'wp-load.php'); 引入 WordPress 核心代码,然后执行 WP_Query 获取特定的日志,然后就发生下面的错误: Fatal error: Call to a member function...get() on a non-object in [path to site]\site\wp-includes\query.php on line 27 这是因为全局变量不正确使用引起的问题,默认
{SaveMode, SparkSession} object EtlDataService { /** * etl用户注册信息 * * @param ssc * @param...object DwdMemberDao { def getDwdMember(sparkSession: SparkSession) = { sparkSession.sql("select...{SaveMode, SparkSession} object DwsMemberService { def importMemberUseApi(sparkSession: SparkSession...{SaveMode, SparkSession} object AdsMemberService { /** * 统计各项指标 使用api * * @param sparkSession...import org.apache.spark.sql.SparkSession object DwdMemberController { def main(args: Array[String
public void someMethod(){ long threadSafeInt = 0; threadSafeInt++; } Local Object References 局部的引用变量相对有点不同...method2(localObject); } public void method2(LocalObject localObject){ localObject.setValue("value"); } Object...Member Variables 我们都知道成员变量是存储在堆上的,因此如果两个线程调用同一个对象的方法,这个方法更新了对象成员变量的话,这个方法就不是线程安全的。
源码 package com.buwenbuhuo.spark.sql.day01 import org.apache.spark.sql....MyCSDN : https://buwenbuhuo.blog.csdn.net/ * */ object DataFrameDemo { def main(args: Array...源码 package com.buwenbuhuo.spark.sql.day01 import org.apache.spark.sql....MyCSDN : https://buwenbuhuo.blog.csdn.net/ * */ object CreateDF { def main(args: Array[...MyCSDN : https://buwenbuhuo.blog.csdn.net/ * */ object CreateDS { def main(args: Array[String]):
{DataFrame, SparkSession} object DataFrameApp { def main(args: Array[String]): Unit = { val spark...{DataFrame, SparkSession} object DataFrameApp { def main(args: Array[String]): Unit = { val spark...{DataFrame, SparkSession} object DataFrameApp { def main(args: Array[String]): Unit = { val spark...{DataFrame, SparkSession} object DataFrameApp { def main(args: Array[String]): Unit = { val spark...{DataFrame, SparkSession} object DataFrameApp { def main(args: Array[String]): Unit = { val spark
{DataFrame, Encoder, SparkSession} case class People(name :String,age:Int) object DataFrameNote {...:Int) object DataFrameNote { def main(args: Array[String]): Unit = { val spark: SparkSession...{DataFrame, SparkSession} object StudentApp { case class Student(id:Int,name:String,phone:String,email...package cn.bx.spark import org.apache.spark.sql.SparkSession object Parquetpp { def main(args: Array...cn.bx.spark import org.apache.spark.sql.SparkSession object JDBCNote { def main(args: Array[String
源码 package com.buwenbuhuo.spark.sql.day02 import org.apache.spark.sql....23 ** * MyCSDN : https://buwenbuhuo.blog.csdn.net/ * */ object DataSourceDemo { def main...源码 package com.buwenbuhuo.spark.sql.day02 import org.apache.spark.sql....源码 package com.buwenbuhuo.spark.sql.day02.jdbc import org.apache.spark.sql.SparkSession /** ** *...MyCSDN : https://buwenbuhuo.blog.csdn.net/ * */ object JDBCRead1 { def main(args: Array
object QzCourseDao { def getDwdQzSiteCourse(sparkSession: SparkSession, dt: String) = { sparkSession.sql...object QzMajorDao { def getQzMajor(sparkSession: SparkSession, dt: String) = { sparkSession.sql...object QzQuestionDao { def getQzQuestion(sparkSession: SparkSession, dt: String) = { sparkSession.sql...{SaveMode, SparkSession} object DwsQzService { def saveDwsQzChapter(sparkSession: SparkSession, dt...{SaveMode, SparkSession} object AdsQzService { def getTarget(sparkSession: SparkSession, dt: String
对象实例通过建造者模式构建,代码如下: 其中①表示导入SparkSession所在的包,②表示建造者模式构建对象和设置属性,③表示导入SparkSession类中implicits对象object中隐式转换函数...{DataFrame, SaveMode, SparkSession} /** * Author itcast * Desc 演示SparkSQL */ object SparkSQLDemo00...{DataFrame, SparkSession} /** * Author itcast * Desc 演示基于RDD创建DataFrame--使用样例类 */ object CreateDataFrameDemo1...{DataFrame, SparkSession} /** * Author itcast * Desc 演示基于RDD创建DataFrame--使用类型加列名 */ object CreateDataFrameDemo2...{DataFrame, Row, SparkSession} /** * Author itcast * Desc 演示基于RDD创建DataFrame--使用StructType */ object
源码 package com.buwenbuhuo.spark.sql.day02.hive import org.apache.spark.sql.SparkSession /** ** *...源码 package com.buwenbuhuo.spark.sql.day02.hive import org.apache.spark.sql.SparkSession /** ** *...3.2.2 df.svaeAsTable(" ") 源码 package com.buwenbuhuo.spark.sql.day02.hive import org.apache.spark.sql...MyCSDN : https://buwenbuhuo.blog.csdn.net/ * */ object HiveWrite { def main(args: Array...MyCSDN : https://buwenbuhuo.blog.csdn.net/ * */ object HiveWrite { def main(args: Array
Spark 调用外部数据源包的是 org.apache.spark.sql,首先了解下 Spark SQL 提供的的扩展数据源相关的接口。...在 Nebula Graph 的 Spark Connector 中,我们实现了将 Nebula Graph 作为 Spark SQL 的外部数据源,通过 sparkSession.read 形式进行数据的读取...v1.0 git@github.com:vesoft-inc/nebula-java.git cd nebula-java/tools/nebula-spark mvn clean compile package
