EXPDP/IMPDP 中的并行度PARALLEL参数

2023-10-08 00:48

本文主要是介绍EXPDP/IMPDP 中的并行度PARALLEL参数,希望对大家解决编程问题提供一定的参考价值,需要的开发者们随着小编来一起学习吧!

    如果设置 EXPDP parallel=4 必须要设置4个EXPDP文件,不然PARALLEL是有问题的,同时EXPDP会使用一个WORKER进程导出METADATA,其他WORKER进程会同时出数据。如果EXPDP作业低于250M,只会启动一个WORKER进程。如果是500M会启动2个,1000M及会启动4个WOKER进程。一般来说加上%U来设置多个文件。
    而IMPDP有所不同,会先启动一个WOKER进程METADATA导入,然后启动多个WORKER进程导入,所以再前期只会看到WOKER在导入METADATA,而且IMPDP如果PARALLE=4也需要不少于4个DMP文件,也可以使用%U来进行导入。
nohup expdp system/**** PARALLEL=2 JOB_NAME=full_bak_job full=y dumpfile=exptest:back_%U.dmp logfile=exptest:back.log &
impdp system/*** PARALLEL=2 EXCLUDE=STATISTICS JOB_NAME=full_imp cluster=no full=y dumpfile=test:back_%U.dmp logfile=test:back_imp.log;
    而在11GR2后EXPDP和IMDP的WORKER进程会在多个INSTANCE启动,所以DIRECTORY必须在共享磁盘上,如果没有设置共享磁盘还是指定cluster=no 来防止报错。
当观察EXPDP/IMPDP woker的时候如下:
Import> status
Job: FULL_IMP
  Operation: IMPORT                        
  Mode: FULL                          
  State: EXECUTING                     
  Bytes Processed: 150,300,713,536
  Percent Done: 80
  Current Parallelism: 6
  Job Error Count: 0
  Dump File: /expdp/back_%u.dmp
  Dump File: /expdp/back_01.dmp
  Dump File: /expdp/back_02.dmp
  Dump File: /expdp/back_03.dmp
  Dump File: /expdp/back_04.dmp
  Dump File: /expdp/back_05.dmp
  Dump File: /expdp/back_06.dmp
  Dump File: /expdp/back_07.dmp
  Dump File: /expdp/back_08.dmp
 
Worker 1 Status:
  Process Name: DW00
  State: EXECUTING                     
  Object Schema: ACRUN
  Object Name: T_PLY_UNDRMSG
  Object Type: DATABASE_EXPORT/SCHEMA/TABLE/TABLE_DATA
  Completed Objects: 3
  Completed Rows: 3,856,891
  Completed Bytes: 1,134,168,200
  Percent Done: 83
  Worker Parallelism: 1
 
Worker 2 Status:
  Process Name: DW01
  State: EXECUTING                     
  Object Schema: ACRUN
  Object Name: T_FIN_PAYDUE
  Object Type: DATABASE_EXPORT/SCHEMA/TABLE/TABLE_DATA
  Completed Objects: 5
  Completed Rows: 2,646,941
  Completed Bytes: 1,012,233,224
  Percent Done: 93
  Worker Parallelism: 1
 
Worker 3 Status:
  Process Name: DW02
  State: EXECUTING                     
  Object Schema: ACRUN
  Object Name: MLOG$_T_FIN_CLMDUE
  Object Type: DATABASE_EXPORT/SCHEMA/TABLE/TABLE_DATA
  Completed Objects: 6
  Completed Bytes: 382,792,584
  Worker Parallelism: 1
 
Worker 4 Status:
  Process Name: DW03
  State: EXECUTING                     
  Object Schema: ACRUN
  Object Name: T_PAY_CONFIRM_INFO
  Object Type: DATABASE_EXPORT/SCHEMA/TABLE/TABLE_DATA
  Completed Objects: 5
  Completed Rows: 2,443,790
  Completed Bytes: 943,310,104
  Percent Done: 83
  Worker Parallelism: 1
 
Worker 5 Status:
  Process Name: DW04
  State: EXECUTING                     
  Object Schema: ACRUN
  Object Name: T_PLY_TGT
  Object Type: DATABASE_EXPORT/SCHEMA/TABLE/TABLE_DATA
  Completed Objects: 6
  Completed Rows: 2,285,353
  Completed Bytes: 822,501,496
  Percent Done: 64
  Worker Parallelism: 1
 
Worker 6 Status:
  Process Name: DW05
  State: EXECUTING                     
  Object Schema: ACRUN
  Object Name: T_FIN_PREINDRCT_CLMFEE
  Object Type: DATABASE_EXPORT/SCHEMA/TABLE/TABLE_DATA
  Completed Objects: 5
  Completed Rows: 6,042,384
  Completed Bytes: 989,435,088
  Percent Done: 79
  Worker Parallelism: 1
 
英文如下:
    For Data Pump Export, the value that is specified for the parallel parameter should be less than or equal to the number of files in the dump file set. Each worker or Parallel Execution Process requires exclusive access to the dump file, so having fewer dump files than the degree of parallelism will mean that some workers or PX processes will be unable to write the information they are exporting. If this occurs, the worker processes go into an idle state and will not be doing any work until more files are added to the job. See the explanation of the DUMPFILE parameter in the Database Utilities guide for details on how to specify multiple dump files for a Data Pump export job.
    For Data Pump Import, the workers and PX processes can all read from the same files. However, if there are not enough dump files, the performance may not be optimal because multiple threads of execution will be trying to access the same dump file. The performance impact of multiple processes sharing the dump files depends on the I/O subsystem containing the dump files. For this reason, Data Pump Import should not have a value for the PARALLEL parameter that is significantly larger than the number of files in the dump file set.
 
