"joinLeft" is not working when joining a clock with a parque-based DataFrame

@icexelloss

(even though the "timeColumn" argument error can be bypassed by renaming the column in question to `time`) the `joinLeft` is not working for me:
```
print( sc.version )
print( tm )

n = df.filter( df['Container'] == 'dbc94d4e3af6' ).select( tm, 'MemPercentG', 'CpuPercentG' )

from ts.flint import FlintContext, clocks
from ts.flint import utils

fc = FlintContext( sqlContext )

r = fc.read \
.option('isSorted', False) \
.option('timeUnit', 's') \
.dataframe( n )

r.show(truncate=False)
print( r )
r.printSchema()

l = clocks.uniform(fc, '30s', begin_date_time='2018-8-1 5:55:35', end_date_time='2018-08-01 05:59:05')
print( type( l ) )
print( l )
l.printSchema()
# l.show(truncate=False)
j = l.leftJoin( r )
```

With the output being:
```
2.3.1
time
+-------------------+---------------+------------+
|time |MemPercentG |CpuPercentG |
+-------------------+---------------+------------+
|2018-08-01 05:55:35|0.0030517578125|0.002331024 |
|2018-08-01 05:58:05|0.0030517578125|0.0031538776|
|2018-08-01 05:59:05|0.0030517578125|0.0030176123|
+-------------------+---------------+------------+

TimeSeriesDataFrame[time: timestamp, MemPercentG: double, CpuPercentG: float]
root
|-- time: timestamp (nullable = true)
|-- MemPercentG: double (nullable = true)
|-- CpuPercentG: float (nullable = true)

TimeSeriesDataFrame[time: timestamp]
root
|-- time: timestamp (nullable = true)

java.lang.NoSuchMethodError: org.apache.spark.sql.catalyst.expressions.codegen.ExprCode.value()Ljava/lang/String;
---------------------------------------------------------------------------
Py4JJavaError Traceback (most recent call last)
in ()
22 l.printSchema()
23 # l.show(truncate=False)
---> 24 j = l.leftJoin( r )
25
26

/databricks/python/lib/python3.5/site-packages/ts/flint/dataframe.py in leftJoin(self, right, tolerance, key, left_alias, right_alias)
606 tolerance = self._timedelta_ns('tolerance', tolerance, default='0ns')
607 scala_key = utils.list_to_seq(self._sc, key)
--> 608 tsrdd = self.timeSeriesRDD.leftJoin(right.timeSeriesRDD, tolerance, scala_key, left_alias, right_alias)
609 return TimeSeriesDataFrame._from_tsrdd(tsrdd, self.sql_ctx)
610

/databricks/spark/python/lib/py4j-0.10.7-src.zip/py4j/java_gateway.py in __call__(self, *args)
1255 answer = self.gateway_client.send_command(command)
1256 return_value = get_return_value(
-> 1257 answer, self.gateway_client, self.target_id, self.name)
1258
1259 for temp_arg in temp_args:

/databricks/spark/python/pyspark/sql/utils.py in deco(*a, **kw)
61 def deco(*a, **kw):
62 try:
---> 63 return f(*a, **kw)
64 except py4j.protocol.Py4JJavaError as e:
65 s = e.java_exception.toString()

/databricks/spark/python/lib/py4j-0.10.7-src.zip/py4j/protocol.py in get_return_value(answer, gateway_client, target_id, name)
326 raise Py4JJavaError(
327 "An error occurred while calling {0}{1}{2}.\n".
--> 328 format(target_id, ".", name), value)
329 else:
330 raise Py4JError(

Py4JJavaError: An error occurred while calling o3324.leftJoin.
: java.lang.NoSuchMethodError: org.apache.spark.sql.catalyst.expressions.codegen.ExprCode.value()Ljava/lang/String;
at org.apache.spark.sql.TimestampCast$class.doGenCode(TimestampCast.scala:77)
at org.apache.spark.sql.NanosToTimestamp.doGenCode(TimestampCast.scala:31)
at org.apache.spark.sql.catalyst.expressions.Expression$$anonfun$genCode$2.apply(Expression.scala:111)
at org.apache.spark.sql.catalyst.expressions.Expression$$anonfun$genCode$2.apply(Expression.scala:108)
at scala.Option.getOrElse(Option.scala:121)
at org.apache.spark.sql.catalyst.expressions.Expression.genCode(Expression.scala:108)
at org.apache.spark.sql.catalyst.expressions.Alias.genCode(namedExpressions.scala:143)
at org.apache.spark.sql.catalyst.expressions.codegen.CodegenContext$$anonfun$24$$anonfun$apply$5.apply(CodeGenerator.scala:1367)
at org.apache.spark.sql.catalyst.expressions.codegen.CodegenContext$$anonfun$24$$anonfun$apply$5.apply(CodeGenerator.scala:1366)
at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
at scala.collection.immutable.List.foreach(List.scala:381)
at scala.collection.TraversableLike$class.map(TraversableLike.scala:234)
at scala.collection.immutable.List.map(List.scala:285)
at org.apache.spark.sql.catalyst.expressions.codegen.CodegenContext$$anonfun$24.apply(CodeGenerator.scala:1366)
