使用“主机”网络时是否可以运行多个apache-server实例？就像使用“桥接”网络和端口映射一样可能吗？
还是要映射“主机”网络实例旁边的其他实例 ，以便映射另一个可能已经使用的端口（而不是80）？
答案 0 :(得分：0)
任何使用主机网络运行的东西都使用主机网络。容器，其他由主机联网的容器以及直接在主机上运行的进程之间没有隔离。如果您在主机上运行Apache，并且有两个Py4JJavaError Traceback (most recent call last)
~/vishal/lib/python3.5/site-packages/pyspark/sql/utils.py in deco(*a, **kw)
62 try:
---> 63 return f(*a, **kw)
64 except py4j.protocol.Py4JJavaError as e:
~/vishal/lib/python3.5/site-packages/py4j/protocol.py in get_return_value(answer, gateway_client, target_id, name)
327 "An error occurred while calling {0}{1}{2}.\n".
--> 328 format(target_id, ".", name), value)
329 else:
Py4JJavaError: An error occurred while calling o24.sql.
: org.apache.spark.sql.AnalysisException: Hive support is required to CREATE Hive TABLE (AS SELECT);;
'CreateTable `employee`, org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe, Ignore
at org.apache.spark.sql.execution.datasources.HiveOnlyCheck$$anonfun$apply$12.apply(rules.scala:392)
at org.apache.spark.sql.execution.datasources.HiveOnlyCheck$$anonfun$apply$12.apply(rules.scala:390)
at org.apache.spark.sql.catalyst.trees.TreeNode.foreach(TreeNode.scala:117)
at org.apache.spark.sql.execution.datasources.HiveOnlyCheck$.apply(rules.scala:390)
at org.apache.spark.sql.execution.datasources.HiveOnlyCheck$.apply(rules.scala:388)
at org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$checkAnalysis$2.apply(CheckAnalysis.scala:386)
at org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$checkAnalysis$2.apply(CheckAnalysis.scala:386)
at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
at org.apache.spark.sql.catalyst.analysis.CheckAnalysis$class.checkAnalysis(CheckAnalysis.scala:386)
at org.apache.spark.sql.catalyst.analysis.Analyzer.checkAnalysis(Analyzer.scala:95)
at org.apache.spark.sql.catalyst.analysis.Analyzer$$anonfun$executeAndCheck$1.apply(Analyzer.scala:108)
at org.apache.spark.sql.catalyst.analysis.Analyzer$$anonfun$executeAndCheck$1.apply(Analyzer.scala:105)
at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper$.markInAnalyzer(AnalysisHelper.scala:201)
at org.apache.spark.sql.catalyst.analysis.Analyzer.executeAndCheck(Analyzer.scala:105)
at org.apache.spark.sql.execution.QueryExecution.analyzed$lzycompute(QueryExecution.scala:57)
at org.apache.spark.sql.execution.QueryExecution.analyzed(QueryExecution.scala:55)
at org.apache.spark.sql.execution.QueryExecution.assertAnalyzed(QueryExecution.scala:47)
at org.apache.spark.sql.Dataset$.ofRows(Dataset.scala:78)
at org.apache.spark.sql.SparkSession.sql(SparkSession.scala:642)
at sun.reflect.GeneratedMethodAccessor3.invoke(Unknown Source)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:498)
at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)
at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)
at py4j.Gateway.invoke(Gateway.java:282)
at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)
at py4j.commands.CallCommand.execute(CallCommand.java:79)
at py4j.GatewayConnection.run(GatewayConnection.java:238)
at java.lang.Thread.run(Thread.java:748)
During handling of the above exception, another exception occurred:
AnalysisException Traceback (most recent call last)
<ipython-input-23-16ed5164dfb1> in <module>
1 with open("/home/data/Downloads/Sample-SQL-File-10-Rows.sql") as fr:
2 query = fr.read()
----> 3 results = sqlContext.sql(query)
~/vishal/lib/python3.5/site-packages/pyspark/sql/context.py in sql(self, sqlQuery)
356 [Row(f1=1, f2=u'row1'), Row(f1=2, f2=u'row2'), Row(f1=3, f2=u'row3')]
357 """
--> 358 return self.sparkSession.sql(sqlQuery)
359
360 @since(1.0)
~/vishal/lib/python3.5/site-packages/pyspark/sql/session.py in sql(self, sqlQuery)
765 [Row(f1=1, f2=u'row1'), Row(f1=2, f2=u'row2'), Row(f1=3, f2=u'row3')]
766 """
--> 767 return DataFrame(self._jsparkSession.sql(sqlQuery), self._wrapped)
768
769 @since(2.0)
~/vishal/lib/python3.5/site-packages/py4j/java_gateway.py in __call__(self, *args)
1255 answer = self.gateway_client.send_command(command)
1256 return_value = get_return_value(
-> 1257 answer, self.gateway_client, self.target_id, self.name)
1258
1259 for temp_arg in temp_args:
~/vishal/lib/python3.5/site-packages/pyspark/sql/utils.py in deco(*a, **kw)
67 e.java_exception.getStackTrace()))
68 if s.startswith('org.apache.spark.sql.AnalysisException: '):
---> 69 raise AnalysisException(s.split(': ', 1)[1], stackTrace)
70 if s.startswith('org.apache.spark.sql.catalyst.analysis'):
71 raise AnalysisException(s.split(': ', 1)[1], stackTrace)
AnalysisException: "Hive support is required to CREATE Hive TABLE (AS SELECT);;\n'CreateTable `employee`, org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe, Ignore\n"
Apache容器，并且它们都试图绑定到0.0.0.0端口80，则它们将发生冲突。您需要使用特定于应用程序的配置来解决此问题。主机联网模式下没有端口映射的概念。
尤其对于简单的HTTP / TCP服务，几乎不需要主机网络。如果您使用标准的桥接网络，则容器中的应用程序不会相互冲突或托管进程。您可以将端口重新映射到任何您方便的位置，而不必担心重新配置应用程序。