


Tencent Cloud Database was once again recognized by top conferences, and the paper was successfully selected for VLDB2023
At the 49th VLDB Conference, the top international database conference, two papers of Tencent Cloud TDSQL were successfully included in VLDB 2023, once again proving that innovative technology is recognized by the top international conference VLDB
As a leader in the database field One of the three top conferences, each VLDB conference focuses on displaying the most cutting-edge directions of current database research and the latest applications in the industry, attracting the participation of many of the world's top technology companies and research institutions. Because the conference has extremely high requirements on system innovation, completeness, experimental design, etc., the paper acceptance rate of the VLDB conference is generally low (about 18%).
Among the selected papers, the "Efficient Black-box Checking of Snapshot Isolation in Databases" solution jointly developed by Tencent Cloud, Nanjing University and ETH Zurich proposes a novel black-box checking The processor - PolySI, can efficiently check Snapshot isolation (SI) and provide understandable counterexamples when a violation is detected.
Snapshot isolation is a common weak isolation level, which avoids the performance loss caused by serialization and can prevent many common data anomalies. However, some production cloud databases that claim to provide snapshot isolation guarantees will still generate SI data anomalies, which will have a huge impact, especially in the financial field. Existing similar tools in the industry either do not support snapshot isolation level testing or are less efficient. Given the complexity of database systems and the fact that internal information within the database is often inaccessible, a black-box snapshot isolation checker is urgently needed in the industry.
In order to solve this problem, we proposed and designed the "PolySI" algorithm and tools. The theoretical basis of PolySI is the SI characterization theorem based on Generalized Polygraphs (GPs), which guarantees the correctness and completeness of PolySI. PolySI adopts an SMT solver (MonoSAT) and leverages GPs’ compact constraint encoding scheme along with domain-specific optimizations to accelerate SMT solving
Currently, through extensive evaluation, PolySI successfully reproduces known SI anomalies and New SI anomalies were detected in three production cloud databases, providing understandable counterexamples. PolySI outperforms current state-of-the-art SI black-box checkers under multiple classes of workloads and is able to scale to large-scale workloads.
According to our understanding, the paper "Online Schema Evolution is (Almost) Free for Snapshot Databases" jointly completed by Tencent Cloud and Simon Fraser University introduces a new online and transactional schema evolution called "Tesseract" Method, designed to solve the challenges faced in the process of online database modification of schema
Currently, modern database applications often make schema changes according to changing needs. The main advantage of online database modification of schema is that there is no need to stop the database service or Structural modifications can be made by interrupting ongoing transactions, allowing the database to meet dynamic changes without requiring downtime for maintenance or restarting the database.
In existing database systems, although online and transactional schema (schema) evolution are supported, they also face some challenges. The first is the issue of data consistency. When making structural modifications, in order to ensure the consistency of the data, transactions or other mechanisms need to be used to ensure the integrity and correctness of the data. Secondly, there is the problem of long running time. Some structural modifications may take a long time to complete, especially for large databases or modifications of complex structures, which may have a certain impact on the performance of the database. Therefore, modifications need to be made in an appropriate time window to minimize the impact on the business
In past solutions, an ad hoc approach was often used to "patch" the schema evolution and apply it to the existing system. This resulted in many edge cases and incomplete functionality. Therefore, applications often require carefully scheduled downtime to make schema changes, thus sacrificing availability
To avoid the above shortcomings, Tesseract comes into play. In widely used multi-version database systems, schema evolution can be modeled as data modification operations on the entire table, which is the so-called Data Definition as Modification (DDaM). In this way, Tesseract can support the pattern at almost zero cost by leveraging the concurrency control protocol
In Tesseract application testing, we made a simple adjustment to the existing snapshot isolation protocol. Under workloads on 40-core servers, Tesseract is able to achieve online, transactional schema evolution without downtime, and maintain high application performance during the evolution process
By participating in the top database conference VLDB, Tencent Cloud will The latest technological breakthroughs and innovative directions in the database field are shared with global technology developers simultaneously, which also provides extremely valuable reference cases for technology and industrial development in the database field. In the future, Tencent Cloud will continue to improve database-related technologies, products and ecological capabilities to provide convenient and easy-to-use database services for all walks of life.
The above is the detailed content of Tencent Cloud Database was once again recognized by top conferences, and the paper was successfully selected for VLDB2023. For more information, please follow other related articles on the PHP Chinese website!

