Load Balancing Algorithms
Last Updated :
07 Aug, 2025
To control traffic across servers in a network, load-balancing algorithms are important.
- The goal is to make sure that no single server is overloaded when too many users visit an application.
- Load balancing algorithms can be broadly categorized into two types: Dynamic load balancing and Static load balancing.

Static Load Balancing Algorithms
Static load balancing involves predetermined assignment of tasks or resources without considering real-time variations in the system. This approach relies on a fixed allocation of workloads to servers or resources, and it doesn’t adapt to changes during runtime.
Types of Static Load Balancing Algorithms are:
1. Round Robin Load Balancing Algorithm
The Round Robin algorithm is a simple static load balancing approach in which requests are distributed across the servers in a sequential or rotational manner. It is easy to implement but it doesn’t consider the load already on a server so there is a risk that one of the servers receives a lot of requests and becomes overloaded.
Lets say you have a group of friends, and you want to share a bag of candies equally with all of them. You give one candy to each friend in a circle, and then you start over. This is like Round Robin – making sure everyone gets a fair share.
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We need to implement a basic Round Robin load balancing algorithm. The goal is to distribute incoming requests evenly among a list of servers. The first request goes to the first server, the second one goes to the second server, the third request goes to the third server and it continues further for all the requests.
Below is the implementation of the Round Robin Load Balancing Algorithm:
Java
import java.util.ArrayList;
import java.util.List;
class LoadBalancer {
private List<String> servers;
private int currentIndex;
public LoadBalancer(List<String> servers) {
this.servers = new ArrayList<>(servers);
this.currentIndex = 0;
}
public String getNextServer() {
String nextServer = servers.get(currentIndex);
currentIndex = (currentIndex + 1) % servers.size();
return nextServer;
}
}
public class RoundRobinExample {
public static void main(String[] args) {
// Sample list of servers
List<String> serverList = new ArrayList<>();
serverList.add("Server1");
serverList.add("Server2");
serverList.add("Server3");
// Create a load balancer with the server list
LoadBalancer loadBalancer = new LoadBalancer(serverList);
// Simulate requests to the load balancer
for (int i = 0; i < 10; i++) {
String nextServer = loadBalancer.getNextServer();
System.out.println("Request " + (i + 1) + ": Routed to " + nextServer);
}
}
}
Output
Request 1: Routed to Server1
Request 2: Routed to Server2
Request 3: Routed to Server3
Request 4: Routed to Server1
Request 5: Routed to Server2
Request 6: Routed to Server3
Request 7: Routed to Serve...
Here is the explanation of the above code:
- Load Balancer Class (
LoadBalancer
):- Maintain a list of server names.
- Implement a method (
getNextServer
) that returns the next server in a round-robin fashion. - Keep track of the current index to determine the next server.
- Main Class (
RoundRobinExample
):- Create a list of server names to be used in the load balancer.
- Instantiate a
LoadBalancer
object with the list of servers. - Simulate a series of requests by repeatedly calling the
getNextServer
method. - Print the server to which each request is routed.
When to use Round Robin Load Balancing Algorithm
- Ideal for applications where all servers have similar capacity and performance.
- Works well for evenly distributed workloads, such as basic web requests.
- Best suited for simple environments without complex resource needs.
- Useful in setups where request order matters less than balanced distribution across servers.
Benefits and Drawbacks of Round Robin Load Balancing Algorithm
- Benefits:
- Simplicity: Easy to implement and understand.
- Fairness: Ensures that each server gets an equal share of the load.
- Drawbacks:
- Unequal Capacities: Doesn't consider the varying capacities of servers; treats all servers equally.
- Predictability: May not be optimal for scenarios with heterogeneous server capacities.
2. Weighted Round Robin Load Balancing Algorithm
The Weighted Round Robin algorithm is also a static load balancing approach which is much similar to the round-robin technique. The only difference is, that each of the resources in a list is provided a weighted score. Depending on the weighted score the request is distributed to these servers.
- Servers with higher weights are given a larger proportion of the requests.
- The distribution is cyclic, similar to the round-robin technique, but with each server receiving a number of requests proportional to its weight.
- If a server reaches its processing capacity, it may start rejecting or queuing additional requests, depending on the server's specific behavior.
For example:
let's say your friends have different levels of candy cravings. You want to be fair, so you give more candies to the friend who loves them the most. Weighted Round Robin does something similar – it gives more tasks to the friends who can handle them better.
Let's say you have three servers with weights: Server1 (weight 0.3), Server2 (weight 0.2), and Server3 (weight 0.1). The total weight is 0.3 + 0.2 + 0.1 = 0.6. During each cycle, Server1 would receive 0.3/0.6 (50%) of the requests, Server2 would receive 0.2/0.6 (33.33%), and Server3 would receive 0.1/0.6 (16.67%).
