[
https://issues.apache.org/jira/browse/SPARK-22678?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16279814#comment-16279814
]
geosmart commented on SPARK-22678:
----------------------------------
so nobody answered！
but I resolve it by split the onehot pipeline
{code:java}
def feature_vectoring(self, configPath, modelPath, inputTable, outputTable,
scalerType="minmax"):
joinKey = "withdraw_no"
linkColumns = [joinKey, "idcard_no_md5", "bank_card", "dt_apply"]
featureConfig = json.load(open(configPath))
categoryFeatures, continousFeatures = self.splitFeature(configPath,
featureConfig)
if self.isDebug:
categoryFeatures1 = [categoryFeatures[c] for c in range(0, 5, 1)]
categoryFeatures2 = [categoryFeatures[c] for c in range(115,
len(categoryFeatures), 1)]
continousFeatures = [continousFeatures[0], continousFeatures[1],
continousFeatures[2]]
pre.init_mock_data(tableName=inputTable)
else:
center = int(len(categoryFeatures) / 2)
categoryFeatures1 = [categoryFeatures[c] for c in range(0, center,
1)]
categoryFeatures2 = [categoryFeatures[c] for c in range(center,
len(categoryFeatures), 1)]
log.info("categoryFeatures: {}".format(len(categoryFeatures)))
log.info("continousFeatures: {}".format(len(continousFeatures)))
df = self.spark.table(inputTable).select(linkColumns + categoryFeatures
+ continousFeatures)
log.info("load spark table: {}".format(inputTable))
df = self.convert_data_type(continousFeatures, df)
assemblerVector = "assembler_vector"
featureScalar = "scalar_vector"
pipeline1, pipeline2, pipeline3 =
self.get_pipeline_split(assemblerVector, categoryFeatures1, categoryFeatures2,
continousFeatures, df, featureScalar, modelPath, scalerType)
inputCol_pipeline1 = linkColumns
inputCol_pipeline2 = [joinKey]
inputCol_pipeline3 = [joinKey]
inputCol_pipeline1.extend([c for c in df.columns if c in
categoryFeatures1])
inputCol_pipeline2.extend([c for c in df.columns if c in
categoryFeatures2])
inputCol_pipeline3.extend([c for c in df.columns if c in
continousFeatures])
df_pipeline1 = df.select(inputCol_pipeline1)
df_pipeline2 = df.select(inputCol_pipeline2)
df_pipeline3 = df.select(inputCol_pipeline3)
# remove temp columns
excludeColumns = []
for categoryFeature in categoryFeatures:
categoryFeatureIndex = categoryFeature + "_index"
excludeColumns.append(categoryFeature)
excludeColumns.append(categoryFeatureIndex)
for continousFeature in continousFeatures:
excludeColumns.append(continousFeature)
excludeColumns.append(assemblerVector)
t0 = datetime.now()
log.info("start pipeline1 transform,pipeline stages [{}], df
size[{}]".format(len(pipeline1.stages), df.count()))
df_onehot1 = pipeline1.transform(df_pipeline1)
log.info("save temp Table [{}]".format(outputTable + "_onehotEncoder1"))
df_onehot1.select([c for c in df_onehot1.columns if c not in
excludeColumns]) \
.write.saveAsTable(outputTable + "_onehotEncoder1")
log.info("start pipeline2 transform,pipeline stages [{}], df
size[{}]".format(len(pipeline2.stages), df.count()))
df_onehot2 = pipeline2.transform(df_pipeline2)
log.info("save temp Table [{}]".format(outputTable + "_onehotEncoder2"))
df_onehot2.select([c for c in df_onehot2.columns if c not in
excludeColumns]) \
.write.saveAsTable(outputTable + "_onehotEncoder2")
log.info("start pipeline3 transform,pipeline stages [{}], df
size[{}]".format(len(pipeline3.stages), df.count()))
df_scalar = pipeline3.transform(df_pipeline3)
# log.info("save temp Table [{}]".format(outputTable + "_scaler"))
df_scalar = df_scalar.select([c for c in df_scalar.columns if c not in
excludeColumns])
# .write.saveAsTable(outputTable + "_scaler")
tt = datetime.now() - t0
log.info("done pipeline transform in [{}s]".format(tt.seconds))
log.info("join transformed columns")
df_onehot1 = self.spark.table(outputTable + "_onehotEncoder1")
df_onehot2 = self.spark.table(outputTable + "_onehotEncoder2")
# df_scalar = self.spark.table(outputTable + "_scaler")
df_vector = df_onehot1.join(df_onehot2, joinKey,
"left").join(df_scalar, joinKey, "left")
log.info("df_vector printSchema...")
