity": 40,
          "hvac_state": "heating",
          "is_locked": true,
          "locked_temp_min_f": 65,
          "locked_temp_max_f": 80
          }
        },
        "smoke_co_alarms": {
          "RTMTKxsQTCxzVcsySOHPxKoF4OyCifrs": {
            "device_id": "RTMTKxsQTCxzVcsySOHPxKoF4OyCifrs",
            "locale": "en-US",
            "software_version": "1.01",
            "structure_id": "VqFabWH21nwVyd4RWgJgNb292wa7hG_dUwo2i2SG7j3-BOLY0BA4sw",
            "where_name": "Jane's Room",
            "last_connection": "2016-10-31T23:59:59.000Z",
            "is_online": true,
            "battery_health": "ok",
            "co_alarm_state": "ok",
            "smoke_alarm_state": "ok",
            "is_manual_test_active": true,
            "last_manual_test_time": "2016-10-31T23:59:59.000Z",
            "ui_color_state": "gray"
            }
          },
       "cameras": {
        "awJo6rH0IldT2YlIVtYaGQ": {
          "device_id": "awJo6rH",
          "software_version": "4.0",
          "structure_id": "VqFabWH21nwVyd4RWgJgNb292wa7hG_dUwo2i2SG7j3-BOLY0BA4sw",
          "where_name": "Foyer",
          "is_online": true,
          "is_streaming": true,
          "is_audio_input_enabled": true,
          "last_is_online_change": "2016-12-29T18:42:00.000Z",
          "is_video_history_enabled": true,
          "web_url": "https://home.nest.com/cameras/device_id?auth=access_token",
          "app_url": "nestmobile://cameras/device_id?auth=access_token",
          "is_public_share_enabled": true,
          "activity_zones": { "name": "Walkway", "id": 244083 },
          "last_event": "2016-10-31T23:59:59.000Z"
          }
        }
      }
     }""").toDS

通过创建一个简单的dataset，我们可以使用所有的dataset的方法来进行ETL操作，比如from_json(), to_json(), explode() and selectExpr()。

val nestDF2 = spark                            // spark session 
 .read                             //  get DataFrameReader
 .schema(nestSchema2)             //  use the defined schema above and read format as JSON
 .json(nestDataDS2.rdd)

2，将整个json对象，转化为一个json string

val stringJsonDF = nestDF2.select(to_json(struct($"*"))).toDF("nestDevice")

3，将三个json object 的map对象抓化为三个单独的map列，然后可以是使用explode方法访问其属性。

val mapColumnsDF = nestDF2.select($"devices".getItem("smoke_co_alarms").alias ("smoke_alarms"),
 $"devices".getItem("cameras").alias ("cameras"),
 $"devices".getItem("thermostats").alias ("thermostats"))

转化为三个dataframe

val explodedThermostatsDF = mapColumnsDF.select(explode($"thermostats"))
val explodedCamerasDF = mapColumnsDF.select(explode($"cameras"))
//or you could use the original nestDF2 and use the devices.X notation
val explodedSmokedAlarmsDF =  nestDF2.select(explode($"devices.smoke_co_alarms"))

查看其schema

explodedThermostatsDF.printSchema

访问三个map内部的元素

val thermostateDF = explodedThermostatsDF.select($"value".getItem("device_id").alias("device_id"),
 $"value".getItem("locale").alias("locale"),
 $"value".getItem("where_name").alias("location"),
 $"value".getItem("last_connection").alias("last_connected"),
 $"value".getItem("humidity").alias("humidity"),
 $"value".getItem("target_temperature_f").alias("target_temperature_f"),
 $"value".getItem("hvac_mode").alias("mode"),
 $"value".getItem("software_version").alias("version"))

val cameraDF = explodedCamerasDF.select($"value".getItem("device_id").alias("device_id"),
 $"value".getItem("where_name").alias("location"),
 $"value".getItem("software_version").alias("version"),
 $"value".getItem("activity_zones").getItem("name").alias("name"),
 $"value".getItem("activity_zones").getItem("id").alias("id"))

val smokedAlarmsDF = explodedSmokedAlarmsDF.select($"value".getItem("device_id").alias("device_id"),
 $"value".getItem("where_name").alias("location"),
 $"value".getItem("software_version").alias("version"),
 $"value".getItem("last_connection").alias("last_connected"),
 $"value".getItem("battery_health").alias("battery_health"))

查看内部数据

cameraDF.show

通过version进行join操作

val joineDFs = thermostateDF.join(cameraDF, "version")

四，总结

这篇文章的重点是介绍几个好用的工具，去获取复杂的嵌套的json数据格式。一旦你将嵌套数据扁平化之后，再进行访问，就跟普通的数据格式没啥区别了。

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原始发表：2017-08-08，如有侵权请联系 cloudcommunity@tencent.com 删除

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