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Below is the implementation of the Weighted Round Robin Load Balancing Algorithm:
Java
import java.util.ArrayList;
import java.util.List;
import java.util.Random;
class WeightedRoundRobinBalancer {
private List<Server> servers;
private int[] cumulativeWeights;
private int totalWeight;
private int currentIndex;
private Random random;
public WeightedRoundRobinBalancer(List<Server> servers) {
this.servers = new ArrayList<>(servers);
this.totalWeight = calculateTotalWeight(servers);
this.cumulativeWeights = calculateCumulativeWeights(servers);
this.currentIndex = 0;
this.random = new Random();
}
private int calculateTotalWeight(List<Server> servers) {
int totalWeight = 0;
for (Server server : servers) {
totalWeight += server.getWeight();
}
return totalWeight;
}
private int[] calculateCumulativeWeights(List<Server> servers) {
int[] cumulativeWeights = new int[servers.size()];
cumulativeWeights[0] = servers.get(0).getWeight();
for (int i = 1; i < servers.size(); i++) {
cumulativeWeights[i] = cumulativeWeights[i - 1] + servers.get(i).getWeight();
}
return cumulativeWeights;
}
public Server getNextServer() {
int randomValue = random.nextInt(totalWeight);
for (int i = 0; i < cumulativeWeights.length; i++) {
if (randomValue < cumulativeWeights[i]) {
currentIndex = i;
break;
}
}
return servers.get(currentIndex);
}
// Inner class representing a server with a weight
static class Server {
private String name;
private int weight;
public Server(String name, int weight) {
this.name = name;
this.weight = weight;
}
public String getName() {
return name;
}
public int getWeight() {
return weight;
}
}
}
public class WeightedRoundRobinExample {
public static void main(String[] args) {
// Sample list of servers with weights
List<WeightedRoundRobinBalancer.Server> serverList = new ArrayList<>();
serverList.add(new WeightedRoundRobinBalancer.Server("Server1", 3));
serverList.add(new WeightedRoundRobinBalancer.Server("Server2", 2));
serverList.add(new WeightedRoundRobinBalancer.Server("Server3", 1));
// Create a weighted round-robin load balancer with the server list
WeightedRoundRobinBalancer balancer = new WeightedRoundRobinBalancer(serverList);
// Simulate requests to the load balancer
for (int i = 0; i < 10; i++) {
WeightedRoundRobinBalancer.Server nextServer = balancer.getNextServer();
System.out.println("Request " + (i + 1) + ": Routed to " + nextServer.getName());
}
}
}
Output
Request 1: Routed to Server1
Request 2: Routed to Server2
Request 3: Routed to Server2
Request 4: Routed to Server3
Request 5: Routed to Server3
Request 6: Routed to Server3
Request 7: Routed to Serve...
Here is the explanation of the above code:
WeightedRoundRobinBalancer
Class:- Manages a list of servers with weights.
- Calculates total and cumulative weights during initialization.
- Provides a method (
getNextServer
) to retrieve the next server based on weighted round-robin. - Contains an inner class (
Server
) representing a server with a name and weight.
WeightedRoundRobinExample
Class:- Demonstrates the usage of the
WeightedRoundRobinBalancer
. - Creates a sample list of servers with weights.
- Instantiates a
WeightedRoundRobinBalancer
object. - Simulates requests and prints the server to which each request is routed.
When to use Weighted Round Robin Load Balancing Algorithm?
- When servers have different capacities or performance levels.
- Ideal for environments where servers vary in resources (CPU, memory, etc.).
- Useful when you want to maximize resource utilization across all servers.
- Helps in preventing smaller servers from overloading while efficiently using larger servers.
Benefits and Drawbacks of Weighted Round Robin Load Balancing Algorithm
- Benefits:
- Capacity Consideration: Accounts for different server capacities by assigning weights.
- Flexibility: Can be adjusted to handle varying workloads effectively.
- Drawbacks:
- Complexity: More complex than simple Round Robin.
- Maintenance: Requires adjusting weights as server capacities change.
3. Source IP Hash Load Balancing Algorithm
The Source IP Hash Load Balancing Algorithm is a method used in network load balancing to distribute incoming requests among a set of servers based on the hash value of the source IP address. This algorithm aims to ensure that requests originating from the same source IP address are consistently directed to the same server.
If the load balancer is configured for session persistence, it ensures that subsequent requests from the same source IP address are consistently directed to the same server. This is beneficial for applications that require maintaining session information or state.
For example:
Think of your friends coming to your house, and you want to remember who gets which toy every time they visit. IP Hash is like remembering which friend played with which toy last time, so you always give them the same one.
