Scale your cloud native application.

SCALE FROM 10 REQUESTS PER SECOND TO
100,000 REQUESTS PER SECOND IN A
CLOUD NATIVE ENVIRONMENT:
@saktib_

Evaluate current system performance:
 Before scaling an application, it is important to
have a clear understanding of the current system
performance.This can be achieved by measuring
and analyzing system metrics such as response
time, throughput, and resource utilization.

Optimize existing code and database
queries:
 Optimize your code to reduce latency, improve
response times, and lower resource
consumption.This can include techniques such
as caching, lazy loading, optimizing algorithms,
and using asynchronous programming.
Improving the performance of existing database
queries can significantly enhance the system's
ability to handle more requests.This could
involve caching frequently accessed data, and
minimizing the number of database queries.

Use asynchronous processing:
 Implementing asynchronous processing can
allow the system to handle more requests
concurrently by leveraging the power of multiple
CPU cores.This could involve using message
queues or event-driven architectures.

Design for scalability:
 When designing your application, make sure that
it is built to scale.This means using a distributed
architecture that can handle multiple instances
of the application running in parallel, as well as a
data storage layer that can scale horizontally.

Use containerization:
 Containerization allows you to package your
application into small, lightweight containers
that can be easily deployed and scaled.This also
allows for faster deployments and rollbacks if
necessary.

Use an orchestration tool:
 An orchestration tool like Kubernetes can help
manage the deployment, scaling, and
monitoring of your application. Kubernetes can
automatically scale your application based on
demand, distribute traffic across multiple
instances, and restart failed instances.

Use auto-scaling:
 Auto-scaling enables you to automatically adjust
the number of instances of a service based on
demand.You can set up rules for scaling up or
down based on metrics such as CPU usage or
network traffic, or you can consider using
serverless computing allows you to run code
without having to manage servers or
infrastructure.This can be especially useful for
handling bursts of traffic, as the cloud provider
will automatically scale up the number of
instances needed to handle the traffic.

Use a load balancer:
 A load balancer can distribute incoming traffic
across multiple instances of your application,
ensuring that the load is evenly distributed and
no single instance becomes overloaded.You can
use a cloud provider load balancer or a
Kubernetes Ingress Controller.

Use a distributed database:
 Use a distributed database that can scale
horizontally to handle the increased load.This
will ensure that the database can handle a higher
volume of read and write requests.

Use a CDN:
 A content delivery network (CDN) can cache
static assets and distribute them across multiple
locations around the world, reducing the load on
your application servers and improving
performance for users in different regions.

Use a message queue:
 Use a message queue to decouple your
microservices and reduce the risk of service
failures. A message queue can also help you
handle bursts of traffic by buffering requests
during peak times.

Monitor and optimize:
 Finally, it's important to monitor your
application's performance and optimize it for
scalability.This includes monitoring the
performance of your infrastructure, analyzing
logs and metrics to identify bottlenecks, and
optimizing your code for efficiency.

Scale your cloud native application.

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