Apache Flink - A Must-Have For Your Streams | Systems Design Interview 0 to 1 With Ex-Google SWE
Summary
TLDRThe 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
🍽️ 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.
🛡️ 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.
🚀 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.
Mindmap
Keywords
💡Stream Processing Frameworks
💡Fault Tolerance
💡Data Joins
💡Caching
💡Hash Join
💡Checkpointing
💡Exactly-Once Semantics
💡Barrier Messages
💡Replayable Queues
💡State Snapshots
💡Consumer
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
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