SparkSQL-Scala与POM

时间：2016-06-30 13:06:45

标签： eclipse scala apache-spark apache-spark-sql cloudera-quickstart-vm

我遇到Cloudera VM和Spark的问题。首先，我是Spark的新手，我的老板要求我在虚拟机上运行Spark on Scala进行一些测试。

我已经在Virtual Box环境下载了虚拟机，所以我打开了Eclipse，我在Maven上有了一个新项目。 不知不觉中，我之前运行Cloudera环境并启动所有服务，如Spark，Yarn，Hive等。 所有服务都很好，Cloudera服务中的所有检查都是绿色的。我用Impala做了一些测试，效果很好。

使用Eclipse和Scala-Maven环境，事情变得最糟糕：这是我在Scala中非常简单的代码：

package org.test.spark

import org.apache.spark.SparkConf
import org.apache.spark.SparkContext
import org.apache.spark.sql.SQLContext

object TestSelectAlgorithm {

  def main(args: Array[String]) = {
    val conf = new SparkConf()
      .setAppName("TestSelectAlgorithm")
      .setMaster("local")
    val sc = new SparkContext(conf)

    val sqlContext = new SQLContext(sc)

    val df = sqlContext.sql("SELECT * FROM products").show()
  }
}

测试非常简单，因为表“产品”存在：如果我在Impala上复制并粘贴相同的查询，查询工作正常！

在Eclipse环境中，否则，我有一些问题：

Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
16/06/30 05:43:17 INFO SparkContext: Running Spark version 1.6.0
16/06/30 05:43:18 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
16/06/30 05:43:18 WARN Utils: Your hostname, quickstart.cloudera resolves to a loopback address: 127.0.0.1; using 10.0.2.15 instead (on interface eth0)
16/06/30 05:43:18 WARN Utils: Set SPARK_LOCAL_IP if you need to bind to another address
16/06/30 05:43:18 INFO SecurityManager: Changing view acls to: cloudera
16/06/30 05:43:18 INFO SecurityManager: Changing modify acls to: cloudera
16/06/30 05:43:18 INFO SecurityManager: SecurityManager: authentication disabled; ui acls disabled; users with view permissions: Set(cloudera); users with modify permissions: Set(cloudera)
16/06/30 05:43:19 INFO Utils: Successfully started service 'sparkDriver' on port 53730.
16/06/30 05:43:19 INFO Slf4jLogger: Slf4jLogger started
16/06/30 05:43:19 INFO Remoting: Starting remoting
16/06/30 05:43:19 INFO Remoting: Remoting started; listening on addresses :[akka.tcp://sparkDriverActorSystem@10.0.2.15:39288]
16/06/30 05:43:19 INFO Utils: Successfully started service 'sparkDriverActorSystem' on port 39288.
16/06/30 05:43:19 INFO SparkEnv: Registering MapOutputTracker
16/06/30 05:43:19 INFO SparkEnv: Registering BlockManagerMaster
16/06/30 05:43:19 INFO DiskBlockManager: Created local directory at /tmp/blockmgr-7d685fc0-ea88-423a-9335-42ca12db85da
16/06/30 05:43:19 INFO MemoryStore: MemoryStore started with capacity 1619.3 MB
16/06/30 05:43:20 INFO SparkEnv: Registering OutputCommitCoordinator
16/06/30 05:43:20 INFO Utils: Successfully started service 'SparkUI' on port 4040.
16/06/30 05:43:20 INFO SparkUI: Started SparkUI at http://10.0.2.15:4040
16/06/30 05:43:20 INFO Executor: Starting executor ID driver on host localhost
16/06/30 05:43:20 INFO Utils: Successfully started service 'org.apache.spark.network.netty.NettyBlockTransferService' on port 57294.
16/06/30 05:43:20 INFO NettyBlockTransferService: Server created on 57294
16/06/30 05:43:20 INFO BlockManagerMaster: Trying to register BlockManager
16/06/30 05:43:20 INFO BlockManagerMasterEndpoint: Registering block manager localhost:57294 with 1619.3 MB RAM, BlockManagerId(driver, localhost, 57294)
16/06/30 05:43:20 INFO BlockManagerMaster: Registered BlockManager
Exception in thread "main" org.apache.spark.sql.AnalysisException: Table not found: products;
    at org.apache.spark.sql.catalyst.analysis.package$AnalysisErrorAt.failAnalysis(package.scala:42)
    at org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveRelations$.getTable(Analyzer.scala:306)
    at org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveRelations$$anonfun$apply$9.applyOrElse(Analyzer.scala:315)
    at org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveRelations$$anonfun$apply$9.applyOrElse(Analyzer.scala:310)
    at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan$$anonfun$resolveOperators$1.apply(LogicalPlan.scala:57)
    at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan$$anonfun$resolveOperators$1.apply(LogicalPlan.scala:57)
    at org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:53)
    at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.resolveOperators(LogicalPlan.scala:56)
    at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan$$anonfun$1.apply(LogicalPlan.scala:54)
    at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan$$anonfun$1.apply(LogicalPlan.scala:54)
    at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$4.apply(TreeNode.scala:265)
    at scala.collection.Iterator$$anon$11.next(Iterator.scala:328)
    at scala.collection.Iterator$class.foreach(Iterator.scala:727)
    at scala.collection.AbstractIterator.foreach(Iterator.scala:1157)
    at scala.collection.generic.Growable$class.$plus$plus$eq(Growable.scala:48)
    at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:103)
    at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:47)
    at scala.collection.TraversableOnce$class.to(TraversableOnce.scala:273)
    at scala.collection.AbstractIterator.to(Iterator.scala:1157)
    at scala.collection.TraversableOnce$class.toBuffer(TraversableOnce.scala:265)
    at scala.collection.AbstractIterator.toBuffer(Iterator.scala:1157)
    at scala.collection.TraversableOnce$class.toArray(TraversableOnce.scala:252)
    at scala.collection.AbstractIterator.toArray(Iterator.scala:1157)
    at org.apache.spark.sql.catalyst.trees.TreeNode.transformChildren(TreeNode.scala:305)
    at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.resolveOperators(LogicalPlan.scala:54)
    at org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveRelations$.apply(Analyzer.scala:310)
    at org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveRelations$.apply(Analyzer.scala:300)
    at org.apache.spark.sql.catalyst.rules.RuleExecutor$$anonfun$execute$1$$anonfun$apply$1.apply(RuleExecutor.scala:83)
    at org.apache.spark.sql.catalyst.rules.RuleExecutor$$anonfun$execute$1$$anonfun$apply$1.apply(RuleExecutor.scala:80)
    at scala.collection.LinearSeqOptimized$class.foldLeft(LinearSeqOptimized.scala:111)
    at scala.collection.immutable.List.foldLeft(List.scala:84)
    at org.apache.spark.sql.catalyst.rules.RuleExecutor$$anonfun$execute$1.apply(RuleExecutor.scala:80)
    at org.apache.spark.sql.catalyst.rules.RuleExecutor$$anonfun$execute$1.apply(RuleExecutor.scala:72)
    at scala.collection.immutable.List.foreach(List.scala:318)
    at org.apache.spark.sql.catalyst.rules.RuleExecutor.execute(RuleExecutor.scala:72)
    at org.apache.spark.sql.execution.QueryExecution.analyzed$lzycompute(QueryExecution.scala:36)
    at org.apache.spark.sql.execution.QueryExecution.analyzed(QueryExecution.scala:36)
    at org.apache.spark.sql.execution.QueryExecution.assertAnalyzed(QueryExecution.scala:34)
    at org.apache.spark.sql.DataFrame.<init>(DataFrame.scala:133)
    at org.apache.spark.sql.DataFrame$.apply(DataFrame.scala:52)
    at org.apache.spark.sql.SQLContext.sql(SQLContext.scala:817)
    at org.test.spark.TestSelectAlgorithm$.main(TestSelectAlgorithm.scala:18)
    at org.test.spark.TestSelectAlgorithm.main(TestSelectAlgorithm.scala)
16/06/30 05:43:22 INFO SparkContext: Invoking stop() from shutdown hook
16/06/30 05:43:22 INFO SparkUI: Stopped Spark web UI at http://10.0.2.15:4040
16/06/30 05:43:22 INFO MapOutputTrackerMasterEndpoint: MapOutputTrackerMasterEndpoint stopped!
16/06/30 05:43:22 INFO MemoryStore: MemoryStore cleared
16/06/30 05:43:22 INFO BlockManager: BlockManager stopped
16/06/30 05:43:22 INFO BlockManagerMaster: BlockManagerMaster stopped
16/06/30 05:43:22 INFO OutputCommitCoordinator$OutputCommitCoordinatorEndpoint: OutputCommitCoordinator stopped!
16/06/30 05:43:22 INFO SparkContext: Successfully stopped SparkContext
16/06/30 05:43:22 INFO ShutdownHookManager: Shutdown hook called
16/06/30 05:43:22 INFO ShutdownHookManager: Deleting directory /tmp/spark-29d381e9-b5e7-485c-92f2-55dc57ca7d25

