Prática de Shadowing: Redis Crash Course - the What, Why and How to use Redis as your primary database - Aprenda a falar inglês com vídeo

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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.

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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.

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

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

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

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

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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,

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

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

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

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

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making your data more durable Great,

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

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

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

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

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

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

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So how does this work?

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It's a pretty simple concept actually.

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

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

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

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

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

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and this way saving you infrastructure costs.

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

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

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

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

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

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We have several options for that.

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

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

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This way you can scale Redis to handle more requests.

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

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

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

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

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

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

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and you will have a downtime.

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

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

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Well, that seems good enough.

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

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Plus, we have scaled the reads in the database.

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So all the requests that basically just query the data,

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

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So, what is the solution here?

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

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

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

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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.

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

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

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

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

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

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

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

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

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

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

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this could be a lot of unwanted effort.

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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.

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

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

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But we have the two following use cases.

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

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

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

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

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

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

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

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

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

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This is a feature available in Redis Enterprise

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

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

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

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

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

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they can sync up those changes again.

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

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

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So if the same data changed in multiple regions,

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

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And how does it ensure data consistency?

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

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which stands for conflict free replicated data types.

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

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So basically Redis itself has a mechanism for merging the changes

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

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

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

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which are the most optimal for that specific data type.

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

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all the parallel changes are kept and intelligently resolved.

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

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

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

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

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

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

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So how does that work?

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

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

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set up and run a highly available Redis database.

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

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

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

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And you would basically have to manage that setup yourself.

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

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

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So having an easier alternative is important here as well.

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

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

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

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

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

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to operate a database for you.

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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.

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The Redis enterprise, on Kubernetes operators specifically,

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

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

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

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

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

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

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

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

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Vocabulário e dicas de fala para esta lição

Este vídeo tem 231 frases e 3446 palavras para praticar shadowing. A fala dura 23:32. O falante mantém um ritmo estável de cerca de 146 palavras por minuto, confortável para o shadowing. Apenas 80% das palavras estão entre as 3.000 mais comuns do inglês, por isso o vocabulário é exigente.

Vocabulário principal deste vídeo

15 palavras do vídeo que vale a pena aprender, com pronúncia e significado:

Palavra	Pronúncia	Significado
database substantivo	/ˈdeɪtəˌbeɪs/	banco de dados, base de dados
server substantivo	/ˈsɝvɚ/	servidor, server
cluster substantivo	/ˈklʌstɚ/	aglomeração, aglomerado
replica substantivo	/ˈɹɛplɪkə/	réplica, cópia exata
disk substantivo	/dɪsk/	disco
replicate verbo	/ˈɹɛplɪkeɪt/	replicar
persist verbo	/pɚˈsɪst/	persistir
enterprise substantivo	/ˈɛntəˌpɹaɪz/	empresa
cloud substantivo	/ˈklaʊ̯d/	nuvem, nimbo
infrastructure substantivo	/ˈɪnfɹəˌstɹʌkt͡ʃə/	infraestrutura
operator substantivo	/ˈɑpəˌɹeɪtɚ/	telefonista
cache substantivo	/kæʃ/	esconderijo
recover verbo	/ɹɪˈkʌvɚ/	recuperar
resolve verbo	/ɹɪˈzɑlv/	resolver, decidir
distribute verbo	/dɪˈstɹɪbjuːt/	distribuir, parcelar

Phrasal verbs que você vai ouvir

Palavra	Pronúncia	Significado
go down verbo		descer
take over verbo		tomar posse de, apoderar-se de
check out verbo		verificar, sacar só
set up verbo	/ˌsɛt ˈʌp/	preparar

Gramática neste vídeo

As estruturas que o falante mais usa, com as palavras exatas do vídeo:

Estrutura	No vídeo
Frases condicionais if + oração, will/would + verbo — uma condição e seu resultado	if that EC2 instance died, you won't have
Voz passiva be + particípio passado — o foco está no que acontece, não em quem faz	be deployed · will be reduced · will be stored
Orações relativas who / which + oração — informação extra sobre uma pessoa ou coisa	core, which is · Flash, which is · clustering, which means
Present perfect have/has + particípio passado — uma ação passada que ainda importa agora	have partnered · we've talked · have scaled

Pronúncia para ficar de olho

O falante usa 21 contrações e formas reduzidas, como don't, doesn't, isn't. Diga-as na forma curta, do jeito que você ouve.

Os sons de “sh” e “zh”: cache /kæʃ/, flash /flæʃ/, sufficient /səˈfɪʃənt/, suggestion /səˈd͡ʒɛst.ʃən/, configuration /kənˌfɪɡ.əˈɹeɪ̯.ʃən/
Palavras longas — acerte a sílaba tônica: infrastructure /ˈɪnfɹəˌstɹʌkt͡ʃə/, operator /ˈɑpəˌɹeɪtɚ/, availability /əˌveɪ.ləˈbɪl.ɪ.ti/, mechanism /ˈmɛk.ə.nɪ.zəm/, consistency /kənˈsɪs.tən.si/

Como praticar com este vídeo

Ouça o vídeo inteiro uma vez sem falar e anote as palavras que você não conhece.
Faça shadowing frase por frase na velocidade normal, repetindo cada uma até o seu ritmo ficar igual ao do falante.
Grave a sua voz e compare com o original, prestando atenção a palavras como database, server, cluster.

O que é a Técnica de Shadowing?

Shadowing é uma técnica de aprendizado de idiomas com base científica, originalmente desenvolvida para o treinamento de intérpretes profissionais. O método é simples, mas poderoso: você ouve áudio em inglês nativo e repete imediatamente em voz alta — como uma sombra seguindo o falante com 1-2 segundos de atraso. Pesquisas mostram melhora significativa na precisão da pronúncia, entonação, ritmo, sons conectados, compreensão auditiva e fluência na fala.

Técnica de shadowing: leia o guia completo passo a passo →

Prática de Shadowing: Redis Crash Course - the What, Why and How to use Redis as your primary database - Aprenda a falar inglês com vídeo

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Vocabulário e dicas de fala para esta lição

Vocabulário principal deste vídeo

Phrasal verbs que você vai ouvir

Gramática neste vídeo

Pronúncia para ficar de olho

Como praticar com este vídeo

O que é a Técnica de Shadowing?