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运行简单的Spark SQL
spark.sql("select count(1) from edw.result_base").show().我得到这个错误
py4j.protocol.Py4JJavaError: An error occurred while calling o69.sql. java.lang.AssertionError: assertion failed表格格式为parquet，Spark版本为2.4.8。
我尝试设置下面的属性，仍然得到一个错误。
spark.conf.set("spark.sql.hive.convertMetastoreOrc", "true")错误的完整堆栈跟踪如下所示：
java.lang.AssertionError: assertion failed
at scala.Predef$.assert(Predef.scala:208)
at org.apache.spark.sql.hive.HiveMetastoreCatalog.convertToLogicalRelation(HiveMetastoreCatalog.scala:261)
at org.apache.spark.sql.hive.HiveMetastoreCatalog.convert(HiveMetastoreCatalog.scala:137)
at org.apache.spark.sql.hive.RelationConversions$$anonfun$apply$4.applyOrElse(HiveStrategies.scala:220)
at org.apache.spark.sql.hive.RelationConversions$$anonfun$apply$4.applyOrElse(HiveStrategies.scala:207)
at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.$anonfun$resolveOperatorsDown$2(AnalysisHelper.scala:108)
at org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:72)
at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.$anonfun$resolveOperatorsDown$1(AnalysisHelper.scala:108)
at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper$.allowInvokingTransformsInAnalyzer(AnalysisHelper.scala:194)
at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.resolveOperatorsDown(AnalysisHelper.scala:106)
at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.resolveOperatorsDown$(AnalysisHelper.scala:104)
at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.resolveOperatorsDown(LogicalPlan.scala:29)
at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.$anonfun$resolveOperatorsDown$4(AnalysisHelper.scala:113)
at org.apache.spark.sql.catalyst.trees.TreeNode.$anonfun$mapChildren$1(TreeNode.scala:376)
at org.apache.spark.sql.catalyst.trees.TreeNode.mapProductIterator(TreeNode.scala:214)
at org.apache.spark.sql.catalyst.trees.TreeNode.mapChildren(TreeNode.scala:374)
at org.apache.spark.sql.catalyst.trees.TreeNode.mapChildren(TreeNode.scala:327)
at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.$anonfun$resolveOperatorsDown$1(AnalysisHelper.scala:113)
at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper$.allowInvokingTransformsInAnalyzer(AnalysisHelper.scala:194)
at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.resolveOperatorsDown(AnalysisHelper.scala:106)
at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.resolveOperatorsDown$(AnalysisHelper.scala:104)
at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.resolveOperatorsDown(LogicalPlan.scala:29)
at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.resolveOperators(AnalysisHelper.scala:73)
at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.resolveOperators$(AnalysisHelper.scala:72)
at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.resolveOperators(LogicalPlan.scala:29)
at org.apache.spark.sql.hive.RelationConversions.apply(HiveStrategies.scala:207)
at org.apache.spark.sql.hive.RelationConversions.apply(HiveStrategies.scala:191)
at org.apache.spark.sql.catalyst.rules.RuleExecutor.$anonfun$execute$2(RuleExecutor.scala:130)
at scala.collection.IndexedSeqOptimized.foldLeft(IndexedSeqOptimized.scala:60)
at scala.collection.IndexedSeqOptimized.foldLeft$(IndexedSeqOptimized.scala:68)
at scala.collection.mutable.ArrayBuffer.foldLeft(ArrayBuffer.scala:49)
at org.apache.spark.sql.catalyst.rules.RuleExecutor.$anonfun$execute$1(RuleExecutor.scala:127)
at org.apache.spark.sql.catalyst.rules.RuleExecutor.$anonfun$execute$1$adapted(RuleExecutor.scala:119)
at scala.collection.immutable.List.foreach(List.scala:392)
at org.apache.spark.sql.catalyst.rules.RuleExecutor.execute(RuleExecutor.scala:119)
at org.apache.spark.sql.catalyst.analysis.Analyzer.org$apache$spark$sql$catalyst$analysis$Analyzer$$executeSameContext(Analyzer.scala:168)
at org.apache.spark.sql.catalyst.analysis.Analyzer.execute(Analyzer.scala:162)
at org.apache.spark.sql.catalyst.analysis.Analyzer.execute(Analyzer.scala:122)
at org.apache.spark.sql.catalyst.rules.RuleExecutor.$anonfun$executeAndTrack$1(RuleExecutor.scala:98)
at org.apache.spark.sql.catalyst.QueryPlanningTracker$.withTracker(QueryPlanningTracker.scala:88)
at org.apache.spark.sql.catalyst.rules.RuleExecutor.executeAndTrack(RuleExecutor.scala:98)
at org.apache.spark.sql.catalyst.analysis.Analyzer.$anonfun$executeAndCheck$1(Analyzer.scala:146)
at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper$.markInAnalyzer(AnalysisHelper.scala:201)
at org.apache.spark.sql.catalyst.analysis.Analyzer.executeAndCheck(Analyzer.scala:145)
at org.apache.spark.sql.execution.QueryExecution.$anonfun$analyzed$1(QueryExecution.scala:66)
at org.apache.spark.sql.catalyst.QueryPlanningTracker.measurePhase(QueryPlanningTracker.scala:111)
at org.apache.spark.sql.execution.QueryExecution.analyzed$lzycompute(QueryExecution.scala:63)
at org.apache.spark.sql.execution.QueryExecution.analyzed(QueryExecution.scala:63)
at org.apache.spark.sql.execution.QueryExecution.assertAnalyzed(QueryExecution.scala:55)
2条答案
按热度按时间fcy6dtqo1#
在查看了一下之后，通过查看
spark.sql.hive.convertMetastoreOrc参数，似乎您走上了正确的道路（例如，参见this和this类似的错误）。由于您使用parquet作为底层存储，因此您需要spark.sql.hive.convertMetastoreParquet参数。尝试将
spark.sql.hive.convertMetastoreParquet设置为false而不是true。true是默认值，因此这不会让您走得太远，因为默认行为是导致此错误的原因。bq9c1y662#
此Assert错误表示Hive表
edw.result_base的逻辑架构与底层parquet文件的物理架构不匹配。以下是HiveMetastoreCatalog.scala中的相关代码：当然，你可以简单地禁用
spark.sql.hive.convertMetastoreParquet，但我认为更安全的方法是找出差异所在，否则它们可能会导致不正确的查询结果（特别是如果数据类型不匹配或表def中有额外的列）。还请注意，在较新的Spark版本中，这个
assert被重构为抛出更详细和准确的AnalysisExceptions。对于在xmlyter中设置驱动程序类路径，this SO answer似乎可以工作，尽管我认为在您的情况下不需要任何额外的jar，因为您可以很好地从parquet推断模式。