Apache Flink - A Must-Have For Your Streams | Systems Design Interview 0 to 1 With Ex-Google SWE

Summary

The video script delves into stream processing frameworks, focusing on fault tolerance and state management in real-time data streams. It contrasts batch processing with real-time processing, highlighting the benefits of low latency in the latter. The script explains the importance of checkpointing in Flink to ensure exactly-once processing semantics, using barrier messages for causal consistency across nodes. It emphasizes the efficiency of Flink's snapshot mechanism for quick recovery without the need to replay all messages, making it a crucial technology for robust stream processing systems.

Takeaways

• 🍽 IHOP is offering a $5 unlimited pancakes promotion, unrelated to the main content of the video.
• 💡 The video discusses stream processing frameworks, which are essential for handling data joins in streams efficiently.
• 🔄 Stream processing requires caching events and fault tolerance to manage consumer failures without data loss.
• 📈 Stream processing frameworks ensure that each event affects the state of each consumer only once, which is crucial for accurate data processing.
• 🌐 Examples of stream processing frameworks include Flink, Spark Streaming, Tez, and Storm, with Flink being the focus of the video.
• 🚀 Flink is chosen for its real-time processing capabilities and lower latency compared to micro-batching approaches like Spark Streaming.
• 📝 Flink and Spark Streaming are declarative, allowing for high-level specification of data processing tasks without detailing the computation.
• 🔗 Stream processing frameworks are often confused with message brokers, but they are distinct, with frameworks focusing on stream consumers.
• 🛑 Fault tolerance in stream processing is challenging due to the complexities of state management and message duplication upon consumer failure.
• 🔒 Flink uses checkpointing to ensure fault tolerance, storing the state of consumers in S3 and allowing for state restoration in case of node failure.
• 🚦 Barrier messages in Flink help maintain causal consistency across all nodes, ensuring that snapshots are taken only after all input cues have been processed.
• 🔄 Flink's snapshot and replay mechanism minimizes the need for extensive message replays, making it efficient for large-scale stream processing.

Q & A

What is the main topic of the video script?

The main topic of the video script is stream processing frameworks, with a focus on fault tolerance and exactly-once processing guarantees.

Why is the IHOP five dollar unlimited Pancakes offer mentioned at the beginning of the script?

The IHOP five dollar unlimited Pancakes offer is mentioned as an unrelated piece of life advice, serving as a casual introduction to the video before diving into the technical content.

What is the significance of caching in the context of stream processing?

Caching is significant in stream processing because it allows the system to store the results of events from multiple streams, which is necessary for operations like data joins.

What challenges does fault tolerance present in stream processing?

Fault tolerance in stream processing is challenging because if a consumer goes down, the in-memory state can be lost, leading to potential data duplication or loss upon recovery.

What is the difference between micro-batches and real-time processing in the context of Spark Streaming and Flink?

Micro-batches, as used in Spark Streaming, process events in small batches, whereas real-time processing, as in Flink, handles events as they come in, which can potentially lower the latency of message processing.

Why are stream processing frameworks not the same as message brokers?

Stream processing frameworks are focused on the consumers and the processing of the streams, whereas message brokers are responsible for the messaging system infrastructure, such as queues and message delivery.

What is the purpose of checkpointing in stream processing frameworks like Flink?

Checkpointing is used to save the state of the system at regular intervals, allowing for fault tolerance by restoring the system to a consistent state in the event of a failure.

What is a barrier message in the context of Flink's checkpointing?

A barrier message is a special type of message in Flink that signals all input streams to take a snapshot of their state, ensuring that all nodes in the system are synchronized for checkpointing.

How do barrier messages help in achieving causal consistency in stream processing?

Barrier messages ensure that a node only takes a snapshot of its state after receiving barrier messages from all its input streams, thus maintaining a consistent state across all nodes in the system.

What is the advantage of Flink's snapshot mechanism for fault tolerance?

Flink's snapshot mechanism allows for lightweight and quick snapshots without locking the state. It ensures that in the event of a node failure, the system can restore from the snapshot and only replay a minimal number of messages, rather than all messages.

Why is it important to minimize the number of messages replayed after a node failure in stream processing?

Minimizing the number of messages replayed after a node failure is important to ensure that the stream processing system can quickly recover and maintain high availability and performance, especially when dealing with large volumes of messages.

