rowid高速分页解析
--分页第一步 获取数据物理地址select t.rowid rid, t.lastdate from t_test t order by t.lastdate desc;--分页第二步 取得最大页数select rownum rn, rid from (select t.rowid rid, t.lastdate from t_test t order by t.lastdate desc) where rownum = 1
--分页第一步 获取数据物理地址 select t.rowid rid, t.lastdate from t_test t order by t.lastdate desc; --分页第二步 取得最大页数 select rownum rn, rid from (select t.rowid rid, t.lastdate from t_test t order by t.lastdate desc) where rownum <= 10; --分页第三步 取得最小页数 select rn,rid from (select rownum rn, rid from (select t.rowid rid, t.lastdate from t_test t order by t.lastdate desc) where rownum <= 10) where rn > 5; --分页第四步 再根据物理地址,查询出具体数据 select t1.*,t1.rowid from t_test t1 where t1.rowid in (select rid from (select rownum rn, rid from (select rowid rid, t.lastdate from t_test t order by t.lastdate desc) where rownum <= 10000) where rn > 5000);
在8i以前rowid由file#+block#+row# 组成,占用6个bytes的空间,10 bit的file# ,22 bit 的block#,16 bit 的row#。
其中oracle的dba(data block address)是32 bits的,包括10 bit 的 file# 和 22 bit 的block#。
由于不存在0编号文件,oracle中的文件最大数量2^10-1=1023
而datafile能达到的最大size就是2^22*db_block_size
如果db_block_size为4k的datafile max size就是16G
如果db_block_size为8k的datafile max size就是32G
从oracle 8开始rowid变成了extend rowid,由data_object_id#+rfile#+block#+row#组成,占用10个bytes的空间,
32bit的 data_object_id#,10 bit 的 rfile#,22bit 的 block#,16 bit 的 row#.由于rowid的组成从file#变成了rfile#,
所以数据文件数的限制也从整个库不能超过1023个变成了每个data_object_id不能超过1023个数据文件。当然,你或许要问,
为什么oracle不调整rowid中表示 file# 的 bit数量,这个应该是由于兼容性的引起的,在 oracle7 的索引中存储的rowid
就是 file# + block# + row# ,因为这样处理后关于索引的存储,oracle8和oracle7没有发生变化。
虽然oracle使用了extend rowid,但是在普通索引里面依然存储了bytes的rowid,只有在global index中存储的是10bytes
的extend rowid,而extend rowid也是global index出现的一个必要条件。
1. rowid基本概念:
1) rowid是一个伪列,是用来确保表中行的唯一性,它并不能指示出行的物理位置,但可以用来定位行
2) rowid是存储在索引中的一组既定的值(当行确定后)。我们可以像表中普通的列一样将它选出来
3) 利用rowid是访问表中一行的最快方式
4) rowid需要10个字节来存储,显示为18位的字符串。
2. 什么情况下rowid会发生变化
一般来说,当表中的行确定后,rowid就不会发生变化。 但当如下情况发生时,rowid将发生改变:
1) 对一个表做表空间的移动后
2) 对一个表进行了EXP/IMP后

Hot AI Tools

Undresser.AI Undress
AI-powered app for creating realistic nude photos

AI Clothes Remover
Online AI tool for removing clothes from photos.

Undress AI Tool
Undress images for free

Clothoff.io
AI clothes remover

AI Hentai Generator
Generate AI Hentai for free.

Hot Article

Hot Tools

Notepad++7.3.1
Easy-to-use and free code editor

SublimeText3 Chinese version
Chinese version, very easy to use

Zend Studio 13.0.1
Powerful PHP integrated development environment

Dreamweaver CS6
Visual web development tools

SublimeText3 Mac version
God-level code editing software (SublimeText3)

Hot Topics



DDREASE is a tool for recovering data from file or block devices such as hard drives, SSDs, RAM disks, CDs, DVDs and USB storage devices. It copies data from one block device to another, leaving corrupted data blocks behind and moving only good data blocks. ddreasue is a powerful recovery tool that is fully automated as it does not require any interference during recovery operations. Additionally, thanks to the ddasue map file, it can be stopped and resumed at any time. Other key features of DDREASE are as follows: It does not overwrite recovered data but fills the gaps in case of iterative recovery. However, it can be truncated if the tool is instructed to do so explicitly. Recover data from multiple files or blocks to a single

