Unable to send data to MongoDB using Kafka-Spark Structured Streaming - mongodb

Question

I am unable to save structured streaming data from Kafka into MongoDB. This is the first time i am implementing Kafka-Spark Structured Streaming data to MongoDB sink. I have followed this article https://learningfromdata.blog/2017/04/16/real-time-data-ingestion-with-apache-spark-structured-streaming-implementation/
It suggests to build a MongoForeachWriter and a Helper class along with the structured streaming program. However, following so i havent been successful in seeing the data in MongoDB collection. Could someone see and correct where i am going wrong???
Error:
Error:
Current State: ACTIVE
Thread State: RUNNABLE
Logical Plan:
Project [cast(value#8 as string) AS value#21]
+- StreamingExecutionRelation KafkaSource[Subscribe[TOPIC_WITH_COMP_P2_R2, TOPIC_WITH_COMP_P2_R2.DIT, TOPIC_WITHOUT_COMP_P2_R2.DIT]], [key#7, value#8, topic#9, partition#10, offset#11L, timestamp#12, timestampType#13]
at org.apache.spark.sql.execution.streaming.StreamExecution.org$apache$spark$sql$execution$streaming$StreamExecution$$runStream(StreamExecution.scala:295)
at org.apache.spark.sql.execution.streaming.StreamExecution$$anon$1.run(StreamExecution.scala:189)
Caused by: org.apache.spark.SparkException: Job aborted due to stage failure: Task 2 in stage 1.0 failed 1 times, most recent failure: Lost task 2.0 in stage 1.0 (TID 8, localhost, executor driver): java.lang.ClassCastException: java.lang.String cannot be cast to [B
at example_new.MongoDBForeachWriter.process(MongoDBForeachWriter.scala:42)
at example_new.MongoDBForeachWriter.process(MongoDBForeachWriter.scala:15)
at org.apache.spark.sql.execution.streaming.ForeachSink$$anonfun$addBatch$1.apply(ForeachSink.scala:53)
at org.apache.spark.sql.execution.streaming.ForeachSink$$anonfun$addBatch$1.apply(ForeachSink.scala:49)
at org.apache.spark.rdd.RDD$$anonfun$foreachPartition$1$$anonfun$apply$29.apply(RDD.scala:929)
at org.apache.spark.rdd.RDD$$anonfun$foreachPartition$1$$anonfun$apply$29.apply(RDD.scala:929)
at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:2067)
at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:2067)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:87)
at org.apache.spark.scheduler.Task.run(Task.scala:109)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:345)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
at java.lang.Thread.run(Thread.java:748)
Driver stacktrace:
at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1599)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1587)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1586)
at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1586)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:831)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:831)
at scala.Option.foreach(Option.scala:257)
at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:831)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:1820)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1769)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1758)
at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48)
at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:642)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2027)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2048)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2067)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2092)
at org.apache.spark.rdd.RDD$$anonfun$foreachPartition$1.apply(RDD.scala:929)
at org.apache.spark.rdd.RDD$$anonfun$foreachPartition$1.apply(RDD.scala:927)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112)
at org.apache.spark.rdd.RDD.withScope(RDD.scala:363)
at org.apache.spark.rdd.RDD.foreachPartition(RDD.scala:927)
