跟读练习: 1. Roadmap for backend from first principles - 通过视频学习英语口语

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Backend engineering is a very wide scope.

And when I say backend engineering, it is much more than building a set of CRUD APIs.

The way I think backend engineering is about building reliable, scalable, fault tolerant and maintainable code bases and efficient systems.

And if one were to start today to learn backend development,

there are a lot of resources and at least a thousand resources.

But how do you decide what to learn?

How do you prioritize?

How do you see the big picture?

When and how all these different concepts come together?

And this is the reason it takes people years to get their head around a lot of these concepts and principles.

Because people primarily start with a limited scope of training, whether it is from a college or a boot camp or a simple Crud API course.

and they eventually build on top of that with trial and error and with help of other developers over time.

Now I am a backend engineer and I have faced this struggle initially when I started out.

I had to constantly search for resources, learn from other developers, I have read a lot of books on backend development,

I have studied hundreds of open source code bases to see how people in the industry are building stuff

and it was a very time -consuming procedure

and the second problem is people start with back -end development from a particular language

or framework's point of view it could be express or springboot

or ruby on rails and the problem with

that is you look at the problems

that you solve with the lens of your particular language and ecosystem and there are blind spots in that.

Let's imagine you have to switch to a different language.

Let's say you were working with Ruby on Rails for years

and one day your company decided to migrate to Golang for performance reasons.

In that situation, how much of your knowledge can you transfer if you don't understand the underlying systems?

So here I have decided to put together a comprehensive list of videos

which are based on foundational concepts of a backend system.

And these are from various

There's books that I've read over the years, the open source code base.

We'll start with a very high level understanding of how backend systems work behind the scenes.

We'll look at how requests from browser flows through different hops, the network firewalls over the internet and how it is routed to our backend server

that is situated in a remote AWS server

and how it responds to the request and we'll look at what the response looks like.

And that should give us a pretty vivid idea of how systems communicate, how a client communicates with the server and how the server responds.

From there, we'll move to understanding HTTP protocol.

What is the role it plays and how the communication is established through HTTP

and how HTTP ROM messages look like and what are the HTTP headers? what are the role of the headers,

the different types of headers like request headers or representational headers, general headers and security headers.

And we'll look at different types of HTTP methods like get method,

post, put, delete and when to use them and what are the semantics and what are the principles behind them.

We'll look at what is the course flow and how does it work.

We'll look at how simple requests differs from a pre -flight request

and how a pre -flight request for looks like from our browser to the server and back to our browser.

We'll look at HTTP responses, the structure of it and different status codes that server returns

and when to return which type of code and what are the most commonly used at HTTP status codes.

Then we'll look at HTTP caching.

What are the different types of caching techniques using HTTP?

We have ETags, we have max age headers.

Then we'll look at the differences between HTTP 1 .1

and HTTP 2 .0 and HTTP 3 .0 and what are the differences between them.

We will look at how content negotiation looks like between client and server using different headers.

We'll see how persistent connections work in HTTP.

We'll look at HTTP compression, different types of compression techniques like GZIP and deflate and BR and which is the commonly used technique.

We'll see the security aspect of it, the SSL, TLS and HTTPS.

Then we'll move on to routing.

How routing maps URLs to server -side logic

and what is the connection between routing

and http methods different components of routes like path parameters

and query parameters different types of routes static routes dynamic routes nested routes hierarchical routes sketch all wildcard routes

and regular expression based routes how to do api versioning using

http different types of versioning techniques we'll see what is the best way to deprecate a route

and what are the best practices in the industry we look at the benefits of route grouping

and how it helps with version

We will see how to secure routes, how to optimize route matching performance.

Then we'll move on to serialization and deserialization.

This basically means how before sending it over to the network, our server translates the data into a particular format and after receiving the data from,

let's say client over the internet and how

and how it translates the data received from the client over the internet to its own native format.

It is called deserialization and we'll see what is the need of it

and how it helps with the interoperability standard the different formats that are used in serialization and deserialization.

We have text -based formats, which is JSON or XML.

We have binary formats, which is protobuf.

And what are the performance differences between these two and when to use which one?

We'll look at how different programming languages implement serialization and deserialization.

We will explore a popular text -based format for serialization and deserialization, which is JSON.

The structure of JSON, the different data types like strings, numbers, booleans, arrays, and objects.

