我想使用Twitter + Apache Spark处理Kafka数据。我为此创建了一个模式。但是，当我运行它时，出现以下错误。我在很多地方搜索了此错误，但是我找不到想要的解决方案，或者它没有用。上次我使用较小的内存空间运行Spark时，以为内存不足，但仍然遇到相同的错误。
这是我收到此错误的代码：
from __future__ import print_function
from pyspark import SparkConf
from pyspark import SparkContext
from pyspark.streaming import StreamingContext
from pyspark.streaming.kafka import KafkaUtils
import json
# os.environ['PYSPARK_SUBMIT_ARGS'] = '--packages org.apache.spark:spark-streaming-kafka-0-8_2.11:2.0.2 pyspark-shell'
def main():
conf = SparkConf().setAll(
[('spark.executor.memory', '2g'), ('spark.executor.cores', '2'), ('spark.cores.max', '2')])
sc = SparkContext(conf=conf)
print(sc.getConf().getAll())
ssc = StreamingContext(sc,10)
kafkaStream = KafkaUtils.createStream(ssc, 'localhost:2181', 'spark-streaming', {'twitter':1})
parsed = kafkaStream.map(lambda v: json.loads(v[1]))
user_counts = parsed.map(lambda tweet: (tweet['user']["screen_name"], 1)).reduceByKey(lambda x, y: x + y)
user_counts.pprint()
ssc.start()
ssc.awaitTermination()
if __name__ == '__main__':
main()
这是我的错误：
2018-12-05 00:32:39 ERROR Executor:91 - Exception in task 0.0 in stage 20.0 (TID 20)
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$$anonfun$10.apply(Executor.scala:402)
at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1360)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:408)
at java.base/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1167)
at java.base/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:641)
at java.base/java.lang.Thread.run(Thread.java:844)
2018-12-05 00:32:39 WARN TaskSetManager:66 - Lost task 0.0 in stage 20.0 (TID 20, localhost, executor driver): java.lang.AbstractMethodError
at org.apache.spark.internal.Logging$class.initializeLogIfNecessary(Logging.scala:99)
at org.apache.spark.streaming.kafka.KafkaReceiver.initializeLogIfNecessary(KafkaInputDStream.scala:68)
at org.apache.spark.internal.Logging$class.log(Logging.scala:46)
at org.apache.spark.streaming.kafka.KafkaReceiver.log(KafkaInputDStream.scala:68)
at org.apache.spark.internal.Logging$class.logInfo(Logging.scala:54)
at org.apache.spark.streaming.kafka.KafkaReceiver.logInfo(KafkaInputDStream.scala:68)
at org.apache.spark.streaming.kafka.KafkaReceiver.onStart(KafkaInputDStream.scala:90)
at org.apache.spark.streaming.receiver.ReceiverSupervisor.startReceiver(ReceiverSupervisor.scala:149)
at org.apache.spark.streaming.receiver.ReceiverSupervisor.start(ReceiverSupervisor.scala:131)
at org.apache.spark.streaming.scheduler.ReceiverTracker$ReceiverTrackerEndpoint$$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$10.apply(Executor.scala:402)
at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1360)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:408)
at java.base/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1167)
at java.base/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:641)
at java.base/java.lang.Thread.run(Thread.java:844)
请帮助我。我一直在尝试解决这个问题。