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 <[email protected]>
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 <[email protected]> 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
>>
>
>