at org.apache.spark.sql.execution.streaming.ForeachSink.addBatch(ForeachSink.scala:49)
at org.apache.spark.sql.execution.streaming.MicroBatchExecution$$anonfun$org$apache$spark$sql$execution$streaming$MicroBatchExecution$$runBatch$3$$anonfun$apply$16.apply(MicroBatchExecution.scala:477)
at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:77)
at org.apache.spark.sql.execution.streaming.MicroBatchExecution$$anonfun$org$apache$spark$sql$execution$streaming$MicroBatchExecution$$runBatch$3.apply(MicroBatchExecution.scala:475)
at org.apache.spark.sql.execution.streaming.ProgressReporter$class.reportTimeTaken(ProgressReporter.scala:271)
at org.apache.spark.sql.execution.streaming.StreamExecution.reportTimeTaken(StreamExecution.scala:58)
at org.apache.spark.sql.execution.streaming.MicroBatchExecution.org$apache$spark$sql$execution$streaming$MicroBatchExecution$$runBatch(MicroBatchExecution.scala:474)
at org.apache.spark.sql.execution.streaming.MicroBatchExecution$$anonfun$runActivatedStream$1$$anonfun$apply$mcZ$sp$1.apply$mcV$sp(MicroBatchExecution.scala:133)
at org.apache.spark.sql.execution.streaming.MicroBatchExecution$$anonfun$runActivatedStream$1$$anonfun$apply$mcZ$sp$1.apply(MicroBatchExecution.scala:121)
at org.apache.spark.sql.execution.streaming.MicroBatchExecution$$anonfun$runActivatedStream$1$$anonfun$apply$mcZ$sp$1.apply(MicroBatchExecution.scala:121)
at org.apache.spark.sql.execution.streaming.ProgressReporter$class.reportTimeTaken(ProgressReporter.scala:271)
at org.apache.spark.sql.execution.streaming.StreamExecution.reportTimeTaken(StreamExecution.scala:58)
at org.apache.spark.sql.execution.streaming.MicroBatchExecution$$anonfun$runActivatedStream$1.apply$mcZ$sp(MicroBatchExecution.scala:121)
at org.apache.spark.sql.execution.streaming.ProcessingTimeExecutor.execute(TriggerExecutor.scala:56)
at org.apache.spark.sql.execution.streaming.MicroBatchExecution.runActivatedStream(MicroBatchExecution.scala:117)
at org.apache.spark.sql.execution.streaming.StreamExecution.org$apache$spark$sql$execution$streaming$StreamExecution$$runStream(StreamExecution.scala:279)
... 1 more
Caused by: java.lang.ClassCastException: java.lang.String cannot be cast to [B
at example_new.MongoDBForeachWriter.process(MongoDBForeachWriter.scala:42)
at example_new.MongoDBForeachWriter.process(MongoDBForeachWriter.scala:15)
at org.apache.spark.sql.execution.streaming.ForeachSink$$anonfun$addBatch$1.apply(ForeachSink.scala:53)
at org.apache.spark.sql.execution.streaming.ForeachSink$$anonfun$addBatch$1.apply(ForeachSink.scala:49)
at org.apache.spark.rdd.RDD$$anonfun$foreachPartition$1$$anonfun$apply$29.apply(RDD.scala:929)
at org.apache.spark.rdd.RDD$$anonfun$foreachPartition$1$$anonfun$apply$29.apply(RDD.scala:929)
at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:2067)
at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:2067)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:87)
at org.apache.spark.scheduler.Task.run(Task.scala:109)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:345)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
at java.lang.Thread.run(Thread.java:748)
2019-04-22 19:40:26 INFO SparkContext:54 - Invoking stop() from shutdown hook
2019-04-22 19:40:26 INFO AbstractConnector:318 - Stopped Spark#907f2b7{HTTP/1.1,[http/1.1]}{0.0.0.0:4040}
2019-04-22 19:40:26 INFO SparkUI:54 - Stopped Spark web UI at http://W10BZVGSQ2.aus.amer.dell.com:4040
2019-04-22 19:40:26 INFO MapOutputTrackerMasterEndpoint:54 - MapOutputTrackerMasterEndpoint stopped!
2019-04-22 19:40:26 INFO MemoryStore:54 - MemoryStore cleared
2019-04-22 19:40:26 INFO BlockManager:54 - BlockManager stopped
2019-04-22 19:40:26 INFO BlockManagerMaster:54 - BlockManagerMaster stopped
2019-04-22 19:40:26 INFO OutputCommitCoordinator$OutputCommitCoordinatorEndpoint:54 - OutputCommitCoordinator stopped!