主要错误是（对我来说）：

Exception in thread "main" org.apache.spark.sql.AnalysisException: Table not found: products;

我搜索了其他网站和文档，我发现问题与Hive表有关...但我不使用Hive表，我使用SparkSql ...

有人可以帮帮我吗？ 谢谢你的回复。

2 个答案:

答案 0 :(得分：2)

在spark中，对于impala，没有直接支持，因为hive有。所以，你必须加载文件。如果是csv，你可以使用spark-csv，

<!-- https://mvnrepository.com/artifact/com.databricks/spark-csv_2.10 -->
<dependency>
    <groupId>com.databricks</groupId>
    <artifactId>spark-csv_2.10</artifactId>
    <version>1.4.0</version>
</dependency>

spark-csv的pom依赖

val sqlContext = new SQLContext(sc)

val df = sqlContext.read.avro("your .avro file location")

import sqlContext.implicits._
import sqlContext._

df.registerTempTable("products")


val result= sqlContext.sql("select * from products")
val result.show()

 result.write
    .format("com.databricks.spark.avro")
    .save("Your ouput location")

对于avro有spark-avro 

 <!-- http://mvnrepository.com/artifact/com.databricks/spark-avro_2.10 -->
            <dependency>
                <groupId>com.databricks</groupId>
                <artifactId>spark-avro_2.10</artifactId>
                <version>2.0.1</version>
            </dependency>

avro的pom依赖

    val sqlContext = new SQLContext(sc)
    val parquetFile = sqlContext.read.parquet("your parquet file location")

    parquetFile.registerTempTable("products")

    sqlContext.sql("select * from products").show()

和镶木地板火花有内置支持

sys.objects

答案 1 :(得分：1)

你能检查 /user/cloudera/.sparkStaging/stagingArea 位置还是包含.avro文件？请更改＆＃34;您的输出位置＆＃34;按目录位置。
请查看avro github页面了解更多详情。 https://github.com/databricks/spark-avro

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