• Architecture Architecture is the art and science of designing structures that organize and enclose space for practical and symbolic purposes. Because architecture grows out of human needs and aspirat...
Architecture
Architecture is the art and science of designing structures that organize and enclose space for practical and symbolic purposes. Because architecture grows out of human needs and aspirations, it clearly communicates cultural values. Of all the visual arts, architecture affects our lives most directly for it determines the character of the human environment in major ways.
Architecture is a three-dimensional form. It utilizes space, mass, texture, line, light, and color. To be architecture, a building must achieve a working harmony with a variety of elements. Humans instinctively seek structures that will shelter and enhance their way of life. It is the work of architects to create buildings that are not simply constructions but also offer inspiration and delight. Buildings contribute to human life when they provide shelter, enrich space, complement their site, suit the climate, and are economically feasible. The client who pays for the building and defines its function is an important member of the architectural team. The mediocre design of many contemporary buildings can be traced to both clients and architects.
In order for the structure to achieve the size and strength necessary to meet its purpose, architecture employs methods of support that, because they are based on physical laws, have changed little since people first discovered them—even while building materials have changed dramatically. The world’s architectural structures have also been devised in relation to the objective limitations of materials. Structures can be analyzed in terms of how they deal with downward forces created by gravity. They are designed to withstand the forces of compression (pushing together), tension (pulling apart), bending, or a combination of these in different parts of the structure.
Even development in architecture has been the result of major technological changes. Materials and methods of construction are integral parts of the design of architecture structures. In earlier times it was necessary to design structural systems suitable for the materials that were available, such as wood, stone, brick. Today technology has progressed to the point where it is possible to invent new building materials to suit the type of structure desired. Enormous changes in materials and techniques of construction within the last few generations have made it possible to enclose space with much greater ease and speed and with a minimum of material. Progress in this area can be measured by the difference in weight between buildings built now and those of comparable size built one hundred years ago.
Modern architectural forms generally have three separate components comparable to elements of the human body: a supporting skeleton or frame, an outer skin enclosing the interior spaces, and equipment, similar to the body’s vital organs and systems. The equipment includes plumbing, electrical wiring, hot water, and air-conditioning. Of course in early architecture—such as igloos and adobe structures—there was no such equipment, and the skeleton and skin were often one.
Much of the world’s great architecture has been constructed of stone because of its beauty, permanence, and availability. In the past, whole cities grew from the arduous task of cutting and piling stone upon. Some of the world’s finest stone architecture can be seen in the ruins of the ancient Inca city of Machu Picchu highin theeastern Andes Mountains of Peru. The doorways and windows are made possible by placing over the open spaces thick stone beams that support the weight from above. A structural invention had to be made before the physical limitations of stone could be overcome and new architectural forms could be created. That invention was the arch, a curved structure originally made of separate stone or brick segments. The arch was used by the early cultures of the Mediterranean area chiefly for underground drains, but it was the Romans who first developed and used the arch extensively in aboveground structures. Roman builders perfected the semicircular arch made of separate blocks of stone. As a method of spanning space, the arch can support greater weight than a horizontal beam. It works in compression to divert the weight above it out to the sides, where the weight is borne by the vertical elements on either side of the arch. The arch is among the many important structural breakthroughs that have characterized architecture throughout the centuries.
转载于:https://www.cnblogs.com/techyu/p/10873390.html
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• 1 企业架构发展历史 企业架构（TOGAF）学习
0.

0.1 三层架构、单体架构

0.2 SOA

SOA（Service-Oriented Architecture，面向服务架构）是一种架构模型。通过接口发布服务，通过协议通信。

0.3 Web Services

实现SOA的最常见技术标准是Web Services。
Web Services是独立的、模块化的应用，能够通过互联网来描述、发布、定位以及调用。
* SOAP(Simple Object Access Protocol)
SOAP协议，即简单对象访问协议，是SOA架构实现的线缆级协议，定义了服务请求者和服务提供者之间的消息传输规范。
* WSDL（Web Services Description Language）
WSDL为服务提供者提供以XML格式描述WebServices 请求的标准格式，将网络服务描述为能够进行消息交换的通信端点的集合，以表达一个Web Services能做什么、它的位置在哪里、如何调用它等。
* UDDI（Universal Discovery,Description,Integration）
UDDI是Web Services的信息注册规范，以便被需要该服务的用户发现和使用它。
0.2,0.3详见

0.4 Apache CXF

Apache CXF是一个开源的Web Services框架。
详见

0.5 JAX-WS

全称是JavaTM API forXML-Based Web Services。Apache CXF实现JAX-WS。

0.6 JAX-RS

全称是JavaTM API forRESTful Web Services 。Apache CXF、Jersey实现JAX-RS。

0.7 Jersey

Jersey是JAX-RS标准的参考实现。

0.8 ROA

REST是一组被称之为面向资源架构（ROA）的架构准则。
详见

1. 操作系统

1.0. 虚拟存储

段式管理
页式管理
段页式管理

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• Practical Software Architecture Solutions from the Legendary Robert C. Martin ("Uncle Bob"). Robert describes the Clean Architecture as an architecture that pushes us to separate stable business rules...
• Chris Eidhof, Matt Gallagher, Florian Kugler联合出品 喵神主持翻译 App Architecture: iOS Application Design Patterns in Swift 包含Source code 有钱请支持正版 没钱请默默学习 原书地址: ...
• Architecture pattern: context + problem -> solution Architecture style: solution part of architecture pattern So architecture style is analogous to the solution part of the architecture patt...

Architecture pattern: context + problem -> solution
Architecture style: solution part of architecture pattern
So architecture style is analogous to the solution part of the architecture pattern. It's often used in books dealing with architecture documentation where the focus is on the solution and not how the context and problem came about.

