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