How serialization of nested objects and collections are handled in JSON, how deserializing into data structures, the native data structures works like in let's say

Python dictionary or Golang structs or JavaScript object.

What are the common errors while dealing with JSON like handling missing or extra fields,

dealing with null values or date serialization issues and time zone issues

and how to implement custom serialization while before sending or serializing data into JSON.

We'll look at error handling in serialization

and deserialization for example invalid data data conversion errors unknown fields

look at the security concerns of it like injection attacks why to do validation before deserialization

and validating json schemas before processing data using json schema validation

we'll see the performance aspect of it like reducing the serialized data through compression

and eliminating unnecessary fields like serialization performance between text -based

and binary formats like json versus protobuf the trade -offs between readability

and performance because in text -based format you can easily check the payload

and see that does not work the same in binary formats

but binary formats are faster so when you use a binary format

and when you use a text -based format

that is a valid trade -off then we'll move to authentication

and authorization why do we use it different types of authentication like stateful state layers we have basic authentication, bearer token authentication, we'll look at sessions,

JWTs, cookies, we'll deep dive on OAuth protocol and OpenID Connect, we'll see how API keys work, how multi -factor authentication work,

what is salting, hashing and different cryptographic techniques used in authorization, we'll explore ABAC, RBAC, REBAC,

we'll look at what are the best practices in security like securing cookies, avoiding CSRF, XSS, MITM, audit logging,

which basically means recording authentication and authorization events for audits, and monitoring failed login attempts, privilege escalation, and access to sensitive resources.

We'll look at obfuscating authenticated related error messages, preventing information leakage to attackers through detailed error messages, and handling edge cases, for example,

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consistency in responses across different failure modes like rate limiting and account lockout.

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We'll see how to avoid timing attacks.

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For example, attackers can exploit time differences in error responses to infer valid credentials. For example,

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an error for a wrong password might take longer than an error for a valid username

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because to check for a password you have to do some kind of hashing

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or use some kind of cryptographic technique which takes a little bit of time.

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So people can general the tiny bit of timing difference between them

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and guess passwords even though that's very difficult

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but we don't want to keep any security holes then the next topic we'll explore is validation

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and transformation at different types of validation like syntactic validation for example checking whether a string is an email

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or not whether it is a valid phone number or not

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or whether it is a valid date format

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or not there is semantic validation for example a date a date of birth cannot be in the future

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or the age of a person should be between 1 and 120.

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These are called semantic validations.

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Then we have type validation.

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For example, checking the input values match expected types, whether it is a string or not, whether it is an integer or not,

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whether it is an array or not, whether it is an object or not.

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These types of checks are called type validation.

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We'll see what are the best practices for validation.

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What is the difference between client side validation and what is the need of server side validation?

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The importance of server -side validation even if client -side validation is already implemented

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because client -side validation improves user experience by providing instant feedback

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but server -side validation is the true security implementation because that is the gateway to your business logic.

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We'll look at the importance of failing fast by reducing unnecessary processing by returning early

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and we'll look at why to keep consistency between front -end validation and back -end validation.

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Then there is transformations for example typecasting converting string to number or number to string because

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in query parameters or path parameters whatever you receive is a string

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But let's say we are expecting an ID field, which is a number.

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So before we send it to our handlers, we have to convert that string into a number.

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So that step is called a transformation, which we have to take care of in our validation pipeline and different date formats.

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For example, the front end might send a different format or we might be expecting a timestamp.

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So that also has to taken care of in the validation pipeline.

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Then there is normalization, for example, converting an email to lowercase or trimming white space from a string or adding country code to a phone number.

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These are called normalizations.

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Then there is sanitization for security issues.

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For example, we have to sanitize a string that is submitted by the user to prevent attacks like SQL injection.

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Then there is complex validation logic.

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For example, relationships.

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Let's say user submitted a form and it has two fields.

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One is password and another is confirmed password.

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So we have to check whether the two strings are the same

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or not so that is a relationship based validation then there is conditional validation

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so let's say in the form there are two fields one is partner name

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and the second is married which is a boolean true

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or false so a partner field might only be required

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if the married is true so that is a conditional validation

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so these kinds of checks

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we have to do then there is chain validation like converting a string to lower case then revoving special characters

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and then checking its length

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Then we'll look at error handling invalidation, like sending meaningful error messages to frontend so that the user can fix them,

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and aggregating all validation errors in one response for client side display or obfuscating error messages.