.webp)
We need to implement a load balancing algorithm that distributes incoming requests across a set of servers based on the hash of the source IP address. The goal is to ensure that requests coming from the same source IP address are consistently routed to the same server.
Below is the implementation of the Source IP Hash Load Balancing Algorithms:
Java
import java.util.HashMap;
import java.util.Map;
class SourceIpHashLoadBalancer {
private Map<String, String> ipToServerMap;
public SourceIpHashLoadBalancer() {
this.ipToServerMap = new HashMap<>();
}
public void addServer(String serverName) {
// Add server to the mapping
ipToServerMap.put(serverName, serverName);
}
public String getServerForIp(String sourceIp) {
// Calculate hash of the source IP
int hash = sourceIp.hashCode();
// Get the list of available servers
String[] servers = ipToServerMap.keySet().toArray(new String[0]);
// Map the hash value to a server index
int serverIndex = Math.abs(hash) % servers.length;
// Return the selected server
return servers[serverIndex];
}
}
public class SourceIpHashLoadBalancerExample {
public static void main(String[] args) {
// Create a source IP hash load balancer
SourceIpHashLoadBalancer loadBalancer = new SourceIpHashLoadBalancer();
// Add servers to the load balancer
loadBalancer.addServer("Server1");
loadBalancer.addServer("Server2");
loadBalancer.addServer("Server3");
// Simulate requests with different source IPs
String[] sourceIps = {"192.168.1.1", "10.0.0.1", "172.16.0.1"};
for (String sourceIp : sourceIps) {
String selectedServer = loadBalancer.getServerForIp(sourceIp);
System.out.println("Request from " + sourceIp + " routed to " + selectedServer);
}
}
}
Output
Request from 192.168.1.1 routed to Server2
Request from 10.0.0.1 routed to Server3
Request from 172.16.0.1 routed to Server1
Here is the explanation of the above code:
SourceIpHashLoadBalancer
Class:- Fields:
ipToServerMap
: A mapping of server names to server names (used for consistent hashing).
- Methods:
addServer
: Adds a server to the load balancer.getServerForIp
: Calculates the hash of the source IP and determines the server to handle the request.
SourceIpHashLoadBalancerExample
Class:- Demonstrates the usage of the
SourceIpHashLoadBalancer
. - Creates an instance of the load balancer, adds servers, and simulates requests from different source IPs.
When to use Source IP Hash Load Balancing Algorithm?
- Ideal for applications needing session consistency, like online banking, where the same user must connect to the same server throughout a session.
- Useful when users from specific regions should connect to dedicated servers for better performance or compliance.
- Effective when a few IPs generate most of the traffic, ensuring balanced load distribution without random switching.
Benefits and Drawbacks of Source IP Hash Load Balancing Algorithm:
- Benefits:
- Consistency: Ensures requests from the same source IP always go to the same server, maintaining session state.
- Predictability: Useful when connection persistence is critical.
- Drawbacks:
- Limited Distribution: May lead to uneven load distribution if certain source IPs are more active.
- Scaling Challenges: Adding or removing servers may disrupt session persistence.
Dynamic Load Balancing Algorithms
Dynamic load balancing involves making real-time decisions about how to distribute incoming network traffic or computational workload across multiple servers or resources. This approach adapts to the changing conditions of the system, such as variations in server load, network traffic, or resource availability.
The choice between dynamic and static load balancing depends on the characteristics of the system, the nature of the workload, and the desired level of adaptability. Dynamic load balancing is often favored in dynamic, high-traffic environments, while static load balancing may be suitable for more predictable scenarios.
1. Least Connection Method Load Balancing Algorithm
The Least Connections algorithm is a dynamic load balancing approach that assigns new requests to the server with the fewest active connections. The idea is to distribute incoming workloads in a way that minimizes the current load on each server, aiming for a balanced distribution of connections across all available resources.
- To do this load balancer needs to do some additional computing to identify the server with the least number of connections.
- This may be a little bit costlier compared to the round-robin method but the evaluation is based on the current load on the server.
For example:
Lets say you're at a playground, and some kids are playing on different swings. You want to join the swing with the fewest kids so that it's not too crowded. Least Connection is like choosing the swing with the least number of kids already on it.
.webp)
We need to implement a Load Balancing Algorithm that distribute incoming requests across a set of servers and should aim to minimize the number of active connections on each server by directing new requests to the server with the fewest active connections. This ensures a balanced distribution of the workload and prevents overload on individual servers.
