쉐도잉 연습: Redis Deep Dive w/ a Ex-Meta Senior Manager - 영상으로 영어 말하기 배우기

So hello and welcome to the first of what we hope will be many deep dives on core technologies.

But today we're going to be talking about Redis.

I'm Stefan.

I am one of the co-founders of Hello Interview.

I've conducted north of 2,000 interviews in my career.

And our hope with these sessions is to give you some of the depth necessary to be successful in system design interviews.

System design is certainly about solving problems from end to end

but the interviewers in these sessions are going to be asking

you questions to try to test your knowledge to make sure

that you don't just have a cursory understanding of the concepts and the technologies that are involved.

And so what we're trying to do here is to give you some of

that foundation or at least give you a start to that foundation if you don't have it.

And Redis is a great starting point.

So why are we learning about Redis to begin with?

Well, Redis is an incredibly versatile technology.

You can use it in a number of different circumstances from caches, distributed locks, leaderboards.

It can be used as a replacement for Kafka in certain instances.

And so there's a lot of bang for your buck with learning about Redis

and how it can apply to various system design problems.

The second reason that we really prefer Redis in a lot of circumstances is because it's very simple to understand.

The conceptual model of Redis is actually quite simple and

that means that you can both explain your choices

but also understand their implications

if you need to dive into the details of how a

sql query planner is operating in postgres forget it you've got

to learn 30 years of history in order to get there meanwhile

if you need to know why a redis query is slow it's actually pretty straightforward for you to reason through

and so redis and particularly this session is hopefully a really good return on your time

so let's talk about redis starting with what it looks like from a end user or developer perspective.

Then we'll talk about how it actually operates underneath the covers.

And finally, we'll merge the two to tell you about how you can use it in various system design scenarios

and some of the pitfalls that you might encounter in those various problems.

So the first thing that you should know about Redis is it is a single threaded in-memory data structure server.

All of those words actually mean something.

Single-threaded is unusual to hear in distributed systems, mostly because it fails to utilize multi-core systems,

but it actually simplifies things quite a lot.

In many databases, the order of operations is hard to grok.

And in Redis, it's quite simple.

The first request that comes in is the first one that actually runs, and everybody else waits.

This has some dramatic implications for how you actually use Redis, but it's a great simplifier for how it's working under the covers.

The second thing to know about Redis is it is in memory.

That means it is lightning fast and can operate in sub-millisecond times, especially for simple operations like sets and gets.

It also has some implications for how you use it in your system.

You can't necessarily guarantee the durability of data, but it means you can use Redis in places that you might not others.

With a SQL database, you need to make sure that you batch all of your requests together, or you run risks of that N plus one problem.

With Redis, you can fire off a thousand requests, and the server will happily return its results to you.

It really changes how you think about operating with a database.

The last piece is that Redis is a data structure server.

What does that mean?

Well, the core of Redis is this kind of key value dictionary.

But Redis values don't necessarily need to be strings or numbers or binary blobs.

They can also be sorted sets or hashes or geospatial indexes or bloom filters.

The way that Redis operates is trying to mirror what you might think of from a data structures and algorithms course,

that you learn about a bunch of really primitive data structures, and Redis just gives you a way to use them in a distributed fashion,

which can be super convenient because it means you can take all of the knowledge

that you've accumulated in building up data structures and algorithms expertise and lift it into a system design context, which is quite nice.

Now, how does Redis actually operate?

It's really simple.

You can brew install Redis on your laptop and actually get a CLI that you can use.

And the wire protocol is very basic.

It accepts commands like what you see here.

So I can call set and I set it, I call get and I get it, I call anchor and it increments it atomically.

Each of these operates on a key.

Remember that the core of Redis is this dictionary of keys to these values,

and the commands are basically ways to manipulate the various data structures.

If you look in the Redis documentation, they are organized by the type of the data, so it wouldn't make sense, for instance, to call anchor on a hash,

but there's another command that will allow you to increment the values of a hash.

Now these commands can get pretty sophisticated.

So here's an example of a way to add an item to a stream.

That's not really important just yet.

The important thing is that you have a command type.

In this case, the add operates on a stream.

That's what the X prefix is.

And then there's the key.

That's where it's operating.

Now, why do the keys matter?

Well, the keys are really how Redis handles the multi-node environments.

So you can just run Redis on a single node.