df_vector.printSchema()
# onehot output SparseVector，convert to multi columns
log.info("start save preprocess vector result as
table[{}]".format(outputTable))
if self.isDebug:
df_vector.write.parquet("../../res/data/{}".format(outputTable),
mode="overwrite")
df_vector.write.saveAsTable(outputTable, mode="overwrite")
else:
df_vector.write.saveAsTable(outputTable, mode="overwrite")
log.info("vector success ,drop temp Table [{}]".format(outputTable
+ "_onehotEncoder"))
# df.show(n=2)
log.info("done save preprocess vector result as
table[{}]".format(outputTable))
{code}
> Schema Exception of SaveAsTable with SparkML Pipeline.transform Result
> DataFrame
> ---------------------------------------------------------------------------------
>
> Key: SPARK-22678
> URL: https://issues.apache.org/jira/browse/SPARK-22678
> Project: Spark
> Issue Type: Bug
> Components: ML
> Affects Versions: 2.2.0
> Environment: Spark-2.2.0 standlone Cluster
>
> I run the app by spark-submit：
> ```
> spark-submit --driver-memory 8g --executor-memory 30G --total-executor-cores
> 200 --executor-cores 12 Main.py >preprocess.log &
> ```
> Reporter: geosmart
> Original Estimate: 1m
> Remaining Estimate: 1m
>
> I run the app by spark-submit：
> ```
> spark-submit --driver-memory 8g --executor-memory 30G --total-executor-cores
> 200 --executor-cores 12 Main.py >preprocess.log &
> ```
> 1. Situatiuon
> Input：I have a table of 2500 features to vectoring，only 740000 rows；
> Action：do onehotEncoder for category features and minmax or standard scaler
> for continues features，
> Output：a new table with vectoring results（UDT）；
> 2. Problem
> 2.1 saveAsTable throws
> Caused by: java.lang.RuntimeException: Couldn't find
> ALL_FIRST_ATTEMPT_SIGN_1_DATE_ALL_vector#12558 in
> [cardno#0,ALL_NUM_PAY_BILLS_1_ALL#1,ALL_NUM_PAY_BILLS_1_7D#2,
> 2.2 I fit the pipeline spend 1 hour, any one knows how to speed up?
> When do pipeline transform ,the spark only start a worker process，why not
> transforming in parallel，the features have no dependency？
> and transformed is very slowly ( 10 minutes)， no matter how small the input
> dataset is !
> 3. Code
> 3.1. Load dataframe
> #feature array from json config
> {code:python}
> categoryFeatures, continousFeatures = self.splitFeature(configPath,
> featureConfig)
> log.info("categoryFeatures: {}".format(categoryFeatures))
> log.info("continousFeatures: {}".format(continousFeatures))
> df = self.spark.table(inputTable)
> df = df.select(categoryFeatures + continousFeatures)
> {code}
> 3.2. Load or Generate pipeline
> {code:python}
> def get_onehot_scalar_pipeline(self, categoryFeatures, continousFeatures, df,
> modelPath, scalerType="minmax"):
> """
> Pipeline：onehot，min-max scalar
> :param categoryFeatures:
> :param df:
> :param modelPath:
> :return:
> """
> pipelineModel = None
> if self.isDebug:
> isLoadModel = os.path.isdir(modelPath)
> else:
> isLoadModel = self.is_path_exist(modelPath)
