跟讀練習: Redis Crash Course - the What, Why and How to use Redis as your primary database - 通過影片學習英語口語

選擇練習模式

每種模式都會開啟全屏練習室。

231 句

如果句子過短或過長，請點選 Edit 進行調整。

In this video, we're going to talk about Redis and how Redis can be used as a primary database

for complex applications that need to store data in multiple formats.

First, we will see what Redis is and its usages as well as why it is suitable for modern complex microservice applications.

We will talk about how Redis supports storing multiple data formats for different purposes through its modules.

Next we will see how Redis as an in -memory database can persist data and recover from data loss. on Flash.

Then we will see very interesting use cases of scaling Redis and replicating it across multiple geographic regions.

And finally, since one of the most popular platforms for running microservices is Kubernetes,

and since running stateful applications in Kubernetes is a bit challenging, we will see how you can easily run Redis on Kubernetes.

I'm proud to say that I have partnered up with Redis to make this video.

So big thanks to Redis for sponsoring and making this video possible.

Now, first of all, Redis, which actually stands for Remote Dictionary Server, is an in -memory database.

So, many people have used it as a cache on top of other databases to improve the application performance.

However, what many people don't know is

that Redis is a fully -fledged primary database that can be used to store and persist multiple data formats for complex applications.

Let's see the use cases and examples for that.

Let's look at a common setup for a microservices application.

Let's say we have a complex social media application with millions of users.

And let's say our microservices application uses a relational database like MySQL to store the data.

In addition, because we are collecting tons of data daily, we have an Elasticsearch database for fast filtering and searching the data.

Now the users are all connected to each other, so we need a graph database to represent these connections.

Plus, our application has a lot of media content that users share with each other daily.

And for that we have a document database.

And finally, for a better performance for the application, we have a cache service that caches the data from other databases and makes it accessible.

faster.

Now, it's obvious that this is a pretty complex setup.

But let's see what are the challenges of this setup.

First of all, all these data services need to be deployed, run and maintained, right?

This means your team needs to have some kind of knowledge on how to operate all these data services.

Plus, of course, for high availability and better performance, you would want to scale your services and each of these data services scales differently,

has different infrastructure requirements.

And that could be an additional challenge.

So overall, using application increases the effort of maintaining your whole application setup.

Of course, as an easier alternative to running and managing the services yourself, you can use the managed data services from cloud providers.

But this could be very expensive because on cloud platforms you pay for each managed data service separately.

Now, on the development side,

your application code also gets pretty complex because you need to talk to multiple data services and for each service,

you would need a separate connector and separate logic.

And this of course makes testing your applications also quite challenging.

And finally, the more number of services that talk to each other, the higher the latency.

Because even though each service may be fast on its own.

Each connection step between the services or each network hub will add some latency to your application.

In comparison with a multi -model database like Redis, you resolve most of these challenges.

First of all, you run and maintain just one data service.

So your application also needs to talk to a single data store.

And that requires only one programmatic interface for that

Data service and latency will be reduced by going to a single data endpoint and eliminating several internal network hops.

So having one database like Redis that allows you to store different types of data,

or basically allows you to have multiple types of databases in one as well as act as a cache solves such challenges.

So let's see how Redis actually works.

First of all, how does Redis support multiple data formats in a single database?

The way it works is that you have Redis core, which is the key value store that already supports storing multiple types of data.

And then you can extend that core with what's called modules for different data types which your application needs for different purposes.

So for example, Ready Search for search functionality like Elastic Search or Redis Graph for graph data storage and so on.

And a great thing about this is that it's modular.

So these different types of database functionalities are not tightly integrated into one database as in many other multi -model databases,

but rather you can pick and choose exactly which data service functionality that module.

And of course, when using Redis as a primary database, you don't need an additional cache because you have that automatically out of the box with Redis.

That means, again, less complexity in your application because you don't need to implement the logic for managing,

populating, and invalidating cache.

And finally, as an in -memory database, Redis is of course super fast and performant, which of course makes the application itself faster.

But in addition, it also makes running the application tests way faster as well.

Because Redis doesn't need a schema like other databases,

so it doesn't need time to initialize the database tests.