It runs in a single thread and it can basically write the commands that successfully execute out to disk.

You can configure the interval.

The default, I think, is every one second, which basically means Redis can lose some data.

But in the ideal scenario, what will happen if the process fails is Redis will go down and when it comes back up, it will read from that file and recover somewhat gracefully.

In practice, this is horrible.

So for most people, what they're going to do is actually have a replica.

And the way that this works is you have some main or master,

and then you have a secondary that's reading from that append only file or basically from the log.

This works much like change data capture.

When a command runs successfully here, it gets run on the secondary.

There is some special behavior in Redis where if there's been five minutes or an hour and I haven't got an update,

then the secondary will basically fully rebuild from the contents of the master or main.

But in most instances, they're going to, and ideally, they're going to be caught up to one another.

But that's not very interesting.

That really constrains us to the throughput of this single instance.

We can, I guess, technically scale our read throughput indefinitely if we want to put a bunch of replicas.

But how do we scale out writes?

And this is where the key space starts to come into the picture.

So Redis has an internal concept they call a slot, which is basically a hash of the key.

I think it's usually a CRC modulo some number 16384 I think and that is the slot that that key occupies.

Theoretically when the cluster isn't resizing a single master

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or main will own that slot and clients should be aware of all of the nodes in the cluster.

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So if I have a request for foo, I'm going to take the hash of foo, the key, I'm going to look up the slot that it occupies

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and then decide which node in the cluster I need to route my request to.

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Each of the nodes in the cluster communicates via a gossip protocol, so they all know about the existence of each other as well as which keys they have,

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which slots they have.

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And if you make a request to the wrong host, it will tell you that key doesn't exist here, it's been moved.

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But for performance sake, it's way more beneficial if the client knows exactly which host to go to.

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And so that's why when you start up a client in Redis, you make it aware of all of the hosts that are available.

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You theoretically aren't making requests that bounce across many different nodes.

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And so this is where the key space becomes incredibly important.

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The only way to shard Redis is through choosing your keys.

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And then when you are choosing how to shard, you're functionally choosing how to spread your data across the key space.

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So let's take a canonical example for a moment.

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If you have a cache, one of the major difficulties that you usually have is what's called a hot key problem,

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where many of your requests are going to the same key.

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Well, how does this break Redis?

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If one of your keys is located on this node, and the aggregate traffic to this node exceeds what this node can handle,

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then it doesn't matter that these other hosts are out there.

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The uneven distribution of traffic is going to kill you.

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So what can you do?

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Well, with Redis, one of the very common patterns is to simply append a random number to the key.

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So that way you write the key to multiple hosts, and you can read it from multiple hosts.

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So this provides a really crude way to distribute the load across the cluster.

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But if you're thinking about how you scale Redis, you should be thinking about your key space.

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This is really essential to how you reason about how Redis scales.

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Okay, so let's walk through some common instances of using Redis in actual designs

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and talk about the types of things that might come up in your deep dives and conversations with your interviewer.

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So the first use of Redis, which is the most common, is to use it like a cache.

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And the usage here is pretty simple, but let me just explain it quickly.

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You have a database where you need to make heavy queries.

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Maybe they're analytic queries, maybe this is a deep search index, whatever.

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And you wanna be able to scale this.

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So what do you do?

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Well, we're going to create a Redis cache off to the side, and our service is first gonna check the cache.

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It's gonna be very, very fast.

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See if there's an entry in there for whatever query we're gonna make.

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If there isn't, we're gonna go to the database, grab it, and then store the result in the cache.

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If there is, we're just gonna go and take the result.

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This is appropriate in basically any case where you can tolerate some staleness or inconsistency.

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This is great when you have an eventually consistent system, and in a lot of cases, that's appropriate.

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There are some places where this obviously doesn't matter.

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Now, you set up a cache like this, you've really got two concerns that you need to address.

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The first is the hot key issue, which I just talked about earlier.

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And so we need to make sure

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that our cache is spreading out the load amongst all of the instances as much as possible.

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The way we do this in Redis is by assigning keys.

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And so we might append values to our keys such

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that we are evenly distributing the requests across all of our Redis cache.