Below is the implementation of the Least Connection Method Load Balancing Algorithms:
Java
import java.util.ArrayList;
import java.util.HashMap;
import java.util.List;
import java.util.Map;
class LeastConnectionLoadBalancer {
private Map<String, Integer> serverConnections;
public LeastConnectionLoadBalancer() {
this.serverConnections = new HashMap<>();
}
public void addServer(String serverName) {
// Add a server to the load balancer with 0 initial connections
serverConnections.put(serverName, 0);
}
public String getServerWithLeastConnections() {
// Find the server with the least active connections
int minConnections = Integer.MAX_VALUE;
String selectedServer = null;
for (Map.Entry<String, Integer> entry : serverConnections.entrySet()) {
if (entry.getValue() < minConnections) {
minConnections = entry.getValue();
selectedServer = entry.getKey();
}
}
// Increment the connection count for the selected server
if (selectedServer != null) {
serverConnections.put(selectedServer, minConnections + 1);
}
return selectedServer;
}
}
public class LeastConnectionLoadBalancerExample {
public static void main(String[] args) {
// Create a Least Connection load balancer
LeastConnectionLoadBalancer loadBalancer = new LeastConnectionLoadBalancer();
// Add servers to the load balancer
loadBalancer.addServer("Server1");
loadBalancer.addServer("Server2");
loadBalancer.addServer("Server3");
// Simulate requests and print the server to which each request is routed
for (int i = 0; i < 10; i++) {
String selectedServer = loadBalancer.getServerWithLeastConnections();
System.out.println("Request " + (i + 1) + ": Routed to " + selectedServer);
}
}
}
Output
Request 1: Routed to Server1
Request 2: Routed to Server2
Request 3: Routed to Server3
Request 4: Routed to Server1
Request 5: Routed to Server2
Request 6: Routed to Server3
Request 7: Routed to Serve...
Here is the explanation of the above code:
LeastConnectionLoadBalancer
Class:- Fields:
serverConnections
: A map that tracks the number of active connections for each server.
- Methods:
addServer
: Adds a server to the load balancer with an initial connection count of 0.getServerWithLeastConnections
: Determines the server with the least active connections and increments its connection count.
LeastConnectionLoadBalancerExample
Class:- Main Method:
- Creates an instance of the
LeastConnectionLoadBalancer
. - Adds servers to the load balancer.
- Simulates requests and prints the server to which each request is routed based on the least connection algorithm.
When to use Least Connection Load Balancing Algorithm?
- Ideal for applications where some requests take longer to process than others (e.g., video streaming or large file uploads).
- Useful when some connections stay active longer, as it ensures new requests go to servers with fewer active connections.
- Great for systems with fluctuating traffic, as it balances based on real-time server load rather than just counting requests.
Benefits and Drawbacks of Least Connection Load Balancing Algorithm:
- Benefits:
- Balanced Load: Distributes traffic to servers with the fewest active connections, preventing overloading.
- Dynamic: Adapts to changing server workloads.
- Drawbacks:
- Ignored Capacities: Ignores server capacities; a server with fewer connections may still have less capacity.
- Sticky Sessions: May not be suitable for scenarios requiring session persistence.
2. Least Response Time Method Load Balancing Algorithm
The Least Response method is a dynamic load balancing approach that aims to minimize response times by directing new requests to the server with the quickest response time.
- It considers the historical performance of servers to make decisions about where to route incoming requests, optimizing for faster processing.
- The dynamic aspect comes from the continuous monitoring of server response times and the adaptive nature of the algorithm to route incoming requests to the server with the historically lowest response time.
For example:
Picture yourself at a snack bar where you can order food from different servers. You notice that some servers are faster than others. You choose the server that seems to serve food the quickest each time you go. Least Response Time is like picking the server with the shortest line.
.webp)
We need to implement a Load Balancing Algorithm that distribute incoming requests across a set of servers and should aim to minimize the response time by directing new requests to the server with the least accumulated response time. This ensures a balanced distribution of the workload and helps optimize the overall system performance.
Below is the implementation of the Least Response Time Load Balancing Algorithms:
Java
import java.util.HashMap;
import java.util.Map;
class LeastResponseLoadBalancer {
private Map<String, Long> serverResponseTimes;
public LeastResponseLoadBalancer() {
this.serverResponseTimes = new HashMap<>();
}
public void addServer(String serverName) {
// Add a server to the load balancer with 0 initial response time
serverResponseTimes.put(serverName, 0L);
}
public String getServerWithLeastResponseTime() {
// Find the server with the least accumulated response time
long minResponseTime = Long.MAX_VALUE;
String selectedServer = null;
for (Map.Entry<String, Long> entry : serverResponseTimes.entrySet()) {
if (entry.getValue() < minResponseTime) {
minResponseTime = entry.getValue();
selectedServer = entry.getKey();
}
}
// Increment the response time for the selected server
if (selectedServer != null) {
serverResponseTimes.put(selectedServer, minResponseTime + 1);
}
return selectedServer;
}
}
public class LeastResponseLoadBalancerExample {
public static void main(String[] args) {
// Create a Least Response load balancer
LeastResponseLoadBalancer loadBalancer = new LeastResponseLoadBalancer();
// Add servers to the load balancer
loadBalancer.addServer("Server1");
loadBalancer.addServer("Server2");
loadBalancer.addServer("Server3");
// Simulate requests and print the server to which each request is routed
for (int i = 0; i < 10; i++) {
String selectedServer = loadBalancer.getServerWithLeastResponseTime();
System.out.println("Request " + (i + 1) + ": Routed to " + selectedServer);
}
}
}
Output
Request 1: Routed to Server1
Request 2: Routed to Server2
Request 3: Routed to Server3
Request 4: Routed to Server1
Request 5: Routed to Server2
Request 6: Routed to Server3
Request 7: Routed to Serve...
