└── README.md /README.md: -------------------------------------------------------------------------------- 1 | # 50 Essential Load Balancing Interview Questions in 2025 2 | 3 |
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5 | 6 | software-architecture-and-system-design 7 | 8 |

9 | 10 | #### You can also find all 50 answers here πŸ‘‰ [Devinterview.io - Load Balancing](https://devinterview.io/questions/software-architecture-and-system-design/load-balancing-interview-questions) 11 | 12 |
13 | 14 | ## 1. Define _load balancing_ in the context of modern web services. 15 | 16 | **Load balancing** is about evenly distributing incoming network traffic across a group of backend servers or resources to optimize system performance, reliability, and uptime. 17 | 18 | This distribution increases the throughput of the system by minimizing the response time and maximizing the resources' usage. 19 | 20 | ### Key Objectives 21 | 22 | - **High Availability**: Ensuring that the provided service is robust and uninterrupted, even in the face of server failures. 23 | - **Scalability**: Accommodating varying levels of traffic without forfeiting performance or reliability. 24 | - **Reliability**: Consistently providing high-performing and equitable access to resources or services. 25 | 26 | ### Load Balancing Strategies 27 | 28 | #### Round Robin 29 | 30 | - **Mechanism**: Requests are allocated to a list of servers in sequential order. 31 | - **Pros**: Simple to implement; Equal distribution under normal operating conditions. 32 | - **Cons**: Does not take server load or other performance metrics into account; Effectiveness can vary. 33 | 34 | #### Weighted Round Robin 35 | 36 | - **Mechanism**: Servers still rotate in a sequence, but each has a specified weight, influencing how many requests it's assigned. 37 | - **Pros**: Allows for rough load level management without more complex metric tracking. 38 | - **Cons**: Inadequate granularity and adaptability for dynamic workloads. 39 | 40 | #### Least Connections 41 | 42 | - **Mechanism**: Channels traffic to the server with the fewest existing connections. 43 | - **Pros**: Can lead to more balanced server loads in many cases. 44 | - **Cons**: Not always effective with persistent connections or when requests vary significantly in resource requirements. 45 | 46 | #### Least Response Time 47 | 48 | - **Mechanism**: Routes new requests to the server with the most efficient and fastest response time. 49 | - **Pros**: Optimizes for real-time system efficiency. 50 | - **Cons**: Can become unreliable if server speeds fluctuate or if connections exhibit latency or instability. 51 | 52 | ### Advanced Load Balancing Strategies 53 | 54 | #### IP Hashing 55 | 56 | - **Mechanism**: Uses a hash of the client's IP address to decide the server to which it will be sent. 57 | - **Pros**: Useful for session-specific apps and databases; Can maintain consistency of stateful connections. 58 | 59 | #### Content-Based Routing 60 | 61 | - **Mechanism**: Analyzes specific attributes of the incoming request, such as the URL or HTTP header content, to strategically dispatch the traffic. 62 | - **Pros**: Valuable for multifaceted architectures or when particular requests need to be managed differently than others. Can be combined with other methods for nuanced traffic control. 63 | 64 | #### Health Monitoring and Adaptive Routing 65 | 66 | - **Mechanism**: Actively monitors server health using various criteria and dynamically adjusts routing based on the assessments. 67 | - **Pros**: Crucial for maintaining system reliability and performance, especially in highly dynamic and volatile environments. 68 | 69 | ### Load Balancing Algorithms 70 | 71 | - **Adaptive Algorithms**: Utilize real-time data to make traffic distribution decisions. 72 | - **Non-Adaptive Algorithms**: Rely on predefined parameters to allocate traffic consistently. 73 | 74 | #### Basics of Adaptive and Non-Adaptive Algorithms 75 | 76 | - **Adaptive**: Assessments of traffic and server performance are periodic or continuous, using dynamic data to make informed routing choices. 77 | - **Non-Adaptive**: Traffic and server performance are evaluated based on fixed parameters, making routing choices consistent over time. 78 | 79 | ### Web Service Example 80 | 81 | In the context of a modern web service, imagine a popular e-commerce website that uses load balancing. The site operates multiple backend servers, distributing the traffic using a round-robin approach. Each server is designated a specific weight, and the server with the least number of connections receives the next incoming request. 82 | 83 | The service also employs adaptive routing algorithms. Regular health checks and performance assessments are conducted, and servers that display signs of deterioration, such as a sudden increase in response time, are temporarily removed from the pool of active servers to ensure the utmost reliability for incoming client requests. 84 |
