Cloudera环境PySpark迁移Greenplum到HiveSQL遇AssertionError报错求助
错误场景
在Cloudera环境开展PySpark开发，通过Spark SQL实现Greenplum到Hive的数据迁移时，执行insertInto写入Hive表触发如下错误：
An error occurred while calling o72.insertInto. :
java.lang.AssertionError: assertion failed
at scala.Predef$.assert(Predef.scala:156)
at org.apache.spark.sql.hive.HiveMetastoreCatalog.convertToLogicalRelation(HiveMetastoreCatalog.scala:255)
at org.apache.spark.sql.hive.HiveMetastoreCatalog.convert(HiveMetastoreCatalog.scala:135)
at org.apache.spark.sql.hive.RelationConversions$$anonfun$apply$4.applyOrElse(HiveStrategies.scala:201)
at org.apache.spark.sql.hive.RelationConversions$$anonfun$apply$4.applyOrElse(HiveStrategies.scala:195)
at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper$$anonfun$resolveOperatorsDown$1$$anonfun$2.apply(AnalysisHelper.scala:108)
at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper$$anonfun$resolveOperatorsDown$1$$anonfun$2.apply(AnalysisHelper.scala:108)
at org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:71)
at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper$$anonfun$resolveOperatorsDown$1.apply(AnalysisHelper.scala:107)
at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper$$anonfun$resolveOperatorsDown$1.apply(AnalysisHelper.scala:106)
at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper$.allowInvokingTransformsInAnalyzer(AnalysisHelper.scala:194)
at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper$class.resolveOperatorsDown(AnalysisHelper.scala:106)
at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.resolveOperatorsDown(LogicalPlan.scala:29)
at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper$class.resolveOperators(AnalysisHelper.scala:73)
at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.resolveOperators(LogicalPlan.scala:29)
at org.apache.spark.sql.hive.RelationConversions.apply(HiveStrategies.scala:195)
at org.apache.spark.sql.hive.RelationConversions.apply(HiveStrategies.scala:179)
at org.apache.spark.sql.catalyst.rules.RuleExecutor$$anonfun$execute$1$$anonfun$apply$1.apply(RuleExecutor.scala:87)
at org.apache.spark.sql.catalyst.rules.RuleExecutor$$anonfun$execute$1$$anonfun$apply$1.apply(RuleExecutor.scala:84)
at scala.collection.IndexedSeqOptimized$class.foldl(IndexedSeqOptimized.scala:57)
at scala.collection.IndexedSeqOptimized$class.foldLeft(IndexedSeqOptimized.scala:66)
at scala.collection.mutable.ArrayBuffer.foldLeft(ArrayBuffer.scala:48)
at org.apache.spark.sql.catalyst.rules.RuleExecutor$$anonfun$execute$1.apply(RuleExecutor.scala:84)
at org.apache.spark.sql.catalyst.rules.RuleExecutor$$anonfun$execute$1.apply(RuleExecutor.scala:76)
at scala.collection.immutable.List.foreach(List.scala:392)
at org.apache.spark.sql.catalyst.rules.RuleExecutor.execute(RuleExecutor.scala:76)
at org.apache.spark.sql.catalyst.analysis.Analyzer.org$apache$spark$sql$catalyst$analysis$Analyzer$$executeSameContext(Analyzer.scala:127)
at org.apache.spark.sql.catalyst.analysis.Analyzer.execute(Analyzer.scala:121)
at org.apache.spark.sql.catalyst.analysis.Analyzer$$anonfun$executeAndCheck$1.apply(Analyzer.scala:106)
at org.apache.spark.sql.catalyst.analysis.Analyzer$$anonfun$executeAndCheck$1.apply(Analyzer.scala:105)
at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper$.markInAnalyzer(AnalysisHelper.scala:201)
at org.apache.spark.sql.catalyst.analysis.Analyzer.executeAndCheck(Analyzer.scala:105)
at org.apache.spark.sql.execution.QueryExecution.analyzed$lzycompute(QueryExecution.scala:68)
at org.apache.spark.sql.execution.QueryExecution.analyzed(QueryExecution.scala:66)
at org.apache.spark.sql.execution.QueryExecution.org$apache$spark$sql$execution$QueryExecution$$writePlans(QueryExecution.scala:215)
at org.apache.spark.sql.execution.QueryExecution.toString(QueryExecution.scala:230)
at org.apache.spark.sql.execution.SQLExecution$$anonfun$withNewExecutionId$1.apply(SQLExecution.scala:77)
at org.apache.spark.sql.execution.SQLExecution$.withSQLConfPropagated(SQLExecution.scala:127)
at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:75)
at org.apache.spark.sql.DataFrameWriter.runCommand(DataFrameWriter.scala:684)
at org.apache.spark.sql.DataFrameWriter.insertInto(DataFrameWriter.scala:334)
at org.apache.spark.sql.DataFrameWriter.insertInto(DataFrameWriter.scala:320)
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)
根因说明
该错误为Spark访问Hive元数据做表逻辑转换时的断言校验失败，常见诱因包括：源端Greenplum导出数据的schema与Hive目标表定义不匹配、目标Hive为Spark不支持的ACID事务表、Spark侧元数据缓存与Hive Metastore实际数据不一致、Cloudera发行版Spark与Hive的版本兼容问题。
解决措施
强制对齐schema
写入前主动将源DataFrame的字段顺序、类型和目标Hive表完全对齐，避免Spark隐式转换异常，示例代码：from pyspark.sql.functions import col # 读取目标Hive表的标准schema target_table = "your_database.your_hive_table" target_schema = spark.table(target_table).schema # 源DataFrame按目标schema做字段和类型转换 source_df = source_df.select(*[col(field.name).cast(field.dataType) for field in target_schema.fields]) # 执行写入 source_df.write.insertInto(target_table)重点校验Greenplum的decimal/numeric精度、时间类型格式是否和Hive表定义一致。
校验Hive表属性
执行Hive命令desc formatted your_database.your_hive_table查看表属性，若transactional = true说明是ACID事务表，低版本Spark不支持写入，可关闭表事务属性，或改用Hive SQL执行写入操作。刷新元数据缓存
写入前执行元数据刷新命令，避免缓存不一致导致的校验失败：spark.sql(f"REFRESH TABLE {target_table}") # 必要时可清理全量缓存 spark.catalog.clearCache()调整Spark配置
在Spark作业提交参数中增加如下配置，关闭元数据自动推断逻辑：--conf spark.sql.hive.convertMetastoreParquet=false --conf spark.sql.hive.caseSensitiveInferenceMode=NEVER_INFER --conf spark.sql.sources.partitionColumnTypeInference.enabled=false若目标表为ORC存储格式，可额外增加配置
--conf spark.sql.hive.convertMetastoreOrc=false。替换写入API
上述方案无效时，可将insertInto替换为saveAsTable指定写入模式为追加：source_df.write.mode("append").saveAsTable(target_table)
内容的提问来源于stack exchange，提问作者Developer Rajinikanth
