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What is DevOps?

Learn how DevOps unifies people, process, and technology to bring better products to customers faster

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DevOps definition

A compound of development (Dev) and operations (Ops), DevOps is the union of people, process, and technology to continually provide value to customers.

What does DevOps mean for teams? DevOps enables formerly siloed roles—development, IT operations, quality engineering, and security—to coordinate and collaborate to produce better, more reliable products. By adopting a DevOps culture along with DevOps practices and tools, teams gain the ability to better respond to customer needs, increase confidence in the applications they build, and achieve business goals faster.

The benefits of DevOps

Teams that adopt DevOps culture, practices, and tools become high-performing, building better products faster for greater customer satisfaction. This improved collaboration and productivity is also integral to achieving business goals like these:

Accelerating time to market

Adapting to the market and competition

Maintaining system stability and reliability

Improving the mean time to recovery

DevOps and the application lifecycle

DevOps influences the application lifecycle throughout its plan, develop, deliver, and operate phases. Each phase relies on the others, and the phases are not role-specific. In a true DevOps culture, each role is involved in each phase to some extent.


In the plan phase, DevOps teams ideate, define, and describe features and capabilities of the applications and systems they are building. They track progress at low and high levels of granularity—from single-product tasks to tasks that span portfolios of multiple products. Creating backlogs, tracking bugs, managing agile software development with Scrum, using Kanban boards, and visualizing progress with dashboards are some of the ways DevOps teams plan with agility and visibility.


The develop phase includes all aspects of coding—writing, testing, reviewing, and the integration of code by team members—as well as building that code into build artifacts that can be deployed into various environments. DevOps teams seek to innovate rapidly without sacrificing quality, stability, and productivity. To do that, they use highly productive tools, automate mundane and manual steps, and iterate in small increments through automated testing and continuous integration.


Delivery is the process of deploying applications into production environments in a consistent and reliable way. The deliver phase also includes deploying and configuring the fully governed foundational infrastructure that makes up those environments.

In the deliver phase, teams define a release management process with clear manual approval stages. They also set automated gates that move applications between stages until they’re made available to customers. Automating these processes makes them scalable, repeatable, controlled. This way, teams who practice DevOps can deliver frequently with ease, confidence, and peace of mind.


The operate phase involves maintaining, monitoring, and troubleshooting applications in production environments. In adopting DevOps practices, teams work to ensure system reliability, high availability, and aim for zero downtime while reinforcing security and governance. DevOps teams seek to identify issues before they affect the customer experience and mitigate issues quickly when they do occur. Maintaining this vigilance requires rich telemetry, actionable alerting, and full visibility into applications and the underlying system. Continue reading “DevOps”

Cache synchronization strategies


A system of record is the authoritative data source when information is scattered among various data providers. When we introduce a caching solution, we automatically duplicate our data. To avoid inconsistent reads and data integrity issues, it’s very important to synchronize the database and the cache (whenever a change occurs in the system).

There are various ways to keep the cache and the underlying database in sync and this article will present some of the most common cache synchronization strategies.


The application code can manually manage both the database and the cache information. The application logic inspects the cache before hitting the database and it updates the cache after any database modification.

Cache Aside

Mixing caching management and application is not very appealing, especially if we have to repeat these steps in every data retrieval method. Leveraging an Aspect-Oriented caching interceptor can mitigate the cache leaking into the application code, but it doesn’t exonerate us from making sure that both the database and the cache are properly synchronized.


Instead of managing both the database and the cache, we can simply delegate the database synchronization to the cache provider. All data interaction is, therefore, done through the cache abstraction layer. Continue reading “Cache synchronization strategies”

Cache-Aside pattern

Load data on demand into a cache from a data store. This can improve performance and also helps to maintain consistency between data held in the cache and data in the underlying data store.

Context and problem

Applications use a cache to improve repeated access to information held in a data store. However, it’s impractical to expect that cached data will always be completely consistent with the data in the data store. Applications should implement a strategy that helps to ensure that the data in the cache is as up-to-date as possible, but can also detect and handle situations that arise when the data in the cache has become stale.


Many commercial caching systems provide read-through and write-through/write-behind operations. In these systems, an application retrieves data by referencing the cache. If the data isn’t in the cache, it’s retrieved from the data store and added to the cache. Any modifications to data held in the cache are automatically written back to the data store as well.

