RMAN Backup Formats:Backup Sets

本文介绍了RMAN如何通过备份集这种逻辑结构存储备份信息。备份集可以包含一个或多个数据文件、归档重做日志或控制文件等内容,并且可以在默认情况下自动跳过未使用的数据块,从而减小备份集的大小并加快备份速度。

RMAN can also store backup information a logical structure called a backup set. A
backup set contains the data from one or more datafiles, archived redo logs, or control
files or SPFILE. (Datafiles and archivelogs cannot be mixed together in the same
backup set.) You can also back up existing backup sets into another backup set.

A backup set consists of one or more files in an RMAN-specific format, known as
backup pieces. By default, a backup set consists of one backup piece. For example,
you can back up ten datafiles into a single backup set consisting of a single backup
piece (that is, one backup piece will be produced as output, the backup set consists of
the single file containing the backup piece, and the backup piece and the backup set
that contains it will be recorded in the RMAN repository). A file cannot be split across
backup sets.

When backing up datafiles to backup sets, RMAN is able to skip some datafile blocks
that do not currently contain data, reducing the size of backup sets and the time
required to create them. This behavior, known as unused block compression, means
that backups of datafiles as backup sets are generally smaller than image copy backups
and take less time to write. This behavior is fundamental to how RMAN writes datafiles
into backup pieces, and cannot be disabled.

[@more@]

来自 “ ITPUB博客 ” ,链接:http://blog.itpub.net/10599713/viewspace-1005181/,如需转载,请注明出处,否则将追究法律责任。

转载于:http://blog.itpub.net/10599713/viewspace-1005181/

内容概要:本文介绍了ENVI Deep Learning V1.0的操作教程,重点讲解了如何利用ENVI软件进行深度学习模型的训练与应用,以实现遥感图像中特定目标(如集装箱)的自动提取。教程涵盖了从数据准备、标签图像创建、模型初始化与训练,到执行分类及结果优化的完整流程,并介绍了精度评价与通过ENVI Modeler实现一键化建模的方法。系统基于TensorFlow框架,采用ENVINet5(U-Net变体)架构,支持通过点、线、面ROI或分类图生成标签数据,适用于多/高光谱影像的单一类别特征提取。; 适合人群:具备遥感图像处理基础,熟悉ENVI软件操作,从事地理信息、测绘、环境监测等相关领域的技术人员或研究人员,尤其是希望将深度学习技术应用于遥感目标识别的初学者与实践者。; 使用场景及目标:①在遥感影像中自动识别和提取特定地物目标(如车辆、建筑、道路、集装箱等);②掌握ENVI环境下深度学习模型的训练流程与关键参数设置(如Patch Size、Epochs、Class Weight等);③通过模型调优与结果反馈提升分类精度,实现高效自动化信息提取。; 阅读建议:建议结合实际遥感项目边学边练,重点关注标签数据制作、模型参数配置与结果后处理环节,充分利用ENVI Modeler进行自动化建模与参数优化,同时注意软硬件环境(特别是NVIDIA GPU)的配置要求以保障训练效率。
评论
添加红包

请填写红包祝福语或标题

红包个数最小为10个

红包金额最低5元

当前余额3.43前往充值 >
需支付:10.00
成就一亿技术人!
领取后你会自动成为博主和红包主的粉丝 规则
hope_wisdom
发出的红包
实付
使用余额支付
点击重新获取
扫码支付
钱包余额 0

抵扣说明:

1.余额是钱包充值的虚拟货币,按照1:1的比例进行支付金额的抵扣。
2.余额无法直接购买下载,可以购买VIP、付费专栏及课程。

余额充值