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



It is also a Tusheng video, but PaintsUndo has taken a different route. ControlNet author LvminZhang started to live again! This time I aim at the field of painting. The new project PaintsUndo has received 1.4kstar (still rising crazily) not long after it was launched. Project address: https://github.com/lllyasviel/Paints-UNDO Through this project, the user inputs a static image, and PaintsUndo can automatically help you generate a video of the entire painting process, from line draft to finished product. follow. During the drawing process, the line changes are amazing. The final video result is very similar to the original image: Let’s take a look at a complete drawing.

The AIxiv column is a column where this site publishes academic and technical content. In the past few years, the AIxiv column of this site has received more than 2,000 reports, covering top laboratories from major universities and companies around the world, effectively promoting academic exchanges and dissemination. If you have excellent work that you want to share, please feel free to contribute or contact us for reporting. Submission email: liyazhou@jiqizhixin.com; zhaoyunfeng@jiqizhixin.com The authors of this paper are all from the team of teacher Zhang Lingming at the University of Illinois at Urbana-Champaign (UIUC), including: Steven Code repair; Deng Yinlin, fourth-year doctoral student, researcher

The AIxiv column is a column where this site publishes academic and technical content. In the past few years, the AIxiv column of this site has received more than 2,000 reports, covering top laboratories from major universities and companies around the world, effectively promoting academic exchanges and dissemination. If you have excellent work that you want to share, please feel free to contribute or contact us for reporting. Submission email: liyazhou@jiqizhixin.com; zhaoyunfeng@jiqizhixin.com In the development process of artificial intelligence, the control and guidance of large language models (LLM) has always been one of the core challenges, aiming to ensure that these models are both powerful and safe serve human society. Early efforts focused on reinforcement learning methods through human feedback (RL

If the answer given by the AI model is incomprehensible at all, would you dare to use it? As machine learning systems are used in more important areas, it becomes increasingly important to demonstrate why we can trust their output, and when not to trust them. One possible way to gain trust in the output of a complex system is to require the system to produce an interpretation of its output that is readable to a human or another trusted system, that is, fully understandable to the point that any possible errors can be found. For example, to build trust in the judicial system, we require courts to provide clear and readable written opinions that explain and support their decisions. For large language models, we can also adopt a similar approach. However, when taking this approach, ensure that the language model generates

cheers! What is it like when a paper discussion is down to words? Recently, students at Stanford University created alphaXiv, an open discussion forum for arXiv papers that allows questions and comments to be posted directly on any arXiv paper. Website link: https://alphaxiv.org/ In fact, there is no need to visit this website specifically. Just change arXiv in any URL to alphaXiv to directly open the corresponding paper on the alphaXiv forum: you can accurately locate the paragraphs in the paper, Sentence: In the discussion area on the right, users can post questions to ask the author about the ideas and details of the paper. For example, they can also comment on the content of the paper, such as: "Given to

Show the causal chain to LLM and it learns the axioms. AI is already helping mathematicians and scientists conduct research. For example, the famous mathematician Terence Tao has repeatedly shared his research and exploration experience with the help of AI tools such as GPT. For AI to compete in these fields, strong and reliable causal reasoning capabilities are essential. The research to be introduced in this article found that a Transformer model trained on the demonstration of the causal transitivity axiom on small graphs can generalize to the transitive axiom on large graphs. In other words, if the Transformer learns to perform simple causal reasoning, it may be used for more complex causal reasoning. The axiomatic training framework proposed by the team is a new paradigm for learning causal reasoning based on passive data, with only demonstrations

Recently, the Riemann Hypothesis, known as one of the seven major problems of the millennium, has achieved a new breakthrough. The Riemann Hypothesis is a very important unsolved problem in mathematics, related to the precise properties of the distribution of prime numbers (primes are those numbers that are only divisible by 1 and themselves, and they play a fundamental role in number theory). In today's mathematical literature, there are more than a thousand mathematical propositions based on the establishment of the Riemann Hypothesis (or its generalized form). In other words, once the Riemann Hypothesis and its generalized form are proven, these more than a thousand propositions will be established as theorems, which will have a profound impact on the field of mathematics; and if the Riemann Hypothesis is proven wrong, then among these propositions part of it will also lose its effectiveness. New breakthrough comes from MIT mathematics professor Larry Guth and Oxford University

The AIxiv column is a column where this site publishes academic and technical content. In the past few years, the AIxiv column of this site has received more than 2,000 reports, covering top laboratories from major universities and companies around the world, effectively promoting academic exchanges and dissemination. If you have excellent work that you want to share, please feel free to contribute or contact us for reporting. Submission email: liyazhou@jiqizhixin.com; zhaoyunfeng@jiqizhixin.com. Introduction In recent years, the application of multimodal large language models (MLLM) in various fields has achieved remarkable success. However, as the basic model for many downstream tasks, current MLLM consists of the well-known Transformer network, which