85 | 86 | ## 2. What are the primary objectives of implementing _load balancing_? 87 | 88 | **Load balancing** primarily aims to achieve **optimal resource utilization**, **enhanced reliability**, and **improved performance** across a network. 89 | 90 | ### Key Objectives 91 | 92 | - **Optimizing Resources**: Evenly distributing workload across servers to prevent individual servers from becoming overloaded, and resources from being underutilized. 93 | - **Enhancing Reliability**: Minimizing service downtime and ensuring high availability by rerouting traffic to healthy servers in case of failures. 94 | - **Improving Performance**: Reducing response times and ensuring smooth user experience by directing requests to servers that can handle them most efficiently. 95 | 96 | ### How Does it Work? 97 | 98 | - **Load Distribution**: Incoming traffic is efficiently distributed among multiple servers, ensuring no single server is overwhelmed. 99 | 100 | - **Health Monitoring**: Systems continuously evaluate server health, removing or redirecting traffic from failing or slow servers to healthy ones. 101 | 102 | ### Load Balancing Algorithms 103 | 104 | 1. **Round Robin**: Cyclically routes traffic to each server sequentially. While simple, it doesn't consider real-time server load. 105 | 106 | 2. **Least Connections**: Routes traffic to the server with the fewest active connections, ensuring a more balanced workload. 107 | 108 | 3. **Weighted Round Robin**: Servers are assigned "weights" representing their capacity. Higher-weighted servers receive more traffic, suitable for disparities in server capabilities. 109 | 110 | 4. **IP Hash**: The client's IP address is hashed to determine which server will handle the request. It's useful in maintaining session persistence. 111 | 112 | #### Implementations 113 | 114 | - **Hardware Load Balancers**: Specialized devices designed for traffic management. They ensure high performance but can be costly. 115 | 116 | - **Software Load Balancers**: Software-based solutions offer flexibility and can be hosted in the cloud or on-premises. Examples include Nginx, HAProxy, and AWS Elastic Load Balancing. 117 | 118 | - **Content Delivery Networks (CDNs)**: They use globally distributed servers to cache and deliver web content, reducing load on origin servers and improving performance. Common CDN providers include Cloudflare, Akamai, and Fastly. 119 |
120 | 121 | ## 3. Explain the difference between _hardware_ and _software load balancers_. 122 | 123 | Both hardware and software load balancers are used to ensure **efficient traffic distribution** and to offer services like SSL termination or DDoS protection. 124 | 125 | Let's look at the Benefits and Limitations of each. 126 | 127 | ### Benefits of Hardware Load Balancer 128 | 129 | - **Optimized Processing**: When specialized hardware is the central controller, the distribution of tasks is generally quicker, leading to better overall performance. 130 | - **Streamlined Setup**: Many hardware solutions come preconfigured, requiring minimal or **no additional setup**, which is beneficial in quick deployment scenarios. 131 | - **Reliability**: Hardware is engineered to be robust and resilient, making it a dependable choice. 132 | 133 | ### Limitations of Hardware Load Balancer 134 | 135 | - **Cost**: Typically, hardware-based solutions are costlier as they involve a separate physical device. 136 | - **Scalability**: Hardware load balancers can be inflexible when it comes to scaling. Expanding beyond the initial hardware's capacity can be challenging and might even require complete replacements at times. This can lead to **downtime**. 137 | 138 | ### Benefits of Software Load Balancer 139 | 140 | - **Cost Efficiency**: Software-based solutions are often more cost-effective due to their dependency on existing infrastructure, like virtual machines or containers. 141 | - **Scalability**: Most software load balancers are designed with scalability in mind. They can be **easily replicated** or **scaled up or down**, catering to varying loads and traffic patterns. 142 | - **Customizability**: Software solutions are flexible and can be tailored to the specific needs of an application or deployment. 143 | 144 | ### Limitations of Software Load Balancer 145 | 146 | - **Learning Curve**: Setting up and configuring software load balancers might require technical expertise and more time compared to hardware alternatives. 147 | - **Resource Usage**: On shared infrastructure, there can be competition for resources, leading to potential bottlenecks. 148 | - **Performance**: In some cases, especially if the host is under heavy load or lacks the necessary resources, the performance might be inferior to hardware solutions. 149 |