2019-04-22 19:40:26 WARN SparkEnv:87 - Exception while deleting Spark temp dir: C:\Users\raheem_mohammed\AppData\Local\Temp\spark-f9296938-c32b-42ff-af71-f90efcd49b10\userFiles-ee595b18-8c75-41be-b20e-f8c30628c765
java.io.IOException: Failed to delete: C:\Users\raheem_mohammed\AppData\Local\Temp\spark-f9296938-c32b-42ff-af71-f90efcd49b10\userFiles-ee595b18-8c75-41be-b20e-f8c30628c765
at org.apache.spark.util.Utils$.deleteRecursively(Utils.scala:1070)
at org.apache.spark.SparkEnv.stop(SparkEnv.scala:103)
at org.apache.spark.SparkContext$$anonfun$stop$11.apply$mcV$sp(SparkContext.scala:1940)
at org.apache.spark.util.Utils$.tryLogNonFatalError(Utils.scala:1357)
at org.apache.spark.SparkContext.stop(SparkContext.scala:1939)
at org.apache.spark.SparkContext$$anonfun$2.apply$mcV$sp(SparkContext.scala:572)
at org.apache.spark.util.SparkShutdownHook.run(ShutdownHookManager.scala:216)
at org.apache.spark.util.SparkShutdownHookManager$$anonfun$runAll$1$$anonfun$apply$mcV$sp$1.apply$mcV$sp(ShutdownHookManager.scala:188)
at org.apache.spark.util.SparkShutdownHookManager$$anonfun$runAll$1$$anonfun$apply$mcV$sp$1.apply(ShutdownHookManager.scala:188)
at org.apache.spark.util.SparkShutdownHookManager$$anonfun$runAll$1$$anonfun$apply$mcV$sp$1.apply(ShutdownHookManager.scala:188)
at org.apache.spark.util.Utils$.logUncaughtExceptions(Utils.scala:1988)
at org.apache.spark.util.SparkShutdownHookManager$$anonfun$runAll$1.apply$mcV$sp(ShutdownHookManager.scala:188)
at org.apache.spark.util.SparkShutdownHookManager$$anonfun$runAll$1.apply(ShutdownHookManager.scala:188)
at org.apache.spark.util.SparkShutdownHookManager$$anonfun$runAll$1.apply(ShutdownHookManager.scala:188)
at scala.util.Try$.apply(Try.scala:192)
at org.apache.spark.util.SparkShutdownHookManager.runAll(ShutdownHookManager.scala:188)
at org.apache.spark.util.SparkShutdownHookManager$$anon$2.run(ShutdownHookManager.scala:178)
at org.apache.hadoop.util.ShutdownHookManager$1.run(ShutdownHookManager.java:54)
2019-04-22 19:40:26 INFO SparkContext:54 - Successfully stopped SparkContext
2019-04-22 19:40:26 INFO ShutdownHookManager:54 - Shutdown hook called
2019-04-22 19:40:26 INFO ShutdownHookManager:54 - Deleting directory C:\Users\raheem_mohammed\AppData\Local\Temp\spark-f9296938-c32b-42ff-af71-f90efcd49b10
2019-04-22 19:40:26 ERROR ShutdownHookManager:91 - Exception while deleting Spark temp dir: C:\Users\raheem_mohammed\AppData\Local\Temp\spark-f9296938-c32b-42ff-af71-f90efcd49b10
java.io.IOException: Failed to delete: C:\Users\raheem_mohammed\AppData\Local\Temp\spark-f9296938-c32b-42ff-af71-f90efcd49b10
at org.apache.spark.util.Utils$.deleteRecursively(Utils.scala:1070)
at org.apache.spark.util.ShutdownHookManager$$anonfun$1$$anonfun$apply$mcV$sp$3.apply(ShutdownHookManager.scala:65)
at org.apache.spark.util.ShutdownHookManager$$anonfun$1$$anonfun$apply$mcV$sp$3.apply(ShutdownHookManager.scala:62)
at scala.collection.IndexedSeqOptimized$class.foreach(IndexedSeqOptimized.scala:33)
at scala.collection.mutable.ArrayOps$ofRef.foreach(ArrayOps.scala:186)
at org.apache.spark.util.ShutdownHookManager$$anonfun$1.apply$mcV$sp(ShutdownHookManager.scala:62)
at org.apache.spark.util.SparkShutdownHook.run(ShutdownHookManager.scala:216)
at org.apache.spark.util.SparkShutdownHookManager$$anonfun$runAll$1$$anonfun$apply$mcV$sp$1.apply$mcV$sp(ShutdownHookManager.scala:188)
at org.apache.spark.util.SparkShutdownHookManager$$anonfun$runAll$1$$anonfun$apply$mcV$sp$1.apply(ShutdownHookManager.scala:188)
at org.apache.spark.util.SparkShutdownHookManager$$anonfun$runAll$1$$anonfun$apply$mcV$sp$1.apply(ShutdownHookManager.scala:188)
at org.apache.spark.util.Utils$.logUncaughtExceptions(Utils.scala:1988)
at org.apache.spark.util.SparkShutdownHookManager$$anonfun$runAll$1.apply$mcV$sp(ShutdownHookManager.scala:188)
at org.apache.spark.util.SparkShutdownHookManager$$anonfun$runAll$1.apply(ShutdownHookManager.scala:188)
