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Today, I will talk to you about how to quickly build Lambda architecture based on Amazon AWS. Many people may not know much about it. In order to make you understand better, the editor has summarized the following content for you. I hope you can get something according to this article.

How to quickly build Lambda Architecture based on Amazon AWS-

In Amazon Web Services (AWS), Lambda is one of the most popular services. To define it, Lambda is serverless computing software that automatically uploads to it any code you upload as a function. With AWS Lambda, developers don't have to write too much code, start the server, configure the server, or pay for them to run. Lambda makes it possible to save costs and resources to get tasks done. It also allows users to write simple functions and then wire them to a request or an event, after which Lambda executes the function whenever a request / event occurs. In addition, users charge only when the code is running.

Because all AWS services are integrated with other services, Lambda can use several other AWS services (including DynamoDB, Alexa, S3, and so on) to use events in these services to activate user actions. So Lambda is an effective way to test Amazon cloud and develop what everyone needs.

Users can start using Lambda in 12 steps.

Step 1: create an AWS account

The first step is to create an AWS account. Many people already have an account because they can use the same Amazon account as shopping. Once users have the account, they can rent computers and deploy whatever they like.

Step 2: planning the architecture

It's best not to start writing code using the examples in Amazon right away. Ideally, users should imagine the whole process: the way data is created, analyzed, and distributed in the application.

Step 3: select an Amazon data source

This is an important step in data storage. This is critical for all applications. Once the user has identified the data flow, they should make a decision about its storage. This becomes difficult because there are too many options available in Amazon. S3 storage is sufficient to store basic bit blocks; for more structured data, Aurora, DynamoDB, and other complex options can be used in Amazon to analyze and organize data.

You should consider the data structure and the code used to analyze the same data structure. For example, Aurora is a sql-enabled database that functions similar to PostgreSQL/MySQL; users who can use SQL code and apply it directly to reap the benefits of advanced queries and existing table structures.

Step 4: prepare a list of events

The Lambda function is prompted by events, such as the start of a new file or when a user visits a web page. This list of events will help you understand the Lamda application and explain what it has done with data and time.

Almost all Amazon services can create Lambda functions. For example, it can be as simple as calling a web address.

Step 5: take a look at other AWS services

AWS provides many services to help users. Therefore, it is more feasible for users to explore whether they can skip coding by using Amazon Run API, which allows users to get their work done faster. AWS provides services in analytics, security, machine learning, game development and other areas. Lambda can be thought of as a link to all AWS options. Using the Amazon service, you can replace individual sites along the pipeline.

Step 6: select a programming language

Lambda directly supports several programming languages, including Java, Python, c #, and JavaScript. If the user needs to use some other code from any other programming language, it is likely that it can be copied by Java or JavaScript.

After users choose a programming language, they should download the development tools and familiarize themselves with the demo examples. What needs to be clear is that Lambda is different from any other development program, such as Python or Java. With Lambda, developers will write only one function, which has a short life cycle. Users will not experience the flexibility of other platforms. The Lambda runtime assumes that the developer's code will receive a small packet of data, then make a decision, and then choose to continue processing the data.

Step 7: build the development environment

Users should choose the right environment for the programming language they choose. Using popular tools and models, Amazon tries to attract developers, even if there may be overlap. Amazon also provides a good web-based editor for any function, which means that users don't have to do much work locally.

Step 8: write a function

Write a function. This function will enable the user to call other functions, such as libraries, to others; this is no different from writing a large application.

Step 9: package the application

Users' Lambda applications are likely to be deployed before they do, especially if they use cloud development tools. Amazon provides multiple levels of production and staging, enabling users to set up a better test workflow and development.

Step 10: be aware of these limitations

When users write software for their computers, they can regard their software as the ultimate authority. They should remember that their machines are controlled by their software. The lambda code they write is like a client running according to AWS lambda. Therefore, the function they write should know its position in the hands of AWS or the risk of termination.

There are many limitations to most simple tasks that fit the Lambda model. For example, the user's function has a response time of only 300 seconds, otherwise it will be turned off. It can only use 3008mb memory, 1024 file descriptors, and 1024 threads.

Most of them may not apply to all users. However, it is better to be proficient in limits than to make assumptions. Lambda will certainly expand users' applications to help meet their needs, but it won't extend it in all possible ways.

Step 11: debug the application

The simplest debugging method can be done in the development environment on the user's local machine. It can be implemented through Eclipse, the command line, or Visual Studio. It is possible to solve most simple bug cases here.

Lambda's built-in logging functions can perform more complex debugging. Lambda logging is a simple and effective way to monitor data flow and user code's response to events.

Step 12: practice

Lambda may seem economical at first. However, once users exceed the first million requests and the first 400000 GB of free computing time, they have to pay a small percentage of cents. It is difficult to predict exactly how much the application will cost in the future.

For the frequency of events generated, users can benefit from simple architectural decisions. For example, if they are developing sensor networks and applying them to AWS cloud computing, they can make these sensor networks call every second or every hour. While it seems attractive to provide near-real-time and good services, they should always keep in mind that the first method uses 3600 calls before the second. So before making a decision, they should consider the trade-off, demand, evaluation and calculation, and then make a decision on the cost that the market can bear.

Deployment acceptance

Users can relax after installing and running the code. Unlike many web applications that require continuous monitoring and management, Lambda automates most tasks, including checking for full / overloaded servers

However, users should still monitor regularly to ensure that their code is working and functioning properly. After it has been up and running for more than a week, they can get a stable tag and know how much it will cost to run it. If the cost is not feasible, it is time for them to explore other options to simplify the work of the code, thereby reducing the cost.

After reading the above, do you have any further understanding of how to quickly build a Lambda architecture based on Amazon AWS? If you want to know more knowledge or related content, please follow the industry information channel, thank you for your support.

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