An architectural style (Base et al. 1997) and an architectural pattern
(Buschmann et al. 1996) are essentially synonymous.
Based on some more googling, this is what i think might be one possible way to differentiate the two
An architectural style is a conceptual way of how the system will be created / will work
An architectural pattern describes a solution for implementing a style at the level of subsystems or modules and their relationships.
How an architectural pattern will differ from a Design pattern i.e Adapter, observer is basically by the level of Granularity at which they are applied (I know this isnt part of the question but its related, i think)

Conclusion
As I mentioned in the beginning of this post, it’s all about the scope:
An Architectural Style is the application design at the highest level of abstraction;
An Architectural Pattern is a way to implement an Architectural Style;
A Design Pattern is a way to solve a localised problem.

https://stackoverflow.com/questions/3958316/whats-the-difference-between-architectural-patterns-and-architectural-styles

https://en.wikipedia.org/wiki/Software_design_pattern

转载于:https://www.cnblogs.com/feng9exe/p/10255124.html
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• Lambda architecture and kappa architecture. From Mastering Azure Analytics by Zoiner Tejada 阅读笔记 Lambda Architecture Lambda architecture was originally proposed by the creator of Apache Storm, ...
Lambda architecture and kappa architecture.
From

Mastering Azure Analytics by Zoiner Tejada
Getting Started with Kudu

Lambda Architecture
Lambda architecture was originally proposed by the creator of Apache Storm, Nathan Marz. In his book, Big Data: Principles and Best Practices of Scalable Realtime Data Systems (Manning), he proposed a pipeline architecture that aims to reduce the com‐ plexity seen in real-time analytics pipelines by constraining any incremental compu‐ tation to only a small portion of this architecture.
In lambda architecture, there are two paths for data to flow in the pipeline
• A “hot” path where latency-sensitive data (e.g., the results need to be ready in
seconds or less) flows for rapid consumption by analytics clients
• A “cold” path where all data goes and is processed in batches that can tolerate greater latencies (e.g., the results can take minutes or even hours) until results are ready

When data flows into the “cold” path, this data is immutable. Any changes to the value of particular datum are reflected by a new, timestamped datum being stored in the system alongside any previous values. This approach enables the system to re- compute the then-current value of a particular datum for any point in time across the history of the data collected. Because the “cold” path can tolerate a greater latency until the results are ready, the computation can afford to run across large data sets,and the types of calculation performed can be time-intensive. The objective of the “cold” path can be summarized as: take the time you need, but make the results extremely accurate.
When data flows into the “hot” path, this data is mutable and can be updated in place. In addition, the hot path places a latency constraint on the data (as the results are typ‐ ically desired in near–real time). The impact of this latency constraint is that the types of calculations that can be performed are limited to those that can happen quickly enough. This might mean switching from an algorithm that provides perfect accuracy to one that provides an approximation. An example of this involves counting the number of distinct items in a data set (e.g., the number of visitors to your website): you can either count each individual datum (which can be very high latency if the volume is high) or you can approximate the count using algorithms like HyperLogLog. The objective of the hot path can be summarized as: trade off some amount of accuracy in the results in order to ensure that the data is ready as quickly as possible.
lambda architecture captures all data entering the pipeline into immutable storage, labeled “Master Data” in the diagram.  is data is processed by the batch layer and output to a serving layer in the form of batch views. Latency-sensitive calcula‐ tions are applied on the input data by the speed layer and exposed as real-time views. Analytics clients can consume the data from either the speed layer views or the serving layer views depending on the time frame of the data required. In some implementations, the serving layer can host both the real-time views and the batch views.
The hot and cold paths ultimately converge at the analytics client application. The cli‐ ent must choose the path from which it acquires the result. It can choose to use the less accurate but most up-to-date result from the hot path, or it can use the less timely but more accurate result from the cold path. An important component of this deci‐ sion relates to the window of time for which only the hot path has a result, as the cold path has not yet computed the result. Looking at this another way, the hot path has results for only a small window of time, and its results will ultimately be updated by the more accurate cold path in time. This has the effect of minimizing the volume of data that components of the hot path have to deal with.
Kappa Architecture
Kappa architecture surfaced in response to a desire to simplify the lambda architecture dramatically by making a single change: eliminate the cold path and make all processing happen in a near–real-time streaming mode (Figure 1-3). Recomputation on the data can still occur when needed; it is in effect streamed through the kappa pipeline again. The kappa architecture was proposed by Jay Kreps based on his expe‐ riences at LinkedIn, and particularly his frustrations in dealing with the problem of “code sharing” in lambda architectures—that is, keeping in sync the logic that does the computation in the hot path with the logic that is doing the same calculation in the cold path.

In the kappa architecture, analytics clients get their data only from the speed layer, as all computation happens upon streaming data. Input events can be mirrored to long-term storage to enable recomputation on historical data should the need arise.
Kappa architecture centers on a unified log (think of it as a highly scalable queue), which ingests all data (which are considered events in this architecture). There is a single deployment of this log in the architecture, whereby each event datum collected is immutable, the events are ordered, and the current state of an event is changed only by a new event being appended.the unified log itself is designed to be distributed and fault tolerant, suitable to its place at that heart of the analytics topology. All processing of events is performed on the input streams and persisted as a real-time view (just as in the hot path of the lambda architecture). To support the human-fault-tolerant aspects, the data ingested from the unified log is typically persisted to a scalable, fault-tolerant persistent stor‐ age so that it can be recomputed even if the data has “aged out” of the unified log.
Moving Beyond Lambda Kappa Architectures with Apache Kudu
normal real-time data flow

solution with kudu


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