使用在spark 2.3.0上创建的配置单元上下文查询配置单元数据库

时间：2018-05-18 03:05:09

标签： apache-spark hivecontext

我可以使用：

以编程方式在spark 1.6.0上创建一个hive上下文 
val conf = new SparkConf().setAppName("SparkTest").setMaster("local")
val sc=new SparkContext(conf)
val hc = new HiveContext(sc)
val actualRecordCountHC = hc.sql("select count(*) from hiveorc_replica.appointment")

这对我来说很好。 以同样的方式，我想在spark 2.3.0上创建一个hive上下文，但是在运行程序时，它会抛出以下错误：

org.apache.spark.sql.AnalysisException: 
Table or view not found: `hiveorc_replica`.`appointment`; line 1 pos 21;
'Aggregate [unresolvedalias(count(1), None)]
    'UnresolvedRelation `hiveorc_replica`.`appointment`

我知道HiveContext（sc）在2.3.0中已被弃用，但是当它们作为spark-shell上的命令运行时，它们也会给出结果。另外，我想让两个版本的spark都能使用该程序。有人可以建议一些直接查询配置表的方法而不使用配置单元数据库文件名吗？

以下是我用来远程连接的hive-site.xml - 

<?xml version="1.0" encoding="UTF-8"?>

<!--Autogenerated by Cloudera Manager-->
<configuration>
  <property>
    <name>hive.metastore.uris</name>
    <value>thrift://fqdn:9083</value>
  </property>
  <property>
    <name>hive.metastore.client.socket.timeout</name>
    <value>300</value>
  </property>
  <property>
    <name>hive.metastore.warehouse.dir</name>
    <value>/user/hive/warehouse</value>
  </property>
  <property>
    <name>hive.warehouse.subdir.inherit.perms</name>
    <value>true</value>
  </property>
  <property>
    <name>hive.auto.convert.join</name>
    <value>true</value>
  </property>
  <property>
    <name>hive.auto.convert.join.noconditionaltask.size</name>
    <value>20971520</value>
  </property>
  <property>
    <name>hive.optimize.bucketmapjoin.sortedmerge</name>
    <value>false</value>
  </property>
  <property>
    <name>hive.smbjoin.cache.rows</name>
    <value>10000</value>
  </property>
  <property>
    <name>hive.server2.logging.operation.enabled</name>
    <value>true</value>
  </property>
  <property>
    <name>hive.server2.logging.operation.log.location</name>
    <value>/var/log/hive/operation_logs</value>
  </property>
  <property>
    <name>mapred.reduce.tasks</name>
    <value>-1</value>
  </property>
  <property>
    <name>hive.exec.reducers.bytes.per.reducer</name>
    <value>67108864</value>
  </property>
  <property>
    <name>hive.exec.copyfile.maxsize</name>
    <value>33554432</value>
  </property>
  <property>
    <name>hive.exec.reducers.max</name>
    <value>1099</value>
  </property>
  <property>
    <name>hive.vectorized.groupby.checkinterval</name>
    <value>4096</value>
  </property>
  <property>
    <name>hive.vectorized.groupby.flush.percent</name>
    <value>0.1</value>
  </property>
  <property>
    <name>hive.compute.query.using.stats</name>
    <value>false</value>
  </property>
  <property>
    <name>hive.vectorized.execution.enabled</name>
    <value>false</value>
  </property>
  <property>
    <name>hive.vectorized.execution.reduce.enabled</name>
    <value>false</value>
  </property>
  <property>
    <name>hive.merge.mapfiles</name>
    <value>true</value>
  </property>
  <property>
    <name>hive.merge.mapredfiles</name>
    <value>false</value>
  </property>
  <property>
    <name>hive.cbo.enable</name>
    <value>false</value>
  </property>
  <property>
    <name>hive.fetch.task.conversion</name>
    <value>minimal</value>
  </property>
  <property>
    <name>hive.fetch.task.conversion.threshold</name>
    <value>268435456</value>
  </property>
  <property>
    <name>hive.limit.pushdown.memory.usage</name>
    <value>0.1</value>
  </property>
  <property>
    <name>hive.merge.sparkfiles</name>
    <value>true</value>
  </property>
  <property>
    <name>hive.merge.smallfiles.avgsize</name>