Outlines

00:00

🍽️ Stream Processing Frameworks and Fault Tolerance

The paragraph introduces the topic of stream processing frameworks, emphasizing the importance of fault tolerance in systems design. It discusses the challenges of caching event results in consumer systems and the need for each event to affect the state of each consumer only once. The paragraph also mentions various stream processing frameworks such as Flink, Spark Streaming, and Tez, highlighting the differences in their approaches to handling real-time data streams. The focus is on Flink's ability to handle events as they come in, potentially lowering latency, and its declarative nature, which simplifies the specification of computation details.

05:03

🛡️ Ensuring Exactly-Once Processing with Flink

This paragraph delves into the specifics of how Flink ensures fault tolerance and exactly-once processing semantics. It explains the concept of checkpointing, where the state of consumers is periodically saved to a persistent store like S3. The use of replayable queues, such as those provided by log-based message brokers like Kafka, is crucial for this process. The paragraph also describes the role of barrier messages in achieving causal consistency across all nodes in the system, ensuring that snapshots are taken only after all input streams have received a barrier message. This mechanism allows Flink to restore state from a checkpoint and replay only the necessary messages, thus maintaining system uptime and efficiency.

10:04

🚀 The Efficiency of Flink's Snapshot and Replay Mechanism

The final paragraph discusses the efficiency of Flink's snapshot and replay mechanism, emphasizing the lightweight nature of snapshots and the avoidance of locking during the process. It explains how Flink makes copies of the state to allow for barrier processing without locks, which is then garbage collected after the snapshot is taken. The paragraph concludes by stressing the importance of this technology in stream processing, particularly in the context of systems design interviews where understanding fault tolerance is crucial. It also touches on the practical implications of not having to replay all messages in the event of a node crash, which would be impractical with large volumes of data.

Keywords

💡Stream Processing Frameworks

Stream processing frameworks are software systems designed to handle and analyze data in real-time as it is generated. They are central to the video's theme, which discusses the architecture and considerations for processing continuous data streams. Examples from the script include Flink, Spark Streaming, and Storm, which are all mentioned as frameworks for stream processing.

💡Fault Tolerance

Fault tolerance refers to the ability of a system to continue operating in the event of a component failure. In the context of the video, it is a critical aspect of stream processing frameworks, ensuring that data is not lost and processing continues even if a consumer node fails. The script discusses the challenges of achieving fault tolerance in stream processing.

💡Data Joins

Data joins are a method of combining data from multiple sources into a single stream. The video explains that stream processing often requires data joins, which necessitates caching and joining events from different streams, such as searches and clicks from a user ID.

💡Caching

Caching is the temporary storage of data to improve performance by reducing the need to fetch the same data repeatedly. In the video, caching is discussed as a necessary step in stream processing to store the results of events for data joins.

💡Hash Join

A hash join is a method used in databases and data processing to combine data from two sources based on a common key. The script mentions using a hash join to join events from different streams in a stream processing context.

💡Checkpointing

Checkpointing is a process used in stream processing to save the state of a system at regular intervals, allowing for recovery in case of a failure. The video explains how Flink uses checkpointing to ensure fault tolerance and exactly-once processing semantics.

💡Exactly-Once Semantics

Exactly-once semantics is a guarantee that each event in a stream is processed exactly once, even in the presence of failures. The video discusses the importance of this guarantee in stream processing and how it is achieved through checkpointing and barrier messages.

💡Barrier Messages

Barrier messages are used in stream processing to coordinate the checkpointing process across multiple nodes. The script explains how barrier messages ensure that all nodes in a system take a snapshot of their state at a consistent point in time.

💡Replayable Queues

Replayable queues are message queues that keep a persistent record of messages, allowing for the replay of events in case of a failure. The video mentions that replayable queues, such as those provided by log-based message brokers like Kafka, are a requirement for the checkpointing mechanism in stream processing frameworks.

💡State Snapshots

State snapshots are saved instances of the state of a system at a particular time, used for recovery after a failure. The video describes how Flink creates lightweight state snapshots that can be restored to continue processing after a node failure.

💡Consumer

In the context of stream processing, a consumer refers to a component that subscribes to and processes data from a stream. The script discusses the role of consumers in processing data, their fault tolerance, and the importance of ensuring that each message affects the state of a consumer exactly once.

Highlights

Introduction to the topic of stream processing frameworks, a deviation from the usual systems design content.

Discussion on the importance of caching in stream processing to handle data joins effectively.

The necessity for fault tolerance in stream processing and the challenges it presents.

Ensuring that each event only affects the state of a consumer once, differentiating it from message delivery guarantees.

Examples of stream processing frameworks such as Flink, Spark Streaming, Tez, and Storm.

