Azure Load Balancing Interview Questions : ChatGPT Answers

Azure Load Balancer and Azure Api Gateway are same ?

No, Azure Load Balancer and Azure API Gateway are not the same.

Azure Load Balancer is a Layer 4 (Transport Layer) load balancer that distributes incoming traffic across multiple virtual machines (VMs) or instances in a backend pool. It can help improve application availability, scalability, and performance by distributing traffic among healthy instances and providing redundancy in case of failures. It works with both internal and external traffic.

Azure API Gateway, on the other hand, is a Layer 7 (Application Layer) gateway that acts as a front door for APIs hosted in Azure. It provides features such as API management, authentication, caching, and transformation. It can help simplify API development and management by providing a unified interface for multiple APIs and enabling organizations to monitor and control access to their APIs. It’s designed to work with HTTP/HTTPS traffic and is primarily used for external traffic.

While both Azure Load Balancer and Azure API Gateway are used for traffic distribution, they serve different purposes and operate at different network layers. Azure Load Balancer focuses on distributing traffic among instances, while Azure API Gateway provides API management features and is used primarily for external API traffic.

What are the differences between external api traffic and internal api traffic ?

External API traffic and internal API traffic differ in their source and destination.

Internal API traffic refers to the communication between components or services within a private network, such as within an organization’s intranet. These APIs are not exposed to the public internet and are typically used for internal system integration or communication. The traffic flow is limited to the private network and is not accessible from outside the network.

External API traffic, on the other hand, refers to the communication between the API provider and consumers outside of the private network, over the public internet. These APIs are exposed to the public internet and can be accessed by authorized consumers from anywhere. The traffic flow can traverse through various networks and may require additional security measures such as authentication and encryption to protect against unauthorized access.

External API traffic and internal API traffic can also differ in terms of their use cases and requirements. Internal APIs may be more focused on performance, scalability, and reliability within a private network, while external APIs may need to consider factors such as security, compliance, and usability for a wider range of consumers.

What are the different transport layer ? Why we need multiple transport layer ?

The Transport Layer is the fourth layer of the OSI model and the TCP/IP protocol suite. Its main function is to provide end-to-end communication services between applications running on different hosts. The Transport Layer receives data from the Session Layer and splits it into segments or packets, which are then transmitted over the network to the destination host.

There are two commonly used transport layer protocols: TCP (Transmission Control Protocol) and UDP (User Datagram Protocol).

TCP is a connection-oriented protocol that provides reliable data delivery, error detection and correction, flow control, and congestion control. It establishes a connection between the sender and receiver before transmitting data and ensures that all packets are received in the correct order and without errors. TCP is used for applications that require reliable data delivery, such as email, file transfer, and web browsing.

UDP, on the other hand, is a connectionless protocol that provides faster data delivery but without any error correction or flow control. It simply sends packets without establishing a connection or checking for errors, which makes it faster but less reliable. UDP is used for applications that require fast data transmission, such as streaming media, online gaming, and real-time communication.

We need multiple transport layer protocols because different applications have different requirements for data delivery, reliability, speed, and overhead. Some applications, such as email and file transfer, require reliable data delivery and error correction, which makes TCP a better choice. Other applications, such as streaming media and online gaming, require fast data transmission and low latency, which makes UDP a better choice. By having multiple transport layer protocols, we can choose the appropriate protocol based on the specific needs of each application and optimize performance and reliability.

How load balancer determines healthy instances ?

Load balancers determine healthy instances using a combination of health checks and load balancing algorithms.

Health checks are typically performed by the load balancer at regular intervals to determine the status of each instance in the backend pool. The load balancer sends a health check request to each instance, and if the instance responds with an expected status code or message, it is considered healthy. If the instance does not respond or responds with an error status code, it is considered unhealthy.

The health check request can be a simple TCP connection check, a ping or ICMP request, an HTTP/HTTPS request, or a custom script or application-level check. The health check settings can be customized based on the specific application requirements and network configuration.

Load balancing algorithms are used to distribute incoming traffic across the healthy instances in the backend pool. The most commonly used algorithms include:

• Round-robin: evenly distributes traffic among all healthy instances in the backend pool.
• Least connections: sends traffic to the instance with the fewest active connections.
• IP hash: hashes the source IP address of the client and sends traffic to the instance based on the hash value.