spark2.2在使用的时候使用的是SparkSession,这个SparkSession创建的时候很明显的使用了创建者模式。...通过观察源代码,简单的模拟了下,可以当作以后编码风格的参考: 官方使用 import org.apache.spark.sql.SparkSession val spark = SparkSession...For implicit conversions like converting RDDs to DataFrames import spark.implicits._ 自己写的小例子,模拟一下: package...xingoo.core object SparkSessionBuilderExample { def main(args: Array[String]): Unit = { SparkSession....builder() .config("a","1") .config("b","2") .getOrCreate() } } object SparkSession
* * @author 不温卜火 * @create 2020-08-12 20:45 * MyCSDN : https://buwenbuhuo.blog.csdn.net/ * */ object...* * @author 不温卜火 * @create 2020-08-12 21:45 * MyCSDN : https://buwenbuhuo.blog.csdn.net/ * */ object...先创建sparkSession val spark: SparkSession = SparkSession.builder() .config(rdd.sparkContext.getConf...* * @author 不温卜火 * @create 2020-08-12 22:45 * MyCSDN : https://buwenbuhuo.blog.csdn.net/ * */ object...先创建sparkSession val spark: SparkSession = SparkSession.builder() .config(rdd.sparkContext.getConf
cn.it.logistics.common import java.text.SimpleDateFormat import java.util.Date /** * 时间处理工具类 */ object...cn.it.logistics.common /** * 自定义离线计算结果表 */ object OfflineTableDefine { //快递单明细表 val expressBillDetail...val CustomType = 16 //下单终端类型 val OrderTerminalType = 17 //下单渠道类型 val OrderChannelType = 18 } object...* @param tableName * @param isLoadFullData */ def getKuduSource(sparkSession: SparkSession...*/ def execute(sparkSession: SparkSession) /** * 数据存储 * dwd及dws层的数据都是需要写入到kudu数据库中,写入逻辑相同
id" ) billCount,sum(express_package."..." express_package ON SENDER_INFO."...._ import org.apache.spark.sql.types.IntegerType /** * 客户主题数据的拉宽操作 */ object CustomerDWD extends OfflineApp...._ import scala.collection.mutable.ArrayBuffer /** * 客户主题指标计算 */ object CustomerDWS extends OfflineApp...scala.collection.mutable.ArrayBuffer /** * 客户主题开发 * 读取客户明细宽表的数据,然后进行指标开发,将结果存储到kudu表中(DWS层) */ object
= kafkaStreamDF .select($"value".cast(StringType)) .filter(filter_udf($"value")) 完整代码如下: package...package cn.itcast.spark.deduplication import org.apache.spark.sql.streaming....{DataFrame, SparkSession} /** * StructuredStreaming对流数据按照某些字段进行去重操作,比如实现UV类似统计分析 */ object _03StructuredDeduplication..."), // get_json_object($"value", "$.signal").cast(DoubleType).as("signal"), // get_json_object...package cn.itcast.spark.window import java.sql.Timestamp import org.apache.spark.sql.streaming.
{DataTypes, StructField} import scala.util.Random object AppUdf { def main(args:Array[String]):Unit...{DataTypes, StructField} import scala.util.Random object AppUdf { def main(args:Array[String]):Unit...import org.apache.spark.sql.expressions.Aggregator case class DataBuf(var sum:Double,var count:Int) object...import org.apache.spark.sql.expressions.Aggregator case class DataBuf(var sum:Double,var count:Int) object...{DataTypes, StructField} import scala.util.Random object AppUdf { def main(args:Array[String]):Unit
org.apache.spark.sql.Row"">http://spark.apache.org/docs/latest/api/scala/index.html#org.apache.spark.sql.package...= SparkSession.builder().getOrCreate() val tdwDataFrame = new TDWSQLProvider(sparkSession, tdwUser,...在整个 SparkSession 期间创建一次就好,如果同一个创建了两次车,会报错 val selectDataFrame1 = sparkSession.sql("select ftime, gid..., ... , x(23) ) ) //语法错误:too many elements for tuple:23, allowed:22 //编译报错:object...Tuple23 is not a member of package scala。
from-beginning 模拟数据 模拟设备监控日志数据,字段信息封装到CaseClass样例类【DeviceData】类: 模拟产生日志数据类【MockIotDatas】具体代码如下: package...提取JSON字符串中字段值,编写SQL执行分析,将最终结果打印控制台 代码如下: package cn.itcast.structedstreaming import org.apache.commons.lang3...构建SparkSession会话实例对象,设置属性信息 val spark: SparkSession = SparkSession.builder() .appName(this.getClass.getSimpleName.stripSuffix...query.awaitTermination() query.stop() } } DSL风格 按照业务需求,从Kafka消费日志数据,基于DataFrame数据结构调用函数分析,代码如下: package...构建SparkSession会话实例对象,设置属性信息 val spark: SparkSession = SparkSession.builder() .appName(this.getClass.getSimpleName.stripSuffix
{DataFrame, SparkSession} object CreateDFDS { def main(args: Array[String]): Unit = { //1.创建SparkSession...{DataFrame, Row, SparkSession} object CreateDFDS2 { def main(args: Array[String]): Unit = { /...{DataFrame, SparkSession} object CreateDFDS3 { case class Person(id:Int,name:String,age:Int) def...{Dataset, SparkSession} object UDFDemo { def main(args: Array[String]): Unit = { //1.创建SparkSession...使用SparkSQL操作Hive表 package cn.itcast.sql import org.apache.spark.sql.SparkSession object HiveSupport
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