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From Todd Nist <tsind...@gmail.com>
Subject Re: Spark Sql - Missing Jar ? json_tuple NoClassDefFoundError
Date Thu, 02 Apr 2015 17:40:52 GMT
Hi Akhil,

Tried your suggestion to no avail.  I actually to not see and "jackson" or
"json serde" jars in the $HIVE/lib directory.  This is hive 0.13.1 and
spark 1.2.1

Here is what I did:

I have added the lib folder to the –jars option when starting the
spark-shell,
but the job fails. The hive-site.xml is in the $SPARK_HOME/conf directory.

I start the spark-shell as follows:

./bin/spark-shell --master spark://radtech.io:7077
--total-executor-cores 2 --driver-class-path
/usr/local/spark/lib/mysql-connector-java-5.1.34-bin.jar

and like this

./bin/spark-shell --master spark://radtech.io:7077
--total-executor-cores 2 --driver-class-path
/usr/local/spark/lib/mysql-connector-java-5.1.34-bin.jar --jars
/opt/hive/0.13.1/lib/*

I’m just doing this in the spark-shell now:

import org.apache.spark.sql.hive._val sqlContext = new
HiveContext(sc)import sqlContext._case class MetricTable(path: String,
pathElements: String, name: String, value: String)val mt = new
MetricTable("""path": "/DC1/HOST1/""",
    """pathElements": [{"node": "DataCenter","value": "DC1"},{"node":
"host","value": "HOST1"}]""",
    """name": "Memory Usage (%)""",
    """value": 29.590943279257175""")val rdd1 = sc.makeRDD(List(mt))
rdd1.printSchema()
rdd1.registerTempTable("metric_table")
sql(
    """SELECT path, name, value, v1.peValue, v1.peName
         FROM metric_table
           lateral view json_tuple(pathElements, 'name', 'value') v1
             as peName, peValue
    """)
    .collect.foreach(println(_))

It results in the same error:

15/04/02 12:33:59 INFO ParseDriver: Parsing command: SELECT path,
name, value, v1.peValue, v1.peName         FROM metric_table
lateral view json_tuple(pathElements, 'name', 'value') v1
as peName, peValue
15/04/02 12:34:00 INFO ParseDriver: Parse Completed
res2: org.apache.spark.sql.SchemaRDD =
SchemaRDD[5] at RDD at SchemaRDD.scala:108== Query Plan ==== Physical Plan ==
java.lang.ClassNotFoundException: json_tuple

Any other suggestions or am I doing something else wrong here?

-Todd



On Thu, Apr 2, 2015 at 2:00 AM, Akhil Das <akhil@sigmoidanalytics.com>
wrote:

> Try adding all the jars in your $HIVE/lib directory. If you want the
> specific jar, you could look fr jackson or json serde in it.
>
> Thanks
> Best Regards
>
> On Thu, Apr 2, 2015 at 12:49 AM, Todd Nist <tsindotg@gmail.com> wrote:
>
>> I have a feeling I’m missing a Jar that provides the support or could
>> this may be related to https://issues.apache.org/jira/browse/SPARK-5792.
>> If it is a Jar where would I find that ? I would have thought in the
>> $HIVE/lib folder, but not sure which jar contains it.
>>
>> Error:
>>
>> Create Metric Temporary Table for querying15/04/01 14:41:44 INFO HiveMetaStore: 0:
Opening raw store with implemenation class:org.apache.hadoop.hive.metastore.ObjectStore15/04/01
14:41:44 INFO ObjectStore: ObjectStore, initialize called15/04/01 14:41:45 INFO Persistence:
Property hive.metastore.integral.jdo.pushdown unknown - will be ignored15/04/01 14:41:45 INFO
Persistence: Property datanucleus.cache.level2 unknown - will be ignored15/04/01 14:41:45
INFO BlockManager: Removing broadcast 015/04/01 14:41:45 INFO BlockManager: Removing block
broadcast_015/04/01 14:41:45 INFO MemoryStore: Block broadcast_0 of size 1272 dropped from
memory (free 278018571)15/04/01 14:41:45 INFO BlockManager: Removing block broadcast_0_piece015/04/01
14:41:45 INFO MemoryStore: Block broadcast_0_piece0 of size 869 dropped from memory (free
278019440)15/04/01 14:41:45 INFO BlockManagerInfo: Removed broadcast_0_piece0 on 192.168.1.5:63230
in memory (size: 869.0 B, free: 265.1 MB)15/04/01 14:41:45 INFO BlockManagerMaster: Updated
info of block broadcast_0_piece015/04/01 14:41:45 INFO BlockManagerInfo: Removed broadcast_0_piece0
on 192.168.1.5:63278 in memory (size: 869.0 B, free: 530.0 MB)15/04/01 14:41:45 INFO ContextCleaner:
Cleaned broadcast 015/04/01 14:41:46 INFO ObjectStore: Setting MetaStore object pin classes
with hive.metastore.cache.pinobjtypes="Table,StorageDescriptor,SerDeInfo,Partition,Database,Type,FieldSchema,Order"15/04/01
14:41:46 INFO Datastore: The class "org.apache.hadoop.hive.metastore.model.MFieldSchema" is
tagged as "embedded-only" so does not have its own datastore table.15/04/01 14:41:46 INFO
Datastore: The class "org.apache.hadoop.hive.metastore.model.MOrder" is tagged as "embedded-only"
so does not have its own datastore table.15/04/01 14:41:47 INFO Datastore: The class "org.apache.hadoop.hive.metastore.model.MFieldSchema"
is tagged as "embedded-only" so does not have its own datastore table.15/04/01 14:41:47 INFO
Datastore: The class "org.apache.hadoop.hive.metastore.model.MOrder" is tagged as "embedded-only"
so does not have its own datastore table.15/04/01 14:41:47 INFO Query: Reading in results
for query "org.datanucleus.store.rdbms.query.SQLQuery@0" since the connection used is closing15/04/01
14:41:47 INFO ObjectStore: Initialized ObjectStore15/04/01 14:41:47 INFO HiveMetaStore: Added
admin role in metastore15/04/01 14:41:47 INFO HiveMetaStore: Added public role in metastore15/04/01
14:41:48 INFO HiveMetaStore: No user is added in admin role, since config is empty15/04/01
14:41:48 INFO SessionState: No Tez session required at this point. hive.execution.engine=mr.15/04/01
14:41:49 INFO ParseDriver: Parsing command: SELECT path, name, value, v1.peValue, v1.peName
>>              FROM metric
>>              lateral view json_tuple(pathElements, 'name', 'value') v1
>>                as peName, peValue15/04/01 14:41:49 INFO ParseDriver: Parse CompletedException
in thread "main" java.lang.ClassNotFoundException: json_tuple
>>     at java.net.URLClassLoader$1.run(URLClassLoader.java:372)
>>     at java.net.URLClassLoader$1.run(URLClassLoader.java:361)
>>     at java.security.AccessController.doPrivileged(Native Method)
>>     at java.net.URLClassLoader.findClass(URLClassLoader.java:360)
>>     at java.lang.ClassLoader.loadClass(ClassLoader.java:424)
>>     at java.lang.ClassLoader.loadClass(ClassLoader.java:357)
>>     at org.apache.spark.sql.hive.HiveFunctionWrapper.createFunction(Shim13.scala:141)
>>     at org.apache.spark.sql.hive.HiveGenericUdtf.function$lzycompute(hiveUdfs.scala:261)
>>     at org.apache.spark.sql.hive.HiveGenericUdtf.function(hiveUdfs.scala:261)
>>     at org.apache.spark.sql.hive.HiveGenericUdtf.outputInspector$lzycompute(hiveUdfs.scala:267)
>>     at org.apache.spark.sql.hive.HiveGenericUdtf.outputInspector(hiveUdfs.scala:267)
>>     at org.apache.spark.sql.hive.HiveGenericUdtf.outputDataTypes$lzycompute(hiveUdfs.scala:272)