> if isLoadModel:
> log.info("load pipeline from {}".format(modelPath))
> pipelineModel = PipelineModel.load(modelPath)
> else:
> from pyspark.ml.feature import StringIndexer, OneHotEncoder,
> StandardScaler
> stages = []
> # OneHotEncoder
> for categoryFeature in categoryFeatures:
> categoryFeatureIndex = categoryFeature + "_index"
> categoryFeatureVector = categoryFeature + "_vector"
> stringIndexer = StringIndexer(inputCol=categoryFeature,
> outputCol=categoryFeatureIndex, handleInvalid="skip")
> onehotEncoder = OneHotEncoder(inputCol=categoryFeatureIndex,
> outputCol=categoryFeatureVector,
> dropLast=False)
> stages.append(stringIndexer)
> stages.append(onehotEncoder)
> # MinMaxScaler
> assemblerVector = "assembler_vector"
> featureScalar = "scalar_vector"
> assembler = VectorAssembler(inputCols=continousFeatures,
> outputCol=assemblerVector)
> if scalerType == "standard":
> scaler = StandardScaler(withMean=True, withStd=True,
> inputCol=assemblerVector, outputCol=featureScalar)
> else:
> scaler = MinMaxScaler(min=0.0, max=1.0, inputCol=assemblerVector,
> outputCol=featureScalar)
> stages.append(assembler)
> stages.append(scaler)
> t0 = datetime.now()
> log.info("start fit pipeline,stages:{}".format(len(stages)))
> pipelineModel = Pipeline(stages=stages).fit(df)
> tt = datetime.now() - t0
> log.info("done fit pipeline in [{}s]".format(tt.seconds))
> log.info("save pipeline as model {}".format(modelPath))
> self.model_save(pipelineModel, modelPath)
> return pipelineModel
> {code}
> 3.3 Pipeline Transform and Save Result
> {code:python}
> # load or fit pipeline and transform
> pipeline = self.get_onehot_scalar_pipeline(categoryFeatures,
> continousFeatures, df, modelPath, scalerType)
> t0 = datetime.now()
> log.info("start pipeline transform,pipeline stages [{}], df
> size[{}]".format(len(pipeline.stages), df.count()))
> df_vector = pipeline.transform(df)
> tt = datetime.now() - t0
> log.info("done pipeline transform in [{}s]".format(tt.seconds))
> # remove temp columns
> excludeColumns = []
> for categoryFeature in categoryFeatures:
> categoryFeatureIndex = categoryFeature + "_index"
> excludeColumns.append(categoryFeature)
> excludeColumns.append(categoryFeatureIndex)
> for continousFeature in continousFeatures:
> excludeColumns.append(continousFeature)
> assemblerVector = "assembler_vector"
> excludeColumns.append(assemblerVector)
> log.info("exclude temp columns")
> df_last = df_vector.select([c for c in df_vector.columns if c not in
> excludeColumns])
> log.info("before feature extract...")
> df_last.printSchema()
> # onehot output SparseVector，convert to multi columns
> log.info("start save preprocess vector result as
> table[{}]".format(outputTable))
> # exception happened in saveAsTable
> df_last.write.saveAsTable(outputTable)
> log.info("done save preprocess vector result as
> table[{}]".format(outputTable))
> {code}
> 4. Log
> detail in 
> {code:java}