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The next thing that you need to be concerned with expiration

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and a common question in interviews is what's the expiration policy

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of your cache there's a bunch of different options here

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and redis fundamentally supports them all the most common way to use redis is to use the expire command

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or to basically attach arguments to your sets and gets

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and what that does is after a certain time

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that item won't be readable and

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so basically you can say expire after you know 60 seconds

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if that's your cache time to live and then you can guarantee

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that anyone reading out of your cache won't read

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that stale data another way to configure redis is in its least recently used setup

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and this works a little bit differently the way

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that the least recently used setup works inside redis is basically

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you'll continue to be able to append keys into your cache and functionally indefinitely until you run out of memory.

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And then at that point, Redis will start to evict those least recently used keys from your setup.

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Works very much like memcache and in many cases the drop-in replacement, but a reasonable choice.

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Another use of Redis, which is quite simple and quite popular, is to use it as a rate limiter.

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The way this works is we want to guard this expensive service from getting lots of requests.

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Maybe our downstream has decided that they can only accept five requests every 60 seconds.

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So what we need to do is make sure that if we have multiple instances of this service, that they aren't all making in aggregate more than five requests over 60 seconds.

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So how do we do this?

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Well, I talked about the atomic increment command earlier on in the session, and what it does is it increments a key if it exists.

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If it doesn't exist, it'll set it to one, and it'll return the final value.

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Think of it like plus plus a variable if you're familiar with C++ or other languages that support that syntax.

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The idea is if this value is over the limit, in my case five, then I don't want to make that request.

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I need to wait.

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On the other hand, if it's under that, that means that I've got space and I can proceed with the request.

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And then the next thing I'm going to do

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if I actually get an opportunity to make that request is I'm going to make sure that this key gets expired.

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So I'm going to say that I need to expire this key after 60 seconds.

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And what this is going to do in practice is it's

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going to allow up to N requests to proceed through and then after however many seconds, in this case I've set it to a minute,

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has finished, then that key gets automatically zeroed out and subsequent services can make a request again.

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Now there is some mechanics that you'll have to insert here.

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The service basically needs to make redundant calls after it gets rate limited

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because it needs to check in again when the rate limit has expired.

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And this also doesn't behave particularly well when the rate limits are under extreme stress.

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So if I had maybe 60 requests that I need to make, it means that all of my services are going to be hitting Redis at the same time,

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and I don't have any specific ordering that is going to be enforced.

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So I might starve one service.

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So there's a lot to talk about here in a system design interview between how you assert fairness of your services,

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how you set the limits, what's most appropriate.

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And this is the most basic implementation of a rate limiter.

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There's a lot of other structures that you could potentially use that include windows

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and give clients some idea about when they might be next in line, so on and so forth.

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So design your rate limiter as you see fit, but keep in mind there's a lot of logistics that can go into this depending upon the needs of your system.

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Sometimes something simple is great.

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So far we've talked mostly about use cases of Redis that rely on its key value nature, maybe with some exploration mechanics.

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But the value of Redis and its data structure server is primarily that these data structures can become increasingly powerful.

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And I want to talk about a few instances of that.

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The most powerful and honestly, the most complicated primitive that I think Redis offers is its stream.

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And the idea behind A Kafka paper

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that was written by LinkedIn many years ago that talks about the utility of distributed append only logs in system design.

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You can imagine Redis streams as basically ordered lists of items.

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They're given an ID, which is usually a timestamp as they're inserted.

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Each of these items can have their own keys and values.

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Think of them like JSON objects or hashes.

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The nice thing about the stream is if we've got something

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that we need to make sure all of the items of the stream are processed, then Redis gives us a bunch of primitives to work with this.

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So let's talk about if I needed to build an async job queue.

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So what I want to be able to do is insert items onto

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that queue and have them processed in order and reliably.

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I want to make sure that if an item is inserted onto the queue, that it will eventually get processed.

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What I'm going to do for storing these items is I'm going to create a stream.

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I'll put each of those items on the stream as they're created.

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And then next, I'm going to create this thing called a consumer group.

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You can think of a consumer group like a pointer into the stream.

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And that pointer basically defines where we're at.

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And so a consumer group will keep track of where in the stream it has to keep processing.

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The workers can each query the consumer group for unallocated items.

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So if the consumer group happens to be pointing at item two and no workers have picked that one up, then that item is going to be allocated to that worker.

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There's a final notion within streams and consumer groups around what Redis calls claiming.

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And the idea is at any given moment, only one of these workers can have claimed an item on the consumer group.