Explanation of Flink's real-time processing as opposed to Spark Streaming's micro-batches.

Clarification on the difference between stream processing frameworks and message brokers.

The complexity of achieving fault tolerance in stream processing due to the nature of consumer replicas.

Introduction to Flink's checkpointing mechanism as a solution for fault tolerance.

The requirement for replayable queues in conjunction with checkpointing for fault tolerance.

How barrier messages in Flink ensure causal consistency across all nodes in the system.

The process of taking snapshots in Flink and their role in maintaining system state.

Advantages of Flink's lightweight snapshots and their impact on system performance.

The ability of Flink to restore state from snapshots and only replay a minimal number of messages post-failure.

The conclusion on the significance of Flink's technology in stream processing for fault tolerance and efficiency.

Advice on the importance of understanding Flink for systems design interviews related to stream processing.

Transcripts

hello everybody and welcome back to the

channel I know this is supposed to be a

systems Design Channel but here is a

piece of Life advice that is not systems

design related for a few more days IHOP

is doing five dollar unlimited Pancakes

on an unrelated note for some reason

I've been having really bad shits lately

but anyways let's go ahead and get into

the video because today we're talking

about stream processing Frameworks

alrighty so like I mentioned today we're

going to be discussing stream processing

Frameworks so basically a lot of context

for this video comes from the last video

in which we discussed data joins in

streams and what that really requires

intuitively is that in our consumer of

multiple streams we basically need to be

caching a lot of the results of the

events that we've seen so far so if you

can see here for example if we wanted to

join two streams one for searches from a

user ID and one for clicks on a user ID

we would need to effectively make sure

to cache all of the things that we've

seen so far place them in some sort of

cache map and then put them in a hash

join so that we could join those events

and then put them into some sync queue

but of course that does beg the question

how can we make sure that all of our

consumers are fault tolerant because

when you store things in memory that

consumer can go down and especially when

we've got a lot of consumers this can be

a problem fault tolerance is not easy

and we will discuss why so stream

processing Frameworks that is hopefully

going to allow us to be fault tolerant

and in addition to that even more

importantly ideally make sure that every

single event that we see only affects

the state of each consumer once this

isn't exactly the same as exactly one's

message delivery guarantees because in

theory a message can and will be

replayed right it's not guaranteed that

you know if there's another system

outside of your group of consumers that

they might not be affected multiple

times but within your consumers within

what's managed by your stream processing

framework each message should only

affect your state once so what are some

actual examples of stream processing

Frameworks well we've got flank we've

got spark streaming we've got tez we've

got storm I personally am most familiar

with Flink just through looking into it

the most so I'm going to talk about that

a lot but I can say with certainty at

least between flank and something like

spark streaming the main difference is

that spark streaming uses micro batches

where from an event queue we might pull

five or ten elements at a time whereas

flank is Real Time effectively meaning

that it handles events as they come in

which in theory should lower the latency

of your processing time of each message

which is good another thing about flank

and Spark streaming at least are that

they are declarative meaning that as

opposed to having to specify every

single detail of computation if you have

a partition setup which you likely

probably will you can simply kind of

specify the target of your output format

you know what you're looking for in your

data what type of joins you want to do a

little bit but generally speaking you've

got some sort of job manager and it's

going to handle a lot of the actual

logic under the hood for you which is

very very nice the last thing is that a

lot of people do confuse stream

processing Frameworks with the message

Brokers themselves they are not the same

when I say stream processing Frameworks

generally speaking I am referring it to

the stream consumers

so let's ask ourselves why is it that

fall tolerance is hard because the

low-hanging fruit of fault tolerance is

basically you know just having a bunch

of replicas of a consumer and you know

that's easy enough and and every single

time that a consumer went down we could

just restore one of the replicas and

place that in our Flink setup instead

but uh it's actually not so easy and I

will explain why right now so let's

imagine that we've got the following

setup right we've got one producer node

which is outputting to two different

cues then in addition to that we've got

two consumers of those cues and each of

those consumers is going to in turn be

publishing to sync cues which go to our

last consumer consumer three

so let's imagine now that we've got

consumer two over here and the first

thing that it's going to do is you know

receive a message from the input queue

push a message to its sync queue so

that's step one step two is that

consumer two goes down so it's going to

die and it's going to go down before it

actually has the chance to register with

this guy over here that it processed the

message successfully so now this queue

is not going to know that it is no way

to know that the message over here has

actually been put into this queue over

here and C3 will be able to see it so

the issue with that is that when C4

comes up and replaces C2 C4 is going to

read from this queue it's going to get

that message

and it yet again is going to place it in

this queue and now that message is