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So instead of saying invalid password, we'll have to say invalid credentials to prevent different types of attacks.

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Then we'll see how to gracefully handle failed transformations, for example, an invalid JSON and a failed date conversion,

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and how to let the user know the meaningful message.

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Then we'll look at the performance trade -offs of validation and how to optimize it by returning early, avoiding redundant validations.

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Then our next topic is going to be middlewares.

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We'll look at what is a middleware and when to use them.

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What are the common use cases of middlewares?

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The role of a middleware in a request cycle, for example, a pre -request middleware or a post -response middleware.

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The flow of middlewares, For example,

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techniques like chaining a middleware is exhibited in a sequence passing control to the next middleware until request reaches its final handler.

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How to order middleware appropriately?

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For example, we have to go in this order.

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We have to log the request.

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We have to check whether the user is authenticated or not.

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We have to do validation and we have to do route handling and then we have to do error handling.

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So this order matters in middleware flow.

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We'll see how the next function works in middleware and exiting middleware early.

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How middleware can third circuit the request pipeline by handling 404 errors.

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We'll look at some of the common middlewares like security middlewares

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which add security headers like xcontent type or strict transport security

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or content security policy or middlewares which add appropriate course headers to every single request or response

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and middlewares to avoid csrf attack middlewares to rate limit

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then we have authentication middlewares to reuse the route protecting logic across our apps.

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Then we have logging and monitoring middlewares for request logging or structure logging for observability or easier debugging in production.

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Then we have error handling middlewares which catches and formats application level errors for consistent API responses.

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Then we have compression or performance related middlewares

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which basically compresses response bodies to reduce the size of data sent over the networks.

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Then we have data parsing middlewares, parsing incoming request bodies like json, url encoded forms

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and file uploads handles multi -platform data for the file uploads then we'll look at the performance

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and scalability aspect of middlewares like what are the best practices to keep middlewares lightweight

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and efficient ensuring middleware is applied in the correct order

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or how middleware order can affect the performance

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and security of the application the next topic is going to be request context request context basically means the metadata

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that is often passed through application middlewares, controllers and services.

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It is kind of a request coped state, right?

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The state is only valid for that request.

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So here we'll explore the lifecycle of a request, maintaining state for the duration of a request,

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sharing data across different layers of the application without coupling, how context provides a temporary request coped state.

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We'll look at what are the different components of a request context, the request metadata, for example, the HTTP method, the URL headers,

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the query parameters and the body and there is the session

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and user information for example in the authentication middleware we fetch the user's information

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and then we add it to the request context so for

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that request scope the user's information is injected into the context then we have tracking

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and logging information like unique request ids

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or trace ids then we have request specific data like custom

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data injected during the request life cycle like caching data permission

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checks we'll look at what are the use cases for example

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authentication rate limiting tracing logging we'll explore the connection between middlewares and request context.

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We'll see what are the different types of timeouts, the request timeouts, custom timeouts, cancellation signals.

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We'll see what are the best practices like keeping it lightweight to prevent memory overhead,

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ensuring context data is cleaned up after request lifecycle to prevent memory leaks,

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avoiding tightly coupling components through context or over relying on it for passing data.

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Then we'll move to handlers and controllers, the MVC pattern and what are handlers and controllers and services,

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the responsibilities of all of them and reducing code with middleware.

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Then we have centralized error handling in handlers and consistent success and error message formats and how to implement them in controllers.

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Then we'll look at the different types of CRUD operations like how CRUD operations map HTTP methods

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and what are the common APIs associated with each method.

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For example, POST method is usually used for creation and submissions

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and the status code is usually 201 created or A400

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if it's a bad request

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and get requests are usually associated with fetching a list of resources

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or fetching a single resource and we have put

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and patch to update resources

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and delete to delete resources we look at how to implement pagination

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and how to implement a search api how to do sorting

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and how to do filtering

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and we'll see what are the best practices for example strict

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validation consistent response formatting limiting payload redacting sensitive fields error handling authentication and authorization.

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Then we'll explore what is RESTful architecture and what are the best practices for implementing REST APIs.

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The principle of designing APIs around resources and sticking to HTTP semantics and best practices for filtering and pagination.