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And if, for instance, the worker fails, which it's apt to do,

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then that item can be reclaimed by the consumer group and allocated out to an additional worker.

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So the idea behind a Redis stream is it gives you

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a way to distribute work amongst many workers in a way that's fault tolerant, partially, because you have all the normal caveats about Redis.

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And that is very, very fast.

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And so you don't have to have a bunch of latency that you insert into the process.

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Now, what does this mean in the system design setting?

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Well, there are a bunch of considerations that you should figure out before you go and implement this.

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The first is you need to be able to handle failures of Redis.

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And there are options like using a fork of Redis, like MemoryDB, that will give you more reliability.

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You also can build some redundancy in by having replications or additional shards, basically by additional keys.

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You'll also want to figure out how you can keep these workers allocated the right work.

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The typical way this is accomplished is that the worker, while it's processing an item, will continue to heartbeat back to the consumer group,

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letting it know, hey, I'm still working on this.

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And that way the consumer group isn't snatching back the work item before it's had a chance to finish it.

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But there are definitely scenarios in distributed system where this doesn't work.

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As an example, if worker two loses network connectivity to the consumer group, but maybe still has it to a database or a downstream system,

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it might continue to process that item while the consumer group reclaims it and hands it off to another worker.

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So the behavior here is that your consumer group is going to offer at least once guarantees, but it's not going to guarantee its process exactly once.

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And in some cases, that may be exactly what you need.

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Let's talk about two more data structures that are really helpful in system design contexts.

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For this application, let's pretend we want to keep a leaderboard of the top five most liked tweets

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that contain the word tiger.

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If you look at our tweet search answer key, you'll see a bit about where this might come up.

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So the sorted set commands all start with Z, and their syntax is quite simple.

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I give a key, the ranking value, in this case the number of likes, and then some string identifier.

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In this case, I'm going to have the tweet ID, we'll call it some ID one.

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This first tweet, let's assume it's a tweet about Tiger Woods.

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In the next tweet, we're going to do the same thing, but for some ID two, this might be a tweet about zoo tigers, and we're gonna add the ID one.

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Remember, I call these sorted sets, which means for any given ID, it can only have one ranking value.

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So if, for instance, the Tiger Woods tweet got an additional like, I would run the same command with 501 to update it.

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Now I can run this command zrem range by rank to remove items

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that are in a specific rank or range of ranks.

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And in this case, what I'm doing is I'm removing all but the top five.

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So what I effectively managed here is a heap.

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I've got the top five most liked tweets.

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And every time I add a new tweet, I can remove the ones that are not in there.

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And I will only actually get an incremental example in this list

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when one of those new tweets rises to a number of likes that is greater than my top five.

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Now it's important to remember that these run in typical sorted list times.

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So as an example, when I'm adding things to the list, I basically am eating a log m,

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where m is the number of items in the list, complexity.

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And so being able to run this command keeps my list small and manageable.

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I wouldn't want to maintain an indefinitely growing list of the tweets that contain the word tiger,

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because then every incremental addition needs to be inserted into that sorted list.

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And while it's logarithmic, it still is growing as the number of examples grows.

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Now, this sounds like a pretty good idea, but remember, I'm doing this in a single key.

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So insofar as I need to manage the top like tweets by specific keywords,

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I can probably distribute this across a cluster by having unique keys.

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But if I was using this to keep track of

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all of the tweets and try to find the most liked tweets across my entire setup, then I might have a problem because they're all going to sit on a single node.

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If I want them to sit on multiple nodes, I'm going to need to keep multiple sorted sets and then combine them at the last minute.

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So what I would basically do is take some hash of the tweet ID,

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and then I would keep a sorted set of the items that have that hash.

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And then when I wanted to query this, I would query all of those sorted sets across my cluster.

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I would need to issue however many queries for the number of shards, maybe 16 queries.

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In practice, this is usually not a big deal, especially because Redis happens to be so fast, but it certainly does have limitations on scaling and how you build your systems.

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Once we have the sorted set primitive, there's a lot of things that we can build on top.

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And Redis goes out of their way to implement what I think is one of the most useful geospatial index.

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The use cases for this vary, but essentially, if you have a big list of items

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that have locations and you want to be able to search them by location, this is a great way to do it.

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And so the API looks pretty basic.

