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twosigma / flint

A Time Series Library for Apache Spark
Apache License 2.0
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UDF error #81

Open thbeh opened 4 years ago

thbeh commented 4 years ago

I was testing flint example notebook with Spark 2.4.3 (Python 3.7) and get this error on paragraph 8 -

Py4JJavaError Traceback (most recent call last)

in 10 sp500_decayed_return = sp500_joined_return.summarizeWindows( 11 window = windows.past_absolute_time('7day'), ---> 12 summarizer = {'previous_day_return_decayed_sum': decayed(sp500_joined_return[['previous_day_return']])} 13 ) 14 /opt/conda/lib/python3.7/site-packages/ts/flint/dataframe.py in summarizeWindows(self, window, summarizer, key) 1269 1270 if isinstance(summarizer, collections.Mapping): -> 1271 return self._summarizeWindows_udf(window, summarizer, key) 1272 else: 1273 return self._summarizeWindows_builtin(window, summarizer, key) /opt/conda/lib/python3.7/site-packages/ts/flint/dataframe.py in _summarizeWindows_udf(self, window, columns, key) 1487 windowed = windowed.filter(windowed[base_rows_col_name].isNotNull()) 1488 -> 1489 result = windowed._concatArrowAndExplode(base_rows_col_name, schema_col_names, data_col_names) 1490 1491 return result /opt/conda/lib/python3.7/site-packages/ts/flint/dataframe.py in _concatArrowAndExplode(self, base_rows_col, schema_cols, data_cols) 928 base_rows_col, 929 utils.list_to_seq(self._sc, schema_cols), --> 930 utils.list_to_seq(self._sc, data_cols)) 931 return TimeSeriesDataFrame._from_tsrdd(tsrdd, self.sql_ctx) 932 /vagrant/spark-2.4.3-bin-hadoop2.7/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: /vagrant/spark-2.4.3-bin-hadoop2.7/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() /vagrant/spark-2.4.3-bin-hadoop2.7/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 o459.concatArrowAndExplode. : java.lang.NoSuchMethodError: org.apache.spark.sql.catalyst.expressions.codegen.ExprCode.code()Ljava/lang/String; at org.apache.spark.sql.TimestampCast$class.doGenCode(TimestampCast.scala:76) at org.apache.spark.sql.NanosToTimestamp.doGenCode(TimestampCast.scala:31) at org.apache.spark.sql.catalyst.expressions.Expression$$anonfun$genCode$2.apply(Expression.scala:108) at org.apache.spark.sql.catalyst.expressions.Expression$$anonfun$genCode$2.apply(Expression.scala:105) at scala.Option.getOrElse(Option.scala:121) at org.apache.spark.sql.catalyst.expressions.Expression.genCode(Expression.scala:105) at org.apache.spark.sql.catalyst.expressions.Alias.genCode(namedExpressions.scala:155) at org.apache.spark.sql.execution.ProjectExec$$anonfun$6.apply(basicPhysicalOperators.scala:60) at org.apache.spark.sql.execution.ProjectExec$$anonfun$6.apply(basicPhysicalOperators.scala:60) at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234) at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234) at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59) at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48) at scala.collection.TraversableLike$class.map(TraversableLike.scala:234) at scala.collection.AbstractTraversable.map(Traversable.scala:104) at org.apache.spark.sql.execution.ProjectExec.doConsume(basicPhysicalOperators.scala:60) at org.apache.spark.sql.execution.CodegenSupport$class.consume(WholeStageCodegenExec.scala:189) at org.apache.spark.sql.execution.InputAdapter.consume(WholeStageCodegenExec.scala:374) at org.apache.spark.sql.execution.InputAdapter.doProduce(WholeStageCodegenExec.scala:403) at org.apache.spark.sql.execution.CodegenSupport$$anonfun$produce$1.apply(WholeStageCodegenExec.scala:90) at org.apache.spark.sql.execution.CodegenSupport$$anonfun$produce$1.apply(WholeStageCodegenExec.scala:85) at org.apache.spark.sql.execution.SparkPlan$$anonfun$executeQuery$1.apply(SparkPlan.scala:155) at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151) at org.apache.spark.sql.execution.SparkPlan.executeQuery(SparkPlan.scala:152) at org.apache.spark.sql.execution.CodegenSupport$class.produce(WholeStageCodegenExec.scala:85) at org.apache.spark.sql.execution.InputAdapter.produce(WholeStageCodegenExec.scala:374) at org.apache.spark.sql.execution.ProjectExec.doProduce(basicPhysicalOperators.scala:45) at