3. 研究org.apache.spark.sql.catalyst.expressions包的相关类和转换规则 在Spark SQL中,catalyst.expressions包包含了许多用于执行SQL查询的表达式类。GenericRowWithSchema是这些表达式处理过程中用来存储数据行的一种结构。在编写UDF(用户定义函数)时,需要特别注意传入和返回的数据类型,确保它们与DataFrame中相应列的schema...
A collection of methods for registering user-defined functions (UDF). defversion:String The version of Spark on which this application is running. finaldefwait():Unit finaldefwait(arg0:Long,arg1:Int):Unit finaldefwait(arg0:Long):Unit
故障解决:spark 访问hive 库、表报错 org.apache.spark.sql.AnalysisException: Table or view not found,程序员大本营,技术文章内容聚合第一站。
SparkFunSuite} import org.apache.spark.sql.catalyst.expressions.codegen.CodegenContext import org.apache.spark.sql.types.{IntegerType, StringType} class ScalaUDFSuite extends SparkFunSuite with ExpressionEvalHelper { test("basic") { val intUdf = ScalaUDF((i: Int) => ...
package org.apache.spark.ml.feature import org.apache.spark.ml.linalg.{Vectors, EuclideanDistance, Vector} import org.apache.spark.sql.functions.{col, explode, udf} import org.scalatest.{PropSpec, Matchers, GivenWhenThen} import org.scalatest.prop.GeneratorDrivenPropertyChecks class ReebDiagramTest...
apache.flink.api.common.functions.util.FunctionUtils.closeFunction(FunctionUtils.java:43) at org.apache.flink.streaming.api.operators.AbstractUdfStreamOperator.dispose(AbstractUdfStreamOperator.java:117) at org.apache.flink.streaming.runtime.tasks.StreamTask.disposeAllOperators(StreamTask.java:668) at ...
/org/apache/spark/spark-catalyst_2.11/2.3.2/spark-catalyst_2.11-2.3.2.jar' in project 'Spark' cannot be read or is not a valid ZIP fileSparkBuild path Build Path Proble 解决方法: Spark SQL Catalyst源码分析之UDF CatalystAnalyzer的作用,其中包含了ResolveFunctions这个解析函数的功能。但是随着Spark...
如果您使用的是spark engine，那么是两个执行器）。并且，在每个执行器上，udf将从1开始序列。所以，...
HiveException: java.lang.RuntimeException:无法实例化org.apache.hadoop.hive.ql.metadata.SessionHiveMetaStoreClient今天在使用Hadoop集群上的Hive时，结果出现了以下的情况。 hive (default)> show databases; FAILED: SemanticException org.apache.hadoop.hive.ql.metadata.HiveException: java.lang.RuntimeException...
sql.functions.{col, date_format, from_json, udf, posexplode, unbase64, lit} import org.apache.spark.sql.types.{ArrayType, LongType, StringType, StructType} val bodySchema = new StructType(). add("AppName", StringType, true). add("ClientIP", StringType, true). add("CommandInput", ...