150 | 151 | ## 4. Can you list some common _load balancing algorithms_ and briefly describe how they work? 152 | 153 | **Load Balancing Algorithms** aim to evenly distribute incoming network or application traffic across multiple backend servers. Let's look at the some of the most common methods used for this task. 154 | 155 | ### Round Robin 156 | 157 | **Overview** 158 | This algorithm sequentially distributes incoming connections to the next available server. Once the last server is reached, the algorithm starts over from the first. 159 | 160 | **Advantages** 161 | - Easy to implement. 162 | - Fairly balanced. 163 | 164 | **Limitations** 165 | - Not effective if the servers have varying capabilities or loads. 166 | - Doesn't consider real-time server performance. 167 | 168 | ### Least Connections 169 | 170 | **Overview** 171 | Servers with the least active connections are chosen. This approach is effective in balancing traffic if servers have different capacities. 172 | 173 | **Advantages** 174 | - More effective with servers of varying capabilities and loads. 175 | 176 | **Limitations** 177 | - Can lead to overloading if connections to fast servers are prolonged. 178 | - Requires continuous monitoring of server loads. 179 | 180 | ### IP Hash 181 | 182 | **Overview** 183 | Using a hash function applied to the client's IP address, a server is selected. This ensures that requests from the same client always go to the same server. 184 | 185 | **Advantages** 186 | - Useful for stateful applications like online gaming and shopping carts. 187 | - Simplifies client-side session management. 188 | 189 | **Limitations** 190 | - Doesn't account for server loads. 191 | - Can pose issues for fault tolerance and dynamic scaling. 192 | 193 | ### Weighted Round Robin 194 | 195 | **Overview** 196 | This is an extension of the Round Robin. Here, a weight is assigned to each server based on its capabilities or resources. 197 | The algorithm distributes connections to servers based on their weights. 198 | 199 | **Advantages** 200 | - Allows biasing the distribution based on server capabilities. 201 | - Adaptable to server loads by adjusting weights dynamically. 202 | 203 | **Limitations** 204 | - Administrators must handle weights carefully. 205 | - Conservative approach in dynamically adjusting weights can be a concern in rapidly changing server conditions. 206 | 207 | ### Weighted Least Connections 208 | 209 | **Overview** 210 | Weighted Least Connections adaptively considers both the weights assigned to servers and their current connection loads. 211 | 212 | **Advantages** 213 | - Combines the benefits of Least Connections and Weighted Round Robin. 214 | - Adaptable to server loads and capabilities. 215 | 216 | **Limitations** 217 | - Management of weights becomes crucial. 218 | - Requires continuous monitoring to adjust weights and ensure load is distributed optimally. 219 | 220 | ### Source IP Affinity or IP Persistence 221 | 222 | **Overview** 223 | Requests from a particular source IP (like a user or a client) are persistently served by the same server. If that server is unavailable, another server is chosen. 224 | 225 | **Advantages** 226 | - Useful for stateful applications. 227 | - Helps avoid data inconsistency or loss. 228 | 229 | **Limitations** 230 | - Can neglect server loads. 231 | - Can cause server imbalances due to sticky sessions. 232 | 233 | ### Least Response Time 234 | 235 | **Overview** 236 | This approach selects the server that has the shortest response time or latency to the client. It is an effective means for providing high-quality user experience. 237 | 238 | **Advantages** 239 | - Focuses on minimizing latency for the best UX. 240 | - Adaptable to server performance. 241 | 242 | **Limitations** 243 | - Often requires periodic latency measurements. 244 | - Might not always ensure fair load distribution. 245 | 246 | ### Dynamic Load Balancers 247 | 248 | While the traditional algorithms are static in nature, dynamic load balancers harness AI and machine learning to make real-time adjustments, taking into consideration factors like server health, historical performance, and demand patterns. 249 | 250 | #### Advanced Routing Mechanisms 251 | 252 | **Overview** 253 | Modern load balancers employ sophisticated algorithms such as Performance-Based Routing, which direct traffic based on current server performance, offering low latency and high availability. 254 | 255 | ### Code Example: Round Robin Load Balancing 256 | 257 | Here is the Python code: 258 | 259 | ```python 260 | class RoundRobinBalancer: 261 | def __init__(self, servers): 262 | self.servers = servers 263 | self.index = 0 264 | 265 | def route(self, request): 266 | server = self.servers[self.index] 267 | self.index = (self.index + 1) % len(self.servers) 268 | return server 269 | ``` 270 |
