YAML Syntax

Introduction

YAML is used for configuration files, blueprints, and also in page settings.

YAML is to configuration what markdown is to markup. It’s basically a human-readable structured data format. It is less complex and ungainly than XML or JSON, but provides similar capabilities. It essentially allows you to provide powerful configuration settings, without having to learn a more complex code type like CSS, JavaScript, and PHP.

YAML is built from the ground up to be simple to use. At its core, a YAML file is used to describe data. One of the benefits of using YAML is that the information in a single YAML file can be easily translated to multiple language types.

YAML Basic Rules

There are some rules that YAML has in place to avoid issues related to ambiguity in relation to various languages and editing programs. These rules make it possible for a single YAML file to be interpreted consistently, regardless of which application and/or library is being used to interpret it.

  • YAML files should end in .yaml.
  • YAML is case sensitive.
  • YAML does not allow the use of tabs.

Basic Data Types

YAML excels at working with mappings (hashes / dictionaries), sequences (arrays / lists), and scalars (strings / numbers). While it can be used with most programming languages, it works best with languages that are built around these data structure types. This includes: PHP, Python, Perl, JavaScript, and Ruby.

Scalars

Scalars are a pretty basic concept. They are the strings and numbers that make up the data on the page. A scalar could be a boolean property, like true, integer (number) such as 5, or a string of text, like a sentence or the title of your website.

Scalars are often called variables in programming. If you were making a list of types of animals, they would be the names given to those animals.

Most scalars are unquoted, but if you are typing a string that uses punctuation and other elements that can be confused with YAML syntax (dashes, colons, etc.) you may want to quote this data using single ‘ or double ” quotation marks. Double quotation marks allow you to use escapings to represent ASCII and Unicode characters.

integer: 25
string: "25"
float: 25.0
boolean: true

TIP: Words true, false, null, ~ and dates have special meaning in YAML. Please quote them if you do not want to use them as a boolean, null or datetime type. Same is true with version numbers, they should be quoted to separate them from float values.

Sequences

Here is a simple sequence you. It is a basic list with each item in the list placed in its own line with an opening dash.

- Cat
- Dog
- Goldfish

This sequence places each item in the list at the same level. If you want to create a nested sequence with items and sub-items, you can do so by placing a single space before each dash in the sub-items. YAML uses spaces, NOT tabs, for indentation. You can see an example of this below. Continue reading “YAML Syntax”

ProviewR – An open source process control

可能是市场上唯一能够以真正的面向对象方式设计和编程的开源过程控制系统。Powered By SSAB.

General

ProviewR is an Open Source Process Control System. It is modern, powerful and general and contains all functions normally required for successful sequential control, adjustment, data acquisition, communication, supervision, etc.

The configuration of a ProviewR system is done graphically, making the application adaptation simple, reliable, and flexible. ProviewR is a distributed system, which means that the system can consist of several computers, connected via a network, preferably ethernet. A typical ProviewR system consists of one process control system and one or more operator stations. It is easy to configure one operating station to become the HMI-system of several control systems.

Programming is possible both with a graphical PLC-editor and with high level programming languages (such as C, C++, Java or FORTRAN). The concept of Proview is based on a soft-PLC solution which runs on standard computers with Linux as operating system.

Performance

The great advantage of using standard hardware and soft-PLC is that system size, properties and performance is mainly limited by the hosting operating system and its hardware. In Proview there are no limits in number of I/O, PID loops, PLC programs, counters etc. The minimum cycle time for a PLC loop is less than 1 ms.

Communication

ProviewR can communicate with other computers both via ethernet network (ip) and via serial mechanisms. ProviewR supports several different protocols, such as UDP or TCP sockets via ethernet and Siemens 3964R on serial links.

I/O system

The most common used I/O system in Proview is Profibus/DP, a robust and well-tested field bus. There are also support for Profinet, Ethernet Powerlink, Modbus TCP and RTU, PSS9000, 1-wire, some USB I/O cards etc. The Modular design of the I/O system and the fact that ProviewR is based on Linux and high level languages makes it easy to implement other I/O systems with available drivers or develop new ones.

Object Orientation

ProviewR is the only control system on the market that can work in a truly object oriented way. Programming can be made in a traditional way with simple function blocks and simple signals. ProviewR though has support for creating complex objects and function objects that work upon them.

Object oriented concepts such as inheritance, methods and aggregates are supported.

Open Source

ProviewR is probably the first Open Source system for process control in the world. Originally developed in Sweden by Mandator and SSAB Oxelösund as a process control system based on standard computers, the system has become a fully-fledged, integrated and low-cost solution that is running on standard PC’s with Linux operating system.

