Spark Stuctured Streaming 解析字段不固定的JSON

数据样例

有两个文件,一个是 json: a.json

{
  "createTime": 1532598069,
  "event": {
    "info": {
       "AAA": "one",
       "BBB": "two",
       "DDD": "opps"
    }
  }
}

另一个也是 json: b.json

{
  "createTime": "1532598069",
  "event": {
    "info": {
       "AAA": "three",
       "BBB": "four",
       "CCC": "haha"
    }
  }
}

Kafka Producer

info 里面的字段个数是不固定的。用下面的代码先将 a.json 发送到 Kafka:

from confluent_kafka import Producer


p = Producer({'bootstrap.servers': 'localhost:9092'})

def delivery_report(err, msg):
    """ Called once for each message produced to indicate delivery result.
        Triggered by poll() or flush(). """
    if err is not None:
        print('Message delivery failed: {}'.format(err))
    else:
        print('Message delivered to {} [{}]'.format(msg.topic(), msg.partition()))

with open("/Users/ohmycloud/work/notes/b.json") as f:
  data = f.read()
  p.poll(0)
  p.produce('dynamic-schema', data.encode('utf-8'), callback=delivery_report)

p.flush()

Kafka Consumer

package dynamic.schma.test
import org.apache.spark.sql.SparkSession
import org.apache.spark.sql.functions._
import org.apache.spark.sql.streaming._
import org.apache.spark.sql.types._


object DynamicSchema extends App {
  val spark = SparkSession
    .builder
    .appName("DynamicSchema")
    .master("local[*]")
    .getOrCreate()

  // 定义 schema,包含 json 中的所有可能出现的字段
  val schema = new StructType()
    .add("createTime", StringType)
    .add("event", MapType(StringType, new StructType()
      .add("AAA", StringType, true)
      .add("BBB", StringType, true)
      .add("CCC", StringType, true)
      .add("DDD", StringType, true)
    ))

  val parsed = spark.readStream
    .format("kafka")
    .option("kafka.bootstrap.servers", "localhost:9092")
    .option("subscribe", "dynamic-schema")
    .option("startingOffsets", "earliest")
    .load()
    .select(from_json(col("value").cast("string"), schema).alias("parsed_value"))

  import spark.implicits._

  val event = parsed.select(explode($"parsed_value.event")).select("value.*")

  val console = event.writeStream
    .format("console")
    .outputMode(OutputMode.Append())

  val query = console.start()

  query.awaitTermination()

}

打印出来的结果为:

+---+---+----+----+
|AAA|BBB| CCC| DDD|
+---+---+----+----+
|one|two|null|opps|
+---+---+----+----+

因为 a.json 里面没有 CCC 这个字段,并且 schema 里面设置允许 CCC 的值为 NULL, 所以 OK 的。

然后发送 b.json, 打印的结果为:

+-----+----+----+----+
|  AAA| BBB| CCC| DDD|
+-----+----+----+----+
|three|four|haha|null|
+-----+----+----+----+

b.json 里面没有 DDD, schema 设置 CCC 的值允许为空,所以 NULL OK。

验证了一下 schema 的问题。

你可能感兴趣的:(Spark Stuctured Streaming 解析字段不固定的JSON)