Here is the explanation of the above code:
LeastResponseLoadBalancer
Class:- Fields:
serverResponseTimes
: A map that tracks the accumulated response time for each server.
- Methods:
addServer
: Adds a server to the load balancer with an initial response time of 0.getServerWithLeastResponseTime
: Determines the server with the least accumulated response time and increments its response time.
LeastResponseLoadBalancerExample
Class:- Main Method:
- Creates an instance of the
LeastResponseLoadBalancer
. - Adds servers to the load balancer.
- Simulates requests and prints the server to which each request is routed based on the least response time algorithm.
When to use Least Response Time Load Balancing Algorithm?
- Ideal for applications with heavy, fluctuating user traffic where response time matters.
- Great for apps like e-commerce sites or streaming services, where a quick response improves user experience.
- Works well when servers have different load levels, as it directs traffic to the server that’s both available and responds the fastest.
Benefits and Drawbacks of Least Response Time Load Balancing Algorithm:
- Benefits:
- Optimized Performance: Directs traffic to servers with the quickest response times, optimizing overall system performance.
- Adaptable: Adjusts to changes in server responsiveness over time.
- Drawbacks:
- Historical Bias: Heavily influenced by past response times, may not always reflect current server capabilities.
- Complex Implementation: Requires tracking and managing historical response times.
3. Resource-based Load Balancing Algorithm
The Resource-Based Load Balancing algorithm distributes incoming requests based on the current resource availability of each server, such as CPU usage, memory, or network bandwidth. Rather than just routing traffic equally or based on past performance, this algorithm evaluates the current "resource health" of each server to decide where new requests should go.
Resource-based Load Balancing AlgorithmFor example:
Imagine it like assigning tasks in an office based on each employee’s workload at the moment—some are busy, while others are free. Resource-Based Load Balancing directs requests to the server with the most available resources.
To implement Resource-Based Load Balancing, you need to track each server’s current resources, then route each new request to the server that can handle it best based on real-time data. Here’s an example implementation of a Resource-Based Load Balancing Algorithm. This simple implementation uses CPU load as a metric to decide which server should handle each request.
Java
import java.util.HashMap;
import java.util.Map;
class Server {
String name;
double cpuLoad; // Represents the current CPU load percentage of the server
public Server(String name) {
this.name = name;
this.cpuLoad = 0.0;
}
// Simulate updating the CPU load for the server
public void updateCpuLoad(double load) {
this.cpuLoad = load;
}
public double getCpuLoad() {
return this.cpuLoad;
}
public String getName() {
return this.name;
}
}
class ResourceBasedLoadBalancer {
private Map<String, Server> servers;
public ResourceBasedLoadBalancer() {
this.servers = new HashMap<>();
}
// Adds a server to the load balancer
public void addServer(Server server) {
servers.put(server.getName(), server);
}
// Finds the server with the lowest CPU load and assigns the request to it
public Server getServerWithMostResources() {
Server bestServer = null;
double lowestLoad = Double.MAX_VALUE;
for (Server server : servers.values()) {
if (server.getCpuLoad() < lowestLoad) {
lowestLoad = server.getCpuLoad();
bestServer = server;
}
}
return bestServer;
}
// Simulate handling a request
public void handleRequest() {
Server bestServer = getServerWithMostResources();
if (bestServer != null) {
System.out.println("Routing request to server: " + bestServer.getName() + " with current CPU load: " + bestServer.getCpuLoad() + "%");
// Here, you might update the server's load after handling a request
} else {
System.out.println("No servers available.");
}
}
}
public class ResourceBasedLoadBalancerExample {
public static void main(String[] args) {
ResourceBasedLoadBalancer loadBalancer = new ResourceBasedLoadBalancer();
// Create servers and add them to the load balancer
Server server1 = new Server("Server1");
Server server2 = new Server("Server2");
Server server3 = new Server("Server3");
loadBalancer.addServer(server1);
loadBalancer.addServer(server2);
loadBalancer.addServer(server3);
// Simulate updating CPU load for each server
server1.updateCpuLoad(30.0);
server2.updateCpuLoad(50.0);
server3.updateCpuLoad(20.0);
// Route requests based on current CPU load
loadBalancer.handleRequest(); // This should route to Server3, as it has the lowest CPU load
}
}
OutputRouting request to server: Server3 with current CPU load: 20.0%
Below is the explanation of the above code:
- Server Class: Represents a server in the load balancer with attributes like
name
and cpuLoad
. The updateCpuLoad
method allows the CPU load of each server to be updated, simulating the monitoring of real-time load. - ResourceBasedLoadBalancer Class: Manages a list of servers and routes requests based on available resources.