at org.apache.spark.util.SparkShutdownHookManager$$anonfun$runAll$1.apply(ShutdownHookManager.scala:188)
at scala.util.Try$.apply(Try.scala:192)
at org.apache.spark.util.SparkShutdownHookManager.runAll(ShutdownHookManager.scala:188)
at org.apache.spark.util.SparkShutdownHookManager$$anon$2.run(ShutdownHookManager.scala:178)
at org.apache.hadoop.util.ShutdownHookManager$1.run(ShutdownHookManager.java:54)
2019-04-22 19:40:26 INFO ShutdownHookManager:54 - Deleting directory C:\Users\raheem_mohammed\AppData\Local\Temp\spark-992d4d7e-ea11-4295-9368-c4038b26f895
2019-04-22 19:40:26 INFO ShutdownHookManager:54 - Deleting directory C:\Users\raheem_mohammed\AppData\Local\Temp\temporaryReader-4d362eeb-6ee5-4a48-9da9-3792a22ec1ca
2019-04-22 19:40:26 INFO ShutdownHookManager:54 - Deleting directory C:\Users\raheem_mohammed\AppData\Local\Temp\temporary-add2fc32-1623-4784-8df1-f5cb0a1dd9fc
2019-04-22 19:40:26 INFO ShutdownHookManager:54 - Deleting directory C:\Users\raheem_mohammed\AppData\Local\Temp\spark-f9296938-c32b-42ff-af71-f90efcd49b10\userFiles-ee595b18-8c75-41be-b20e-f8c30628c765
2019-04-22 19:40:26 ERROR ShutdownHookManager:91 - Exception while deleting Spark temp dir: C:\Users\raheem_mohammed\AppData\Local\Temp\spark-f9296938-c32b-42ff-af71-f90efcd49b10\userFiles-ee595b18-8c75-41be-b20e-f8c30628c765
java.io.IOException: Failed to delete: C:\Users\raheem_mohammed\AppData\Local\Temp\spark-f9296938-c32b-42ff-af71-f90efcd49b10\userFiles-ee595b18-8c75-41be-b20e-f8c30628c765
at org.apache.spark.util.Utils$.deleteRecursively(Utils.scala:1070)
at org.apache.spark.util.ShutdownHookManager$$anonfun$1$$anonfun$apply$mcV$sp$3.apply(ShutdownHookManager.scala:65)
at org.apache.spark.util.ShutdownHookManager$$anonfun$1$$anonfun$apply$mcV$sp$3.apply(ShutdownHookManager.scala:62)
at scala.collection.IndexedSeqOptimized$class.foreach(IndexedSeqOptimized.scala:33)
at scala.collection.mutable.ArrayOps$ofRef.foreach(ArrayOps.scala:186)
at org.apache.spark.util.ShutdownHookManager$$anonfun$1.apply$mcV$sp(ShutdownHookManager.scala:62)
at org.apache.spark.util.SparkShutdownHook.run(ShutdownHookManager.scala:216)
at org.apache.spark.util.SparkShutdownHookManager$$anonfun$runAll$1$$anonfun$apply$mcV$sp$1.apply$mcV$sp(ShutdownHookManager.scala:188)
at org.apache.spark.util.SparkShutdownHookManager$$anonfun$runAll$1$$anonfun$apply$mcV$sp$1.apply(ShutdownHookManager.scala:188)
at org.apache.spark.util.SparkShutdownHookManager$$anonfun$runAll$1$$anonfun$apply$mcV$sp$1.apply(ShutdownHookManager.scala:188)
at org.apache.spark.util.Utils$.logUncaughtExceptions(Utils.scala:1988)
at org.apache.spark.util.SparkShutdownHookManager$$anonfun$runAll$1.apply$mcV$sp(ShutdownHookManager.scala:188)
at org.apache.spark.util.SparkShutdownHookManager$$anonfun$runAll$1.apply(ShutdownHookManager.scala:188)
at org.apache.spark.util.SparkShutdownHookManager$$anonfun$runAll$1.apply(ShutdownHookManager.scala:188)
at scala.util.Try$.apply(Try.scala:192)
at org.apache.spark.util.SparkShutdownHookManager.runAll(ShutdownHookManager.scala:188)
at org.apache.spark.util.SparkShutdownHookManager$$anon$2.run(ShutdownHookManager.scala:178)
at org.apache.hadoop.util.ShutdownHookManager$1.run(ShutdownHookManager.java:54)
Created MongoForeachWriter.scala, Helper.scala and the StructuredStreamingProgram.scala
package example
import java.util.concurrent.TimeUnit
import scala.concurrent.Await
import scala.concurrent.duration.Duration
import org.mongodb.scala._
object Helpers {
implicit class DocumentObservable[C](val observable: Observable[Document]) extends ImplicitObservable[Document] {
override val converter: (Document) => String = (doc) => doc.toJson
}
implicit class GenericObservable[C](val observable: Observable[C]) extends ImplicitObservable[C] {
override val converter: (C) => String = (doc) => doc.toString
}
trait ImplicitObservable[C] {
val observable: Observable[C]
val converter: (C) => String
def results(): Seq[C] = Await.result(observable.toFuture(), Duration(10, TimeUnit.SECONDS))
