我知道类似的问题已经得到了简短的回答，但由于缺乏最低声誉，我无法在那里添加我个人的额外怀疑......因此我在这里问它
我想使用 Apache Spark + Kafka 处理 Twitter 数据。我为此创建了一个模式。但是当我运行它时，我收到以下错误。我搜索了很多关于此错误的地方，但我无法得到我想要的解决方案，或者它不起作用。上次我用较小的内存空间运行Spark时，认为内存不足，但我仍然收到同样的错误。这是我收到的此错误的代码：
from kafka import KafkaConsumer
from pyspark.streaming import StreamingContext
import json
import pandas as pd
from pyspark import SparkConf,SparkContext
from pyspark.streaming.kafka import KafkaUtils
#cd /opt/hadoop-3.2.0-7/hadoop/spark $sudo ./bin/spark-submit --packages org.apache.spark:spark-streaming-kafka-0-8_2.11:2.3.0 /opt/twitterConsumer.py
conf = SparkConf()
conf.setAppName("BDA-Twitter-Spark-Kafka")
sc = SparkContext(conf=conf)
sc.setLogLevel("ERROR")
ssc = StreamingContext(sc,1)
KafkaStream = KafkaUtils.createStream(ssc, "localhost:2181",'tks',{"xmas":1}) # directKafkaStream = KafkaUtils.createDirectStream(ssc, [topic], {"metadata.broker.list": brokers})
KafkaStream.pprint()
print("HERE1")
ssc.start()
ssc.awaitTermination()
答我的错误是：
ERROR Executor: Exception in task 0.0 in stage 0.0 (TID 0)
java.lang.AbstractMethodError
at org.apache.spark.internal.Logging$class.initializeLogIfNecessary(Logging.scala:99)
at org.apache.spark.streaming.kafka.KafkaReceiver.initializeLogIfNecessary(KafkaInputDStream.scala:68)
at org.apache.spark.internal.Logging$class.log(Logging.scala:46)
at org.apache.spark.streaming.kafka.KafkaReceiver.log(KafkaInputDStream.scala:68)
at org.apache.spark.internal.Logging$class.logInfo(Logging.scala:54)
at org.apache.spark.streaming.kafka.KafkaReceiver.logInfo(KafkaInputDStream.scala:68)
at org.apache.spark.streaming.kafka.KafkaReceiver.onStart(KafkaInputDStream.scala:90)
at org.apache.spark.streaming.receiver.ReceiverSupervisor.startReceiver(ReceiverSupervisor.scala:149)
at org.apache.spark.streaming.receiver.ReceiverSupervisor.start(ReceiverSupervisor.scala:131)
at org.apache.spark.streaming.scheduler.ReceiverTracker$ReceiverTrackerEndpoint$$anonfun$9.apply(ReceiverTracker.scala:601)
at org.apache.spark.streaming.scheduler.ReceiverTracker$ReceiverTrackerEndpoint$$anonfun$9.apply(ReceiverTracker.scala:591)
at org.apache.spark.SparkContext$$anonfun$37.apply(SparkContext.scala:2212)
at org.apache.spark.SparkContext$$anonfun$37.apply(SparkContext.scala:2212)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)
at org.apache.spark.scheduler.Task.run(Task.scala:121)
at org.apache.spark.executor.Executor$TaskRunner$$anonfun$10.apply(Executor.scala:408)
at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1360)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:414)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
at java.lang.Thread.run(Thread.java:748)
19/12/29 09:57:49 ERROR TaskSetManager: Task 0 in stage 0.0 failed 1 times; aborting job
19/12/29 09:57:49 ERROR ReceiverTracker: Receiver has been stopped. Try to restart it.