>>     at org.apache.spark.sql.hive.HiveGenericUdtf.outputDataTypes(hiveUdfs.scala:272)
>>     at org.apache.spark.sql.hive.HiveGenericUdtf.makeOutput(hiveUdfs.scala:278)
>>     at org.apache.spark.sql.catalyst.expressions.Generator.output(generators.scala:60)
>>     at org.apache.spark.sql.catalyst.plans.logical.Generate$$anonfun$1.apply(basicOperators.scala:50)
>> 	at org.apache.spark.sql.catalyst.plans.logical.Generate$$anonfun$1.apply(basicOperators.scala:50)
>>     at scala.Option.map(Option.scala:145)
>>     at org.apache.spark.sql.catalyst.plans.logical.Generate.generatorOutput(basicOperators.scala:50)
>>     at org.apache.spark.sql.catalyst.plans.logical.Generate.output(basicOperators.scala:60)
>>     at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan$$anonfun$resolveChildren$1.apply(LogicalPlan.scala:118)
>> 	at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan$$anonfun$resolveChildren$1.apply(LogicalPlan.scala:118)
>>     at scala.collection.TraversableLike$$anonfun$flatMap$1.apply(TraversableLike.scala:251)
>> 	at scala.collection.TraversableLike$$anonfun$flatMap$1.apply(TraversableLike.scala:251)
>>     at scala.collection.immutable.List.foreach(List.scala:318)
>>     at scala.collection.TraversableLike$class.flatMap(TraversableLike.scala:251)
>>     at scala.collection.AbstractTraversable.flatMap(Traversable.scala:105)
>>     at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.resolveChildren(LogicalPlan.scala:118)
>>     at org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveReferences$$anonfun$apply$6$$anonfun$applyOrElse$1.applyOrElse(Analyzer.scala:159)
>>     at org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveReferences$$anonfun$apply$6$$anonfun$applyOrElse$1.applyOrElse(Analyzer.scala:156)
>>     at org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:144)
>>     at org.apache.spark.sql.catalyst.plans.QueryPlan.org$apache$spark$sql$catalyst$plans$QueryPlan$$transformExpressionDown$1(QueryPlan.scala:71)
>> 	at org.apache.spark.sql.catalyst.plans.QueryPlan$$anonfun$1$$anonfun$apply$1.apply(QueryPlan.scala:85)
>>     at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244)
>> 	at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244)
>>     at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
>>     at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:47)
>>     at scala.collection.TraversableLike$class.map(TraversableLike.scala:244)
>>     at scala.collection.AbstractTraversable.map(Traversable.scala:105)
>>     at org.apache.spark.sql.catalyst.plans.QueryPlan$$anonfun$1.apply(QueryPlan.scala:84)
>> 	at scala.collection.Iterator$$anon$11.next(Iterator.scala:328)
>>     at scala.collection.Iterator$class.foreach(Iterator.scala:727)
>>     at scala.collection.AbstractIterator.foreach(Iterator.scala:1157)
>>     at scala.collection.generic.Growable$class.$plus$plus$eq(Growable.scala:48)
>>     at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:103)
>>     at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:47)
>>     at scala.collection.TraversableOnce$class.to(TraversableOnce.scala:273)
>>     at scala.collection.AbstractIterator.to(Iterator.scala:1157)
>>     at scala.collection.TraversableOnce$class.toBuffer(TraversableOnce.scala:265)
>>     at scala.collection.AbstractIterator.toBuffer(Iterator.scala:1157)
>>     at scala.collection.TraversableOnce$class.toArray(TraversableOnce.scala:252)
>>     at scala.collection.AbstractIterator.toArray(Iterator.scala:1157)
>>     at org.apache.spark.sql.catalyst.plans.QueryPlan.transformExpressionsDown(QueryPlan.scala:89)
>>     at org.apache.spark.sql.catalyst.plans.QueryPlan.transformExpressions(QueryPlan.scala:60)
>>     at org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveReferences$$anonfun$apply$6.applyOrElse(Analyzer.scala:156)
>>     at org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveReferences$$anonfun$apply$6.applyOrElse(Analyzer.scala:153)
>>     at org.apache.spark.sql.catalyst.trees.TreeNode.transformUp(TreeNode.scala:206)
>>     at org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveReferences$.apply(Analyzer.scala:153)
>>     at org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveReferences$.apply(Analyzer.scala:152)
>>     at org.apache.spark.sql.catalyst.rules.RuleExecutor$$anonfun$apply$1$$anonfun$apply$2.apply(RuleExecutor.scala:61)
>>     at org.apache.spark.sql.catalyst.rules.RuleExecutor$$anonfun$apply$1$$anonfun$apply$2.apply(RuleExecutor.scala:59)
