使用 PySpark 结构化流将 Kafka Stream 接收到 MongoDB

Question

My Spark:

spark = SparkSession\
    .builder\
    .appName("Demo")\
    .master("local[3]")\
    .config("spark.streaming.stopGracefullyonShutdown", "true")\
    .config('spark.jars.packages','org.mongodb.spark:mongo-spark-connector_2.12:3.0.1')\
    .getOrCreate()

Mongo URI:

input_uri_weld = 'mongodb://127.0.0.1:27017/db.coll1'
output_uri_weld = 'mongodb://127.0.0.1:27017/db.coll1'

Function for writing stream batches to Mongo:

def save_to_mongodb_collection(current_df, epoc_id, mongodb_collection_name):
    current_df.write\
      .format("com.mongodb.spark.sql.DefaultSource") \
      .mode("append") \
      .option("spark.mongodb.output.uri", output_uri_weld) \
      .save()

Kafka Stream:

kafka_df = spark.readStream\
.format("kafka")\
.option("kafka.bootstrap.servers", kafka_broker)\
.option("subscribe", kafka_topic)\
.option("startingOffsets", "earliest")\
.load()

Write to Mongo:

mongo_writer = df_parsed.write\
        .format('com.mongodb.spark.sql.DefaultSource')\
        .mode('append')\
        .option("spark.mongodb.output.uri", output_uri_weld)\
        .save()

& my spark.conf file:

spark.jars.packages                org.apache.spark:spark-sql-kafka-0-10_2.12:3.0.1,org.apache.spark:spark-avro_2.12:3.0.1,com.datastax.spark:spark-cassandra-connector_2.12:3.0.0

Error:

java.lang.ClassNotFoundException: Failed to find data source: com.mongodb.spark.sql.DefaultSource. Please find packages at http://spark.apache.org/third-party-projects.html  

Answer 1

I found a solution. Since I couldn't find the right Mongo driver for Structured Streaming, I worked on another solution. Now, I use the direct connection to mongoDb, and use "foreach(...)" instead of foreachbatch(...). My code looks like this in testSpark.py file:

....
import pymongo
from pymongo import MongoClient

local_url = "mongodb://localhost:27017"


def write_machine_df_mongo(target_df):

    cluster = MongoClient(local_url)
    db = cluster["test_db"]
    collection = db.test1

    post = {
            "machine_id": target_df.machine_id,
            "proc_type": target_df.proc_type,
            "sensor1_id": target_df.sensor1_id,
            "sensor2_id": target_df.sensor2_id,
            "time": target_df.time,
            "sensor1_val": target_df.sensor1_val,
            "sensor2_val": target_df.sensor2_val,
            }

    collection.insert_one(post)

machine_df.writeStream\
    .outputMode("append")\
    .foreach(write_machine_df_mongo)\
    .start()