All:
I am accessing Geomesa from Zeppelin via spark. I am able
to run a lot of
great queries. But I have also run into an interesting
issue. If I run a
basic string compare like below I get a syntax error:
select count(objectkey) from linkageview where name = "NGrams"
java.lang.IllegalArgumentException:
argument type mismatch 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:498) at org.locationtech.geomesa.spark.SparkVersions$$anonfun$1$$anonfun$apply$2.apply(SparkVersions.scala:34) at org.locationtech.geomesa.spark.SparkVersions$$anonfun$1$$anonfun$apply$2.apply(SparkVersions.scala:34)
at org.locationtech.geomesa.spark.SparkVersions$.copy(SparkVersions.scala:48) at org.apache.spark.sql.SQLRules$SpatialOptimizationsRule$$anonfun$apply$1.applyOrElse(SQLRules.scala:261) at org.apache.spark.sql.SQLRules$SpatialOptimizationsRule$$anonfun$apply$1.applyOrElse(SQLRules.scala:221)
at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$3.apply(TreeNode.scala:288) at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$3.apply(TreeNode.scala:288) at org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:70) at
org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:287) at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformDown$1.apply(TreeNode.scala:293) at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformDown$1.apply(TreeNode.scala:293)
at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$5.apply(TreeNode.scala:331) at org.apache.spark.sql.catalyst.trees.TreeNode.mapProductIterator(TreeNode.scala:188) at org.apache.spark.sql.catalyst.trees.TreeNode.transformChildren(TreeNode.scala:329)
at org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:293) at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformDown$1.apply(TreeNode.scala:293) at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformDown$1.apply(TreeNode.scala:293)
at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$5.apply(TreeNode.scala:331) at org.apache.spark.sql.catalyst.trees.TreeNode.mapProductIterator(TreeNode.scala:188) at org.apache.spark.sql.catalyst.trees.TreeNode.transformChildren(TreeNode.scala:329)
at org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:293) at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformDown$1.apply(TreeNode.scala:293) at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformDown$1.apply(TreeNode.scala:293)
at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$5.apply(TreeNode.scala:331) at org.apache.spark.sql.catalyst.trees.TreeNode.mapProductIterator(TreeNode.scala:188) at org.apache.spark.sql.catalyst.trees.TreeNode.transformChildren(TreeNode.scala:329)
at org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:293) at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformDown$1.apply(TreeNode.scala:293) at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformDown$1.apply(TreeNode.scala:293)
at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$5.apply(TreeNode.scala:331) at org.apache.spark.sql.catalyst.trees.TreeNode.mapProductIterator(TreeNode.scala:188) at org.apache.spark.sql.catalyst.trees.TreeNode.transformChildren(TreeNode.scala:329)
at org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:293) at org.apache.spark.sql.catalyst.trees.TreeNode.transform(TreeNode.scala:277) at org.apache.spark.sql.SQLRules$SpatialOptimizationsRule$.apply(SQLRules.scala:221) at org.apache.spark.sql.SQLRules$SpatialOptimizationsRule$.apply(SQLRules.scala:143)
at org.apache.spark.sql.catalyst.rules.RuleExecutor$$anonfun$execute$1$$anonfun$apply$1.apply(RuleExecutor.scala:85) at org.apache.spark.sql.catalyst.rules.RuleExecutor$$anonfun$execute$1$$anonfun$apply$1.apply(RuleExecutor.scala:82) at scala.collection.LinearSeqOptimized$class.foldLeft(LinearSeqOptimized.scala:124)
at scala.collection.immutable.List.foldLeft(List.scala:84) at org.apache.spark.sql.catalyst.rules.RuleExecutor$$anonfun$execute$1.apply(RuleExecutor.scala:82) at org.apache.spark.sql.catalyst.rules.RuleExecutor$$anonfun$execute$1.apply(RuleExecutor.scala:74)
at scala.collection.immutable.List.foreach(List.scala:381) at org.apache.spark.sql.catalyst.rules.RuleExecutor.execute(RuleExecutor.scala:74) at org.apache.spark.sql.execution.QueryExecution.optimizedPlan$lzycompute(QueryExecution.scala:73) at org.apache.spark.sql.execution.QueryExecution.optimizedPlan(QueryExecution.scala:73)
at org.apache.spark.sql.execution.QueryExecution.sparkPlan$lzycompute(QueryExecution.scala:79) at org.apache.spark.sql.execution.QueryExecution.sparkPlan(QueryExecution.scala:75) at org.apache.spark.sql.execution.QueryExecution.executedPlan$lzycompute(QueryExecution.scala:84)
at org.apache.spark.sql.execution.QueryExecution.executedPlan(QueryExecution.scala:84) at org.apache.spark.sql.Dataset.withTypedCallback(Dataset.scala:2791) at org.apache.spark.sql.Dataset.head(Dataset.scala:2112) at org.apache.spark.sql.Dataset.take(Dataset.scala:2327)
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:498)
at org.apache.zeppelin.spark.SparkZeppelinContext.showData(SparkZeppelinContext.java:108) at org.apache.zeppelin.spark.SparkSqlInterpreter.interpret(SparkSqlInterpreter.java:135) at org.apache.zeppelin.interpreter.LazyOpenInterpreter.interpret(LazyOpenInterpreter.java:103)
at org.apache.zeppelin.interpreter.remote.RemoteInterpreterServer$InterpretJob.jobRun(RemoteInterpreterServer.java:632) at org.apache.zeppelin.scheduler.Job.run(Job.java:188) at org.apache.zeppelin.scheduler.ParallelScheduler$JobRunner.run(ParallelScheduler.java:162)
at java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:511) at java.util.concurrent.FutureTask.run(FutureTask.java:266) at java.util.concurrent.ScheduledThreadPoolExecutor$ScheduledFutureTask.access$201(ScheduledThreadPoolExecutor.java:180)
at java.util.concurrent.ScheduledThreadPoolExecutor$ScheduledFutureTask.run(ScheduledThreadPoolExecutor.java:293) at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149) at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
at java.lang.Thread.run(Thread.java:748)
If I change the query to:
select count(objectkey) from linkageview where name like
"NGrams%"
The query works just fine.
I am running a custom build of 2.2.2 for SPARK 2.1 and Hadoop
2.7.
Since it looks like this is tied to
org.apache.spark.sql.catalyst.rules
The schema for my data frame looks like the following:
root
|-- __fid__: string (nullable = false) |-- entity2version: string (nullable = true) |-- linklabel: string (nullable = true) |-- linktype: integer (nullable = true) |-- source: string (nullable = true) |-- datecreated: timestamp (nullable = true) |-- title:
string (nullable = true) |-- version: string (nullable = true) |-- entity2name: string (nullable = true) |-- entity2source: string (nullable = true) |-- objectkey: string (nullable = true) |-- lastmodified: timestamp (nullable = true) |-- name: string (nullable
= true) |-- entity2key: string (nullable = true) |-- linkstatus: integer (nullable = true)
This
occurs with any string field.
--
========= mailto:dboyd@xxxxxxxxxxxxxxxxx ============
David W. Boyd
VP, Data Solutions
10432 Balls Ford, Suite 240
Manassas, VA 20109
office: +1-703-552-2862
cell: +1-703-402-7908
============== http://www.incadencecorp.com/ ============
ISO/IEC JTC1 WG9, editor ISO/IEC 20547 Big Data Reference Architecture
Chair ANSI/INCITS TC Big Data
Co-chair NIST Big Data Public Working Group Reference Architecture
First Robotic Mentor - FRC, FTC - www.iliterobotics.org
Board Member- USSTEM Foundation - www.usstem.org
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