Interview Questions for Kong Gateway

Kong Gateway’s architecture is built around a microservices design, primarily utilizing Nginx for its core functionality and providing a decoupled, scalable solution. It comprises several key components:

• Nginx Core: The heart of Kong, providing high performance and reliability for handling requests. It’s responsible for efficient request routing and processing.
• Cassandra/PostgreSQL Database: Kong uses a database (Cassandra being the default, offering high availability and scalability, though PostgreSQL is also supported) to store its configuration data, including APIs, plugins, and consumer information. This allows for persistence and centralized management.
• API Gateway Layer: This is the layer that interacts directly with clients and performs the tasks defined by plugins, such as authentication, rate limiting, and transformation.
• Plugin System: This is the extensible part of Kong allowing for customization through various plugins. Plugins extend the functionality of the gateway without modifying its core code.
• Admin API: A RESTful API enabling automated management and configuration of the gateway, crucial for integration with DevOps workflows and tools.

Think of it like a well-organized kitchen: Nginx is the efficient stovetop, the database is the pantry holding all the ingredients (configurations), the API gateway layer is the chef preparing the dishes (processing requests), the plugins are the various kitchen tools and appliances allowing flexibility, and the admin API is the order management system.

While many API gateways exist, Kong distinguishes itself through its highly extensible plugin architecture, its scalable and distributed nature, and its open-source foundation. Other gateways often provide a more fixed set of features, while Kong allows for near-infinite customization through plugins. For instance, if a specific authentication mechanism is needed, you’re not limited to what the gateway offers out-of-the-box; you could potentially build or use an existing plugin. Furthermore, Kong’s ability to scale horizontally using multiple instances and a database for configuration makes it well-suited for high-traffic environments, unlike some simpler gateways.

For example, consider a scenario needing to integrate with a legacy system that has a unique authentication protocol. With a less flexible gateway, you might be forced to perform cumbersome workarounds. With Kong, you could create a custom plugin to handle this specific authentication, seamlessly integrating the legacy system into your modern architecture.

Kong handles authentication and authorization primarily through its plugin system. Several built-in plugins cater to common authentication needs like OAuth 2.0, JWT (JSON Web Tokens), Basic Authentication, and Key Authentication. Authorization is handled by controlling access based on consumer credentials and plugin configurations.

For instance, the key-auth plugin allows you to assign unique keys to consumers and verify those keys in incoming requests, restricting access based on those keys. The oauth2 plugin provides functionality to authenticate users using an OAuth2 provider. These plugins interact with various databases or external services for user verification, allowing you to integrate with your existing security infrastructure. You can also combine multiple plugins, for example, using JWT for authentication and an ACL (Access Control List) plugin for authorization to create a granular access control system.

Kong’s plugin architecture is its strength. Plugins are essentially small pieces of code written in Lua that extend Kong’s functionality. They run within the Nginx environment and can hook into various stages of the request lifecycle (e.g., pre-request, post-request). To create a custom plugin, you write Lua code that interacts with the Kong API. You then package this code and deploy it to your Kong instance.

A simple example could be a plugin to log all requests to a custom logging service. The plugin would intercept the request, extract relevant information, and send it to the service. The development involves defining the plugin’s schema (defining its configuration options), writing the Lua code for handling the request lifecycle hooks, and packaging it appropriately. Kong provides extensive documentation and examples to guide developers through this process.

Managing and monitoring Kong Gateway instances involves several strategies. The Admin API is fundamental, allowing you to programmatically manage configurations, deploy plugins, and check the health of your instances. For monitoring, you can leverage tools such as Prometheus and Grafana to gather metrics and visualize performance data. Kong also offers built-in metrics that can be accessed through the Admin API or exposed for monitoring systems. Furthermore, using a declarative configuration approach (discussed later) streamlines the management process by allowing you to define your entire Kong configuration as code, facilitating version control, automated deployments, and easier rollbacks.