- addServer: Adds a server to the load balancer.
- getServerWithMostResources: Iterates through the list of servers to find the one with the lowest CPU load, which has the most available resources.
- handleRequest: Simulates handling a request by routing it to the server with the lowest CPU load.
- ResourceBasedLoadBalancerExample Class: This
main
class sets up the load balancer, adds servers, updates their CPU loads, and simulates routing requests.
When to Use Resource-Based Load Balancing Algorithm?
- Useful for applications that perform CPU-intensive or memory-heavy tasks.
- Works well when servers have different resource levels, as the algorithm adapts to each server’s real-time capacity.
- Ensures availability by routing requests to the least overloaded servers, reducing downtime risks.
Benefits and Drawbacks of Resource-Based Load Balancing Algorithm
- Benefits:
- Resource Optimization: Balances workloads based on real-time resource data, improving system efficiency.
- Adaptability: Adjusts dynamically to the current state of each server’s resources.
- Drawbacks:
- Complex Implementation: Requires continuous monitoring of server resources, which can add complexity.
- Higher Overhead: Real-time tracking of resources may consume additional system resources itself.
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System Design Fundamentals
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What is Requirements Gathering Process in System Design?The first and most essential stage in system design is requirements collecting. It identifies and documents the needs of stakeholders to guide developers during the building process. This step makes sure the final system meets expectations by defining project goals and deliverables. We will explore
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Differences between System Analysis and System DesignSystem Analysis and System Design are two stages of the software development life cycle. System Analysis is a process of collecting and analyzing the requirements of the system whereas System Design is a process of creating a design for the system to meet the requirements. Both are important stages
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Horizontal and Vertical Scaling | System DesignIn system design, scaling is crucial for managing increased loads. Horizontal scaling and vertical scaling are two different approaches to scaling a system, both of which can be used to improve the performance and capacity of the system. Why do we need Scaling?We need scaling to built a resilient sy
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Capacity Estimation in Systems DesignCapacity Estimation in Systems Design explores predicting how much load a system can handle. Imagine planning a party where you need to estimate how many guests your space can accommodate comfortably without things getting chaotic. Similarly, in technology, like websites or networks, we must estimat
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Object-Oriented Analysis and Design(OOAD)Object-Oriented Analysis and Design (OOAD) is a way to design software by thinking of everything as objects similar to real-life things. In OOAD, we first understand what the system needs to do, then identify key objects, and finally decide how these objects will work together. This approach helps m
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How to Answer a System Design Interview Problem/Question?System design interviews are crucial for software engineering roles, especially senior positions. These interviews assess your ability to architect scalable, efficient systems. Unlike coding interviews, they focus on overall design, problem-solving, and communication skills. You need to understand r
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Functional vs. Non Functional RequirementsRequirements analysis is an essential process that enables the success of a system or software project to be assessed. Requirements are generally split into two types: Functional and Non-functional requirements. functional requirements define the specific behavior or functions of a system. In contra
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Communication Protocols in System DesignModern distributed systems rely heavily on communication protocols for both design and operation.Communication protocols facilitate smooth coordination and communication in distributed systems by defining the norms and guidelines for message exchange between various components.By choosing the right
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Web Server, Proxies and their role in Designing SystemsIn system design, web servers and proxies are crucial components that facilitate seamless user-application communication. Web pages, images, or data are delivered by a web server in response to requests from clients, like browsers. A proxy, on the other hand, acts as a mediator between clients and s
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Scalability in System Design
Databases in Designing Systems
Complete Guide to Database Design - System DesignDatabase design is key to building fast and reliable systems. It involves organizing data to ensure performance, consistency, and scalability while meeting application needs. From choosing the right database type to structuring data efficiently, good design plays a crucial role in system success. Th
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SQL vs. NoSQL - Which Database to Choose in System Design?When designing a system, one of the most critical system design choices you will face is choosing the proper database management system (DBMS). The choice among SQL vs. NoSQL databases can drastically impact your system's overall performance, scalability, and usual success. This is why we have broug
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File and Database Storage Systems in System DesignFile and database storage systems are important to the effective management and arrangement of data in system design. These systems offer a structure for data organization, retrieval, and storage in applications while guaranteeing data accessibility and integrity. Database systems provide structured