Load balancers may also support more advanced algorithms, such as weighted round-robin, where each instance is assigned a weight based on its capacity, or session persistence, where traffic from the same client is always sent to the same instance to maintain session state.

By combining health checks and load balancing algorithms, load balancers can ensure that traffic is distributed only to healthy instances and that the distribution is optimized based on the specific requirements of the application.

What are the key differences between azure load balancer and api gateway ?

Azure Load Balancer and Azure API Gateway are both used to manage and distribute traffic, but they serve different purposes and have different features. Here are the key differences between Azure Load Balancer and API Gateway:

1. Traffic Management: Azure Load Balancer is primarily used for managing network traffic and distributing it across multiple instances or VMs within a backend pool. It operates at the transport layer (Layer 4) and is used for both internal and external traffic. On the other hand, Azure API Gateway is used for managing and distributing API traffic, and it operates at the application layer (Layer 7). It provides features like API management, authentication, and caching.
2. Load Balancing Algorithms: Azure Load Balancer uses standard load balancing algorithms such as round-robin, least connections, and IP hash to distribute traffic. In contrast, Azure API Gateway provides more advanced load balancing algorithms that can distribute traffic based on various factors such as client IP address, request URL, or custom headers.
3. Protocol Support: Azure Load Balancer supports both TCP and UDP protocols, while Azure API Gateway supports HTTP and HTTPS protocols for API traffic.
4. SSL Offloading: Azure Load Balancer provides SSL offloading, which allows SSL/TLS decryption to take place at the load balancer rather than at the backend pool instances. This can help improve performance and reduce the workload on backend pool instances. Azure API Gateway also provides SSL termination and offloading.
5. API Management: Azure API Gateway provides advanced API management features such as authentication, authorization, rate limiting, caching, and transformation. These features can help simplify API development, improve security, and enhance performance. Azure Load Balancer does not provide these features.

Overall, Azure Load Balancer is best suited for managing network traffic, while Azure API Gateway is best suited for managing API traffic and providing advanced API management features. Organizations may use both services together to optimize traffic management and API delivery for their specific needs.

Is there any conflict if both are setup in azure at the same time ?

There should not be any conflict if both Azure Load Balancer and Azure API Gateway are set up in Azure at the same time, as they serve different purposes and operate at different layers of the network stack.

Azure Load Balancer operates at the transport layer (Layer 4) and is used to distribute traffic across multiple instances or VMs in a backend pool, while Azure API Gateway operates at the application layer (Layer 7) and is used to manage and distribute API traffic.

In fact, it is common for organizations to use both services together to optimize traffic management and API delivery for their specific needs. For example, an organization may use Azure Load Balancer to distribute traffic across multiple instances of an application, and use Azure API Gateway to manage and distribute API traffic for that same application.

However, it is important to ensure that the two services are configured properly and do not conflict with each other. For example, if both services are set up to use the same port, there may be a conflict. Therefore, it is important to carefully plan and configure both services to avoid any conflicts or issues.

How we can test both to make sure application is working smooth for any kind of load ?

To test Azure Load Balancer and Azure API Gateway and ensure that the application is working smoothly for any kind of load, you can perform load testing or stress testing. Load testing involves simulating user traffic on the application to measure its performance under different loads, while stress testing involves pushing the application beyond its expected capacity to measure its resilience and ability to handle unexpected spikes in traffic.

Here are some steps you can follow to test the application with Azure Load Balancer and Azure API Gateway:

1. Set up load testing tools: There are many load testing tools available, such as Apache JMeter, Gatling, and Locust. Choose a tool that fits your needs and set it up to simulate user traffic on the application.
2. Configure load testing scenarios: Define different load testing scenarios that simulate different traffic volumes and patterns, such as peak traffic hours, different user profiles, and varying traffic loads.
3. Execute load testing: Run the load testing tool to simulate the defined scenarios and measure the application’s performance. You can use Azure Monitor to monitor the performance of the application and identify any bottlenecks or performance issues.
4. Analyze results: Analyze the load testing results to identify any performance issues or bottlenecks. If any issues are identified, you can use Azure Load Balancer and Azure API Gateway features such as load balancing algorithms, SSL offloading, or API management to optimize the application’s performance.
5. Repeat testing: Repeat the load testing process to ensure that any changes made to the application or infrastructure have improved its performance and resilience under different loads.