271 | 272 | ## 5. Describe the term "_sticky session_" in _load balancing_. 273 | 274 | **Sticky sessions**, also known as session persistency, are a method used by load balancers to ensure that a user's subsequent requests are directed to the same server that handled their initial request. 275 | 276 | This approach becomes necessary in cases when the user's session or client state is tied to a specific server. This commonly occurs in stateful applications or certain web frameworks where persistence mechanisms like local server storage or in-memory caching are used. 277 | 278 | ### Benefits and Drawbacks 279 | 280 | - **Benefits**: Simplifies session management and can enhance application performance by reducing the need for repeated session data lookups or storage updates. 281 | 282 | - **Drawbacks**: Can lead to uneven server loads (if client sessions aren't perfectly balanced). It can also reduce the robustness of the system as a whole (because if a server goes down, all the clients with sticky sessions to that server will lose their session). 283 | 284 | ### Implementations 285 | 286 | Several load balancing tools and technologies, such as HAProxy, Nginx, and Amazon Elastic Load Balancer (ELB), offer sticky session support. This usually involves configuration and sometimes also specific code or architecture requirements in the backend applications. 287 | 288 | ### Code Example: Using Sticky Sessions in Nginx 289 | 290 | Here is the Nginx configuration block to use sticky session: 291 | 292 | ```nginx 293 | upstream backend { 294 | server backend1.example.com; 295 | server backend2.example.com; 296 | hash $request_uri consistent; 297 | } 298 | 299 | server { 300 | listen 80; 301 | location / { 302 | proxy_pass http://backend; 303 | proxy_http_version 1.1; 304 | proxy_set_header Host $host; 305 | } 306 | } 307 | ``` 308 | 309 | The `hash` and `consistent` modifiers ensure that requests matching a particular hash value (commonly based on a client's attributes, like IP address or session ID) are consistently directed to the same server. 310 |
311 | 312 | ## 6. How does _load balancing_ improve _application reliability_? 313 | 314 | **Load balancing** can greatly enhance the reliability of applications. It achieves this through several key mechanisms, making it an integral part of high-availability infrastructures. 315 | 316 | ### Key Benefits 317 | 318 | - **Increased Throughput**: Load balancers can distribute incoming requests across a cluster of servers, ensuring efficient use of resources. 319 | 320 | - **Redundancy**: In several real-world scenarios, multiple application servers exist to prevent a single point of failure. A load balancer directs traffic away from any failing instances. 321 | 322 | - **Resource Optimization**: Load balancers enable dynamic allocation of resources, adjusting server workloads based on current traffic conditions. 323 | 324 | - **SSL Termination**: Certain load balancers can offload SSL/TLS encryption and decryption work from the servers, improving their overall efficiency. 325 | 326 | - **Health Monitoring**: Load balancers often perform health checks on servers in the pool, ensuring they are operational before sending traffic their way. 327 | 328 | ### How Load Balancing Enhances Reliability 329 | 330 | - **SSL Termination**: Instead of individual servers handling SSL/TLS connections, the load balancer does so, reducing this overhead on the server's end. 331 | 332 | - **Session Persistence**: When necessary, load balancers can ensure that requests from the same client are sent to the same server. This is important for maintaining session state in stateful applications. 333 | 334 | - **Database Connection Pooling**: Load balancers manage the pool of connections, providing a consistent connection experience for clients. 335 | 336 | - **Fault Tolerance With Clustering**: Load balancers are often set up in clusters themselves for fault tolerance, ensuring uninterrupted service. 337 |
