SparkSQL-Scala with POM - eclipse

Question

I have some problem with Cloudera VM and Spark. First of all, I'm completely new on Spark, and my boss asked to me to run Spark on Scala in a Virtual Machine for some test.
I have downloaded the Virtual Machine on Virtual Box environment, so I open Eclipse and I had a new Project on Maven.
Obliviously, after I run previously the Cloudera environment and start all services, like Spark, Yarn, Hive and so on.
All services work fine, and all check, in Cloudera services are green. I had do some test with Impala and that works perfectly.
With Eclipse and Scala-Maven environment, the things became worst: that is my very simple code in Scala:
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()
}
}
The test is very simple, because the table "products" exist: if I copy-and-paste the same query on Impala, the query works fine!
On the Eclipse environment, otherwise, I have some problem:
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
The main error is (for me):
Exception in thread "main" org.apache.spark.sql.AnalysisException: Table not found: products;
I searched on other site and documentation, and I founded that the problem is connected with the Hive table... but I don't use the Hive table, I use SparkSql...
Can anyone help me, please?
Thank you for any reply.

Answer 1

In spark, For impala there is no direct support as hive has .So, You have to load file. If it is csv you can use spark-csv,
val df = sqlContext.read
.format("com.databricks.spark.csv")
.option("header", "true")
.option("inferSchema", "true")
.load("your .csv file location")
import sqlContext.implicits._
import sqlContext._
df.registerTempTable("products")
sqlContext.sql("select * from products").show()
pom dependency for 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>
for avro there is spark-avro
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")
pom dependency for 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>
and parquet spark has in-build support
val sqlContext = new SQLContext(sc)
val parquetFile = sqlContext.read.parquet("your parquet file location")
parquetFile.registerTempTable("products")
sqlContext.sql("select * from products").show()

Answer 2

Can you check /user/cloudera/.sparkStaging/stagingArea location exist or it contains .avro file?? And please change "Your ouput location" by directory location.
Please check avro github page for more detail. https://github.com/databricks/spark-avro