Latest Performance & Scalability Articles

Consistent hashing is a data-distribution technique that minimizes how many keys need to move when servers are added to or removed from a distributed system.

Vertical scaling, also called scaling up , increases a system's capacity by giving an existing machine more resources: additional CPU cores, memory, faster storage, higher network bandwidth, or a larger cloud instance.

Horizontal scaling is the practice of increasing a system's capacity by adding more machines, instances, containers, or nodes instead of making a single machine more powerful. If one application server can handle 2,000 requests per second, horizontal scaling might run five servers behind a load bala

Scalability is the ability of a software system to handle increasing load without becoming unacceptably slow, unstable, or expensive.

Horizontal scaling becomes significantly easier when application instances are interchangeable. A request can reach any healthy instance, failed instances can disappear without losing business state, and new capacity can join the fleet without synchronizing local files or sessions.

System design decisions depend heavily on scale. An architecture serving 100 requests per second can look very different from one handling 100,000 requests per second, billions of stored objects, or millions of persistent connections.

Production load balancing is not simply about distributing requests across several servers. A reliable traffic layer must continuously decide where traffic can safely go, how failures affect routing, how capacity changes, and how application instances enter and leave production .

Running an application in multiple regions can reduce latency, increase geographic resilience, and protect against regional infrastructure failures. The difficult part is deciding which region should receive each request and what should happen when that region becomes unavailable .

Deploying a new application version without downtime requires more than starting new instances. Production traffic must move from the old version to the new version without routing requests to unready instances, interrupting in-flight work, or exposing an unhealthy release to every client .

Load balancers normally distribute requests across multiple interchangeable application instances. This works best when any healthy instance can process any request. Problems appear when an application stores user-specific state locally and later requests must return to the same server.

A load balancer can protect an application from individual backend failures, but only if the load-balancing layer itself is highly available . Replacing one application server with one load balancer simply moves the single point of failure.

A load balancer needs more than a list of healthy servers. For every incoming request or connection, it must decide which backend should receive the traffic . That decision is controlled by the load-balancing algorithm.

Load balancing distributes incoming requests across multiple servers, containers, or service instances so that no single backend becomes a bottleneck. It is one of the fundamental building blocks behind scalable and highly available production systems .

Modern applications rarely do only one thing at a time. They call APIs, query databases, read files, send notifications, process messages, and wait for external services. If every operation runs one after another, the application often spends most of its time waiting instead of doing useful work.

Cloning is the first and often most practical way to scale a system: run more identical copies of the same application so more requests can be handled in parallel.

Scalability is the ability of a software system to handle growth without unacceptable performance degradation. Growth can mean more users, more requests, more data, more tenants, more background jobs, or more complex queries.