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What are the different types of versioning like URI versioning, header versioning, query string, media type.

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We'll see how to design APIs with OpenAPI spec in mind.

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We'll see content negotiation, capturing exceptions and providing meaningful messages,

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supporting client side caching, e -tags and optimizing large requests and responses.

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After that, we'll move on to a very important topic which are databases.

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In databases, we'll look at relational and non -relational, what are the differences and when to use which.

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We'll look at some of the theoretical concepts like ACID and CAP theorem.

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We'll take a look at basic querying and joins and database design best practices like schema design, indexing.

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We'll look at different optimization methods like query optimization, caching, connection pooling then we have data integrity like constraints

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and validations transactions

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and concurrency we'll see how orms work whether to use an orm what are the trade -offs

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and we'll look at what our database migrations to

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that we'll move on to business logic layer

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which is also called vll what is the role of it

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what are the different layers of a request cycle for example

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we have validation layer we have routing we have middlewares

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and we have handlers and controllers which all of them fall under presentation layer

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because they deal with users data whether it is accepting users data

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or sending a user data so those are part of a presentation layer after

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that we have business logic layer which is the middle one

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which deals with our core business logic and after

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that we have the data access layer which deals with databases performs querying

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or inserts or deletions

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and business logic layer uses the data access layer behind the

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scenes we'll look different design principles like separation of concerns single

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responsibility open close dependency inversion what are the components of a business logic layer for example

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we have services we have domain models which represent core entities like a user

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or an order then we have business tools

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then we have business validation logic we'll look at service layer

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design best practices we'll look at how to handle errors properly

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and how to propagate those errors from our service layer to our presentation layer after

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that we have caching we'll discuss what is the need of caching

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and how it differs from database persistence what are the different types of caching

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we have in -memory caching browser caching database caching

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and what is the need of client -side caching

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and server -side caching the different caching strategies for example cache aside right through right behind

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or right back read through the different cache eviction strategies for example lru lfu ttl

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and fifo need for cache invalidation like manual cache invalidation time to live invalidation or event -based invalidation.

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The different levels of caching, level 1 which is in -memory, level 2 which is network distributed and there is the

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hierarchical caching which combines level 1

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and level 2 caching strategies where frequently used data is stored in a fast small cache

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which is level 1 cache and the less frequently used data is stored in a slower

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or large cache which is the level 2 cache.

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We'll see how caching for web apps looks like

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that is caching static assets

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or caching api responses using headers we'll see how to cache

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with databases for example query caching like storing the results of heavy joins in redis

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and we'll look at cache hit and cache miss ratio

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and how to optimize them after

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that we'll move on to transactional emails what are the use

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of them what are the common use cases the anatomy of

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a transactional email the subject the pre -header the body header main content cta footer

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and how to personalize with different dynamic parameters then we have task queuing

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and scheduling what are the common use cases for example queuing might be used for sending emails

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or processing image files and third -party api integration like payment processing or webhooks or offloading heavy computation like batch processing.

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For example, a user clicks a button to clear all my data.

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So to clear all the user's data, we have to call, we have to execute different queries for different tables to clear all the user's data

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and that might take some time.

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So instead of blocking the request, we return the response instantly and we trigger a background job by pushing into the task queue.

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We'll look at scheduling, what are the use cases, for example, for example, running database backups,

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recurring notifications and reminders, data synchronization or maintenance related issues, for example, clearing logs or caches,

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the different components of a task queue, there is the producer, queue, consumer, broker, backend, the flow of a task dependency,

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for example, it might be chain dependency or it might have parent -child relationship.

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We look at task groups, executing multiple tasks concurrently and waiting for all of them to complete at the same time.

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We look at how to handle errors and implement retries in task use.

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We'll look at task prioritization and rate limiting.

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For example, giving importance to tasks like payment processing before reprocess tasks like sending notifications.

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After that, we'll move on to Elasticsearch.

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Why do we use Elasticsearch and how does it work behind the scenes?

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The different techniques that are used, for example, inverter index, term frequency and inverse document frequency, segments and shards.

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What are the use cases of Elasticsearch?