338 | 339 | ## 7. How do _load balancers_ perform _health checks_ on _backend servers_? 340 | 341 | **Health checks**, also known as **heartbeat monitoring**, are vital for ensuring that backend servers in a load balancer pool are operational. Health checks can be either active or passive. 342 | 343 | ### Active Health Checks 344 | 345 | Active health checks involve the load balancer directly testing the server's health. Typically, it sends a small, defined request and expects a specific response to determine the server's current status. If the server meets the predefined criteria, it is considered healthy and stays in rotation. Otherwise, it is marked as unhealthy and taken out. 346 | 347 | This process is useful in detecting a wide range of potential problems, from the server being offline to specific service misconfigurations. 348 | 349 | #### Example: HTTP GET Request 350 | 351 | The load balancer sends an HTTP GET request to a predefined endpoint, like `/health`. It expects a 200 OK response to consider the server healthy. 352 | 353 | ### Passive Health Checks 354 | 355 | Passive health checks rely on external signals to determine the server's state. These signals may come from several sources, such as the servers themselves reporting their status or other monitoring systems sending data to the load balancer. 356 | 357 | ### Benefits of Passive Health Checks 358 | 359 | Multiple signals indicating a problem can give a more accurate picture of the server's state, reducing false positives and negatives. 360 | 361 | For example, a server may still be serving HTTP 200 OK, but warning logs could indicate an impending issue effectively. 362 | 363 | ### Code Example: Implementing Health Checks in Node.js 364 | 365 | Here is the Node.js code: 366 | 367 | ```javascript 368 | const http = require('http'); 369 | 370 | // Define a health-check endpoint 371 | const healthCheckPath = '/_healthcheck'; 372 | 373 | // Set up a simple HTTP server 374 | const server = http.createServer((req, res) => { 375 | if (req.url === healthCheckPath) { 376 | res.writeHead(200); 377 | res.end(); 378 | } else { 379 | // Process normal traffic here 380 | } 381 | }); 382 | 383 | // Start the server 384 | server.on('listening', () => { 385 | console.log(`Server is listening on port ${server.address().port}`); 386 | }); 387 | 388 | server.listen(3000); 389 | 390 | // Gracefully handle server shut down events 391 | process.on('SIGINT', () => { 392 | server.close(() => { 393 | console.log('Server gracefully shut down.'); 394 | }); 395 | }); 396 | ``` 397 |
398 | 399 | ## 8. What are the advantages and disadvantages of _round-robin load balancing_? 400 | 401 | **Round-robin load balancing** distributes traffic evenly across servers, and is especially effective when servers exhibit homogenous performance. 402 | 403 | ### Advantages 404 | 405 | - **Simplicity**: The round-robin algorithm is straightforward to implement and manage. 406 | 407 | - **Equal Distribution**: It ensures that all servers receive an equivalent number of requests, promoting balance in the system. 408 | 409 | ### Disadvantages 410 | 411 | - **No Benefit from Caching**: The lack of session affinity, or sticky sessions, might hinder the advantages of caching, instead leading to cache misses. 412 | 413 | - **Performance Variability**: Servers' differing performance or computational loads might not be optimally accommodated, potentially leading to latency or throughput issues. 414 |
415 | 416 | ## 9. In _load balancing_, what is the significance of the _least connections_ method? 417 | 418 | **Least Connections** is a key method for **load balancing**, ensuring that traffic is consistently directed to the server with the fewest active connections. 419 | 420 | ### Importance of Load Balancing 421 | 422 | - **Efficient Resource Usage**: By evenly distributing incoming traffic, the system strives to prevent any single application or server from being overburdened, leading to optimal resource employment. 423 | - **High Availability**: In the event of malfunctions or traffic spikes, a load balancer can redirect requests away from problematic servers to those better equipped to handle the load. 424 | - **Improved Scalability**: Load balancers are a pivotal tool in auto-scaling scenarios as they can seamlessly distribute incoming traffic among a larger number of instances. 425 | - **Enhanced Security**: Certain load balancing strategies enhance security, such as SSL offloading, which allows the load balancer to manage encryption and decryption, alleviating this task from backend servers. 426 | 427 | ### The Significance of "Least Connections" Method 428 | 429 | - **Fair Request Allocation**: This method aims to manage server workload effectively by directing traffic to the least busy server, ensuring servers aren't overloaded. 