     In a typical export that includes both data and metadata, the first worker process will unload the metadata: tablespaces, schemas, grants, roles, tables, indexes, and so on. This single worker unloads the metadata, and all the rest unload the data, all at the same time. If the metadata worker finishes and there are still data objects to unload, it will start unloading the data too. The examples in this document assume that there is always one worker busy unloading metadata while the rest of the workers are busy unloading table data objects.
 
    If the external tables method is chosen, Data Pump will determine the maximum number of PX processes that can work on a table data object. It does this by dividing the estimated size of the table data object by 250 MB and rounding the result down. If the result is zero or one, then PX processes are not used to unload the table
 
    The PARALLEL parameter works a bit differently in Import than Export. Because there are various dependencies that exist when creating objects during import, everything must be done in order. For Import, no data loading can occur until the tables are created because data cannot be loaded into tables that do not yet exist

这篇关于EXPDP/IMPDP 中的并行度PARALLEL参数的文章就介绍到这儿,希望我们推荐的文章对编程师们有所帮助!



http://www.chinasem.cn/article/161563

相关文章

Andrej Karpathy最新采访:认知核心模型10亿参数就够了,AI会打破教育不公的僵局

夕小瑶科技说 原创  作者 | 海野 AI圈子的红人,AI大神Andrej Karpathy,曾是OpenAI联合创始人之一,特斯拉AI总监。上一次的动态是官宣创办一家名为 Eureka Labs 的人工智能+教育公司 ,宣布将长期致力于AI原生教育。 近日,Andrej Karpathy接受了No Priors(投资博客)的采访,与硅谷知名投资人 Sara Guo 和 Elad G

C++11第三弹:lambda表达式 | 新的类功能 | 模板的可变参数

🌈个人主页: 南桥几晴秋 🌈C++专栏: 南桥谈C++ 🌈C语言专栏: C语言学习系列 🌈Linux学习专栏: 南桥谈Linux 🌈数据结构学习专栏: 数据结构杂谈 🌈数据库学习专栏: 南桥谈MySQL 🌈Qt学习专栏: 南桥谈Qt 🌈菜鸡代码练习: 练习随想记录 🌈git学习: 南桥谈Git 🌈🌈🌈🌈🌈🌈🌈🌈🌈🌈🌈🌈🌈�

如何在页面调用utility bar并传递参数至lwc组件

1.在app的utility item中添加lwc组件: 2.调用utility bar api的方式有两种: 方法一,通过lwc调用: import {LightningElement,api ,wire } from 'lwc';import { publish, MessageContext } from 'lightning/messageService';import Ca

4B参数秒杀GPT-3.5:MiniCPM 3.0惊艳登场!

​ 面壁智能 在 AI 的世界里,总有那么几个时刻让人惊叹不已。面壁智能推出的 MiniCPM 3.0,这个仅有4B参数的"小钢炮",正在以惊人的实力挑战着 GPT-3.5 这个曾经的AI巨人。 MiniCPM 3.0 MiniCPM 3.0 MiniCPM 3.0 目前的主要功能有: 长上下文功能:原生支持 32k 上下文长度,性能完美。我们引入了

AI(文生语音)-TTS 技术线路探索学习:从拼接式参数化方法到Tacotron端到端输出

AI(文生语音)-TTS 技术线路探索学习:从拼接式参数化方法到Tacotron端到端输出 在数字化时代,文本到语音(Text-to-Speech, TTS)技术已成为人机交互的关键桥梁,无论是为视障人士提供辅助阅读,还是为智能助手注入声音的灵魂,TTS 技术都扮演着至关重要的角色。从最初的拼接式方法到参数化技术,再到现今的深度学习解决方案,TTS 技术经历了一段长足的进步。这篇文章将带您穿越时

如何确定 Go 语言中 HTTP 连接池的最佳参数?

确定 Go 语言中 HTTP 连接池的最佳参数可以通过以下几种方式: 一、分析应用场景和需求 并发请求量: 确定应用程序在特定时间段内可能同时发起的 HTTP 请求数量。如果并发请求量很高,需要设置较大的连接池参数以满足需求。例如,对于一个高并发的 Web 服务,可能同时有数百个请求在处理,此时需要较大的连接池大小。可以通过压力测试工具模拟高并发场景,观察系统在不同并发请求下的性能表现,从而

多路转接之select(fd_set介绍,参数详细介绍),实现非阻塞式网络通信

目录 多路转接之select 引入 介绍 fd_set 函数原型 nfds readfds / writefds / exceptfds readfds  总结  fd_set操作接口  timeout timevalue 结构体 传入值 返回值 代码 注意点 -- 调用函数 select的参数填充  获取新连接 注意点 -- 通信时的调用函数 添加新fd到

struts2中的json返回指定的多个参数

要返回指定的多个参数,就必须在struts.xml中的配置如下: <action name="goodsType_*" class="goodsTypeAction" method="{1}"> <!-- 查询商品类别信息==分页 --> <result type="json" name="goodsType_findPgae"> <!--在这一行进行指定,其中lis是一个List集合,但

mybatis if test 之 0当做参数传入出问题

首先前端传入了参数 if(StringUtils.isNotBlank(status)){requestParam.setProperty("status", Integer.parseInt(status));}List<SuperPojo> applicationList = groupDao.getApplicationListByReviewStatusAndMember(req

Linux的系统性能监测参数获取方法介绍

目前的工程需要简单的监测一下Linux系统的:CPU负载、内存消耗情况、几个指定目录的磁盘空间、磁盘I/O、swap的情况还有就是网络流量。   Linux下的性能检测工具其实都有很多。   mrtg(http://people.ee.ethz.ch/~oetiker/webtools/mrtg/)就是一个很不错的选择。不过用mrtg就要装sysstat、apache、snmp、pe