如何将流数据从Spark汇到Mongodb？

我正在使用pyspark从Kafka读取流数据，然后我想将这些数据汇入mongodb . 我已经包含了所有必需的包，但它会引发错误
UnsupportedOperationException: Data source com.mongodb.spark.sql.DefaultSource does not support streamed writing

以下链接与我的问题无关

Writing to mongoDB from Spark

Spark to MongoDB via Mesos

这是完整的错误堆栈跟踪

Traceback（最近一次调用最后一次）：文件“/home/b3ds/kafka-spark.py”，第85行，在.option中（“com.mongodb.spark.sql.DefaultSource”，“mongodb：// localhost：27017 /twitter.test“）\ File”/home/b3ds/hdp/spark/python/lib/pyspark.zip/pyspark/sql/streaming.py“，第827行，在启动文件”/ home / b3ds / hdp / spark中/python/lib/py4j-0.10.4-src.zip/py4j/java_gateway.py“，第1133行，在调用文件中”/home/b3ds/hdp/spark/python/lib/pyspark.zip/pyspark/sql/ utils.py“，第63行，在deco文件中”/home/b3ds/hdp/spark/python/lib/py4j-0.10.4-src.zip/py4j/protocol.py“，第319行，在get_return_value py4j.protocol中.Py4JJavaError：调用o122.start时发生错误 . ：java.lang.UnsupportedOperationException：数据源com.mongodb.spark.sql.DefaultSource不支持org.apache中org.apache.spark.sql.execution.datasources.DataSource.createSink（DataSource.scala：287）的流式写入.spark.sql.streaming.DataStreamWriter.start（DataStreamWriter.scala：272）at sun.reflect.NativeMethodAccessorImpl.invoke0（Native Method）at sun.reflect.NativeMethodAccessorImpl.invoke（NativeMethodAccessorImpl.java:62）at sun.reflect.DelegatingMethodAccessorImpl .invoke（DelegatingMethodAccessorImpl.java:43）位于py4j.reflection.Metod上的java.lang.reflect.Method.invoke（Method.java:498）py4j.reflection.MododInvoker.invoke（MethodInvoker.java:244） ReflectionEngine.java:357）py4j.Gateway.invoke（Gateway.java:280）py4j.commands.AbstractCommand.invokeMethod（AbstractCommand.java:132）py4j.commands.CallCommand.execute（CallCommand.java:79）at java.lang.Thread.run中的py4j.GatewayConnection.run（GatewayConnection.java:214）（Thread.java:748）

这是我的pyspark代码

from __future__ import print_function
import sys
from pyspark.sql.functions import udf
from pyspark.sql import SparkSession
from pyspark import SparkContext
from pyspark.streaming import StreamingContext
from pyspark.streaming.kafka import KafkaUtils
from pyspark.sql.types import StructType
from pyspark.sql.types import *
import json
from pyspark.sql.functions import struct
from pyspark.sql.functions import *
import datetime

json_schema = StructType([
  StructField("twitterid", StringType(), True),
  StructField("created_at", StringType(), True),
  StructField("tweet", StringType(), True),
  StructField("screen_name", StringType(), True)
])

def parse_json(df):
    twitterid   = json.loads(df[0])['id']
    created_at  = json.loads(df[0])['created_at']
    tweet       = json.loads(df[0])['text']
    tweet       = json.loads(df[0])['text']
    screen_name = json.loads(df[0])['user']['screen_name']
    return [twitterid, created_at, tweet, screen_name]

def convert_twitter_date(timestamp_str):
    output_ts = datetime.datetime.strptime(timestamp_str.replace('+0000 ',''), '%a %b %d %H:%M:%S %Y')
    return output_ts

if __name__ == "__main__":

        spark = SparkSession\
                        .builder\
                        .appName("StructuredNetworkWordCount")\
                        .config("spark.mongodb.input.uri","mongodb://192.168.1.16:27017/twitter.test")\
                        .config("spark.mongodb.output.uri","mongodb://192.168.1.16:27017/twitter.test")\
                        .getOrCreate()
        events = spark\
                        .readStream\
                        .format("kafka")\
                        .option("kafka.bootstrap.servers", "localhost:9092")\
                        .option("subscribe", "twitter")\
                        .load()
        events = events.selectExpr("CAST(value as String)")

        udf_parse_json = udf(parse_json , json_schema)
        udf_convert_twitter_date = udf(convert_twitter_date, TimestampType())
        jsonoutput = events.withColumn("parsed_field", udf_parse_json(struct([events[x] for x in events.columns]))) \
                                        .where(col("parsed_field").isNotNull()) \
                                        .withColumn("created_at", col("parsed_field.created_at")) \
                                        .withColumn("screen_name", col("parsed_field.screen_name")) \
                                        .withColumn("tweet", col("parsed_field.tweet")) \
                                        .withColumn("created_at_ts", udf_convert_twitter_date(col("parsed_field.created_at")))

        windowedCounts = jsonoutput.groupBy(window(jsonoutput.created_at_ts, "1 minutes", "15 seconds"),jsonoutput.screen_name)$

        mongooutput = jsonoutput \
                        .writeStream \
                        .format("com.mongodb.spark.sql.DefaultSource")\
                        .option("com.mongodb.spark.sql.DefaultSource","mongodb://localhost:27017/twitter.test")\
                        .start()
        mongooutput.awaitTermination()

I have seen the mongodb documentation which says it supports spark to mongo sink

https://docs.mongodb.com/spark-connector/master/scala/streaming/