430 | - **Optimum Throughput**: As stated above, this method helps make sure connections are distributed evenly among available servers, thereby avoiding server bottlenecks and maintaining optimal performance. 431 | - **Consistent Response**: Even though perfect balance might be challenging in practice, load balancer algorithms like "Least Connections" ensure attempts are made to keep response time and throughput consistent across servers. 432 | 433 | ### Code Example: Least Connections Algorithm 434 | 435 | Here is the Python code: 436 | 437 | ```python 438 | def least_connections_server(servers): 439 | return min(servers, key=lambda server: server.active_connections) 440 | ``` 441 | 442 | In the above example: 443 | 444 | - `servers` is a list of available server objects. 445 | - `active_connections` represents the current number of active connections on a server. 446 | - The `min` function, with `key` set to a lambda function, returns the server object with the minimum active connections. 447 |
448 | 449 | ## 10. Explain how a _load balancer_ might handle _failure_ in one of the _servers_ it manages. 450 | 451 | **Load Balancers** manage multiple servers. When one fails, the **Load Balancer** no longer directs traffic to it, ensuring seamless functionality. Here is a list of mechanisms it uses. 452 | 453 | ### Server Health Checks 454 | 455 | The load balancer continuously monitors server health, often through periodic checks. If a server misses several health checks, it's presumed **unavailable** and taken out of rotation. 456 | 457 | ### Example: Nginx Configuration 458 | 459 | Here’s a `Nginx` configuration using the `upstream` module for load balancing and server monitoring: 460 | 461 | ```nginx 462 | http { 463 | upstream myapp1 { 464 | server backend1.example.com weight=5; 465 | server backend2.example.com; 466 | server backend3.example.com; 467 | 468 | # Health check 469 | check interval=3000 rise=2 fall=3 timeout=1000; 470 | 471 | # Backup server 472 | backup; 473 | } 474 | 475 | ... 476 | } 477 | ``` 478 | 479 | ### Load Balancing Algorithms 480 | 481 | Load balancers deploy various **algorithms** to route traffic to available servers: 482 | 483 | - **Round Robin**: Cyclically directs traffic to servers in a queue. 484 | - **Least Connections**: Sends traffic to the server with the fewest active connections, promoting efficiency. 485 | - **Weighted Round Robin**: Accounts for server capacity by assigning weights. Servers with higher weights handle more traffic. 486 | 487 | ### SSL Termination 488 | 489 | For secure connections using HTTPS, the load balancer might employ SSL Termination, decrypting incoming requests and re-encrypting responses. This can create performance discrepancies. 490 | 491 | ### Sticky Sessions 492 | 493 | When a user establishes a session, the load balancer routes subsequent requests from that user to the same server. This ensures session state is maintained. 494 | 495 | ### Redundant Load Balancers 496 | 497 | To avert potential load balancer failures, a backup or secondary load balancer might be deployed, ensuring no single point of failure. 498 | 499 | ### Elastic Scalability 500 | 501 | Modern load balancers, especially in cloud environments, support elastic scalability. This means they can quickly adapt, managing more servers in response to increased traffic. 502 | 503 | ### Ensuring Data Consistency 504 | 505 | With multiple servers catering to database operations, maintaining data consistency is crucial. Load balancers may use techniques such as server affinity or database locking to ensure this. 506 | 507 | ### Service Health Metrics 508 | 509 | Additionally, a load balancer might record various metrics of server health, such as response times and error rates. 510 | 511 | ### Deploying Backup Servers 512 | 513 | Some load balancers are designed to have a pool of backup servers, which only become active when the primary servers fail. This setup can be particularly useful for managing unexpected spikes in traffic or cloud infrastructure issues. 514 |
515 | 516 | ## 11. How does a _load balancer_ distribute traffic in a _stateless_ vs _stateful_ scenario? 517 | 518 | **Load balancers** handle traffic distribution differently in stateless and stateful scenarios, often using different algorithms for each. 519 | 520 | ### Stateless Behavior 521 | 522 | In a "stateless" setup, each **user request** is independent. 523 | 524 | #### Traffic Distribution 525 | 526 | - **Algorithm**: Round Robin or IP Hash 527 | - **Mechanism**: The load balancer selects the next available server, bearing in mind server weights if applicable. 528 | 529 | ### Stateful Behavior 530 | 531 | In a "stateful" setup, there is a **persistent connection** between a user and a server due to ongoing **session data**. 532 | 533 | #### Traffic Distribution 534 | 535 | - **Algorithm**: Least Connection or Session Stickiness 536 | - **Mechanism**: To maintain session continuity, the load balancer consistently directs a user to the server where the session was initially established. Common methods for achieving this include **source-IP affinity** and **HTTP cookie-based persistence**. 537 |