ProviewR is Open Source and the license is GNU/GPL. You can download ProviewR, use it, modify it and redistribute it as much as you like as long as you follow the license terms.

Official site:

www.proview.se

Source Codes:

https://sourceforge.net/projects/proview/files/proview/

https://github.com/siamect/proview

 

老生常谈,TCP三次握手、四次挥手过程及原理

本文来自于互联网,原始出处已无法考证

TCP 协议简述

TCP 提供面向有连接的通信传输,面向有连接是指在传送数据之前必须先建立连接,数据传送完成后要释放连接。
无论哪一方向另一方发送数据之前,都必须先在双方之间建立一条连接。在TCP/IP协议中,TCP协议提供可靠的连接服务,连接是通过三次握手进行初始化的。
同时由于TCP协议是一种面向连接的、可靠的、基于字节流的运输层通信协议,TCP是全双工模式,所以需要四次挥手关闭连接。

TCP包首部

网络中传输的数据包由两部分组成:一部分是协议所要用到的首部,另一部分是上一层传过来的数据。首部的结构由协议的具体规范详细定义。在数据包的首部,明确标明了协议应该如何读取数据。反过来说,看到首部,也就能够了解该协议必要的信息以及所要处理的数据。包首部就像协议的脸。

所以我们在学习TCP协议之前,首先要知道TCP在网络传输中处于哪个位置,以及它的协议的规范,下面我们就看看TCP首部的网络传输起到的作用:

下面的图是TCP头部的规范定义,它定义了TCP协议如何读取和解析数据:

TCP首部承载这TCP协议需要的各项信息,下面我们来分析一下: Continue reading “老生常谈,TCP三次握手、四次挥手过程及原理”

YARP, 强大的可编程的反向代理

YARP, 在Nginx、Apache、Ocelot等之外,一个.Net Core下Reverse Proxy的新起之秀,Microsoft官方维护开源,从此你拥有了强大的可编程的反向代理。

原文链接:YARP Documentation (microsoft.github.io)

YARP is a library to help create reverse proxy servers that are high-performance, production-ready, and highly customizable. Right now it’s still in preview, but please provide us your feedback by going to the GitHub repository.

We found a bunch of internal teams at Microsoft who were either building a reverse proxy for their service or had been asking about APIs and tech for building one, so we decided to get them all together to work on a common solution, this project.

YARP is built on .NET using the infrastructure from ASP.NET and .NET (.NET Core 3.1 and .NET 5.0). The key differentiator for YARP is that it’s been designed to be easily customized and tweaked via .NET code to match the specific needs of each deployment scenario.

We expect YARP to ship as a library, project template, and a single-file exe, to provide a variety of choices for building a robust, performant proxy server. Its pipeline and modules are designed so that you can then customize the functionality for your needs. For example, while YARP supports configuration files, we expect that many users will want to manage the configuration programmatically based on their own configuration management system, YARP will provide a configuration API to enable that customization in-proc. YARP is designed with customizability as a primary scenario rather than requiring you to break out to script or rebuild the library from source.

支持(仅列举部分):

  • Header Routing

Proxy routes specified in config or via code must include at least a path or host to match against. In addition to these, a route can also specify one or more headers that must be present on the request.

  • Authentication and Authorization

The reverse proxy can be used to authenticate and authorize requests before they are proxied to the destination servers. This can reduce load on the destination servers, add a layer of protection, and ensure consistent policies are implemented across your applications.

  • Cross-Origin Requests (CORS)

The reverse proxy can handle cross-origin requests before they are proxied to the destination servers. This can reduce load on the destination servers and ensure consistent policies are implemented across your applications.

  • Session Affinity:

Session affinity is a mechanism to bind (affinitize) a causally related request sequence to the destination handled the first request when the load is balanced among several destinations. It is useful in scenarios where the most requests in a sequence work with the same data and the cost of data access differs for different nodes (destinations) handling requests. The most common example is a transient caching (e.g. in-memory) where the first request fetches data from a slower persistent storage into a fast local cache and the others work only with the cached data thus increasing throughput.

  • Load Balancing

Whenever there are multiple healthy destinations available, YARP has to decide which one to use for a given request. YARP ships with built-in load-balancing algorithms, but also offers extensibility for any custom load balancing approach.

  • Transforms

When proxying a request it’s common to modify parts of the request or response to adapt to the destination server’s requirements or to flow additional data such as the client’s original IP address. This process is implemented via Transforms. Types of transforms are defined globally for the application and then individual routes supply the parameters to enable and configure those transforms. The original request objects are not modified by these transforms, only the proxy requests.