    <value>16777216</value>
  </property>
  <property>
    <name>hive.merge.size.per.task</name>
    <value>268435456</value>
  </property>
  <property>
    <name>hive.optimize.reducededuplication</name>
    <value>true</value>
  </property>
  <property>
    <name>hive.optimize.reducededuplication.min.reducer</name>
    <value>4</value>
  </property>
  <property>
    <name>hive.map.aggr</name>
    <value>true</value>
  </property>
  <property>
    <name>hive.map.aggr.hash.percentmemory</name>
    <value>0.5</value>
  </property>
  <property>
    <name>hive.optimize.sort.dynamic.partition</name>
    <value>false</value>
  </property>
  <property>
    <name>hive.execution.engine</name>
    <value>mr</value>
  </property>
  <property>
    <name>spark.executor.memory</name>
    <value>268435456</value>
  </property>
  <property>
    <name>spark.driver.memory</name>
    <value>268435456</value>
  </property>
  <property>
    <name>spark.executor.cores</name>
    <value>1</value>
  </property>
  <property>
    <name>spark.yarn.driver.memoryOverhead</name>
    <value>26</value>
  </property>
  <property>
    <name>spark.yarn.executor.memoryOverhead</name>
    <value>26</value>
  </property>
  <property>
    <name>spark.dynamicAllocation.enabled</name>
    <value>true</value>
  </property>
  <property>
    <name>spark.dynamicAllocation.initialExecutors</name>
    <value>1</value>
  </property>
  <property>
    <name>spark.dynamicAllocation.minExecutors</name>
    <value>1</value>
  </property>
  <property>
    <name>spark.dynamicAllocation.maxExecutors</name>
    <value>2147483647</value>
  </property>
  <property>
    <name>hive.metastore.execute.setugi</name>
    <value>true</value>
  </property>
  <property>
    <name>hive.support.concurrency</name>
    <value>true</value>
  </property>
  <property>
    <name>hive.zookeeper.quorum</name>
    <value>fqdn</value>
  </property>
  <property>
    <name>hive.zookeeper.client.port</name>
    <value>2181</value>
  </property>
  <property>
    <name>hive.zookeeper.namespace</name>
    <value>hive_zookeeper_namespace_CD-HIVE-WAyDdBlP</value>
  </property>
  <property>
    <name>hive.cluster.delegation.token.store.class</name>
    <value>org.apache.hadoop.hive.thrift.MemoryTokenStore</value>
  </property>
  <property>
    <name>hive.server2.enable.doAs</name>
    <value>true</value>
  </property>
  <property>
    <name>hive.metastore.sasl.enabled</name>
    <value>true</value>
  </property>
  <property>
    <name>hive.metastore.kerberos.principal</name>
    <value>hive/_HOST@EXAMPLE.COM</value>
  </property>
  <property>
    <name>hive.server2.authentication.kerberos.principal</name>
    <value>hive/_HOST@EXAMPLE.COM</value>
  </property>
  <property>
    <name>spark.shuffle.service.enabled</name>
    <value>true</value>
  </property>
  <property>
    <name>hive.server2.authentication</name>
    <value>LDAP</value>
  </property>
</configuration>

这里，fqdn在运行时被主机hdfs FQDN取代，并且正在为spark 1.6.0完美运行。

1 个答案:

答案 0 :(得分：0)

在spark 2.x.x中，您需要在创建SparkSession时使用enableHiveSupport() 

val spark = SparkSession.builder()
      .appName("Example")
      .master("local")
      .config("hive.metastore.uris","thrift://B:PortNumber")
      .enableHiveSupport()                // <---- This line here
      .getOrCreate()

如果你想要泛型 - 我认为你只需要分别创建SparkContext和HiveContext：

if (sparkVersion <= 2.x.x) {
    // create the old way
}
else 
{
   //create spark session and then get SparkContext and HiveContext from it.
}

Here您可以找到如何以编程方式了解spark版本

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