spark.sql.hive.caseSensitiveInferenceMode INFER_AND_SAVE Sets the action to take when a case-sensitive schema cannot be read from a Hive table’s properties. Although Spark SQL itself is not case-sensitive, Hive compatible file formats such as Parquet are. Spark SQL must use a case-preserving...
//调用的主函数是parse override def parsePlan(sqlText: String): LogicalPlan = parse(sqlText) { parser => //从 singleStatement 结点开始,遍历语法树,将结点转换为逻辑计划 astBuilder.visitSingleStatement(parser.singleStatement()) match { case plan: LogicalPlan => plan case _ => val position = ...
The language of Spark statement. Accepted values:CSharp, PySpark, Python, SQL, Scala, Spark, SparkDotNet --session-id The id of Spark session. --spark-pool-name The name of the Spark pool. --workspace-name The name of the workspace. ...
setState public SparkStatement setState(LivyStatementStates state) Set the state property: The state property. Parameters: state - the state value to set. Returns: the SparkStatement object itself.Applies to Azure SDK for Java Preview在GitHub 上與我們共同作業 您可以在 GitHub 上找到此內容的...
而spark.sql.hive.verifyPartitionPath参数默认是false,当设置为true的时候会在获得分区路径时对分区路径是否存在做一个校验,过滤掉不存在的分区路径,这样就会避免上面的错误。 spark.files.ignoreCorruptFiles && spark.files.ignoreMissingFiles 这两个参数和上面的spark.sql.files.ignoreCorruptFiles很像,但是区别是很大...
SparkSqlParser没有该方法的实现,具体是现在其父类 AbstractSqlParser中,如下: /**Creates LogicalPlan for a given SQL string.*///TODO 根据 sql语句生成 逻辑计划 LogicalPlanoverride def parsePlan(sqlText: String): LogicalPlan = parse(sqlText) { parser =>val singleStatementContext: SqlBaseParser.Sin...
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/parser/ParseDriver.scala Original file line numberDiff line numberDiff line change @@ -289,8 +289,8 @@ class ParseException( } def withCommand(cmd: String): ParseException = { // PARSE_EMPTY_STATEMENT error class overrides the PARSE...
Apache Hive是一个构建于Hadoop顶层的数据仓库,可以将结构化的数据文件映射为一张数据库表,并提供简单的SQL查询功能,可以将SQL语句转换为MapReduce任务进行运行。需要注意的是,Hive它并不是数据库。 Hive依赖于HDFS和MapReduce,其对HDFS的操作类似于SQL,我们称之为HQL,它提供了丰富的SQL查询方式来分析存储在HDFS中的...
Like most platform technologies, the maturation of Hadoop has led to a stable computing environment that is general enough to build specialist tools for tasks such as graph processing, micro-batch processing, SQL querying, data warehousing, and machine learning. However, as Hadoop became more ...
Another interesting thing to notice is thatcolumns are identified bycolobjects. In this case,we are letting Spark infer what Dataframe those columns belong to. We can use the syntaxexecution_plan_variable["column_name"]tospecify from which execution plan the column is coming. Using this alternati...