duplicated C3 is going to see it twice

so how is it within flank that we can

actually guarantee that every single

message is going to be processed not

just at least once but also only once

well the main answer is that we are

going to do this via checkpointing so

typically what Flink will do is we've

got all this state in a bunch of our

different consumers and occasionally

we're going to checkpoint it right over

here into S3 that way if a single node

goes down of our consumers what we can

actually do is go ahead and restore our

state from our checkpoint because the

checkpoint is going to contain the state

from all of our consumers and then from

there we can actually replay the

messages in our cues the reason we could

do that is because we require replayable

cues now if you remember from two videos

ago generally speaking that's going to

be log based message Brokers things like

Kafka where effectively all of the

events stay on disk and are not removed

from the queue after they are read but

instead are persistent so how do we

actually go ahead and take these

checkpoints well like I mentioned flank

under the hood has some sort of job

manager right in that job manager you

don't really have to worry about it too

much it's just one of your nodes

probably attached to a zookeeper

instance somehow just to keep track of

which you know consumers are running

which are up which are down same for all

of your Kafka cues or anything like that

and so let's imagine we have a similar

setup to the previous example that I

just mentioned where effectively you

know we've got a producer publishing to

two consumers and then those consumers

themselves are publishing to a third

consumer and we kind of have a join

there of two cues so what the job

manager will actually do is the

following as you can see we've got this

B right here and what that's called is a

barrier message so here's how the

barrier message is actually going to

work it is going to make its way through

the queue

get over here to the end and then once

any single node receives a barrier

message for example C1 is going to see

that b it is then going to checkpoint

its state

and place it in S3 simple enough right

same thing for C2 eventually the B is

going to come here checkpoint it's state

place it in S3 now the really really

cool thing about barrier messages is

that they allow us to make sure that all

of our snapshots are causally consistent

the reason being that a node for example

such as this one right here C3 is only

going to take its snapshot once it

receives a barrier message from all of

its input cues so it's not enough if we

only see this guy right here up top we

have to make sure that we actually see

every single barrier message from all of

the input cues and what that allows us

to do is it basically gives us some

amount of state or rather some point in

time where all of the messages that have

been processed are the same on every

single node in our system right anything

else that could have been additionally

processed we know is not going to be a

part of our checkpoint and I'll make

sure to kind of Link the semantic

reasoning for this below because I don't

really want to do a full proof in like a

10 minute video but the gist is that we

have actually

we effectively get a consistent state of

our data where we know that when we

resume from our snapshot every single

event that has been played has been

played in all of our consumers and

similarly every event that has not been

played has been played in none of our

consumers and so as a result of that we

can actually rely on our snapshots a

node goes down we back up from our

snapshot we replay as expected because

keep in mind these Kafka cues actually

will keep the state

of every consumer

so they remember exactly where each

consumer was during this snapshot

because you know we're just going to

read right after the barrier node so

wherever the barrier node was from one

message on right there that's where

we're resuming from the snapshot which

is great

and so

as a result of that Flink is able to not

only ensure fault tolerance but let's

say C3 went down

as opposed to having to replay every

single message that was in both of these

cues in order to restore its state

instead we can just restore from a

snapshot

and then basically go ahead and only

have to replay a few of those messages

hopefully this generally makes sense so

what's the conclusion of a technology

like Flink well first of all their

snapshots are super lightweight the

reasoning being that uh you're not

actually doing any locking or directly

writing to the objects or the state and

memory but flank will actually make

copies of the state specifically so that

you don't have to lock when you receive

barriers which is really great for

keeping things lightweight and

relatively quick once the snapshot is

taken then all of that duplicate State

can be garbage collected additionally

this is going to allow us to ensure that

all messages affect State on our

consumers exactly once their great

guarantees about the snapshots which

ensure fault tolerance but also more

importantly ensure that we don't have to

replay every single message of all time

in the event of a node crash there could

literally be hundreds of thousands if

not millions of messages and if a node

went down and we had to replay a million

message our entire stream processing

setup would be down for probably days so

as a result it's extremely important

that we can snapshot restore from that

snapshot and then only have to replay a

few messages from there to get back to

where we want to be so again guys this

is a super useful technology for stream

processing and it's very important that

if you come up on a systems design

interview question where it actually has

to do with stream processing and

somebody asks you how you're going to

make sure that your consumers are fault

tolerant you want to know how something

like Flink might work because you never

know when someone might ask you to

redesign something similar anyways guys

have a good day and I will see you for

the next one