I think you need to include the jar file through --jars option that contains the hive definition (code) of UDF json_tuple. That should solve your problem.
On Fri, Apr 3, 2015 at 3:57 PM, Todd Nist <tsind...@gmail.com> wrote: > I placed it there. It was downloaded from MySql site. > > On Fri, Apr 3, 2015 at 6:25 AM, ÐΞ€ρ@Ҝ (๏̯͡๏) <deepuj...@gmail.com> wrote: > >> Akhil >> you mentioned /usr/local/spark/lib/mysql-connector-java-5.1.34-bin.jar . >> how come you got this lib into spark/lib folder. >> 1) did you place it there ? >> 2) What is download location ? >> >> >> On Fri, Apr 3, 2015 at 3:42 PM, Todd Nist <tsind...@gmail.com> wrote: >> >>> Started the spark shell with the one jar from hive suggested: >>> >>> ./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/apache-hive-0.13.1-bin/lib/hive-exec-0.13.1.jar >>> >>> Results in the same error: >>> >>> scala> sql( | """SELECT path, name, value, v1.peValue, v1.peName >>> | FROM metric_table | lateral view >>> json_tuple(pathElements, 'name', 'value') v1 | as peName, >>> peValue | """) >>> 15/04/03 06:01:30 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 >>> 15/04/03 06:01:31 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 >>> >>> I will try the rebuild. Thanks again for the assistance. >>> >>> -Todd >>> >>> >>> On Fri, Apr 3, 2015 at 5:34 AM, Akhil Das <ak...@sigmoidanalytics.com> >>> wrote: >>> >>>> Can you try building Spark >>>> <https://spark.apache.org/docs/1.2.0/building-spark.html#building-with-hive-and-jdbc-support%23building-with-hive-and-jdbc-support> >>>> with hive support? Before that try to run the following: >>>> >>>> ./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/hive-exec.jar >>>> >>>> Thanks >>>> Best Regards >>>> >>>> On Fri, Apr 3, 2015 at 2:55 PM, Todd Nist <tsind...@gmail.com> wrote: >>>> >>>>> Hi Akhil, >>>>> >>>>> This is for version 1.2.1. Well the other thread that you reference >>>>> was me attempting it in 1.3.0 to see if the issue was related to 1.2.1. I >>>>> did not build Spark but used the version from the Spark download site for >>>>> 1.2.1 Pre Built for Hadoop 2.4 or Later. >>>>> >>>>> Since I get the error in both 1.2.1 and 1.3.0, >>>>> >>>>> 15/04/01 14:41:49 INFO ParseDriver: Parse Completed Exception in >>>>> thread "main" java.lang.ClassNotFoundException: json_tuple at >>>>> java.net.URLClassLoader$1.run( >>>>> >>>>> It looks like I just don't have the jar. Even including all jars in >>>>> the $HIVE/lib directory did not seem to work. Though when looking in >>>>> $HIVE/lib for 0.13.1, I do not see any json serde or jackson files. I do >>>>> see that hive-exec.jar contains >>>>> the org/apache/hadoop/hive/ql/udf/generic/GenericUDTFJSONTuple class. Do >>>>> you know if there is another Jar that is required or should it work just >>>>> by >>>>> including all jars from $HIVE/lib? >>>>> >>>>> I can build it locally, but did not think that was required based on >>>>> the version I downloaded; is that not the case? >>>>> >>>>> Thanks for the assistance. >>>>> >>>>> -Todd >>>>> >>>>> >>>>> On Fri, Apr 3, 2015 at 2:06 AM, Akhil Das <ak...@sigmoidanalytics.com> >>>>> wrote: >>>>> >>>>>> How did you build spark? which version of spark are you having? >>>>>> Doesn't this thread already explains it? >>>>>> https://www.mail-archive.com/user@spark.apache.org/msg25505.html >>>>>> >>>>>> Thanks >>>>>> Best Regards >>>>>> >>>>>> On Thu, Apr 2, 2015 at 11:10 PM, Todd Nist <tsind...@gmail.com> >>>>>> wrote: >>>>>> >>>>>>> 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 < >>>>>>> ak...@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 <tsind...@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 >>>>>>>>> >>>>>>>> >>>>>>>> >>>>>>> >>>>>> >>>>> >>>> >>> >> >> >> -- >> Deepak >> >> > -- Deepak