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For example, providing a type ahead experience or log analytics

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or social media search for example full text search for user profiles posts comments we'll see how to create

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and manage indexes we'll see how to search

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and query different types of searching basic searching full text search

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relevance scoring we'll see how to optimize search performance by tweaking text versus keyword fields understanding analyzers and boosting

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and pagination we'll take a look at some of the advanced search patterns for example filtering aggregation fuzzy search

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then we'll see how kibana works

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and how to use elastic search in a user -friendly way and different best practices for example defining field mappings explicitly

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optimizing the number of shards indexing data in batches

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and avoiding wildcards then we have error handling the different types

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of errors in our apps could be syntax errors runtime errors logical errors

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and different error handling strategies for example fail safe

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or fail fast graceful degradation

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or prevention of errors different practices for error handling for example

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catching early not swallowing errors custom error types failing gracefully logging errors

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and using stack traces we'll look at how global error handlers

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work we'll see how to appropriately handle user facing errors like providing friendly error messages

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and providing actionable feedbacks we'll see the importance of monitoring logging in error handling

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and different tools like sentry or elk stack

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and different error alerts like email based alerts and slack based alerts after

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that we have config management what is exactly config management

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and how does it help with flexibility

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and decouples environment specific settings from application logic and what are the use cases for example

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different environments using config management safely managing sensitive data such as api keys database passwords

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and private certificates dynamically enabling

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and disabling features without changing code base what are the best

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practices of conflict management different types of conflicts for example static conflicts like db credentials

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and api endpoints dynamic conflicts like feature flags rate limits and sensitive conflicts like credentials tokens secrets

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and different sources of conflicts for example it could be dot env file

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or json or yaml and what are the differences between using environment variables versus command line flags versus static files

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After that, we have logging, monitoring and observability, a very important topic.

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We'll see what are the differences between logging, tracing, monitoring and observability, the different types of logging like system logging,

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application access, security logs, the different levels of logs like debug, info, warn, error, fatal.

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And we'll see the difference between structured logging and unstructured logging and the best practices for logging like centralized logging,

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log rotation and retention, contextual and meaningful logs

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and avoiding sensitive data like passwords

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and api keys then we will look at monitoring different types

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of monitoring like infrastructure monitoring application performance monitoring uptime monitoring the different tools

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that are used in monitoring like prometheus grafana and how to manage alerts

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and notifications by defining thresholds creating alerts

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and avoiding alert fatigue by only creating actionable alerts and ensuring that alerts are meaningful and necessary

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Then we'll take a look at observability, the three pillars of observability, which are logs, metrics and traces, the best practices around it,

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the security and compliance of log management.

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After that, we'll move on to graceful shutdown.

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Why do we need graceful shutdown?

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And how does it work behind the scenes?

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What are the different use cases?

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For example, you might need it when server restarts or scaling in cloud environments or microservices or long running jobs.

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How it works, like signal handling, sigterm, sigint and kill signals.

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What are the different steps of graceful shutdown?

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For example, it starts with capturing a signal and then it stops accepting requests.

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Then it completes any in -flight requests and then it closes external resources like database connections or any open files,

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etc. And at last it terminates the app.

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After that, we'll move on to security, the different aspects of security in a backend codebase avoiding different security attacks like SQL injection,

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no SQL injection, XSS, CSRF, broken authentication, insecure serialization, and principles of a secure software design for example least privilege,

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defense in depth, failed secure defaults, separation of duties, security by design.

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Then we'll look at the importance of input validation and sanitization and rate limits and content security policy,

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course and same -site cookie and the importance of monitoring events.

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After that, we have scaling and performance, the different metrics of performance like response time, resource utilization, identifying bottlenecks, caching,

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and database optimization, for example, avoiding n plus one query problems and ensuring proper use of joins and using lazy loading where appropriate.

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Then we have using database indexes to speed up read operations on frequent query fields like indexing foreign keys or search fields,

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how to process data in batches to minimize database load and improve performance for large datasets.

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How to avoid memory leaks like closing file handles, database connections or cleaning up after a long process.

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Minimizing network overhead by reducing payload size and using compression.

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We'll look at how to do performance testing and profiling.

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We'll look at some of the best practices for writing performance code like focusing on writing clear

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and maintainable code first without premature optimization

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and writing modular code to make it easier to optimize individual components without affecting the entire system ensuring

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that if a particular resource is under load

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or unavailable the system degrades gracefully without crashing

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and how to offload non -critical tasks like sending emails

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or logging to background processes

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or task queues to free up resources for more critical operations then we have concurrency

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and parallelism what are the difference between concurrency and parallelism

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and how concurrency helps in io bound tasks and how parallelism helps in cpu bound tasks

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Then we have object storage and large files.