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Block, Object, and File Storage in System DesignStorage is a key part of system design, and understanding the types of storage can help you build efficient systems. Block, object, and file storage are three common methods, each suited for specific use cases. Block storage is like building blocks for structured data, object storage handles large,
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Database Sharding - System DesignDatabase sharding is a technique for horizontal scaling of databases, where the data is split across multiple database instances, or shards, to improve performance and reduce the impact of large amounts of data on a single database.Table of ContentWhat is Sharding?Methods of ShardingKey Based Shardi
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Database Replication in System DesignDatabase replication is essential to system design, particularly when it comes to guaranteeing data scalability, availability, and reliability. It involves building and keeping several copies of a database on various servers to improve fault tolerance and performance.Table of ContentWhat is Database
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High Level Design(HLD)
What is High Level Design? - Learn System DesignHigh-level design or HLD is an initial step in the development of applications where the overall structure of a system is planned. Focuses mainly on how different components of the system work together without getting to know about internal coding and implementation. Helps everyone involved in the p
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Availability in System DesignA system or service's readiness and accessibility to users at any given moment is referred to as availability. It calculates the proportion of time a system is available and functional. Redundancy, fault tolerance, and effective recovery techniques are usually used to achieve high availability, whic
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Consistency in System DesignConsistency in system design refers to the property of ensuring that all nodes in a distributed system have the same view of the data at any given point in time, despite possible concurrent operations and network delays.Importance of Consistency in System DesignConsistency plays a crucial role in sy
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Reliability in System DesignReliability is crucial in system design, ensuring consistent performance and minimal failures. System reliability refers to how consistently a system performs its intended functions without failure over a given period under specified operating conditions. It means the system can be trusted to work c
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CAP Theorem in System DesignAccording to the CAP theorem, only two of the three desirable characteristicsâconsistency, availability, and partition toleranceâcan be shared or present in a networked shared-data system or distributed system.The theorem provides a way of thinking about the trade-offs involved in designing and buil
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What is API Gateway?An API Gateway is a key component in system design, particularly in microservices architectures and modern web applications. It serves as a centralized entry point for managing and routing requests from clients to the appropriate microservices or backend services within a system. An API Gateway serv
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What is Content Delivery Network(CDN) in System DesignThese days, user experience and website speed are crucial. Content Delivery Networks (CDNs) are useful in this situation. A distributed network of servers that work together to deliver content (like images, videos, and static files) to users faster and more efficiently.These servers, called edge ser
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What is Load Balancer & How Load Balancing works?A load balancer is a networking device or software application that distributes and balances the incoming traffic among the servers to provide high availability, efficient utilization of servers, and high performance. Works as a âtraffic copâ routing client requests across all serversEnsures that no
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Caching - System Design ConceptCaching is a system design concept that involves storing frequently accessed data in a location that is easily and quickly accessible. The purpose of caching is to improve the performance and efficiency of a system by reducing the amount of time it takes to access frequently accessed data.=Caching a
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Communication Protocols in System DesignModern distributed systems rely heavily on communication protocols for both design and operation.Communication protocols facilitate smooth coordination and communication in distributed systems by defining the norms and guidelines for message exchange between various components.By choosing the right
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Activity Diagrams - Unified Modeling Language (UML)Activity diagrams are an essential part of the Unified Modeling Language (UML) that help visualize workflows, processes, or activities within a system. They depict how different actions are connected and how a system moves from one state to another. By offering a clear picture of both simple and com
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Message Queues - System DesignMessage queues enable communication between various system components, which makes them crucial to system architecture. Serve as buffers and allow messages to be sent and received asynchronously, enabling systems to function normally even if certain components are temporarily or slowly unavailable.