at org.apache.spark.sql.catalyst.expressions.codegen.CodegenContext$$anonfun$24.apply(CodeGenerator.scala:1366)
at org.apache.spark.sql.catalyst.expressions.codegen.CodegenContext.withSubExprEliminationExprs(CodeGenerator.scala:1227)
at org.apache.spark.sql.catalyst.expressions.codegen.CodegenContext.generateExpressionsForWholeStageWithCSE(CodeGenerator.scala:1365)
at org.apache.spark.sql.execution.ProjectExec.doConsume(basicPhysicalOperators.scala:67)
at org.apache.spark.sql.execution.CodegenSupport$class.consume(WholeStageCodegenExec.scala:181)
at org.apache.spark.sql.execution.RDDScanExec.consume(ExistingRDD.scala:176)
at org.apache.spark.sql.execution.RDDScanExec.doProduce(ExistingRDD.scala:221)
at org.apache.spark.sql.execution.CodegenSupport$$anonfun$produce$1.apply(WholeStageCodegenExec.scala:88)
at org.apache.spark.sql.execution.CodegenSupport$$anonfun$produce$1.apply(WholeStageCodegenExec.scala:83)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$executeQuery$5.apply(SparkPlan.scala:190)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
at org.apache.spark.sql.execution.SparkPlan.executeQuery(SparkPlan.scala:187)
at org.apache.spark.sql.execution.CodegenSupport$class.produce(WholeStageCodegenExec.scala:83)
at org.apache.spark.sql.execution.RDDScanExec.produce(ExistingRDD.scala:176)
at org.apache.spark.sql.execution.ProjectExec.doProduce(basicPhysicalOperators.scala:50)
at org.apache.spark.sql.execution.CodegenSupport$$anonfun$produce$1.apply(WholeStageCodegenExec.scala:88)
at org.apache.spark.sql.execution.CodegenSupport$$anonfun$produce$1.apply(WholeStageCodegenExec.scala:83)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$executeQuery$5.apply(SparkPlan.scala:190)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
at org.apache.spark.sql.execution.SparkPlan.executeQuery(SparkPlan.scala:187)
at org.apache.spark.sql.execution.CodegenSupport$class.produce(WholeStageCodegenExec.scala:83)
at org.apache.spark.sql.execution.ProjectExec.produce(basicPhysicalOperators.scala:40)
at org.apache.spark.sql.execution.WholeStageCodegenExec.doCodeGen(WholeStageCodegenExec.scala:530)
at org.apache.spark.sql.execution.WholeStageCodegenExec.doExecute(WholeStageCodegenExec.scala:582)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:150)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:138)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$executeQuery$5.apply(SparkPlan.scala:190)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
at org.apache.spark.sql.execution.SparkPlan.executeQuery(SparkPlan.scala:187)
at org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:138)
at org.apache.spark.sql.execution.QueryExecution.toRdd$lzycompute(QueryExecution.scala:108)
at org.apache.spark.sql.execution.QueryExecution.toRdd(QueryExecution.scala:108)
at com.twosigma.flint.timeseries.NormalizedDataFrameStore.toOrderedRdd(TimeSeriesStore.scala:252)
at com.twosigma.flint.timeseries.NormalizedDataFrameStore.orderedRdd(TimeSeriesStore.scala:237)
at com.twosigma.flint.timeseries.TimeSeriesRDDImpl.orderedRdd(TimeSeriesRDD.scala:1346)
at com.twosigma.flint.timeseries.TimeSeriesRDDImpl.leftJoin(TimeSeriesRDD.scala:1515)
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 py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)
at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:380)
at py4j.Gateway.invoke(Gateway.java:295)
at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)
at py4j.commands.CallCommand.execute(CallCommand.java:79)
at py4j.GatewayConnection.run(GatewayConnection.java:251)
at java.lang.Thread.run(Thread.java:748)
```

By the way, what is the way to even display the contents of the clocks DataFrame?
The second to last commented out line (with the `.show` command) errors out, so I don't understand how `TimeSeriesDataFrame` is inheriting from a regular `DataFrame`, for which that method is available...
The `display` method also fails...

Anyways, what is wrong with the `leftJoin` here? The clock is on the left like you indicated it should be. Swapping left and right data frames also does not help.

Is this reproducible for you?

Please, advise, if I'm not using/calling it correctly or if it's a bug.

The flint libraries (the Scala and the Python ones) I installed on DataBricks via its UI (from the respective online repos, which might be dated) - I can try and install the latest builds from the freshest source code, if you think that will help.

Thanks.