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We look at some of the common use cases

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when we use object storage like AWS S3 and how to manage large files with chunking and streaming.

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And we look at multi -part file uploads.

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Then we have real -time backend systems where we take a look at web sockets or servers and events and pubsub architecture.

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To that, we have testing and code quality.

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Here we take a look at different types of testing, unit testing, integration testing, end -to -end testing, functional testing, regression testing, performance testing,

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load and stress testing, user acceptance testing, security testing.

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We'll take a look at what is test -driven development, how to automate tests in CICD environments,

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how to manage code quality with external linting and formatting tools, and what are the measures of code quality and coverage,

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like quality metrics, like cyclomatic complexity, which measures complexity of a function by counting the number of possible paths through the code.

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And we have maintainability index, which basically quantifies how easy it is to maintain a code based on the complexity, lines of code, and other factors.

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Then we'll take a look at a very interesting set of principles, which is called 12 -factor app.

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After that, we'll move on to OpenAPI standards.

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What is the need of these standards and why should we stick to it?

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What are the benefits of it?

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What are the use cases like documentation, automation, and the ecosystem surrounding it like Swagger,

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core gen postman and what is the history the swagger to open api transition

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and what are the different versions that are currently active

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and what are the key concepts of open api documents for example there is api pass the request

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and response definition there is parameters there is schemas

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and what is the structure of an open api document

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there is metadata there is paths there is components there is security definitions

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and there is responses we'll see what are the new features of open API 3 .0 and 3 .1.

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What are the tools surrounding open API for example swagger UI

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core gen postman what are the best practices like avoiding duplication and sticking to standards

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we'll look at a very interesting development method

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which is API first development where you define your open API standard

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or write your open API spec first and then you start creating APIs after

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that we'll move on to webhooks what are the use cases of webhooks like sending notification third party integration

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What are the differences between API versus webhook for the same use case?

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For example, for API, we might have to use polling, which is client side initiated compared to webhooks, which is pushing this server initiated.

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What are the key components of webhooks?

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For example, the webhook URL, event triggers, payload, HTTP method, the response handling.

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What are the best practices surrounding webhooks?

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Like webhook signature verification by using HTTPS and quick response, retry logic, logging, How to test webhooks with ngrok

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Real world use cases like stype payment processing, github webhooks, slack, discord, twilio etc.

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And at last we will take a look at what are some devops concepts that backend engineers should be familiar with.

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For example, some of the core concepts like continuous integration, continuous delivery, continuous deployment, the devops practices like infrastructure as code,

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config management, version control, and different tools surrounding devops for example creating containers with docker

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or orchestrating container with kubernetes and ci cd pipelines

409

and how to scale your service horizontal scaling versus vertical scaling

410

and different deployment strategies like red green deployment rolling deployment etc

411

and that's about it this is all the concepts

412

that we are going to cover in the next 30 or 40 videos so stay tuned

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FOR YOU

本课的词汇与口语要点

这节口语课以视频“1. Roadmap for backend from first principles”为素材。视频中反复出现的词有：example, error, request, validation, middleware。这段视频共有 412 个句子、5439 个单词可供跟读。讲话部分时长为 31:23。说话人语速自然，每分钟约 173 个词，接近日常对话。只有 74% 的单词属于英语最常用的 3,000 词，词汇难度较高。

视频中的重点词汇

视频中 15 个值得学习的单词，附发音和释义：

单词	发音	释义
validation 名词	/ˌvæl.əˈdeɪ.ʃən/	肯定, 情緒價值 /情绪价值
cache 名词	/kæʃ/	緩存 /缓存, 快取
log 名词	/lɔɡ/	圓木 /圆木
format 名词	/ˈfɔːɹ.mæt/	格式, 開本 /开本
server 名词	/ˈsɝvɚ/	服務器 /服务器, 伺服器
layer 名词	/ˈleɪ̯ɚ/	層 /层
database 名词	/ˈdeɪtəˌbeɪs/	數據庫 /数据库
string 名词	/stɹɪŋ/	線 /线
monitor 名词	/ˈmɑ.nɪ.tɚ/	顯示器 /显示器
query 名词	/ˈkwɪɹ.i/	問題 /问题, 質問 /质问
component 名词	/kəmˈpoʊ.nənt/	元件, 部件
password 名词	/ˈpæs.wɜɹd/	密碼 /密码, 口令
alert 形容词	/əˈlɝt/	警惕
parameter 名词	/pəˈɹæm.ə.tɚ/	參數 /参数, 參量 /参量
valid 形容词	/ˈvælɪd/	有效的