538 | 539 | ## 12. What is the concept of _session persistence_, and why is it important? 540 | 541 | **Session Persistence**, often called **Session Stickiness**, is a technique used in **load balancing** to ensure that all requests from a single client are directed to the same server. 542 | 543 | ### Importance of Session Persistence 544 | 545 | - **User Experience**: Many applications, like e-commerce platforms or social media sites, tailor user experiences based on session-state information. For instance, a shopping cart typically requires persistence. 546 | 547 | - **Database Consistency**: To maintain integrity, certain operations must be carried out consistently on a single server, especially in **multi-tier architecture** and **stateful applications**. 548 | 549 | - **Security**: Ensuring client-server proximity can minimize potential security risks, like cross-site request forgery (**CSRF**). 550 | 551 | ### How Load Balancer Manages Flexibility and Statelessness 552 | 553 | Modern web architectures, favoring statelessness and flexibility, resolve these issues primarily through **intelligent design** and **session storage**. 554 | 555 | #### Techniques 556 | 557 | - **Round-Robin**: This technique rotates request distribution among servers in a circular manner. It's simple but doesn't guarantee that all client requests will go to the same server. 558 | 559 | - **Sticky Sessions**: Also known as **Session Affinity**, this method uses mechanisms, such as **HTTP cookies** or **dynamic rule-designators**, to direct a client to the same server for the duration of its session. 560 | 561 | - **Load Balancer Persistence Modes**: Some advanced load balancers offer specific algorithms to maintain session stickiness. Common modes include: 562 | - Source IP Affinity 563 | - Cookie-Based Persistence 564 | - SSL Persistence 565 | 566 | - **Shared Session State Across Servers**: By centralizing session data in a **shared database** or **cache**, all servers under the load balancer can access this data, ensuring session uniformity. 567 | 568 | ### When to Avoid Session Persistence 569 | 570 | While it can help with efficiency in certain scenarios, it's essential to recognize when **session persistence** might not be the best choice: 571 | 572 | - **Server Overload**: If a particular server is overwhelmed with session-bound traffic, session persistence can exacerbate the problem. 573 | 574 | - **Scalability**: As your traffic grows, session persistence can lead to scalability challenges since it restricts client-server flexibility. 575 | 576 | - **Operational Challenges**: Tools like **content caching** or load balancer alterations can become less effective or difficult to manage with session stickiness in place. 577 |
578 | 579 | ## 13. Discuss the role of _DNS_ in _load balancing_. 580 | 581 | While not a direct load balancer, **Domain Name System (DNS)** skillfully complements load balancing to distribute traffic among multiple servers. 582 | 583 | ### DNS Mechanisms for Load Balancing 584 | 585 | 1. **Round Robin (RR)**: 586 | DNS servers traditionally leverage RR to cycle through multiple IP addresses that correspond to various servers in a load balancing pool. While it's simple and easy to administer, RR can't dynamically adjust traffic based on server loads. 587 | 588 | 2. **Weighted Round Robin (WRR)**: 589 | Builds on RR by assigning each IP address a weight based on server capabilities or capacities, thereby directing more or less traffic to each server. 590 | 591 | 3. **Least Connections**: 592 | More advanced load balancing mechanisms, like Weighted Least Connections, incorporate intelligence about the number of active connections on servers to route traffic effectively. 593 | 594 | 4. **Geographical Load Balancing**: 595 | DNS can also be optimized to manage traffic based on global geography, directing users to the closest server to minimize latency. 596 | 597 | ### Code Example: Simple Round Robin DNS 598 | 599 | Here is the Python code: 600 | 601 | ```python 602 | from itertools import cycle 603 | 604 | # Replace with actual server IPs 605 | server_ips = ["192.168.1.1", "192.168.1.2", "192.168.1.3"] 606 | 607 | # Use cycle for round-robin behavior 608 | server_cycle = cycle(server_ips) 609 | 610 | # Simulate DNS query 611 | def get_next_server(): 612 | return next(server_cycle) 613 | 614 | # Test with multiple queries 615 | for _ in range(6): 616 | print(get_next_server()) 617 | ``` 618 |