>>     at scala.collection.LinearSeqOptimized$class.foldLeft(LinearSeqOptimized.scala:111)
>>     at scala.collection.immutable.List.foldLeft(List.scala:84)
>>     at org.apache.spark.sql.catalyst.rules.RuleExecutor$$anonfun$apply$1.apply(RuleExecutor.scala:59)
>> 	at org.apache.spark.sql.catalyst.rules.RuleExecutor$$anonfun$apply$1.apply(RuleExecutor.scala:51)
>>     at scala.collection.immutable.List.foreach(List.scala:318)
>>     at org.apache.spark.sql.catalyst.rules.RuleExecutor.apply(RuleExecutor.scala:51)
>>     at org.apache.spark.sql.SQLContext$QueryExecution.analyzed$lzycompute(SQLContext.scala:411)
>>     at org.apache.spark.sql.SQLContext$QueryExecution.analyzed(SQLContext.scala:411)
>>     at org.apache.spark.sql.SQLContext$QueryExecution.withCachedData$lzycompute(SQLContext.scala:412)
>>     at org.apache.spark.sql.SQLContext$QueryExecution.withCachedData(SQLContext.scala:412)
>>     at org.apache.spark.sql.SQLContext$QueryExecution.optimizedPlan$lzycompute(SQLContext.scala:413)
>>     at org.apache.spark.sql.SQLContext$QueryExecution.optimizedPlan(SQLContext.scala:413)
>>     at org.apache.spark.sql.SQLContext$QueryExecution.sparkPlan$lzycompute(SQLContext.scala:418)
>>     at org.apache.spark.sql.SQLContext$QueryExecution.sparkPlan(SQLContext.scala:416)
>>     at org.apache.spark.sql.SQLContext$QueryExecution.executedPlan$lzycompute(SQLContext.scala:422)
>>     at org.apache.spark.sql.SQLContext$QueryExecution.executedPlan(SQLContext.scala:422)
>>     at org.apache.spark.sql.SchemaRDD.collect(SchemaRDD.scala:444)
>>     at com.opsdatastore.elasticsearch.spark.ElasticSearchReadWrite$.main(ElasticSearchReadWrite.scala:119)
>>     at com.opsdatastore.elasticsearch.spark.ElasticSearchReadWrite.main(ElasticSearchReadWrite.scala)
>>     at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
>>     at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
>>     at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
>>     at java.lang.reflect.Method.invoke(Method.java:483)
>>     at org.apache.spark.deploy.SparkSubmit$.launch(SparkSubmit.scala:358)
>>     at org.apache.spark.deploy.SparkSubmit$.main(SparkSubmit.scala:75)
>>     at org.apache.spark.deploy.SparkSubmit.main(SparkSubmit.scala)
>>
>> Json:
>>
>> "metric": {
>>
>>     "path": "/PA/Pittsburgh/12345 Westbrook Drive/main/theromostat-1",
>>     "pathElements": [
>>     {
>>         "node": "State",
>>         "value": "PA"
>>     },
>>     {
>>         "node": "City",
>>         "value": "Pittsburgh"
>>     },
>>     {
>>         "node": "Street",
>>         "value": "12345 Westbrook Drive"
>>     },
>>     {
>>         "node": "level",
>>         "value": "main"
>>     },
>>     {
>>         "node": "device",
>>         "value": "thermostat"
>>     }
>>     ],
>>     "name": "Current Temperature",
>>     "value": 29.590943279257175,
>>     "timestamp": "2015-03-27T14:53:46+0000"
>>   }
>>
>> Here is the code that produces the error:
>>
>> // Spark importsimport org.apache.spark.{SparkConf, SparkContext}import org.apache.spark.SparkContext._
>> import org.apache.spark.rdd.RDD
>> import org.apache.spark.sql.{SchemaRDD,SQLContext}import org.apache.spark.sql.hive._
>> // ES importsimport org.elasticsearch.spark._import org.elasticsearch.spark.sql._
>> def main(args: Array[String]) {
>>     val sc = sparkInit
>>
>>     @transient
>>     val hiveContext = new org.apache.spark.sql.hive.HiveContext(sc)
>>
>>     import hiveContext._
>>
>>     val start = System.currentTimeMillis()
>>
>>     /*
>>      * Read from ES and provide some insights with SparkSQL
>>      */
>>     val esData = sc.esRDD(s"${ElasticSearch.Index}/${ElasticSearch.Type}")
>>
>>     esData.collect.foreach(println(_))
>>
>>     val end = System.currentTimeMillis()
>>     println(s"Total time: ${end-start} ms")
>>
>>     println("Create Metric Temporary Table for querying")
>>
>>     val schemaRDD = hiveContext.sql(
>>           "CREATE TEMPORARY TABLE metric     " +
>>           "USING org.elasticsearch.spark.sql " +
>>           "OPTIONS (resource 'device/metric')" )
>>
>>     hiveContext.sql(
>>         """SELECT path, name, value, v1.peValue, v1.peName
>>              FROM metric
>>              lateral view json_tuple(pathElements, 'name', 'value') v1
>>                as peName, peValue
>>         """)
>>         .collect.foreach(println(_))
>>   }
>> }
>>
>> More than likely I’m missing a jar, but not sure what that would be.
>>
>> -Todd
>>
>
>

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