> py4j.protocol.Py4JJavaError: An error occurred while calling o3728.showString.
> : org.apache.spark.SparkException: Job aborted due to stage failure: Task 0
> in stage 729.0 failed 4 times, most recent failure: Lost task 0.3 in stage
> 729.0 (TID 750, 172.31.23.17, executor 0):
> org.apache.spark.sql.catalyst.errors.package$TreeNodeException: Binding
> attribute, tree: ALL_FIRST_ATTEMPT_SIGN_1_DATE_ALL_vector#13746
> at
> org.apache.spark.sql.catalyst.errors.package$.attachTree(package.scala:56)
> at
> org.apache.spark.sql.catalyst.expressions.BindReferences$$anonfun$bindReference$1.applyOrElse(BoundAttribute.scala:88)
> at
> org.apache.spark.sql.catalyst.expressions.BindReferences$$anonfun$bindReference$1.applyOrElse(BoundAttribute.scala:87)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$2.apply(TreeNode.scala:267)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$2.apply(TreeNode.scala:267)
> at
> org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:70)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:266)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode.transform(TreeNode.scala:256)
> at
> org.apache.spark.sql.catalyst.expressions.BindReferences$.bindReference(BoundAttribute.scala:87)
> at
> org.apache.spark.sql.catalyst.expressions.UnsafeProjection$$anonfun$2.apply(Projection.scala:151)
> at
> org.apache.spark.sql.catalyst.expressions.UnsafeProjection$$anonfun$2.apply(Projection.scala:151)
> 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$class.map(TraversableLike.scala:234)
> at scala.collection.AbstractTraversable.map(Traversable.scala:104)
> at
> org.apache.spark.sql.catalyst.expressions.UnsafeProjection$.create(Projection.scala:151)
> at
> org.apache.spark.sql.execution.ProjectExec$$anonfun$9.apply(basicPhysicalOperators.scala:74)
> at
> org.apache.spark.sql.execution.ProjectExec$$anonfun$9.apply(basicPhysicalOperators.scala:73)
> at
> org.apache.spark.rdd.RDD$$anonfun$mapPartitionsWithIndexInternal$1$$anonfun$apply$24.apply(RDD.scala:815)
> at
> org.apache.spark.rdd.RDD$$anonfun$mapPartitionsWithIndexInternal$1$$anonfun$apply$24.apply(RDD.scala:815)
> at
> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:287)
> at
> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:287)
> at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:87)
> at org.apache.spark.scheduler.Task.run(Task.scala:108)
> at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:335)
> 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)
> Caused by: java.lang.RuntimeException: Couldn't find
> ALL_FIRST_ATTEMPT_SIGN_1_DATE_ALL_vector#13746 in