So you can start with an empty Redis database every time and generate data for tests as you need.

And fast tests can really increase your development productivity.

OK, great.

So we understood how Redis works and all its benefits.

But at this point, you may be wondering, how can an in -memory database persist data?

Because if the Redis process or the server on which Redis is running fails, all the data in memory is gone, right?

And if I lose the data, how can I recover it?

So basically, how can I be confident that my data is safe?

Well, the simplest way to have data backups is by replicating Redis.

So if the Redis master instance goes down, the replicas will still be running and have all the data.

So if you have a replicated Redis, the replicas will have the data.

But of course, if all the Redis instances go down, you will lose the data because there will be no replica remaining.

So we need real persistence.

Well, Redis has multiple mechanisms for persisting the data and keeping the data safe.

First one, the snapshots, which you can configure based on time, number of requests, etc. So, snapshots of your data will be stored on a disk

which you can use to recover your data if the whole Redis database is gone.

But note that you will lose the last minutes of data because you usually do snapshotting every five minutes or an hour, depending on your needs.

So as an alternative, Redis uses something called AOF.

which stands for append -only file.

In this case, every change is saved to the disk for persistence continuously.

When restarting Redis or after an outage, Redis will replay the append -only file logs to rebuild the state.

So AOF is more durable but can be slower than snapshotting.

And of course, you can also use a combination of both AOF and snapshots,

where the append -only file is persisting data from memory to disk continuously,

plus you have regular snapshots in between to save the data state in case you need to recover it.

This means that even if the Redis database itself or the servers, the underlying infrastructure where Redis is running,

all fail, you still have all your data safe, and you can easily recreate and restart a new Redis database with all the data.

Now, a very interesting question is, where is that persistent storage?

So where is that disk which holds your snapshots and the append -only file locks located?

Are they on the same servers where Redis is running?

Well, this question actually leads us to the trend or best practice of data persistence in cloud environments,

which is that it's always better to separate the servers that run your application and data services

from the persistent storage that stores your data.

So with a specific example, if your applications and services run in the cloud on let's say AWS EC2 instance,

you should use EBS

or elastic block storage to persist your data instead of storing them on the EC2 instances hard drive, for example.

100

Because if that EC2 instance died, you won't have access to any of its storage, whether it's RAM or disk storage or whatever.

101

So if you want persistence and durability for your data, you must put your data outside the storage.

102

As a result, by separating these two, if the server instance fails or if all the instances fail,

103

you still have the disk and all the data on it unaffected.

104

You just spin up other instances and take the data from the EBS and that's it.

105

And this makes your infrastructure way easier to manage because each server is equal, you don't have any special servers with any special data or files on it,

106

so you don't care if you lose your whole infrastructure

107

because you can just recreate a new one and pull the data from a separate storage and you're good to go again.

108

So going back to Redis example, ready service will be running on the servers, and using server RAM to store the data,

109

while the pen only file logs and snapshots will be persisted on a disk outside those servers.

110

making your data more durable Great,

111

now we know you can persist data with Redis for durability and recovery,

112

while using RAM or memory storage for great performance and speed.

113

So the question you may have here is, isn't storing data in memory expensive.

114

So you would need more servers compared to a database that stores data on disk simply because memory is limited in size.

115

So there's a trade -off between the cost and performance.

116

Well, Redis actually has a way to optimize this using a service called Redis on Flash, which is part of Redis Enterprise.

117

So how does this work?

118

It's a pretty simple concept actually.

119

Redis on Flash extends the RAM to the flash drive or SSD,

120

where frequently used values are stored in RAM and the infrequently used ones are stored on SSD.

121

So for Redis, it's just more RAM on the server.

122

and this means that Redis can use more of the underlying infrastructure or the underlying server resources

123

by using both RAM and SSD drive, to store the data, increasing the storage capacity on each server,

124

and this way saving you infrastructure costs.

125

Alright, so we've talked about data storage for Redis database and how it all works including the best practices.

126

Now another very interesting topic is how do we scale a Redis database?

127

Let's say my one Redis instance runs out of memory,

128

so data becomes too large to hold in memory or Radis becomes a bottleneck and can't handle any more requests.

129

In such case, How do I increase the capacity and memory size for my Redis database.