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When I want to add an item to my geospatial index, I'm going to pass in a longitude and latitude

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and an identifier for my item

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and that's going to add it to the index at this key

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when i want to search

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that index i run the geo search command give it the

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key i'm going to give it a an anchor point

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and then a radius and i can optionally return the distances

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that are associated with it and this works exactly like you'd expect

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if i have a bike rental service and i want to find all the nearby stations

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I add the stations to my geospatial index and query.

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The way that this works under the covers is

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that each of these longitudes and latitudes are geohashed to give them basically a numeric identifier.

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That numeric identifier is the ranking in the sorted set.

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And then Redis under the covers is calculating the bounding boxes given your radius

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and then finding all of those entries that are basically within that range in your sorted set.

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Redis does also, in this case, ensure that you're not returning items outside that radius, so you theoretically wouldn't see an entry with a radius of 6.

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But the important thing here probably isn't the internals.

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The important thing is that this API is super convenient and works in a wide variety of different situations.

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Now from a system design perspective, this isn't without its perils.

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There's a number of cases where you might not want to do this.

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The first is if your items aren't changing location very often, if you're not updating your data,

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it may actually be better to just keep a static list of longitudes and latitudes

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in the service that's actually making these queries, and then just calculate the Haversign distance for all of them.

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If for instance, I have 1000 stores across the globe,

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that's actually not that much to to do some simple floating point arithmetic to go and find the closest place.

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It's certainly faster than making a network call out to Redis.

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The other instance where this can have some trouble is that remember that the index is on a single key, which means it's on a single node.

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If I need to go and separate this out and I need to go and shard this, then I've got to decide on some way to do that.

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There's a lot of natural ways to do this.

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I can calculate the geo hash on my side.

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I can take some of the most significant bits and use those as part of the key.

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I can break this out by continent, where frequently I'm not going to need to query across continents, or if I'm near the border,

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maybe I'll query two, maybe North America and South America.

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But generally speaking, I need to be concerned with how this scales if I've got, say, billions of examples.

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The last capability of Redis that I'm going to talk about today is PubSub.

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And PubSub is really trying to solve for this unique instance

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where your servers need to be able to address one another in a reasonable fashion.

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Canonical example of this would be a chat room where user one is connected to server A,

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maybe via a WebSocket or some persistent TLS connection, and they need to message user three, who unfortunately is connected to server C.

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How does server A know user three is on server C?

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There's a bunch of different potential solutions to this problem.

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Probably the most famous is to use a consistent hash ring.

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So that way user three is always allocated to server C and server A knows that.

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And so can send messages directly there.

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But there's a bunch of incremental problems that happen with these consistent hash rings.

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Notably, it's harder to manage the balance between servers and scaling them up and down requires a bunch of very careful orchestration,

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usually with a service, something like Zookeeper.

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But Redis has this capability called PubSub.

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And the idea with PubSub is that the servers can connect to Redis

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and announce a publication that they're going to be making.

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And then on that topic, other servers can subscribe.

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So if, for instance, user 1 connects to server A, server A is going to tell Redis that I have user 1.

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Any messages sent to the topic for user 1 should come back to me.

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And server C would do the same thing.

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Now, when server A wants to send a message to server C so that user 3 gets it,

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What it's going to do is publish to the topic of user three.

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Redis PubSub is at most once delivery.

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So the idea here is that the message might get to a user and it might not,

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which is surprisingly useful in spite of its reliability issues.

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What it typically means from a system design perspective is if you need to guarantee that messages eventually arrive, you're going to have to try something else.

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But Redis PubSub is going to be very, very fast.

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It's operating on a single box, and all it's doing is bouncing these requests between different services.

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And so it can scale quite well.

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And what this allows us to do is if we wanted to, we could swap user one and user three, they could connect to different hosts.

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And Redis PubSub would be the registry that knew which host each user was connected to.

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This is something that's a little bit harder to pull off with, for instance, a consistent hash ring.

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And it also means if server A were to go down,

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we can migrate user 3 to server B quite quickly because server B will now register its publication to that topic.

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And for a while, those messages will go both to server A and server B, but they'll eventually get their way to user 3.

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Like I said, there's a bunch of considerations with this.

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I think the WhatsApp key that we have on the site talks about some of them in depth, but be happy to answer questions in the comments if you've got them.

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Overall, thank you so much for listening in on this deep dive on Redis.

