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 >>>>>> >>>>> >>>>> >>>> >>> >> >
