Regarding current_date, I think it is not in either Hive 0.12.0 or 0.13.1 (versions that we support). Seems https://issues.apache.org/jira/browse/HIVE-5472 added it Hive recently.
On Tue, Mar 3, 2015 at 6:03 AM, Cheng, Hao <hao.ch...@intel.com> wrote: > The temp table in metastore can not be shared cross SQLContext > instances, since HiveContext is a sub class of SQLContext (inherits all of > its functionality), why not using a single HiveContext globally? Is there > any specific requirement in your case that you need multiple > SQLContext/HiveContext? > > > > *From:* shahab [mailto:shahab.mok...@gmail.com] > *Sent:* Tuesday, March 3, 2015 9:46 PM > > *To:* Cheng, Hao > *Cc:* user@spark.apache.org > *Subject:* Re: Supporting Hive features in Spark SQL Thrift JDBC server > > > > You are right , CassandraAwareSQLContext is subclass of SQL context. > > > > But I did another experiment, I queried Cassandra > using CassandraAwareSQLContext, then I registered the "rdd" as a temp table > , next I tried to query it using HiveContext, but it seems that hive > context can not see the registered table suing SQL context. Is this a > normal case? > > > > best, > > /Shahab > > > > > > On Tue, Mar 3, 2015 at 1:35 PM, Cheng, Hao <hao.ch...@intel.com> wrote: > > Hive UDF are only applicable for HiveContext and its subclass instance, > is the CassandraAwareSQLContext a direct sub class of HiveContext or > SQLContext? > > > > *From:* shahab [mailto:shahab.mok...@gmail.com] > *Sent:* Tuesday, March 3, 2015 5:10 PM > *To:* Cheng, Hao > *Cc:* user@spark.apache.org > *Subject:* Re: Supporting Hive features in Spark SQL Thrift JDBC server > > > > val sc: SparkContext = new SparkContext(conf) > > val sqlCassContext = new CassandraAwareSQLContext(sc) // I used some > Calliope Cassandra Spark connector > > val rdd : SchemaRDD = sqlCassContext.sql("select * from db.profile " ) > > rdd.cache > > rdd.registerTempTable("profile") > > rdd.first //enforce caching > > val q = "select from_unixtime(floor(createdAt/1000)) from profile > where sampling_bucket=0 " > > val rdd2 = rdd.sqlContext.sql(q ) > > println ("Result: " + rdd2.first) > > > > And I get the following errors: > > xception in thread "main" > org.apache.spark.sql.catalyst.errors.package$TreeNodeException: Unresolved > attributes: 'from_unixtime('floor(('createdAt / 1000))) AS c0#7, tree: > > Project ['from_unixtime('floor(('createdAt / 1000))) AS c0#7] > > Filter (sampling_bucket#10 = 0) > > Subquery profile > > Project > [company#8,bucket#9,sampling_bucket#10,profileid#11,createdat#12L,modifiedat#13L,version#14] > > CassandraRelation localhost, 9042, 9160, normaldb_sampling, profile, > org.apache.spark.sql.CassandraAwareSQLContext@778b692d, None, None, > false, Some(Configuration: core-default.xml, core-site.xml, > mapred-default.xml, mapred-site.xml) > > > > at > org.apache.spark.sql.catalyst.analysis.Analyzer$CheckResolution$$anonfun$apply$1.applyOrElse(Analyzer.scala:72) > > at > org.apache.spark.sql.catalyst.analysis.Analyzer$CheckResolution$$anonfun$apply$1.applyOrElse(Analyzer.scala:70) > > at > org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:165) > > at > org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$4.apply(TreeNode.scala:183) > > at scala.collection.Iterator$$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$class.toArray(TraversableOnce.scala:252) > > at scala.collection.AbstractIterator.toArray(Iterator.scala:1157) > > at > org.apache.spark.sql.catalyst.trees.TreeNode.transformChildrenDown(TreeNode.scala:212) > > at > org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:168) > > at > org.apache.spark.sql.catalyst.trees.TreeNode.transform(TreeNode.scala:156) > > at > org.apache.spark.sql.catalyst.analysis.Analyzer$CheckResolution$.apply(Analyzer.scala:70) > > at > org.apache.spark.sql.catalyst.analysis.Analyzer$CheckResolution$.apply(Analyzer.scala:68) > > 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$2.apply(RuleExecutor.scala:59) > > at > scala.collection.IndexedSeqOptimized$class.foldl(IndexedSeqOptimized.scala:51) > > at > scala.collection.IndexedSeqOptimized$class.foldLeft(IndexedSeqOptimized.scala:60) > > at scala.collection.mutable.WrappedArray.foldLeft(WrappedArray.scala:34) > > 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$lzycompute(SQLContext.scala:402) > > at > org.apache.spark.sql.SQLContext$QueryExecution.analyzed(SQLContext.scala:402) > > at > org.apache.spark.sql.SQLContext$QueryExecution.optimizedPlan$lzycompute(SQLContext.scala:403) > > at > org.apache.spark.sql.SQLContext$QueryExecution.optimizedPlan(SQLContext.scala:403) > > at > org.apache.spark.sql.SQLContext$QueryExecution.sparkPlan$lzycompute(SQLContext.scala:407) > > at > org.apache.spark.sql.SQLContext$QueryExecution.sparkPlan(SQLContext.scala:405) > > at > org.apache.spark.sql.SQLContext$QueryExecution.executedPlan$lzycompute(SQLContext.scala:411) > > at > org.apache.spark.sql.SQLContext$QueryExecution.executedPlan(SQLContext.scala:411) > > at org.apache.spark.sql.SchemaRDD.collect(SchemaRDD.scala:438) > > at org.apache.spark.sql.SchemaRDD.take(SchemaRDD.scala:440) > > at org.apache.spark.sql.SchemaRDD.take(SchemaRDD.scala:103) > > at org.apache.spark.rdd.RDD.first(RDD.scala:1091) > > at boot.SQLDemo$.main(SQLDemo.scala:65) //my code > > at boot.SQLDemo.main(SQLDemo.scala) //my code > > > > On Tue, Mar 3, 2015 at 8:57 AM, Cheng, Hao <hao.ch...@intel.com> wrote: > > Can you provide the detailed failure call stack? > > > > *From:* shahab [mailto:shahab.mok...@gmail.com] > *Sent:* Tuesday, March 3, 2015 3:52 PM > *To:* user@spark.apache.org > *Subject:* Supporting Hive features in Spark SQL Thrift JDBC server > > > > Hi, > > > > According to Spark SQL documentation, "....Spark SQL supports the vast > majority of Hive features, such as User Defined Functions( UDF) ", and one > of these UFDs is "current_date()" function, which should be supported. > > > > However, i get error when I am using this UDF in my SQL query. There are > couple of other UDFs which cause similar error. > > > > Am I missing something in my JDBC server ? > > > > /Shahab > > > > >