619 | 620 | ## 14. In what scenarios would you use _weighted load balancing_? 621 | 622 | **Weighted Load Balancing** adjusts server traffic in a manner that doesn't merely distribute load evenly across all servers. Instead, weighted balancing allows for load assignment depending on server capacities and resource allocation levels. 623 | 624 | ### Common Use-Cases 625 | 626 | 1. **Performance Optimization**: In instances with heterogeneous servers, you might benefit from routing more traffic to more capable servers. 627 | 628 | 2. **Cost-Effective Scaling**: In cloud or virtual environments where servers are billed based on resource usage, weighted balancing can be used to minimize costs. 629 | 630 | 3. **Task Segregation**: For unique server tasks, you can distribute load based on task requirements, leveraging weighted balancing. 631 | 632 | 4. **Disaster Recovery Preparedness**: In setups with dedicated backup systems, such as active-passive configurations, keeping a portion of the server capacity unused is crucial for swift transitions in case of calamities. 633 | 634 | ### Code Example: Round-Robin with Weights 635 | 636 | Here is the Python code: 637 | 638 | ```python 639 | from itertools import cycle 640 | 641 | class WeightedRoundRobinBalancer: 642 | def __init__(self, servers): 643 | self.servers = servers 644 | self.weights = {server: servers[server]['weight'] for server in servers} 645 | self.cycle = cycle(self._expand_servers()) 646 | 647 | def _expand_servers(self): 648 | expanded = [[key] * self.weights[key] for key in self.weights] 649 | flattened = [item for sublist in expanded for item in sublist] 650 | return flattened 651 | 652 | def next_server(self): 653 | return next(self.cycle) 654 | ``` 655 | 656 | ### Code Example: Testing the Round-Robin Weighted Balancer 657 | 658 | Here is the Python code: 659 | 660 | ```python 661 | servers = { 662 | 'Server1': {'ip': '192.168.1.1', 'weight': 5}, 663 | 'Server2': {'ip': '192.168.1.2', 'weight': 3}, 664 | 'Server3': {'ip': '192.168.1.3', 'weight': 2} 665 | } 666 | 667 | # Create and initialize WeightedRoundRobinBalancer 668 | wrr = WeightedRoundRobinBalancer(servers) 669 | 670 | # Test the Balancer by firing 10 requests 671 | results = {server: 0 for server in servers} 672 | for _ in range(10): 673 | server = wrr.next_server() 674 | results[server] += 1 675 | 676 | # Print the results 677 | for server, count in results.items(): 678 | print(f'{server} received {count} requests. (Weight: {servers[server]["weight"]}, IP: {servers[server]["ip"]})') 679 | ``` 680 |
681 | 682 | ## 15. How can _load balancers_ help mitigate _DDoS attacks_? 683 | 684 | Load balancers are essential in distributing traffic across servers to optimize performance. They also play a crucial role in mitigating DDoS attacks by detecting and filtering malicious traffic. 685 | 686 | ### How Load Balancers Mitigate DDoS Attacks 687 | 688 | 1. **Traffic Distribution**: Load balancers ensure equitable traffic distribution across multiple servers, which prevents the overloading of a single server and distribution of attack traffic. 689 | 690 | 2. **Layer 4 SYN Flood Protection**: Modern load balancers can mitigate SYN flood attacks, which flood servers with TCP connection requests, by employing intelligent connection tracking and state management. 691 | 692 | 3. **Layer 7 Application DDoS Protection**: Advanced load balancers can detect application layer (Layer 7) DDoS attacks by monitoring HTTP and HTTPS requests. They can also identify and filter out malicious traffic patterns targeting specific URLs or application endpoints. 693 | 694 | 4. **Behavior-based Detection**: Some load balancers leverage real-time traffic analysis that can identify abnormal behavior, such as excessive requests from a single source, and dynamically adjust traffic flow accordingly. 695 | 696 | ### Code Example: SYN Flood Protection with iptables 697 | 698 | Here is the `iptables` configuration 699 | 700 | ```bash 701 | sudo iptables -A INPUT -p tcp --syn -m limit --limit 1/s --limit-burst 3 -j ACCEPT 702 | ``` 703 |
704 | 705 | 706 | 707 | #### Explore all 50 answers here πŸ‘‰ [Devinterview.io - Load Balancing](https://devinterview.io/questions/software-architecture-and-system-design/load-balancing-interview-questions) 708 | 709 |
710 | 711 | 712 | software-architecture-and-system-design 713 | 714 |

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