0.What does this article do? We propose DepthFM: a versatile and fast state-of-the-art generative monocular depth estimation model. In addition to traditional depth estimation tasks, DepthFM also demonstrates state-of-the-art capabilities in downstream tasks such as depth inpainting. DepthFM is efficient and can synthesize depth maps within a few inference steps. Let’s read about this work together ~ 1. Paper information title: DepthFM: FastMonocularDepthEstimationwithFlowMatching Author: MingGui, JohannesS.Fischer, UlrichPrestel, PingchuanMa, Dmytr

The performance of JAX, promoted by Google, has surpassed that of Pytorch and TensorFlow in recent benchmark tests, ranking first in 7 indicators. And the test was not done on the TPU with the best JAX performance. Although among developers, Pytorch is still more popular than Tensorflow. But in the future, perhaps more large models will be trained and run based on the JAX platform. Models Recently, the Keras team benchmarked three backends (TensorFlow, JAX, PyTorch) with the native PyTorch implementation and Keras2 with TensorFlow. First, they select a set of mainstream

Facing lag, slow mobile data connection on iPhone? Typically, the strength of cellular internet on your phone depends on several factors such as region, cellular network type, roaming type, etc. There are some things you can do to get a faster, more reliable cellular Internet connection. Fix 1 – Force Restart iPhone Sometimes, force restarting your device just resets a lot of things, including the cellular connection. Step 1 – Just press the volume up key once and release. Next, press the Volume Down key and release it again. Step 2 – The next part of the process is to hold the button on the right side. Let the iPhone finish restarting. Enable cellular data and check network speed. Check again Fix 2 – Change data mode While 5G offers better network speeds, it works better when the signal is weaker

I cry to death. The world is madly building big models. The data on the Internet is not enough. It is not enough at all. The training model looks like "The Hunger Games", and AI researchers around the world are worrying about how to feed these data voracious eaters. This problem is particularly prominent in multi-modal tasks. At a time when nothing could be done, a start-up team from the Department of Renmin University of China used its own new model to become the first in China to make "model-generated data feed itself" a reality. Moreover, it is a two-pronged approach on the understanding side and the generation side. Both sides can generate high-quality, multi-modal new data and provide data feedback to the model itself. What is a model? Awaker 1.0, a large multi-modal model that just appeared on the Zhongguancun Forum. Who is the team? Sophon engine. Founded by Gao Yizhao, a doctoral student at Renmin University’s Hillhouse School of Artificial Intelligence.

Google Authenticator is a tool used to protect the security of user accounts, and its key is important information used to generate dynamic verification codes. If you forget the key of Google Authenticator and can only verify it through the security code, then the editor of this website will bring you a detailed introduction on where to get the Google security code. I hope it can help you. If you want to know more Users please continue reading below! First open the phone settings and enter the settings page. Scroll down the page and find Google. Go to the Google page and click on Google Account. Enter the account page and click View under the verification code. Enter your password or use your fingerprint to verify your identity. Obtain a Google security code and use the security code to verify your Google identity.

Recently, the military circle has been overwhelmed by the news: US military fighter jets can now complete fully automatic air combat using AI. Yes, just recently, the US military’s AI fighter jet was made public for the first time and the mystery was unveiled. The full name of this fighter is the Variable Stability Simulator Test Aircraft (VISTA). It was personally flown by the Secretary of the US Air Force to simulate a one-on-one air battle. On May 2, U.S. Air Force Secretary Frank Kendall took off in an X-62AVISTA at Edwards Air Force Base. Note that during the one-hour flight, all flight actions were completed autonomously by AI! Kendall said - "For the past few decades, we have been thinking about the unlimited potential of autonomous air-to-air combat, but it has always seemed out of reach." However now,

New SOTA for multimodal document understanding capabilities! Alibaba's mPLUG team released the latest open source work mPLUG-DocOwl1.5, which proposed a series of solutions to address the four major challenges of high-resolution image text recognition, general document structure understanding, instruction following, and introduction of external knowledge. Without further ado, let’s look at the effects first. One-click recognition and conversion of charts with complex structures into Markdown format: Charts of different styles are available: More detailed text recognition and positioning can also be easily handled: Detailed explanations of document understanding can also be given: You know, "Document Understanding" is currently An important scenario for the implementation of large language models. There are many products on the market to assist document reading. Some of them mainly use OCR systems for text recognition and cooperate with LLM for text processing.