130

We have several options for that.

131

First of all, Redis supports clustering, which means you can have a primary or master Redis instance,

132

which can be used to read and write data, and you can have multiple replicas of that primary instance for reading the data.

133

This way you can scale Redis to handle more requests.

134

And in addition, increase the high availability of your database,

135

because if master fails, one of the replicas can take over and your Redis database basically can continue functioning without any issues.

136

Now, these replicas will all hold copies of the data of the primary instance.

137

So the more replicas you have, the more memory space you need.

138

And one server may not have sufficient memory for all your replicas.

139

Plus, if you have all the replicas on one server and that server fails, your whole Redis database is gone.

140

and you will have a downtime.

141

Instead, you want to distribute these replicas among multiple nodes or servers.

142

So for example, your master instance will be on one node and two replicas on other two nodes.

143

Well, that seems good enough.

144

But what if your data set grows too large to fit in a memory on a single server?

145

Plus, we have scaled the reads in the database.

146

So all the requests that basically just query the data,

147

but our master instance is still alone and still has to handle all the writes.

148

So, what is the solution here?

149

For that, we use a concept of sharding, which is a general concept in databases and which Redis also supports.

150

So sharding basically means that, you take your complete data set and divide it into smaller chunks

151

or subsets of data where each shard is responsible for its own subset of data.

152

So that means instead of having one master instance that handles all the writes to the complete data set, you can split it into, let's say, four shards.

153

each of them responsible for reads and writes to a subset of the data.

154

and each shard also needs less memory capacity, because they just have a fourth of the data.

155

This means you can distribute and run shards on smaller nodes and basically scale your cluster horizontally.

156

And of course, as your data set grows and as you need even more resources, you can reshard your Redis database,

157

which basically means you just Split your data in even smaller chunks and create more shards.

158

So having multiple nodes which run multiple replicas of Redis, which are all sharded, gives you a very performant,

159

highly available Redis database that can handle much more requests without creating any bottlenecks.

160

Now, I have to note here that this setup is great, but of course, you would need to manage it yourself.

161

Do the scaling, add nodes, do the sharding and then resharding,

162

etc. So for some teams that are more focused on the application development and more business logic, rather than running and maintaining data services,

163

this could be a lot of unwanted effort.

164

So as an easier alternative, in Redis Enterprise you get this kind of setup automatically because the scaling, sharding, and so on is all managed for you.

165

Now let's consider another interesting scenario for applications that need even higher availability and performance across multiple geographic locations.

166

So let's say we have this replicated, sharded, Redis database cluster in one region in the data center of London, Europe.

167

But we have the two following use cases.

168

First, our users are geographically distributed, so they are accessing the application from all over the world.

169

So we want to distribute our application and data services globally close to the users to give our users better performance.

170

And second, if the complete data center in London Europe,

171

for example, goes down, we want an immediate switch over to another data center so that the ready service stays available.

172

In other words, We want replicas of the whole Redis cluster in data centers in multiple geographic locations or regions.

173

This means a single data should be replicated to many clusters spread across multiple regions,

174

with each cluster being fully able to accept reads and writes.

175

So, in that case, you would have multiple Redis clusters that will act as local Redis instances in each region.

176

and the data will be synced across these geographically distributed clusters.

177

This is a feature available in Redis Enterprise

178

and it's called active -active deployment because you have multiple active databases in different locations.

179

So with this setup, we'll have lower latency for the users.

180

And even if the Redis database in one region completely goes down, the other regions will be unaffected.

181

And if the connection or syncing between the regions breaks for a short time because of some network problem,

182

for example, the Redis clusters in these regions can update the data independently and once connection is reestablished,

183

they can sync up those changes again.

184

Now, of course, when you hear that, the first question that may pop up in your mind is,

185

how does Redis resolve the changes in multiple regions to the same dataset?

186

So if the same data changed in multiple regions,

187

how does Redis make sure that data changes of any region isn't lost and data is correctly synced.

188

And how does it ensure data consistency?

189

Specifically for that, Redis Enterprise uses a concept called CRDTs.

190

which stands for conflict free replicated data types.

191

And this concept is used to resolve any conflicts automatically at the database level and without any data loss.