def headResult() = Await.result(observable.head(), Duration(10, TimeUnit.SECONDS))
def printResults(initial: String = ""): Unit = {
if (initial.length > 0) print(initial)
results().foreach(res => println(converter(res)))
}
def printHeadResult(initial: String = ""): Unit = println(s"${initial}${converter(headResult())}")
}
}
package example
import java.util.Calendar
import org.apache.spark.util.LongAccumulator
import org.apache.spark.sql.Row
import org.apache.spark.sql.ForeachWriter
import org.mongodb.scala._
import org.mongodb.scala.bson.collection.mutable.Document
import org.mongodb.scala.bson._
import example.Helpers._
import scala.util.Try
class MongoDBForeachWriter(p_uri: String,
p_dbName: String,
p_collectionName: String,
p_messageCountAccum: LongAccumulator) extends ForeachWriter[Row] {
val mongodbURI = p_uri
val dbName = p_dbName
val collectionName = p_collectionName
val messageCountAccum = p_messageCountAccum
var mongoClient: MongoClient = null
var db: MongoDatabase = null
var collection: MongoCollection[Document] = null
def ensureMongoDBConnection(): Unit = {
if (mongoClient == null) {
mongoClient = MongoClient(mongodbURI)
db = mongoClient.getDatabase(dbName)
collection = db.getCollection(collectionName)
}
}
override def open(partitionId: Long, version: Long): Boolean = {
true
}
override def process(record: Row): Unit = {
val valueStr = new String(record.getAs[Array[Byte]]("value"))
val doc: Document = Document(valueStr)
doc += ("log_time" -> Calendar.getInstance().getTime())
// lazy opening of MongoDB connection
ensureMongoDBConnection()
val result = collection.insertOne(doc)
// tracks how many records I have processed
if (messageCountAccum != null)
messageCountAccum.add(1)
}
override def close(errorOrNull: Throwable): Unit = {
if(mongoClient != null) {
Try {
mongoClient.close()
}
}
}
}
package example
import org.apache.spark.sql.functions.{col, _}
import org.apache.spark.sql.SparkSession
import org.apache.spark.sql.functions._
import org.apache.spark.sql.streaming.Trigger
import org.apache.spark.util.LongAccumulator
import example.Helpers._
import java.util.Calendar
object StructuredStreamingProgram {
def main(args: Array[String]): Unit = {
val spark = SparkSession
.builder()
.master("local[*]")
.appName("OSB_Streaming_Model")
.getOrCreate()
import spark.implicits._
val df = spark
.readStream
.format("kafka")
.option("kafka.bootstrap.servers", "10.160.172.45:9092, 10.160.172.46:9092, 10.160.172.100:9092")
.option("subscribe", "TOPIC_WITH_COMP_P2_R2, TOPIC_WITH_COMP_P2_R2.DIT, TOPIC_WITHOUT_COMP_P2_R2.DIT")
.load()
val dfs = df.selectExpr("CAST(value AS STRING)")
// sends to MongoDB once every 20 seconds
val mongodb_uri = "mongodb://dstk8sdev06.us.dell.com:27018"
val mdb_name = "HANZO_MDB"
val mdb_collection = "Spark"
val CountAccum: LongAccumulator = spark.sparkContext.longAccumulator("mongostreamcount")
val structuredStreamForeachWriter: MongoDBForeachWriter = new MongoDBForeachWriter(mongodb_uri,mdb_name,mdb_collection,CountAccum)
val query = dfs.writeStream
.foreach(structuredStreamForeachWriter)
.trigger(Trigger.ProcessingTime("20 seconds"))
.start()
while (!spark.streams.awaitAnyTermination(60000)) {
println(Calendar.getInstance().getTime()+" :: mongoEventsCount = "+CountAccum.value)
}
}
}
I need to save the structured streaming data onto Mongo collection

Answer 1

According to the error, you already have a string, (you already did df.selectExpr("CAST(value AS STRING)")), so you should try getting the Row event as a String, and not an Array[Byte]
Start by changing
val valueStr = new String(record.getAs[Array[Byte]]("value"))
to
val valueStr = record.getAs[String]("value")
I understand you may already have a cluster to run Spark code, but I would suggest still looking into the Kafka Connect Mongo Sink Connector so that you don't have to write and maintain your own Mongo writer in Spark code.
Or, you can write Spark datasets to mongo directly as well