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Redis is useful across the board in a number of different instances from geospatial indexes, leaderboards, distributed locks.

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And so it probably has a place in your system designs.

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And I hope this presentation, this video has really shown you that the internals of Redis aren't that complicated, and if you understand them,

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then you can reason through some of the issues that you might expect in production.

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So that's a wrap on our Redis Deep Dive.

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Would love to get any feedback that you all have in the comments, any questions or follow-ups.

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If you'd like to read more, our Redis Deep Dive write-up is available in the description below

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and we're looking forward to making more of these for you all if you find them interesting.

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So let us know what you'd like to see next.

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Thanks.

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I'm Stefan.

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Have a great day.

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Armchair Architects: Resiliency in the Cloud – Core Patterns

Business EnglishUniversity StudentsWorking Adults

이 클립의 영어 회화 연습 목표

이 비디오는 전문적인 주제의 영어 회화를 자연스럽게 이해하고 표현하는 능력을 키우는 데 중점을 둡니다. 특히 기술 용어가 많이 등장하는 대화에서 논리적 흐름을 따라가고 핵심 내용을 정리하는 실력을 기릅니다. 시스템 디자인 인터뷰 준비나 기술 관련 영어 공부를 하는 분들에게 매우 유용한 연습 기회입니다!

핵심 표현 모음

deep dive into - ...에 대한 심층 분석/탐구
cursory understanding - 피상적인 이해
bang for your buck - 투자 대비 효과
under the covers - 내부적으로, 뒤에서
dramatic implications - 큰 영향을 미치는 함의
lightning fast - 번개처럼 빠른

이 비디오로 고칠 수 있는 약점

이 영상은 영어 발음 교정뿐만 아니라 빠른语速에서도 명확하게 들리는 발음을 연습하는 데 도움됩니다. 특히 "distributed systems", "sub-millisecond"와 같은 기술 용어의 발음을 정확히 익히고, "the order of operations"처럼 어순이 중요한 구문의 리듬을 잡을 수 있습니다. shadowspeaks(쉐도우 스피치) 방법을 사용해 화자의 발음, 강세, 휴지시간을 따라하면 자연스러운 영어 회화 실력이 크게 향상될 것입니다. 유튜브 영어 공부를 할 때 이렇게 실제 대화를 모방하는 연습이 가장 효과적이라는 것을 기억하세요!

이 영상의 문법

화자가 가장 많이 쓰는 문형을 영상 속 실제 표현과 함께 정리했습니다.

문형	영상 속 표현
조건문 if + 절, will/would + 동사 — 조건과 그 결과	if the process fails is Redis will go · If it doesn't exist, it'll set
현재완료 have/has + 과거분사 — 과거의 일이 지금도 관련이 있을 때	I've conducted · you've accumulated · has decided
수동태 be + 과거분사 — 누가 하는지보다 무슨 일이 일어나는지에 초점	are involved · be sorted · are organized
관계절 who / which + 절 — 사람이나 사물에 대한 추가 정보	context, which is · slot, which is · Redis, which is

쉐도잉이란? 영어 실력을 빠르게 키우는 과학적 방법

쉐도잉(Shadowing)은 원래 전문 통역사 훈련을 위해 개발된 언어 학습 기법으로, 다언어 학자인 Dr. Alexander Arguelles에 의해 대중화된 방법입니다. 핵심 원리는 간단하지만 매우 강력합니다: 원어민의 영어를 들으면서 1~2초의 짧은 지연으로 즉시 소리 내어 따라 말하는 것——마치 '그림자(shadow)'처럼 화자를 따라가는 것입니다. 문법 공부나 수동적인 청취와 달리, 쉐도잉은 뇌와 입 근육이 동시에 실시간으로 영어를 처리하고 재현하도록 훈련합니다. 연구에 따르면 이 방법은 발음 정확도, 억양, 리듬, 연음, 청취력, 말하기 유창성을 크게 향상시킵니다. IELTS 스피킹 준비와 자연스러운 영어 소통을 원하는 분들에게 특히 효과적입니다.

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Armchair Architects: Resiliency in the Cloud – Core Patterns

이 클립의 영어 회화 연습 목표

핵심 표현 모음

이 비디오로 고칠 수 있는 약점

이 영상의 문법

쉐도잉이란? 영어 실력을 빠르게 키우는 과학적 방법