By performing load testing and stress testing with Azure Load Balancer and Azure API Gateway, you can ensure that the application is optimized for different loads and can handle unexpected spikes in traffic, providing a smooth user experience.

Can we create custom load balancing through some some scripts/code ?

Yes, it is possible to create custom load balancing solutions through scripts or code. In fact, many organizations build custom load balancing solutions tailored to their specific needs, especially when using specialized protocols or requiring specific load balancing algorithms.

Here are some examples of how custom load balancing solutions can be created using scripts or code:

1. Network Load Balancing: Custom network load balancing solutions can be created using scripts or code that leverage network protocols and APIs to distribute traffic across multiple backend servers. For example, you can use scripting languages such as Python or PowerShell to create custom load balancing solutions that leverage the Windows Server Network Load Balancing (NLB) feature.
2. HTTP Load Balancing: Custom HTTP load balancing solutions can be created using code that runs on a server or in a container. For example, you can use a reverse proxy server such as NGINX or HAProxy to implement custom HTTP load balancing solutions that provide advanced features like SSL offloading, session persistence, and content-based routing.
3. Custom Algorithms: Custom load balancing algorithms can be implemented using scripts or code to meet specific load balancing requirements. For example, you can create custom algorithms that take into account server capacity, location, and health status to distribute traffic in an optimal way.

While building custom load balancing solutions can provide flexibility and customization, it requires technical expertise and ongoing maintenance to ensure proper operation and security. As an alternative, using pre-built load balancing solutions like Azure Load Balancer or Azure Application Gateway can provide a more simplified and secure way to distribute traffic in the cloud.

What action taken by load balancer if a particular zone is down ? And how it recognizes when its up ?

If a particular zone is down, the load balancer will stop sending traffic to that zone and route traffic to other available zones. This is to ensure that traffic is distributed to healthy resources and prevent user traffic from being routed to an unhealthy resource, which could lead to poor user experience.

To recognize when a zone is up again, the load balancer uses health probes to continuously monitor the health of the backend resources in each zone. Health probes are requests that the load balancer sends to the backend resources to check their health status. The load balancer can use different types of probes, such as TCP probes, HTTP probes, or custom probes, depending on the service and application requirements.

When a zone is down, the load balancer will detect the failed health probes and mark the resources in that zone as unhealthy. The load balancer will continue to monitor the health of the resources in that zone and, once it detects that the zone is up again, it will start sending traffic to that zone.

To ensure high availability and fault tolerance, it is recommended to distribute resources across multiple zones and use load balancers that support zone redundancy. This helps to ensure that if one zone goes down, traffic can be automatically redirected to another available zone without user interruption. Additionally, using multiple health probes and setting appropriate thresholds can help ensure that the load balancer accurately detects resource health and effectively routes traffic to healthy resources.

What is zone redundancy ?

Zone redundancy is a feature that provides high availability and fault tolerance by replicating resources across multiple zones in a geographic region. In cloud computing, zones refer to physically separate data centers or availability zones within a region.

By replicating resources across multiple zones, zone redundancy helps to ensure that if one zone experiences an outage or service disruption, traffic can be automatically redirected to another available zone without user interruption. This helps to minimize downtime, ensure business continuity, and provide a seamless user experience.

For example, in Microsoft Azure, zone redundancy is supported in services such as Azure Virtual Machines, Azure SQL Database, Azure Storage, and Azure Load Balancer. When zone redundancy is enabled, resources are automatically replicated across multiple availability zones within a region, ensuring that they are available even if one zone fails. Azure Load Balancer can also distribute traffic across multiple availability zones to ensure that it is evenly balanced and routed to healthy resources.

Zone redundancy typically requires additional configuration and may incur additional costs, but it provides a higher level of availability and reliability than single-zone deployments. It is especially important for mission-critical applications and services that require high levels of uptime and reliability.

How does the default load balancer work specifically for container apps?

The default load balancer in container environments typically works by using an ingress controller to distribute traffic across multiple containers running in a Kubernetes cluster.

When a client sends a request to a containerized application, the request first goes to the ingress controller. The ingress controller uses routing rules defined in an ingress resource to determine how to route the request to the appropriate backend service or pod.

