Can you try building Spark <https://spark.apache.org/docs/1.2.0/building-spark.html#building-with-hive-and-jdbc-support#building-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 >>>>> >>>> >>>> >>> >> >