Imagine managing a fleet of servers; centralized management through the Admin API acts like a command center, allowing you to control all your Kong instances. Monitoring tools provide the dashboards, allowing you to keep an eye on the health and performance of this fleet.

Kong’s declarative configuration approach is a game-changer for managing the gateway at scale. It allows you to define your entire Kong configuration using YAML or JSON files. This approach offers several advantages:

• Version Control: Configuration files can be stored in version control systems (like Git), enabling easy tracking of changes and rollbacks.
• Automation: Automated deployments using tools like Terraform or Ansible become possible.
• Consistency: Ensures consistency across multiple Kong instances.
• Collaboration: Allows for better team collaboration on managing configuration.

Instead of manually managing configurations through the Admin API, you define your services, routes, plugins, and consumers in a declarative file. Kong then uses this file to apply the configuration. This approach makes the configuration more manageable and less error-prone, especially in complex environments with many APIs and plugins.

Troubleshooting Kong Gateway issues often involves several approaches:

• Check Kong Logs: Kong logs are crucial. Analyze them for errors and warnings. These logs provide valuable insights into the issues.
• Utilize the Admin API: The Admin API allows querying the status of services, plugins, and consumers. Identifying failing services is frequently the first step.
• Inspect Nginx Configuration (advanced): If necessary, directly examine the Nginx configuration generated by Kong. This can help identify deeper issues relating to Nginx’s internal workings.
• Monitoring Tools: Monitoring tools provide an overview of the Gateway’s performance metrics, helping you quickly identify bottlenecks or areas of concern.
• Plugin Configuration: Carefully review plugin configurations, particularly for custom plugins where subtle errors in code might occur.
• Network Connectivity: Verify network connectivity between Kong and upstream services or external authentication providers. Problems here are frequent root causes of gateway failure.

A systematic approach, starting with log analysis and Admin API checks, is often sufficient to resolve the majority of issues. Remember to use your monitoring dashboards to quickly spot any anomalies that could indicate a problem.

In Kong Gateway, routing and request transformation are core functionalities that allow you to control how requests reach your upstream services. Routing involves directing incoming requests to the appropriate backend service based on criteria like path, method, headers, or even the presence of specific query parameters. Request transformation, on the other hand, allows you to modify the request before it even reaches the upstream service. This might involve adding, removing, or modifying headers, rewriting paths, or even completely altering the request body.

Example: Imagine you have a legacy application accessible via /legacy/users. You might want to route requests to this legacy path to a new microservice at /api/v2/users while adding a header to identify the request origin. Kong’s routing capabilities allow you to define a route mapping /legacy/users to the new microservice’s location and use a plugin like the request-transformer to add the necessary headers. This cleanly handles the migration without disrupting the existing client applications.

Another Example: You could use routing to direct different traffic based on the client’s IP address – perhaps sending traffic from internal IPs directly to a database while routing traffic from external clients through an authentication service first.

Kong Gateway provides robust rate limiting and traffic shaping capabilities through its plugin architecture. Rate limiting prevents a single client or a group of clients from overwhelming your upstream services. Traffic shaping allows you to control the rate at which requests are processed, smoothing out traffic spikes and preventing overload. Both are essential for maintaining the stability and performance of your APIs.

Kong offers several plugins to achieve this: The most common is the rate-limiting plugin, which allows you to define rate limits based on various criteria like IP address, API key, or consumer. You can specify the number of requests allowed within a specific time window. If a client exceeds the limit, Kong can return an appropriate HTTP response, such as 429 Too Many Requests. The traffic-control plugin offers even finer-grained control, allowing for sophisticated traffic shaping techniques to ensure fair distribution of resources.

Example: Let’s say you want to limit users to 100 requests per minute. You’d configure the rate-limiting plugin on a specific service or route with a limit of 100 requests per minute. Any request exceeding this limit will be blocked. The plugin’s configuration provides details on this rate limiting.