视频中出现的短语动词

单词	发音	释义
come together 动词	/ˈkʌm tʊˈɡɛðɚ/	集合, 联合
put together 动词		拼合, 組合 /组合
speed up 动词	/spiːdˈʌp/	加速, 加快

值得反复跟读的句子

视频中简短而完整的句子，可以直接用在日常对话里：

How do you prioritize?
How do you see the big picture?
Then we'll move on to routing.
Then we'll move on to serialization and deserialization.
Why do we need graceful shutdown?

视频中的语法

说话人最常用的结构，并附上视频中的原话：

结构	视频中的用法
被动语态 be + 过去分词 — 强调发生了什么，而不是谁做的	are based · is routed · is situated
定语从句 who / which + 从句 — 补充说明人或事物	formats, which is · deserialization, which is · field, which is
现在完成时 have/has + 过去分词 — 过去发生但与现在仍有关联的事	have faced · have read · have studied

需要注意的发音

说话人用了 96 次缩略和弱读形式，例如 we'll, don't, I've。请按听到的简短形式来说。

“th” 音: authorization /ˌɔːθəɹaɪˈzeɪʃən/, authentication /ɔːˌθɛntɪˈkeɪʃn̩/, authenticate /ɔˈθɛn.tɪ.keɪt/
“sh” 和 “zh” 音: validation /ˌvæl.əˈdeɪ.ʃən/, cache /kæʃ/, compression /kəmˈpɹɛʃ.ən/, integration /ˌɪn.tɪˈɡɹeɪ.ʃən/, injection /ɪnˈd͡ʒɛk.ʃən/
长单词——注意重音位置: validation /ˌvæl.əˈdeɪ.ʃən/, parameter /pəˈɹæm.ə.tɚ/, integration /ˌɪn.tɪˈɡɹeɪ.ʃən/, authorization /ˌɔːθəɹaɪˈzeɪʃən/, optimization /ˌɑptəmaɪˈzeɪʃən/

中文母语者容易读错的音：

/v/ — 不要读成 /w/，上齿轻触下唇: validation /ˌvæl.əˈdeɪ.ʃən/, server /ˈsɝvɚ/, valid /ˈvælɪd/, versus /ˈvɝsəs/, sensitive /ˈsɛn.sɪt.ɪv/
词尾辅音 — 要读清楚，后面不要加元音: cache /kæʃ/, log /lɔɡ/, format /ˈfɔːɹ.mæt/, database /ˈdeɪtəˌbeɪs/, component /kəmˈpoʊ.nənt/
/r/ 和 /l/ 的区别: explore /ɪkˈsploɹ/, principle /ˈpɹɪn.sɪ.pəl/, controller /kənˈtɹoʊlɚ/, rail /ɹeɪ(ə)l/, privilege /ˈpɹɪv.(ɪ.)lɪd͡ʒ/

如何用这段视频练习

先完整听一遍视频，不要开口，记下不认识的单词。
先用 0.75 倍速逐句跟读，熟练之后再回到正常速度。
录下自己的声音并与原声对比，特别注意 validation, cache, log 这类单词。

什么是跟读法？

跟读法 (Shadowing) 是一种有科学依据的语言学习技巧，最初开发用于专业口译员的培训，并由多语言者Alexander Arguelles博士普及。这个方法简单而强大：您在听英语母语原声的同时立即大声重复——就像是一个延迟1-2秒紧跟说话者的影子。与被动听力或语法练习不同，跟读法强迫您的大脑和口腔肌肉同时处理并模仿真实的讲话模式。研究表明它能显着提高发音准确性，语调，节奏，连读，听力理解和口语流利度——使其成为雅思口语备考和真实英语交流最有效的方法之一。

影子跟读法: 阅读完整分步指南 →

跟读练习: 1. Roadmap for backend from first principles - 通过视频学习英语口语

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