This week in Databend #19

Databend aimed to be an open source elastic and reliable cloud warehouse, it offers blazing fast query and combines elasticity, simplicity, low cost of the cloud, built to make the Data Cloud easy.

Big changes

Below is a list of some major changes that we don't want you to miss.

Features

Improvement

Performance Improvement

Bug fixes

Tips

Let's learn a weekly tip from Databend.

How to explore github repos via Databend

Databend now supports GitHub as a data source, and you can read the relevant code at storages/github.

create github-engine based database

Before running databend, your Github Access Token should be set.

export GITHUB_TOKEN=<your_token>;

Create a Github powered database.

databend :) create database datafuselabs engine=github;

0 rows in set. Elapsed: 2.611 sec. 

show all tables

Show all tables in this database, which are currently flattened. This means that Repos, issues and PRs are all in the form of tables.

databend :) use datafuselabs;

0 rows in set. Elapsed: 0.013 sec.

databend :) show tables;

+---------------------------------+
| name                            |
+---------------------------------+
| .github                         |
| .github_comments                |
| .github_issues                  |
| .github_prs                     |
| databend                        |
| databend-playground             |
| databend-playground_comments    |
| databend-playground_issues      |
| databend-playground_prs         |
| databend_comments               |
| databend_issues                 |
| databend_prs                    |
| ...                             |
+---------------------------------+

36 rows in set. Elapsed: 0.053 sec. 

View basic information about a repo

databend :) select * from databend;

+------------+----------+------------+------------+-------------+----------------+-------------------+-------------------+
| reposiroty | language | license    | star_count | forks_count | watchers_count | open_issues_count | subscribers_count |
+------------+----------+------------+------------+-------------+----------------+-------------------+-------------------+
| databend   | Rust     | apache-2.0 |       2661 |         252 |           2661 |               349 |                63 |
+------------+----------+------------+------------+-------------+----------------+-------------------+-------------------+

1 rows in set. Elapsed: 1.368 sec. 

Changlogs

You can check the changelogs of Databend nightly to learn about our latest developments.

Ecosystem/Upstream

From open source, for open source. Our team is also committed to contributing to the Rust ecosystem and upstream dependencies.

Meet Us

Please join the DatafuseLabs Community if you are interested in Databend.

We are looking forward to seeing you try our code. We have a strong team behind you to ensure a smooth experience in trying our code for your projects. If you are a hacker passionate about database internals, feel free to play with our code.

You can submit issues for any problems you find. We also highly appreciate any of your pull requests.

如果想了解更多,可以关注公众号: Databend

基于数据驱动的 Koopman 算子的递归神经网络模型线性化,用于纳米定位系统的预测控制研究(Matlab代码实现)内容概要:本文围绕“基于数据驱动的Koopman算子的递归神经网络模型线性化”展开,旨在研究纳米定位系统的预测控制方法。通过结合数据驱动技术与Koopman算子理论,将非线性系统动态近似为高维线性系统,进而利用递归神经网络(RNN)建模并实现系统行为的精确预测。文中详细阐述了模型构建流程、线性化策略及在预测控制中的集成应用,并提供了完整的Matlab代码实现,便于科研人员复现实验、优化算法并拓展至其他精密控制系统。该方法有效提升了纳米级定位系统的控制精度与动态响应性能。; 适合人群:具备自动控制、机器学习或信号处理背景,熟悉Matlab编程,从事精密仪器控制、智能制造或先进控制算法研究的研究生、科研人员及工程技术人员。; 使用场景及目标:①实现非线性动态系统的数据驱动线性化建模;②提升纳米定位平台的轨迹跟踪与预测控制性能;③为高精度控制系统提供可复现的Koopman-RNN融合解决方案; 阅读建议:建议结合Matlab代码逐段理解算法实现细节,重点关注Koopman观测矩阵构造、RNN训练流程与模型预测控制器(MPC)的集成方式,鼓励在实际硬件平台上验证并调整参数以适应具体应用场景。
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