Kong Gateway offers flexible deployment strategies catering to various infrastructure needs and scalability requirements. The most common approaches include:

• Standalone Deployment: A single Kong instance runs on a single server, suitable for small deployments or development environments. Simple to set up and manage but lacks inherent scalability and redundancy.
• Clustered Deployment: Multiple Kong instances form a cluster, providing high availability and scalability. The instances communicate and share configuration information, allowing for automatic failover and load balancing across the cluster. This is critical for production deployments.
• Kubernetes Deployment: Kong can run as a native Kubernetes service leveraging Kubernetes’s capabilities for automated scaling, deployment, and management. This integrates seamlessly with container orchestration and is ideal for cloud-native environments.
• Cloud Deployments: Cloud providers like AWS, Azure, and GCP offer managed Kong services that abstract away the infrastructure management, simplifying deployments and improving operational efficiency.

The choice of deployment strategy depends heavily on factors such as the scale of your API infrastructure, the level of redundancy required, and your familiarity with the underlying infrastructure.

Securing Kong Gateway itself is paramount. This involves multiple layers of security:

• Authentication and Authorization: Restrict access to the Kong Admin API using robust authentication mechanisms like HTTPS with mutual TLS and strong authentication protocols. Authorization controls define which users have permissions to manage specific aspects of the Kong configuration.
• Network Security: Employ firewalls to control access to the Kong instances. Limit access to the Admin API to authorized IP addresses or networks and isolate Kong from other sensitive parts of your infrastructure.
• Regular Updates and Patching: Maintain Kong and its plugins at their latest versions to benefit from security patches and bug fixes. This is crucial for mitigating known vulnerabilities.
• Regular Security Audits: Conduct periodic security assessments to identify potential weaknesses in your Kong configuration and deployment.
• HSM (Hardware Security Module) Integration: For sensitive use cases, using an HSM to securely manage cryptographic keys adds another layer of defense.

A layered approach, combining these methods, provides comprehensive protection for Kong itself.

Kong’s health checks and load balancing are essential for ensuring high availability and performance of your upstream services. Health checks verify the health status of the upstream services and automatically remove unhealthy services from the load balancing pool. This prevents requests from being routed to unresponsive backends. Kong uses active health checks, periodically sending requests to the upstream services to check their status.

Load balancing distributes traffic across multiple instances of an upstream service, maximizing resource utilization and ensuring resilience. Kong supports various load balancing algorithms, including round-robin, weighted round-robin, and least-connections. The choice of algorithm depends on the specific needs of your application. For instance, weighted round-robin might be preferred if you have different instances with varying capacity.

Example: If one of my backend services becomes unavailable, Kong’s health check detects this failure and automatically removes it from the load balancing pool. This prevents requests from being directed to an unavailable service, safeguarding user experience. My experience involves configuring these health checks and load balancing algorithms based on the specific needs of the deployed services.

Kong Gateway integrates seamlessly with numerous tools and services within a typical ecosystem. Integration often happens through plugins or custom scripts:

• Service Discovery: Kong integrates with service discovery tools like Consul, etcd, and ZooKeeper, allowing it to dynamically discover and manage upstream services without manual configuration changes.
• Authentication Providers: Kong supports integration with various authentication providers like OAuth 2.0, OpenID Connect, and LDAP, enhancing security and streamlining user authentication.
• Monitoring Tools: Kong can be integrated with monitoring tools such as Prometheus and Grafana for real-time visibility into its performance and health.
• Logging and Tracing: Integration with logging tools like Elasticsearch and tracing tools like Jaeger allows for comprehensive monitoring and debugging of API requests.
• CI/CD pipelines: Integrating Kong with CI/CD pipelines allows for automating the deployment and management of Kong configurations.

This interoperability significantly improves the operational efficiency and management of API gateways in complex microservice architectures.