  • Destinations Health Checks

In most of the real-world systems, it’s expected for their nodes to occasionally experience transient issues and go down completely due to a variety of reasons such as an overload, resource leakage, hardware failures, etc. Ideally, it’d be desirable to completely prevent those unfortunate events from occurring in a proactive way, but the cost of designing and building such an ideal system is generally prohibitively high. However, there is another reactive approach which is cheaper and aimed to minimizing a negative impact failures cause on client requests. The proxy can analyze each nodes health and stop sending client traffic to unhealthy ones until they recover. YARP implements this approach in the form of active and passive destination health checks.

Continue reading “YARP, 强大的可编程的反向代理”

Shard (database architecture)

A database shard, or simply a shard, is a horizontal partition of data in a database or search engine. Each shard is held on a separate database server instance, to spread load.

Some data within a database remains present in all shards, but some appears only in a single shard. Each shard (or server) acts as the single source for this subset of data.

Database architecture

Horizontal partitioning is a database design principle whereby rows of a database table are held separately, rather than being split into columns (which is what normalization and vertical partitioning do, to differing extents). Each partition forms part of a shard, which may in turn be located on a separate database server or physical location.

There are numerous advantages to the horizontal partitioning approach. Since the tables are divided and distributed into multiple servers, the total number of rows in each table in each database is reduced. This reduces index size, which generally improves search performance. A database shard can be placed on separate hardware, and multiple shards can be placed on multiple machines. This enables a distribution of the database over a large number of machines, greatly improving performance. In addition, if the database shard is based on some real-world segmentation of the data (e.g., European customers v. American customers) then it may be possible to infer the appropriate shard membership easily and automatically, and query only the relevant shard.

Disadvantages include:

Main section: Disadvantages

  • A heavier reliance on the interconnection between servers.
  • Increased latency when querying, especially where more than one shard must be searched.
  • Data or indexes are often only sharded one way, so that some searches are optimal, and others are slow or impossible.
  • Issues of consistency and durability due to the more complex failure modes of a set of servers, which often result in systems making no guarantees about cross-shard consistency or durability.

In practice, sharding is complex. Although it has been done for a long time by hand-coding (especially where rows have an obvious grouping, as per the example above), this is often inflexible. There is a desire to support sharding automatically, both in terms of adding code support for it, and for identifying candidates to be sharded separately. Consistent hashing is a technique used in sharding to spread large loads across multiple smaller services and servers.

Where distributed computing is used to separate load between multiple servers (either for performance or reliability reasons), a shard approach may also be useful. Continue reading “Shard (database architecture)”

DevOps

The original url of this article is https://azure.microsoft.com/en-us/overview/what-is-devops/

What is DevOps?

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

Explore DevOps solutions

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.

Plan

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.

Develop

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.

Deliver

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.

Operate

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

Introduction

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.

Cache-aside

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.

Read-through

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.

Solution

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”

缓存和DB一致性问题

以下是从网络上摘取的一些缓存一致性方案,供参考。

产生原因

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。

更新缓存的设计模式

1.Cache Aside Pattern(旁路缓存)

这是最常用最常用的pattern了。其具体逻辑如下:

  • 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。

一个是查询操作,一个是更新操作的并发,首先,没有了删除cache数据的操作了,而是先更新了数据库中的数据,此时,缓存依然有效,所以,并发的查询操作拿的是没有更新的数据,但是,更新操作马上让缓存的失效了,后续的查询操作再把数据从数据库中拉出来。而不会像文章开头的那个逻辑产生的问题,后续的查询操作一直都在取老的数据。

要么通过2PC或是Paxos协议保证一致性,要么就是拼命的降低并发时脏数据的概率,而Facebook使用了这个降低概率的玩法,因为2PC太慢,而Paxos太复杂。当然,最好还是为缓存设置上过期时间。

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)

下图自来Wikipedia的Cache词条。其中的Memory你可以理解为就是我们例子里的数据库。

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。

但是,其带来的问题是,数据不是强一致性的,而且可能会丢失(我们知道Unix/Linux非正常关机会导致数据丢失,就是因为这个事)。在软件设计上,我们基本上不可能做出一个没有缺陷的设计,就像算法设计中的时间换空间,空间换时间一个道理,有时候,强一致性和高性能,高可用和高性性是有冲突的。软件设计从来都是取舍Trade-Off。

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 https://ocelot.readthedocs.io

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

../_images/OcelotBasic.jpg

With IdentityServer

../_images/OcelotIndentityServer.jpg

Multiple Instances

../_images/OcelotMultipleInstances.jpg

Continue reading “ocelot brief”