Rails利用Yaml在不同的环境与数据库之间传递数据

介绍了一种名为'yaml_db'的Gem,它提供了一种简便的方法来处理Rails应用中的数据迁移问题,尤其是在从本地环境向生产环境迁移复杂数据时。

Sometimes, you cannot just seed your Rails database. This could be due to the complexity of the data itself, which may turn the creation of your seeds.rb file into a pure nightmare. In such cases, one of the possible solutions is to process all your data manually, through your web app UI, for example.

However, how would you make the data available to your application in the production environment ? Furthermore, you may not be able to cut off your production server while you make the necessary changes.

Fortunately, there is a neat solution to this problem. It’s a gem called 'yaml_db', that provides an intermediary dump format for your data ( by default, it outputs and reads data from db/data.yml ), and two very helpful commands.

How to use it ?

Add this line to your gemfile :

gem 'yaml_db'

Run bundler :

bundle

To dump your data :

bundle exec rake db:data:dump

To load your data : bundle exec rake db:data:load You can specify the environment using RAILS_ENV variable. The following example dumps data from the development database and pushes it to the production db :

RAILS_ENV=development bundle exec rake db:data:dump
RAILS_ENV=production bundle exec rake db:data:load

As a side note, I found this gem to be particularily handy when I have to transfer data from my localhost ( for example ) to a heroku instance. Assuming that you have dumped your database, properly added db/data.yml to the repository, and updated your heroku app with your latest code version, all you have to do is to run the following command :

heroku run bundle exec rake db:data:load

Please note that this method doesn’t reset your data but rather merges your actual database with data.yml content. Be careful not to import it more than once !

You can find more informations about yaml_db in it’s official GitHub repo :

http://github.com/ludicast/yaml_db

内容概要:本文提出了一种基于融合鱼鹰算法和柯西变异的改进麻雀优化算法(OCSSA),用于优化变分模态分解(VMD)的参数,进而结合卷积神经网络(CNN)双向长短期记忆网络(BiLSTM)构建OCSSA-VMD-CNN-BILSTM模型,实现对轴承故障的高【轴承故障诊断】基于融合鱼鹰和柯西变异的麻雀优化算法OCSSA-VMD-CNN-BILSTM轴承诊断研究【西储大学数据】(Matlab代码实现)精度诊断。研究采用西储大学公开的轴承故障数据集进行实验验证,通过优化VMD的模态数和惩罚因子,有效提升了信号分解的准确性稳定性,随后利用CNN提取故障特征,BiLSTM捕捉时间序列的深层依赖关系,最终实现故障类型的智能识别。该方法在提升故障诊断精度鲁棒性方面表现出优越性能。; 适合人群:具备一定信号处理、机器学习基础,从事机械故障诊断、智能运维、工业大数据分析等相关领域的研究生、科研人员及工程技术人员。; 使用场景及目标:①解决传统VMD参数依赖人工经验选取的问题,实现参数自适应优化;②提升复杂工况下滚动轴承早期故障的识别准确率;③为智能制造预测性维护提供可靠的技术支持。; 阅读建议:建议读者结合Matlab代码实现过程,深入理解OCSSA优化机制、VMD信号分解流程以及CNN-BiLSTM网络架构的设计逻辑,重点关注参数优化故障分类的联动关系,并可通过更换数据集进一步验证模型泛化能力。
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