org.apache.spark.sql.execution.CodegenSupport$$anonfun$produce$1.apply(WholeStageCodegenExec.scala:90) at org.apache.spark.sql.execution.CodegenSupport$$anonfun$produce$1.apply(WholeStageCodegenExec.scala:85) at org.apache.spark.sql.execution.SparkPlan$$anonfun$executeQuery$1.apply(SparkPlan.scala:155) at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151) at org.apache.spark.sql.execution.SparkPlan.executeQuery(SparkPlan.scala:152) at org.apache.spark.sql.execution.CodegenSupport$class.produce(WholeStageCodegenExec.scala:85) at org.apache.spark.sql.execution.ProjectExec.produce(basicPhysicalOperators.scala:35) at org.apache.spark.sql.execution.WholeStageCodegenExec.doCodeGen(WholeStageCodegenExec.scala:544) at org.apache.spark.sql.execution.WholeStageCodegenExec.doExecute(WholeStageCodegenExec.scala:598) at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:131) at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:127) at org.apache.spark.sql.execution.SparkPlan$$anonfun$executeQuery$1.apply(SparkPlan.scala:155) at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151) at org.apache.spark.sql.execution.SparkPlan.executeQuery(SparkPlan.scala:152) at org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:127) at org.apache.spark.sql.execution.GenerateExec.doExecute(GenerateExec.scala:80) at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:131) at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:127) at org.apache.spark.sql.execution.SparkPlan$$anonfun$executeQuery$1.apply(SparkPlan.scala:155) at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151) at org.apache.spark.sql.execution.SparkPlan.executeQuery(SparkPlan.scala:152) at org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:127) at org.apache.spark.sql.execution.InputAdapter.inputRDDs(WholeStageCodegenExec.scala:391) at org.apache.spark.sql.execution.FilterExec.inputRDDs(basicPhysicalOperators.scala:121) at org.apache.spark.sql.execution.WholeStageCodegenExec.doExecute(WholeStageCodegenExec.scala:627) at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:131) at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:127) at org.apache.spark.sql.execution.SparkPlan$$anonfun$executeQuery$1.apply(SparkPlan.scala:155) at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151) at org.apache.spark.sql.execution.SparkPlan.executeQuery(SparkPlan.scala:152) at org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:127) at org.apache.spark.sql.execution.python.EvalPythonExec.doExecute(EvalPythonExec.scala:87) at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:131) at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:127) at org.apache.spark.sql.execution.SparkPlan$$anonfun$executeQuery$1.apply(SparkPlan.scala:155) at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151) at org.apache.spark.sql.execution.SparkPlan.executeQuery(SparkPlan.scala:152) at org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:127) at org.apache.spark.sql.execution.InputAdapter.inputRDDs(WholeStageCodegenExec.scala:391) at org.apache.spark.sql.execution.ProjectExec.inputRDDs(basicPhysicalOperators.scala:41) at org.apache.spark.sql.execution.WholeStageCodegenExec.doExecute(WholeStageCodegenExec.scala:627) at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:131) at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:127) at org.apache.spark.sql.execution.SparkPlan$$anonfun$executeQuery$1.apply(SparkPlan.scala:155) at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151) at org.apache.spark.sql.execution.SparkPlan.executeQuery(SparkPlan.scala:152) at org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:127) at org.apache.spark.sql.execution.QueryExecution.toRdd$lzycompute(QueryExecution.scala:80) at org.apache.spark.sql.execution.QueryExecution.toRdd(QueryExecution.scala:80) 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.concatArrowAndExplode(TimeSeriesRDD.scala:1946) 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:357) at py4j.Gateway.invoke(Gateway.java:282) at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132) at py4j.commands.CallCommand.execute(CallCommand.java:79) at py4j.GatewayConnection.run(GatewayConnection.java:238) at java.lang.Thread.run(Thread.java:748)