The ingress controller can also perform advanced traffic management functions such as SSL termination, session persistence, and content-based routing. Once the request has been routed to the appropriate backend service or pod, the default load balancer distributes the traffic across the available replicas of the service or pod.

The default load balancer in Kubernetes uses a round-robin algorithm to distribute traffic evenly across the replicas of the service or pod. When a new replica is added or removed, the load balancer automatically updates its routing rules to ensure that traffic is distributed evenly across the available replicas.

The default load balancer in container environments can also support advanced load balancing algorithms, such as least connections or IP hash, by using third-party ingress controllers or load balancer solutions.

Overall, the default load balancer in container environments provides a scalable and resilient way to distribute traffic across multiple instances of containerized applications, ensuring that users are able to access the application without interruption or downtime.

How to maintain sessions with load balancing ?

Maintaining user sessions with load balancing can be challenging because the session state needs to be shared between multiple backend servers to ensure that users maintain their session data as they move between different servers.

Here are a few techniques that can be used to maintain user sessions with load balancing:

1. Sticky Sessions: This approach, also known as session affinity, involves directing all requests from a particular client to the same backend server. This ensures that all requests from the same user are handled by the same server, allowing session data to be maintained. Sticky sessions can be implemented using load balancers that support cookie-based or IP-based session persistence.
2. Centralized Session Store: In this approach, session data is stored in a central location, such as a database or cache, that can be accessed by all backend servers. When a user makes a request, the backend server looks up the session data from the centralized store and uses it to process the request. This approach can be implemented using technologies such as Redis or memcached.
3. Server-to-Server Session Replication: In this approach, session data is replicated between backend servers using a synchronization mechanism, such as database replication or file synchronization. This ensures that each server has a copy of the session data and can access it when needed.

It’s important to note that each of these techniques has its own trade-offs in terms of complexity, scalability, and performance, and the best approach depends on the specific requirements of the application and the resources available.

Azure load balancer and api gateway can work with application sidecar ?

Yes, both Azure Load Balancer and API Gateway can work with application sidecars in a Kubernetes environment.

In Kubernetes, a sidecar is a container that runs alongside the main application container within a pod. Sidecars are often used to provide additional functionality such as logging, monitoring, or security.

When using Azure Load Balancer or API Gateway with sidecar containers, the traffic from the client is first intercepted by the sidecar container, which can perform any required processing or modifications before forwarding the traffic to the main application container.

To use Azure Load Balancer with sidecars, you can deploy a Kubernetes Service that exposes the sidecar and main application containers as separate endpoints. You can then use the Azure Load Balancer to distribute traffic across these endpoints.

To use API Gateway with sidecars, you can deploy an ingress controller that uses an API Gateway instance as the frontend for incoming traffic. The ingress controller can then route traffic to the appropriate sidecar and main application containers based on the URL path or other routing rules.

In both cases, it’s important to ensure that the sidecar containers and the main application container are configured to work together and that any required traffic routing or load balancing rules are properly configured to ensure that traffic is directed to the appropriate endpoint.

When to use azure front door and traffic manager as load balancer?

Azure Front Door and Traffic Manager are both Azure services that can be used to distribute traffic across multiple backend resources, but they have different use cases and features.

Azure Front Door is a global, scalable, and highly available service that provides layer 7 load balancing capabilities for web applications. It provides advanced traffic routing features such as URL-based routing, path-based routing, and session affinity. It also includes built-in security features such as SSL termination and DDoS protection. Azure Front Door is ideal for applications that require global scale, high availability, and advanced routing and security features.

On the other hand, Azure Traffic Manager is a DNS-based traffic routing service that can be used to distribute traffic across multiple endpoints based on various routing policies such as geographic, performance, and priority-based routing. Traffic Manager is primarily used for scaling and optimizing the delivery of web services that are hosted in different regions or data centers. Traffic Manager is ideal for applications that require global traffic routing capabilities and improved availability by redirecting traffic to healthy endpoints.

In general, Azure Front Door is more suitable for web applications that require advanced traffic routing, security, and scalability features, while Azure Traffic Manager is more suitable for applications that require global traffic routing and improved availability by redirecting traffic to healthy endpoints.

However, in some cases, both services can be used together to provide a more comprehensive load balancing solution. For example, Traffic Manager can be used to route traffic to Front Door instances located in different regions, providing an additional layer of redundancy and failover capabilities.