For caches that don’t provide this functionality, it’s the responsibility of the applications that use the cache to maintain the data.

An application can emulate the functionality of read-through caching by implementing the cache-aside strategy. This strategy loads data into the cache on demand. The figure illustrates using the Cache-Aside pattern to store data in the cache.

Using the Cache-Aside pattern to store data in the cache

If an application updates information, it can follow the write-through strategy by making the modification to the data store, and by invalidating the corresponding item in the cache.

When the item is next required, using the cache-aside strategy will cause the updated data to be retrieved from the data store and added back into the cache. Continue reading “Cache-Aside pattern”




主要有两种情况,会导致缓存和 DB 的一致性问题:

  1. 并发的场景下,导致读取老的 DB 数据,更新到缓存中。
  2. 缓存和 DB 的操作,不在一个事务中,可能只有一个操作成功,而另一个操作失败,导致不一致。

当然,有一点我们要注意,缓存和 DB 的一致性,我们指的更多的是最终一致性。我们使用缓存只要是提高读操作的性能,真正在写操作的业务逻辑,还是以数据库为准。例如说,我们可能缓存用户钱包的余额在缓存中,在前端查询钱包余额时,读取缓存,在使用钱包余额时,读取数据库。


1.Cache Aside Pattern(旁路缓存)


  • 失效:应用程序先从cache取数据,没有得到,则从数据库中取数据,成功后,放到缓存中。
  • 命中:应用程序从cache中取数据,取到后返回。
  • 更新:先把数据存到数据库中,成功后,再让缓存失效。



2.Read/Write Through Pattern

在上面的Cache Aside套路中,我们的应用代码需要维护两个数据存储,一个是缓存(Cache),一个是数据库(Repository)。所以,应用程序比较啰嗦。而Read/Write Through套路是把更新数据库(Repository)的操作由缓存自己代理了,所以,对于应用层来说,就简单很多了。可以理解为,应用认为后端就是一个单一的存储,而存储自己维护自己的Cache。

Read Through

Read Through 套路就是在查询操作中更新缓存,也就是说,当缓存失效的时候(过期或LRU换出),Cache Aside是由调用方负责把数据加载入缓存,而Read Through则用缓存服务自己来加载,从而对应用方是透明的。

Write Through

Write Through 套路和Read Through相仿,不过是在更新数据时发生。当有数据更新的时候,如果没有命中缓存,直接更新数据库,然后返回。如果命中了缓存,则更新缓存,然后再由Cache自己更新数据库(这是一个同步操作)


3.Write Behind Caching Pattern

Write Behind 又叫 Write Back。write back就是Linux文件系统的Page Cache的算法

Write Back套路,一句说就是,在更新数据的时候,只更新缓存,不更新数据库,而我们的缓存会异步地批量更新数据库。

这个设计的好处就是让数据的I/O操作飞快无比(因为直接操作内存嘛 ),因为异步,write back还可以合并对同一个数据的多次操作,所以性能的提高是相当可观的。


另外,Write Back实现逻辑比较复杂,因为他需要track有哪数据是被更新了的,需要刷到持久层上。操作系统的write back会在仅当这个cache需要失效的时候,才会被真正持久起来,比如,内存不够了,或是进程退出了等情况,这又叫lazy write。

在wikipedia上有一张write back的流程图,基本逻辑如下:

参照:左耳朵耗子《缓存更新的套路》 Continue reading “缓存和DB一致性问题”

ocelot brief

The article copyed from

Ocelot is aimed at people using .NET running a micro services / service orientated architecture that need a unified point of entry into their system.

In particular I want easy integration with IdentityServer reference and bearer tokens.

Ocelot is a bunch of middlewares in a specific order.

Ocelot manipulates the HttpRequest object into a state specified by its configuration until it reaches a request builder middleware where it creates a HttpRequestMessage object which is used to make a request to a downstream service. The middleware that makes the request is the last thing in the Ocelot pipeline. It does not call the next middleware. There is a piece of middleware that maps the HttpResponseMessage onto the HttpResponse object and that is returned to the client. That is basically it with a bunch of other features.

The following are configurations that you use when deploying Ocelot.

Basic Implementation


With IdentityServer


Multiple Instances


Continue reading “ocelot brief”