ruby-on-rails - 将 Rails 5.2 部署到生产环境时,哪种资产调试器配置有效?
问题描述
我计划在 Apache 反向代理后面部署我的 Ruby on Rails 5.2 应用程序(我们只有 Windows 服务器)。我尝试将环境从开发切换到生产:
rails server -e production
Puma 服务器很好,但默认配置 (config/environments/production.rb) 提供页面而不格式化布局:似乎没有应用 css。
为了让它工作,我添加了以下配置选项:
config.assets.debug = true
但它会减慢页面的渲染速度。
这是生产实例的结果配置文件:
DataQualityManager::Application.configure do
# Settings specified here will take precedence over those in config/application.rb.
# Code is not reloaded between requests.
config.cache_classes = true
# Eager load code on boot. This eager loads most of Rails and
# your application in memory, allowing both thread web servers
# and those relying on copy on write to perform better.
# Rake tasks automatically ignore this option for performance.
config.eager_load = true
# Full error reports are disabled and caching is turned on.
config.consider_all_requests_local = false
config.action_controller.perform_caching = true
# Enable Rack::Cache to put a simple HTTP cache in front of your application
# Add `rack-cache` to your Gemfile before enabling this.
# For large-scale production use, consider using a caching reverse proxy like nginx, varnish or squid.
# config.action_dispatch.rack_cache = true
# Debug mode disables concatenation and preprocessing of assets.
# This option may cause significant delays in view rendering with a large
# number of complex assets.
config.assets.debug = true
# Disable Rails's static asset server (Apache or nginx will already do this).
config.serve_static_assets = false
# Compress JavaScripts and CSS.
config.assets.js_compressor = :uglifier
# config.assets.css_compressor = :sass
# Do not fallback to assets pipeline if a precompiled asset is missed.
config.assets.compile = true
# Generate digests for assets URLs.
config.assets.digest = true
# Version of your assets, change this if you want to expire all your assets.
config.assets.version = '1.0'
# Specifies the header that your server uses for sending files.
# config.action_dispatch.x_sendfile_header = "X-Sendfile" # for apache
# config.action_dispatch.x_sendfile_header = 'X-Accel-Redirect' # for nginx
# Force all access to the app over SSL, use Strict-Transport-Security, and use secure cookies.
config.force_ssl = false
# Set to :debug to see everything in the log.
config.log_level = :info
# Prepend all log lines with the following tags.
# config.log_tags = [ :subdomain, :uuid ]
# Use a different logger for distributed setups.
# config.logger = ActiveSupport::TaggedLogging.new(SyslogLogger.new)
# Use a different cache store in production.
# config.cache_store = :mem_cache_store
# Enable serving of images, stylesheets, and JavaScripts from an asset server.
# config.action_controller.asset_host = "http://assets.example.com"
# Precompile additional assets.
# application.js, application.css, and all non-JS/CSS in app/assets folder are already added.
# config.assets.precompile += %w( search.js )
# Ignore bad email addresses and do not raise email delivery errors.
# Set this to true and configure the email server for immediate delivery to raise delivery errors.
# config.action_mailer.raise_delivery_errors = false
# Enable locale fallbacks for I18n (makes lookups for any locale fall back to
# the I18n.default_locale when a translation can not be found).
# config.i18n.fallbacks = true
# Send deprecation notices to registered listeners.
config.active_support.deprecation = :notify
# Disable automatic flushing of the log to improve performance.
# config.autoflush_log = false
# Use default logging formatter so that PID and timestamp are not suppressed.
config.log_formatter = ::Logger::Formatter.new
# Store uploaded files locally.
config.active_storage.service = :local
end
你能帮我解决这个问题并保持服务器的性能吗?非常感谢!
解决方案
推荐阅读
- aframe - 实体的定向音频
- javascript - 创建自定义 EventEmitter 时不需要调用 .emit 吗?
- processing - 在 P5.js 中使用 loadBytes 的奇怪行为
- java - Java - ServiceLoader - Intellij 找不到实现类文件
- python - Python - 在where子句中动态传递产品名称
- python-3.x - 在新的 python 程序文件中从套接字编程中读取客户端文件的结果
- r - 在 tibble 的不同级别应用函数
- firebase - React Native with Firebase:任何人都可以看到电子邮件格式不正确的地方吗?
- rdf - 如何使用任何 owl 本体映射大型数据文件
- python - sklearn learning_curve 和 StandardScaler