RadonDB is an open source, Cloud-native MySQL database for unlimited scalability and performance.
RadonDB is a cloud-native database based on MySQL, and architected in fully distributed cluster that enable unlimited scalability (scale-out), capacity and performance. It supported distributed transaction that ensure high data consistency, and leveraged MySQL as storage engine for trusted data reliability. RadonDB is compatible with MySQL protocol, and sup-porting automatic table sharding as well as batch of automation feature for simplifying the maintenance and operation workflow.
For guidance on installation, deployment, and administration, see our Documentation.
RadonDB is a new generation of distributed relational database (MyNewSQL) based on MySQL. It was designed to create the open-source database our developers would want to use: one that has features such as financial high availability、large-capacity database、automatic plane split table、 scalable and strong consistency, this guide sets out to detail the inner-workings of the radon process as a means of explanation.
On SQL syntax level, RadonDB Fully compatible with MySQL.You can view all of the SQL features RadonDB supports here radon_sql_statements_manual
After your SQL node receives a SQL request from a mysql client via proxy, RadonDB parses the statement, creates a query plan, and then executes the plan.
+---------------+
x---------->|node1_Executor |
+--------------------+ x +---------------+
| SQL Node | x
|--------------------| x
+-------------+ | sqlparser | x +---------------+
| query |+----------->| |--x---------->|node2_Executor |
+-------------+ | Distributed Plan | x +---------------+
| | x
+--------------------+ x
x +---------------+
x---------->|node3_Executor |
+---------------+
Parsing
Received queries are parsed by sqlparser (which describes the supported syntax by mysql) and generated Abstract Syntax Trees (AST).
Planning
With the AST, RadonDB begins planning the query's execution by generating a tree of planNodes.This step also includes steps analyzing the client's SQL statements against the expected AST expressions, which include things like type checking.
You can see the a query plan generates using EXPLAIN
(At this stage we only use EXPLAIN
to analysis Table distribution).
Excuting
Executing an Executor in a storage layer in Parallel with a Distributed Execution Plan.
The SQL node is stateless, but in order to guarantee transaction Snapshot Isolation
, it is currently a write-multiple-read mode.
Distributed transaction
RadonDB provides distributed transaction capabilities. If the distributed executor at different storage nodes and one of the nodes failed to execute, then operation of the rest nodes will be rolled back, This guarantees the atomicity of operating across nodes and makes the database in a consistent state.
Isolation Levels
RadonDB achieves the level of SI (Snapshot Isolation) at the level of consistency. As long as a distributed transaction has not commit, or if some of the partitions have committed, the operation is invisible to other transactions.
Transaction with SQL Layer
The SQL node is stateless, but in order to guarantee transaction Snapshot Isolation
, it is currently a write-multiple-read mode.
The integrated github issue trackeris used for this project.
RadonDB is released under the GPLv3. See LICENSE
使用python和radon批量获取代码复杂度 任务概述 我们小组的大作业要从多个维度分析同学们的代码情况,描绘出编程者的用户画像。 为啥用radon 起初,我们小组商量着对同学们的py代码分析出循环深度。不得不说,对文件做词法分析和“循环深度”这样的事情,看着就很烦。 于是我在网上看到了下面这个文章。 如何科学评估代码复杂度? 确实,mi指数或者圈复杂度要比我们想的循环深度要好(再说直接调用就好
其数学基础是 Radon 变换及其逆变换.测试 CT 系统后得到的数据,可用 Matlab 软件提供的 iradon 逆变换函数,得出介质密度分布的图像,根据 CT 系统的标定参数值,把...... ? 信息技术 基于 Radon 变换的文本图像倾斜校正* 吴珅 黄道平 刘少君(... CT 成像的基本数学原理是 Radon 变换及其逆变换, 对于大量精确的投影数据来说,这是一种具有高效率的重建
1.https://zhuanlan.zhihu.com/p/79722768 2.https://blog.csdn.net/qq_33837704/article/details/79504530 3.https://blog.csdn.net/yu132563/article/details/99228303
其数学基础是 Radon 变换及其逆变换.测试 CT 系统后得到的数据,可用 Matlab 软件提供的 iradon 逆变换函数,得出介质密度分布的图像,根据 CT 系统的标定参数值,把...... ? 信息技术 基于 Radon 变换的文本图像倾斜校正* 吴珅 黄道平 刘少君(... CT 成像的基本数学原理是 Radon 变换及其逆变换, 对于大量精确的投影数据来说,这是一种具有高效率的重建