> 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EMPT_SIGN_TO_TODAY_94_ALL#1097,CONSUMERLOAN_DT_FIRST_ATTEMPT_SIGN_TO_TODAY_94_7D#1098,CONSUMERLOAN_DT_FIRST_ATTEMPT_SIGN_TO_TODAY_94_15D#1099,CONSUMERLOAN_DT_FIRST_ATTEMPT_SIGN_TO_TODAY_94_30D#1100,CONSUMERLOAN_DT_FIRST_SUCCESS_SIGN_TO_TODAY_95_ALL#1101,CONSUMERLOAN_DT_FIRST_SUCCESS_SIGN_TO_TODAY_95_7D#1102,CONSUMERLOAN_DT_FIRST_SUCCESS_SIGN_TO_TODAY_95_15D#1103,CONSUMERLOAN_DT_FIRST_SUCCESS_SIGN_TO_TODAY_95_30D#1104,CONSUMERLOAN_DT_FIRST_ATTEMPT_PAY_TO_TODAY_96_ALL#1105,CONSUMERLOAN_DT_FIRST_ATTEMPT_PAY_TO_TODAY_96_7D#1106,CONSUMERLOAN_DT_FIRST_ATTEMPT_PAY_TO_TODAY_96_15D#1107,CONSUMERLOAN_DT_FIRST_ATTEMPT_PAY_TO_TODAY_96_30D#1108,CONSUMERLOAN_DT_FIRST_SUCCESS_PAY_TO_TODAY_97_ALL#1109,CONSUMERLOAN_DT_FIRST_SUCCESS_PAY_TO_TODAY_97_7D#1110,CONSUMERLOAN_DT_FIRST_SUCCESS_PAY_TO_TODAY_97_15D#1111,CONSUMERLOAN_DT_FIRST_SUCCESS_PAY_TO_TODAY_97_30D#1112,CONSUMERLOAN_DT_FIRST_ATTEMPT_WITHDRAW_TO_TODAY_98_ALL#1113,CONSUMERLOAN_DT_FIRST_ATTEMPT_WITHDRAW_TO_TODAY_98_7D#1114,CONSUMERLOAN_DT_FIRST_ATTEMPT_WITHDRAW_TO_TODAY_98_15D#1115,CONSUMERLOAN_DT_FIRST_ATTEMPT_WITHDRAW_TO_TODAY_98_30D#1116,CONSUMERLOAN_DT_FIRST_SUCCESS_WITHDRAW_TO_TODAY_99_ALL#1117,CONSUMERLOAN_DT_FIRST_SUCCESS_WITHDRAW_TO_TODAY_99_7D#1118,CONSUMERLOAN_DT_FIRST_SUCCESS_WITHDRAW_TO_TODAY_99_15D#1119,CONSUMERLOAN_DT_FIRST_SUCCESS_WITHDRAW_TO_TODAY_99_30D#1120,CONSUMERLOAN_DT_FIRST_ATTEMPT_SIGN_TO_FIRST_SUCCESS_SIGN_100_ALL#1121,CONSUMERLOAN_DT_FIRST_ATTEMPT_SIGN_TO_FIRST_SUCCESS_SIGN_100_7D#1122,CONSUMERLOAN_DT_FIRST_ATTEMPT_SIGN_TO_FIRST_SUCCESS_SIGN_100_15D#1123,CONSUMERLOAN_DT_FIRST_ATTEMPT_SIGN_TO_FIRST_SUCCESS_SIGN_100_30D#1124,CONSUMERLOAN_DT_FIRST_ATTEMPT_SIGN_TO_FIRST_ATTEMPT_PAY_101_ALL#1125,CONSUMERLOAN_DT_FIRST_ATTEMPT_SIGN_TO_FIRST_ATTEMPT_PAY_101_7D#1126,CONSUMERLOAN_DT_FIRST_ATTEMPT_SIGN_TO_FIRST_ATTEMPT_PAY_101_15D#1127,CONSUMERLOAN_DT_FIRST_ATTEMPT_SIGN_TO_FIRST_ATTEMPT_PAY_101_30D#1128,CONSUMERLOAN_DT_FIRST_ATTEMPT_SIGN_TO_FIRST_SUCCESS_PAY_102_ALL#1129,CONSUMERLOAN_DT_FIRST_ATTEMPT_SIGN_TO_FIRST_SUCCESS_PAY_102_7D#1130,CONSUMERLOAN_DT_FIRST_ATTEMPT_SIGN_TO_FIRST_SUCCESS_PAY_102_15D#1131,CONSUMERLOAN_DT_FIRST_ATTEMPT_SIGN_TO_FIRST_SUCCESS_PAY_102_30D#1132,CONSUMERLOAN_DT_FIRST_ATTEMPT_SIGN_TO_FIRST_ATTEMPT_WITHDRAW_103_ALL#1133,CONSUMERLOAN_DT_FIRST_ATTEMPT_SIGN_TO_FIRST_ATTEMPT_WITHDRAW_103_7D#1134,CONSUMERLOAN_DT_FIRST_ATTEMPT_SIGN_TO_FIRST_ATTEMPT_WITHDRAW_103_15D#1135,CONSUMERLOAN_DT_FIRST_ATTEMPT_SIGN_TO_FIRST_ATTEMPT_WITHDRAW_103_30D#1136,CONSUMERLOAN_DT_FIRST_ATTEMPT_SIGN_TO_FIRST_SUCCESS_WITHDRAW_104_ALL#1137,CONSUMERLOAN_DT_FIRST_ATTEMPT_SIGN_TO_FIRST_SUCCESS_WITHDRAW_104_7D#1138,CONSUMERLOAN_DT_FIRST_ATTEMPT_SIGN_TO_FIRST_SUCCESS_WITHDRAW_104_15D#1139,CONSUMERLOAN_DT_FIRST_ATTEMPT_SIGN_TO_FIRST_SUCCESS_WITHDRAW_104_30D#1140,CONSUMERLOAN_DT_FIRST_SUCCESS_SIGN_TO_FIRST_ATTEMPT_PAY_105_ALL#1141,CONSUMERLOAN_DT_FIRST_SUCCESS_SIGN_TO_FIRST_ATTEMPT_PAY_105_7D#1142,CONSUMERLOAN_DT_FIRST_SUCCESS_SIGN_TO_FIRST_ATTEMPT_PAY_105_15D#1143,CONSUMERLOAN_DT_FIRST_SUCCESS_SIGN_TO_FIRST_ATTEMPT_PAY_105_30D#1144,CONSUMERLOAN_DT_FIRST_SUCCESS_SIGN_TO_FIRST_SUCCESS_PAY_106_ALL#1145,CONSUMERLOAN_DT_FIRST_SUCCESS_SIGN_TO_FIRST_SUCCESS_PAY_106_7D#1146,CONSUMERLOAN_DT_FIRST_SUCCESS_SIGN_TO_FIRST_SUCCESS_PAY_106_15D#1147,CONSUMERLOAN_DT_FIRST_SUCCESS_SIGN_TO_FIRST_SUCCESS_PAY_106_30D#1148,CONSUMERLOAN_DT_FIRST_SUCCESS_SIGN_TO_FIRST_ATTEMPT_WITHDRAW_107_ALL#1149,CONSUMERLOAN_DT_FIRST_SUCCESS_SIGN_TO_FIRST_ATTEMPT_WITHDRAW_107_7D#1150,CONSUMERLOAN_DT_FIRST_SUCCESS_SIGN_TO_FIRST_ATTEMPT_WITHDRAW_107_15D#1151,CONSUMERLOAN_DT_FIRST_SUCCESS_SIGN_TO_FIRST_ATTEMPT_WITHDRAW_107_30D#1152,CONSUMERLOAN_DT_FIRST_SUCCESS_SIGN_TO_FIRST_SUCCESS_WITHDRAW_108_ALL#1153,CONSUMERLOAN_DT_FIRST_SUCCESS_SIGN_TO_FIRST_SUCCESS_WITHDRAW_108_7D#1154,CONSUMERLOAN_DT_FIRST_SUCCESS_SIGN_TO_FIRST_SUCCESS_WITHDRAW_108_15D#1155,CONSUMERLOAN_DT_FIRST_SUCCESS_SIGN_TO_FIRST_SUCCESS_WITHDRAW_108_30D#1156,CONSUMERLOAN_DT_FIRST_ATTEMPT_PAY_TO_FIRST_ATTEMPT_WITHDRAW_109_ALL#1157,CONSUMERLOAN_DT_FIRST_ATTEMPT_PAY_TO_FIRST_ATTEMPT_WITHDRAW_109_7D#1158,CONSUMERLOAN_DT_FIRST_ATTEMPT_PAY_TO_FIRST_ATTEMPT_WITHDRAW_109_15D#1159,CONSUMERLOAN_DT_FIRST_ATTEMPT_PAY_TO_FIRST_ATTEMPT_WITHDRAW_109_30D#1160,CONSUMERLOAN_DT_FIRST_ATTEMPT_PAY_TO_FIRST_SUCCESS_WITHDRAW_110_ALL#1161,CONSUMERLOAN_DT_FIRST_ATTEMPT_PAY_TO_FIRST_SUCCESS_WITHDRAW_110_7D#1162,CONSUMERLOAN_DT_FIRST_ATTEMPT_PAY_TO_FIRST_SUCCESS_WITHDRAW_110_15D#1163,CONSUMERLOAN_DT_FIRST_ATTEMPT_PAY_TO_FIRST_SUCCESS_WITHDRAW_110_30D#1164,CONSUMERLOAN_DT_FIRST_ATTEMPT_PAY_TO_FIRST_SUCCESS_PAY_111_ALL#1165,CONSUMERLOAN_DT_FIRST_ATTEMPT_PAY_TO_FIRST_SUCCESS_PAY_111_7D#1166,CONSUMERLOAN_DT_FIRST_ATTEMPT_PAY_TO_FIRST_SUCCESS_PAY_111_15D#1167,CONSUMERLOAN_DT_FIRST_ATTEMPT_PAY_TO_FIRST_SUCCESS_PAY_111_30D#1168,CONSUMERLOAN_DT_FIRST_SUCCESS_PAY_TO_FIRST_ATTEMPT_WITHDRAW_112_ALL#1169,CONSUMERLOAN_DT_FIRST_SUCCESS_PAY_TO_FIRST_ATTEMPT_WITHDRAW_112_7D#1170,CONSUMERLOAN_DT_FIRST_SUCCESS_PAY_TO_FIRST_ATTEMPT_WITHDRAW_112_15D#1171,CONSUMERLOAN_DT_FIRST_SUCCESS_PAY_TO_FIRST_ATTEMPT_WITHDRAW_112_30D#1172,CONSUMERLOAN_DT_FIRST_SUCCESS_PAY_TO_FIRST_SUCCESS_WITHDRAW_113_ALL#1173,CONSUMERLOAN_DT_FIRST_SUCCESS_PAY_TO_FIRST_SUCCESS_WITHDRAW_113_7D#1174,CONSUMERLOAN_DT_FIRST_SUCCESS_PAY_TO_FIRST_SUCCESS_WITHDRAW_113_15D#1175,CONSUMERLOAN_DT_FIRST_SUCCESS_PAY_TO_FIRST_SUCCESS_WITHDRAW_113_30D#1176,CONSUMERLOAN_DT_FIRST_ATTEMPT_WITHDRAW_TO_FIRST_SUCCESS_WITHDRAW_114_ALL#1177,CONSUMERLOAN_DT_FIRST_ATTEMPT_WITHDRAW_TO_FIRST_SUCCESS_WITHDRAW_114_7D#1178,CONSUMERLOAN_DT_FIRST_ATTEMPT_WITHDRAW_TO_FIRST_SUCCESS_WITHDRAW_114_15D#1179,CONSUMERLOAN_DT_FIRST_ATTEMPT_WITHDRAW_TO_FIRST_SUCCESS_WITHDRAW_114_30D#1180]
> at scala.sys.package$.error(package.scala:27)
> at
> org.apache.spark.sql.catalyst.expressions.BindReferences$$anonfun$bindReference$1$$anonfun$applyOrElse$1.apply(BoundAttribute.scala:94)
> at
> org.apache.spark.sql.catalyst.expressions.BindReferences$$anonfun$bindReference$1$$anonfun$applyOrElse$1.apply(BoundAttribute.scala:88)
> at
> org.apache.spark.sql.catalyst.errors.package$.attachTree(package.scala:52)
> ... 33 more
> {code}
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