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Here's a problem. You're working at
Netflix and you have millions of users
trying to use the service. For example,
a user might be trying to add movies to
their list, find the next recommended
show, view watch history, update their
profile, and manage their subscriptions.
And while all of this is going on, you
also have a system that also needs to
update internal analytics, billing
systems, email notification service, and
update recommendation algorithm. Now
once you scale this from a single user
activity to millions and millions of
users, you start to get a scope of how
complex this entire operation is and one
of the major complexities here is we can
start out by abstracting away all the
dependent components here into two main
categories producers and consumers. For
example, producers are any entity that
produces facts. So in our case it could
be client apps where users perform
actions like start playback, add to
watch list, search or thumbs up and it
could also be any of the microservices
that create events like register device,
process payment, renew subscription. So
in other words, producers play the role
in creating signals and delegate the
responsibility to an external entity
that can actually take them. Consumers
are components within the system that
actually take the signals created by
producers and react to them. So if an
email relay server needs to send out
forgot password email for example or
your push notification needs to show on
devices or running database searches for
movies and TV shows. This type of
abstraction between producers and
consumers is how Apache CFKA tries to
frame the problem. By essentially acting
as a broker, Apache Kafka can really
focus on getting the messaging pipeline
implemented to prov for example popular
applications like Netflix require
extremely low latency because people
rely on Netflix to work very fast in
providing what they actually need in
seconds. So that means when topics need
to be stored in a way that can be
retrieved at a near instantaneous speed,
Apache Cafka is highly asynchronous by
nature and leverages techniques like
zero copy and sequential logbased
structure where you can essentially
optimize how events are stored and
transferred to reduce the overhead of
writing and transmitting data allowing
topics to flow through the system with
extremely low latency. Other areas that
Apache CFKA helps is what's called
batching. As we saw earlier, when
millions and millions of users are
interacting with their system to
generate events, the producers can
constantly open and close network
requests, which could certainly add
network overhead that could be avoided.
In other words, if we batch multiple
records into one single request before
sending them to the broker on a mass
scale, looking at millions of
interactions that occur, this could
drastically reduce the network load
that's required to handle this highly
responsive and reactive network of
systems. And this type of batching
occurs not only on the producer side but
also in how each topic is partitioned.
Essentially, you can have the topic
contain multiple partitions that all
have their own different consumers that
are designated to process specific
partitions but all in sequence. This
type of partitioning allows true
parallelism and scalability where you
can route one single topic in various
areas within the
How does Kafka keep Netflix running with zero lag? 🎬💨 When millions hit “play,” Kafka makes sure every event — from billing to recommendations — flies through the system in milliseconds. This architecture is a masterclass in scalable design. 💡 Key takeaways: 1️⃣ Kafka separates systems into producers and consumers for efficiency. 2️⃣ Batching reduces network load by grouping events before sending. 3️⃣ Partitioning allows parallel processing, enabling near real‑time performance. Bookmark this one if you’re diving into stream-processing architectures! #Kafka #AWS #NetflixArchitecture #DataStreaming #CloudArchitecture #Engineering #SystemDesign #SolutionArchitect