192

So basically Redis itself has a mechanism for merging the changes

193

which were made to the same data set from multiple sources in a way

194

that none of the data changes are lost and any conflicts are properly resolved.

195

And since, as you learned, Redis supports multiple data types, for each data type they use its own data conflict resolution rules,

196

which are the most optimal for that specific data type.

197

So simply put, instead of just overriding the changes of one source, and just discarding all the others,

198

all the parallel changes are kept and intelligently resolved.

199

And again, this is automatically done for you with this active active geo -replication feature, so you don't need to worry about that.

200

And the last topic I want to address with Redis is running Redis in Kubernetes.

201

As I said, Redis is a great fit for complex microservices

202

that need to support multiple data types and that need an easy scaling of a database without worrying about data consistency.

203

And we also know that the new standard for running microservices is the Kubernetes platform.

204

So running Redis in Kubernetes is a very interesting and common use case.

205

So how does that work?

206

With open source Redis, you can deploy replicated Redis as a Helm chart

207

or Kubernetes manifest files and basically using the replication and scaling rules that we already talked about,

208

set up and run a highly available Redis database.

209

The only difference would be that the hosts where Redis will run will be Kubernetes pods instead of

210

for example EC2 instances or any other physical or virtual servers.

211

But the same sharding, replicating, and scaling concepts apply here as well, when you want to run Redis cluster in Kubernetes.

212

And you would basically have to manage that setup yourself.

213

However, as I mentioned, many teams don't want to have the effort of maintaining these third -party services

214

because they rather invest their time and resources in application development or other tasks.

215

So having an easier alternative is important here as well.

216

So Redis Enterprise has a managed Redis cluster which you can deploy as a Kubernetes operator.

217

If you don't know operators, operator in Kubernetes is basically a concept where you can bundle all the resources

218

needed to operate a certain application or service so that you don't have to

219

manage it yourself or you don't have to operate it yourself.

220

So instead of a human operating a database, you basically have all this logic in an automated form.

221

to operate a database for you.

222

And many databases have operators for Kubernetes, and each such operator has, of course, its own logic based on who wrote them and how they wrote them.

223

The Redis enterprise, on Kubernetes operators specifically,

224

automates deployment and configuration of the whole Redis database in your Kubernetes cluster.

225

It also takes care of scaling, doing the backups and recovering the Redis cluster if needed,

226

etc. So it takes over the complete operation of Redis cluster inside the Kubernetes cluster.

227

Well, I hope you learned a lot in this video and that I was able to answer many of your questions.

228

If you want to try Redis Enterprise Cloud, be sure to check out my special link in the video description for a $200 credit.

229

And if you want to learn more similar technologies and concepts,

230

then make sure to subscribe to my channel because I make videos regularly about different DevOps and cloud technologies.

231

Also, comment below if you have any questions regarding Redis or any new topic suggestions.

下載應用

流利口語所需的一切

AI 發音評分逐句打分

音標練習掌握每個音

詞彙學習建立你的詞庫

詞彙遊戲邊玩邊學

掃碼

Download on theApp Store Get it onGoogle Play

同頻道

FOR YOU

本課的詞彙與口語要點

這段影片共有 231 個句子、3446 個單詞可供跟讀。講話部分時長為 23:32。說話人語速平穩，每分鐘約 146 個詞，很適合跟讀。只有 80% 的單詞屬於英語最常用的 3,000 詞，詞彙難度較高。

影片中的重點詞彙

影片中 15 個值得學習的單詞，附發音和釋義：

單詞	發音	釋義
database 名詞	/ˈdeɪtəˌbeɪs/	資料庫 /資料庫
server 名詞	/ˈsɝvɚ/	伺服器 /伺服器, 伺服器
cluster 名詞	/ˈklʌstɚ/	串, 叢 /叢
replica 名詞	/ˈɹɛplɪkə/	複製品 /複製品, 仿製品 /仿製品
disk 名詞	/dɪsk/	圓盤 /圓盤
persist 動詞	/pɚˈsɪst/	堅持 /堅持
enterprise 名詞	/ˈɛntəˌpɹaɪz/	企業 /企業
cloud 名詞	/ˈklaʊ̯d/	йүн, 雲 /雲
infrastructure 名詞	/ˈɪnfɹəˌstɹʌkt͡ʃə/	基礎設施 /基礎設施
operator 名詞	/ˈɑpəˌɹeɪtɚ/	話務員 /話務員, 接線員 /接線員
node 名詞	/noʊd/	結節 /結節
cache 名詞	/kæʃ/	快取 /快取, 快取
recover 動詞	/ɹɪˈkʌvɚ/	追回, 收復
resolve 動詞	/ɹɪˈzɑlv/	解決 /解決
distribute 動詞	/dɪˈstɹɪbjuːt/	分配