org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 0.0 failed 1 times, most recent failure: Lost task 0.0 in stage 0.0 (TID 0, localhost, executor driver): java.lang.AbstractMethodError
at org.apache.spark.internal.Logging$class.initializeLogIfNecessary(Logging.scala:99)
at org.apache.spark.streaming.kafka.KafkaReceiver.initializeLogIfNecessary(KafkaInputDStream.scala:68)
at org.apache.spark.internal.Logging$class.log(Logging.scala:46)
at org.apache.spark.streaming.kafka.KafkaReceiver.log(KafkaInputDStream.scala:68)
at org.apache.spark.internal.Logging$class.logInfo(Logging.scala:54)
at org.apache.spark.streaming.kafka.KafkaReceiver.logInfo(KafkaInputDStream.scala:68)
at org.apache.spark.streaming.kafka.KafkaReceiver.onStart(KafkaInputDStream.scala:90)
at org.apache.spark.streaming.receiver.ReceiverSupervisor.startReceiver(ReceiverSupervisor.scala:149)
at org.apache.spark.streaming.receiver.ReceiverSupervisor.start(ReceiverSupervisor.scala:131)
at org.apache.spark.streaming.scheduler.ReceiverTracker$ReceiverTrackerEndpoint$$anonfun$9.apply(ReceiverTracker.scala:601)
at org.apache.spark.streaming.scheduler.ReceiverTracker$ReceiverTrackerEndpoint$$anonfun$9.apply(ReceiverTracker.scala:591)
at org.apache.spark.SparkContext$$anonfun$37.apply(SparkContext.scala:2212)
at org.apache.spark.SparkContext$$anonfun$37.apply(SparkContext.scala:2212)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)
at org.apache.spark.scheduler.Task.run(Task.scala:121)
at org.apache.spark.executor.Executor$TaskRunner$$anonfun$10.apply(Executor.scala:408)
at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1360)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:414)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
at java.util.concurrent.ThreadPoolExecutor$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$$failJobAndIndependentStages(DAGScheduler.scala:1889)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1877)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1876)
at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1876)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:926)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:926)
at scala.Option.foreach(Option.scala:257)
at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:926)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:2110)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2059)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2048)
at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:49)
Caused by: java.lang.AbstractMethodError
at org.apache.spark.internal.Logging$class.initializeLogIfNecessary(Logging.scala:99)
at org.apache.spark.streaming.kafka.KafkaReceiver.initializeLogIfNecessary(KafkaInputDStream.scala:68)
at org.apache.spark.internal.Logging$class.log(Logging.scala:46)
at org.apache.spark.streaming.kafka.KafkaReceiver.log(KafkaInputDStream.scala:68)
at org.apache.spark.internal.Logging$class.logInfo(Logging.scala:54)
at org.apache.spark.streaming.kafka.KafkaReceiver.logInfo(KafkaInputDStream.scala:68)
at org.apache.spark.streaming.kafka.KafkaReceiver.onStart(KafkaInputDStream.scala:90)
at org.apache.spark.streaming.receiver.ReceiverSupervisor.startReceiver(ReceiverSupervisor.scala:149)
at org.apache.spark.streaming.receiver.ReceiverSupervisor.start(ReceiverSupervisor.scala:131)
at org.apache.spark.streaming.scheduler.ReceiverTracker$ReceiverTrackerEndpoint$$anonfun$9.apply(ReceiverTracker.scala:601)
at org.apache.spark.streaming.scheduler.ReceiverTracker$ReceiverTrackerEndpoint$$anonfun$9.apply(ReceiverTracker.scala:591)
at org.apache.spark.SparkContext$$anonfun$37.apply(SparkContext.scala:2212)
at org.apache.spark.SparkContext$$anonfun$37.apply(SparkContext.scala:2212)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)
at org.apache.spark.scheduler.Task.run(Task.scala:121)
at org.apache.spark.executor.Executor$TaskRunner$$anonfun$10.apply(Executor.scala:408)
at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1360)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:414)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
at java.lang.Thread.run(Thread.java:748)
如何在此处匹配所有必需工具的版本？
您看到的错误可能来自版本不匹配
Hadoop 和 Spark 需要 Java8
您使用的是"Kafka with Scala 2.12"(Maven：kafka_2.12(，因此您的软件包还必须使用 Scala 2.12(Maven：spark-xyz_2.12(，并且还必须与您的Spark 版本(2.3.1(匹配。您的命令显示您已为 Spark 2.3.0 提取了 Scala 2.11 的 Kafka 流包。另请注意，Spark Streaming 包已弃用，您应该改用 spark-sql-kafka，即结构化流。
您仍然可以在没有Spark和Hadoop的情况下进行实时分析