fak*_*zad 9 scala k-means apache-spark
我用过这段代码
我的错误是:
Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
17/02/03 20:39:24 INFO SparkContext: Running Spark version 2.1.0
17/02/03 20:39:25 WARN NativeCodeLoader: Unable to load native-hadoop
library for your platform... using builtin-java classes where applicable
17/02/03 20:39:25 WARN SparkConf: Detected deprecated memory fraction
settings: [spark.storage.memoryFraction]. As of Spark 1.6, execution and
storage memory management are unified. All memory fractions used in the old
model are now deprecated and no longer read. If you wish to use the old
memory management, you may explicitly enable `spark.memory.useLegacyMode`
(not recommended).
17/02/03 20:39:25 ERROR SparkContext: Error initializing SparkContext.
org.apache.spark.SparkException: A master URL must be set in your
configuration
at org.apache.spark.SparkContext.<init>(SparkContext.scala:379)
at PCA$.main(PCA.scala:26)
at PCA.main(PCA.scala)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:498)
at com.intellij.rt.execution.application.AppMain.main(AppMain.java:144)
17/02/03 20:39:25 INFO SparkContext: Successfully stopped SparkContext
Exception in thread "main" org.apache.spark.SparkException: A master URL must be set in your configuration
at org.apache.spark.SparkContext.<init>(SparkContext.scala:379)
at PCA$.main(PCA.scala:26)
at PCA.main(PCA.scala)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at
sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
at
sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:498)
at com.intellij.rt.execution.application.AppMain.main(AppMain.java:144)
Process finished with exit code 1
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小智 9
如果你单独运行火花架那么
val conf = new SparkConf().setMaster("spark://master") //missing
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并且您可以在提交作业时传递参数
spark-submit --master spark://master
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如果你正在运行spark local
val conf = new SparkConf().setMaster("local[2]") //missing
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您可以在提交作业时传递参数
spark-submit --master local
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如果你在纱线上运行火花那么
spark-submit --master yarn
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错误消息非常清楚,您必须通过SparkContext或通过提供Spark Master节点的地址spark-submit:
val conf =
new SparkConf()
.setAppName("ClusterScore")
.setMaster("spark://172.1.1.1:7077") // <--- This is what's missing
.set("spark.storage.memoryFraction", "1")
val sc = new SparkContext(conf)
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