影片中出現的短語動詞

單詞	釋義
go down 動詞	下降, 下去
take over 動詞	接管, 接手
check out 動詞	退房

影片中的語法

說話人最常用的結構，並附上影片中的原話：

結構	影片中的用法
條件句 if + 從句，will/would + 動詞 — 條件及其結果	if that EC2 instance died, you won't have
被動語態 be + 過去分詞 — 強調發生了什麼，而不是誰做的	be deployed · will be reduced · will be stored
定語從句 who / which + 從句 — 補充說明人或事物	core, which is · Flash, which is · clustering, which means
現在完成時 have/has + 過去分詞 — 過去發生但與現在仍有關聯的事	have partnered · we've talked · have scaled

需要注意的發音

說話人用了 21 次縮略和弱讀形式，例如 don't, doesn't, isn't。請按聽到的簡短形式來說。

“sh” 和 “zh” 音: cache /kæʃ/, flash /flæʃ/, sufficient /səˈfɪʃənt/, suggestion /səˈd͡ʒɛst.ʃən/, configuration /kənˌfɪɡ.əˈɹeɪ̯.ʃən/
長單詞——注意重音位置: infrastructure /ˈɪnfɹəˌstɹʌkt͡ʃə/, operator /ˈɑpəˌɹeɪtɚ/, availability /əˌveɪ.ləˈbɪl.ɪ.ti/, mechanism /ˈmɛk.ə.nɪ.zəm/, consistency /kənˈsɪs.tən.si/

如何用這段影片練習

先完整聽一遍影片，不要開口，記下不認識的單詞。
用正常速度逐句跟讀，每句重複到你的節奏與說話人一致為止。
錄下自己的聲音並與原聲對比，特別注意 database, server, cluster 這類單詞。

什麼是跟讀法？

跟讀法 (Shadowing) 是一種有科學依據的語言學習技巧，最初開發用於專業口譯員的培訓，並由多語言者Alexander Arguelles博士普及。這個方法簡單而強大：您在聽英語母語原聲的同時立即大聲重複——就像是一個延遲1-2秒緊跟說話者的影子。與被動聽力或語法練習不同，跟讀法強迫您的大腦和口腔肌肉同時處理並模模擬實的講話模式。研究表明它能顯著提高發音準確性，語調，節奏，連讀，聽力理解和口語流利度——使其成為雅思口語備考和真實英語交流最有效的方法之一。

影子跟讀法: 閱讀完整分步指南 →

跟讀練習: Redis Crash Course - the What, Why and How to use Redis as your primary database - 通過影片學習英語口語

選擇練習模式

下載應用

同頻道

What is Docker? Docker container concept explained || Docker Tutorial 1

Every Networking Concept Explained In 20 Minutes

Microservices explained - the What, Why and How?

NGINX Explained - What is Nginx

CI/CD Explained in 5 Minutes

Kafka Tutorial for Beginners | Everything you need to get started

If I would start DevOps from 0 - How would I start and what would I learn

推薦影片

Top 5 Redis Use Cases

Microservices with Databases can be challenging...

Give Me 24 minutes, I'll Give You 1,000+ hours of Claude Knowledge

Everything You Need to Know About DNS: Crash Course System Design #4

Rate Limiter System Design in Under 60 Seconds #techprep #programming #systemdesign

what is a database index?

The easy way to keep your repos tidy.

Amazon Athena explained in 120 seconds💡

How to Become Your Own ISP

Database Sharding! Designing Data-Intensive Applications chapter 6

Design a File Upload Service Like a Senior Engineer