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Low Level Design(LLD)
What is Low Level Design or LLD?Low-Level Design (LLD) plays a crucial role in software development, transforming high-level abstract concepts into detailed, actionable components that developers can use to build the system. In simple terms, LLD is the blueprint that guides developers on how to implement specific components of a s
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Difference between Authentication and Authorization in LLD - System DesignTwo fundamental ideas in system design, particularly in low-level design (LLD), are authentication and authorization. While authorization establishes what resources or actions a user is permitted to access, authentication confirms a person's identity. Both are essential for building secure systems b
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Performance Optimization Techniques for System DesignThe ability to design systems that are not only functional but also optimized for performance and scalability is essential. As systems grow in complexity, the need for effective optimization techniques becomes increasingly critical. Here we will explore various strategies and best practices for opti
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Object-Oriented Analysis and Design(OOAD)Object-Oriented Analysis and Design (OOAD) is a way to design software by thinking of everything as objects similar to real-life things. In OOAD, we first understand what the system needs to do, then identify key objects, and finally decide how these objects will work together. This approach helps m
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Data Structures and Algorithms for System DesignSystem design relies on Data Structures and Algorithms (DSA) to provide scalable and effective solutions. They assist engineers with data organization, storage, and processing so they can efficiently address real-world issues. In system design, understanding DSA concepts like arrays, trees, graphs,
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Containerization Architecture in System DesignIn system design, containerization architecture describes the process of encapsulating an application and its dependencies into a portable, lightweight container that is easily deployable in a variety of computing environments. Because it makes the process of developing, deploying, and scaling appli
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Introduction to Modularity and Interfaces In System DesignIn software design, modularity means breaking down big problems into smaller, more manageable parts. Interfaces are like bridges that connect these parts together. This article explains how using modularity and clear interfaces makes it easier to build and maintain software, with tips for making sys
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Unified Modeling Language (UML) DiagramsUnified Modeling Language (UML) is a general-purpose modeling language. The main aim of UML is to define a standard way to visualize the way a system has been designed. It is quite similar to blueprints used in other fields of engineering. UML is not a programming language, it is rather a visual lan
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Data Partitioning Techniques in System DesignUsing data partitioning techniques, a huge dataset can be divided into smaller, easier-to-manage portions. These techniques are applied in a variety of fields, including distributed systems, parallel computing, and database administration. Data Partitioning Techniques in System DesignTable of Conten
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How to Prepare for Low-Level Design Interviews?Low-Level Design (LLD) interviews are crucial for many tech roles, especially for software developers and engineers. These interviews test your ability to design detailed components and interactions within a system, ensuring that you can translate high-level requirements into concrete implementation
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Essential Security Measures in System DesignIn today's digitally advanced and Interconnected technology-driven worlds, ensuring the security of the systems is a top-notch priority. This article will deep into the aspects of why it is necessary to build secure systems and maintain them. With various threats like cyberattacks, Data Breaches, an
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Design Patterns
Software Design Patterns TutorialSoftware design patterns are important tools developers, providing proven solutions to common problems encountered during software development. Reusable solutions for typical software design challenges are known as design patterns. Provide a standard terminology and are specific to particular scenar
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Creational Design PatternsCreational Design Patterns focus on the process of object creation or problems related to object creation. They help in making a system independent of how its objects are created, composed, and represented. Creational patterns give a lot of flexibility in what gets created, who creates it, and how i
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Structural Design PatternsStructural Design Patterns are solutions in software design that focus on how classes and objects are organized to form larger, functional structures. These patterns help developers simplify relationships between objects, making code more efficient, flexible, and easy to maintain. By using structura
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Behavioral Design PatternsBehavioral design patterns are a category of design patterns that focus on the interactions and communication between objects. They help define how objects collaborate and distribute responsibility among them, making it easier to manage complex control flow and communication in a system. Table of Co
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Design Patterns Cheat Sheet - When to Use Which Design Pattern?In system design, selecting the right design pattern is related to choosing the right tool for the job. It's essential for crafting scalable, maintainable, and efficient systems. Yet, among a lot of options, the decision can be difficult. This Design Patterns Cheat Sheet serves as a guide, helping y
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Interview Guide for System Design
How to Crack System Design Interview Round?In the System Design Interview round, You will have to give a clear explanation about designing large scalable distributed systems to the interviewer. This round may be challenging and complex for you because you are supposed to cover all the topics and tradeoffs within this limited time frame, whic
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System Design Interview Questions and Answers [2025]In the hiring procedure, system design interviews play a significant role for many tech businesses, particularly those that develop large, reliable software systems. In order to satisfy requirements like scalability, reliability, performance, and maintainability, an extensive plan for the system's a
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Most Commonly Asked System Design Interview Problems/QuestionsThis System Design Interview Guide will provide the most commonly asked system design interview questions and equip you with the knowledge and techniques needed to design, build, and scale your robust applications, for professionals and newbiesBelow are a list of most commonly asked interview proble
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5 Common System Design Concepts for Interview PreparationIn the software engineering interview process system design round has become a standard part of the interview. The main purpose of this round is to check the ability of a candidate to build a complex and large-scale system. Due to the lack of experience in building a large-scale system a lot of engi
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5 Tips to Crack Low-Level System Design InterviewsCracking low-level system design interviews can be challenging, but with the right approach, you can master them. This article provides five essential tips to help you succeed. These tips will guide you through the preparation process. Learn how to break down complex problems, communicate effectivel
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