Scaling and performance optimization in Kong Gateway are crucial for handling large volumes of traffic. Strategies include:

• Horizontal Scaling: Adding more Kong nodes to a cluster increases overall capacity. Kong’s clustering capabilities handle automatic load balancing and failover.
• Optimized Plugins: Using plugins judiciously and configuring them efficiently minimizes overhead. Avoid using resource-intensive plugins if they aren’t absolutely necessary.
• Caching: Using Kong’s caching capabilities can significantly reduce the load on upstream services. Kong’s built-in caching plugins such as the `response-caching` plugin can store responses and reduce the number of requests sent to upstream services.
• Asynchronous Operations: Using asynchronous plugins or processing can reduce the response time of your API requests.
• Proper Resource Allocation: Ensure that your Kong instances have sufficient CPU, memory, and network resources. Monitoring performance metrics (CPU usage, memory consumption, network traffic, latency) allows you to effectively identify bottlenecks and optimize performance.

Continuous monitoring and analysis of performance metrics allow for proactive identification and resolution of potential bottlenecks, ensuring optimal performance and scalability.

Kong Gateway offers flexibility in choosing its datastore, impacting scalability and persistence. I’ve worked extensively with both Cassandra and PostgreSQL, each with its own strengths and weaknesses. Cassandra, a distributed NoSQL database, excels in high availability and scalability, making it ideal for large-scale deployments with high write throughput. I’ve used it in projects requiring extremely high performance and fault tolerance, where the distributed nature ensured continued operation even with node failures. For instance, in a project with millions of API requests per day, Cassandra’s performance was crucial. In contrast, PostgreSQL, a relational database, offers ACID properties (Atomicity, Consistency, Isolation, Durability), guaranteeing data integrity. This makes it a strong choice when data consistency is paramount, such as in projects requiring precise auditing or complex transactional operations. In a previous project integrating with a legacy system demanding transactional consistency, PostgreSQL was the natural fit. The choice between Cassandra and PostgreSQL ultimately depends on the specific needs of the project, weighing the need for scalability and high availability against data integrity and transactional consistency.

Kong Gateway is a powerful API gateway that offers significant advantages in a microservices architecture. Think of it as a central traffic controller and security guard for your microservices. Its benefits include:

• Centralized API Management: Kong provides a single point of control for managing all your APIs, simplifying routing, authentication, and rate limiting. This eliminates the need for each microservice to handle these tasks individually, reducing complexity and improving maintainability.
• Enhanced Security: Kong offers a wide range of security plugins, such as authentication, authorization, and rate limiting, protecting your APIs from unauthorized access and abuse. This is crucial in a microservices environment where many services may be exposed to the public or internal networks.
• Improved Observability: Kong’s built-in logging and monitoring capabilities provide valuable insights into API traffic, performance, and errors. This information helps in troubleshooting, optimization, and capacity planning.
• Simplified Service Discovery: Kong can integrate with service discovery tools, allowing it to automatically discover and route traffic to available microservices. This simplifies deployments and ensures high availability.
• Flexibility and Extensibility: Kong’s plugin architecture allows you to extend its functionality with custom plugins, tailored to meet the unique needs of your microservices. This is key for adapting to evolving requirements.

For example, in one project, Kong helped us securely expose dozens of microservices to different clients, each with its own authentication and rate-limiting policies. Without Kong, managing this would have been a nightmare.

Kong provides robust logging and monitoring capabilities through its plugins and integrations. I’ve extensively utilized the built-in logging plugin to capture detailed information about API requests, including timestamps, request methods, response codes, and latency. This data is invaluable for debugging and performance analysis. I’ve also integrated Kong with various monitoring tools like Prometheus and Grafana, enabling real-time dashboards for visualizing key metrics like request rates, error rates, and latency. These dashboards provide critical insights into API health and performance. Furthermore, I leveraged Kong’s plugin system to integrate with centralized logging solutions like Elasticsearch and Kibana, creating a comprehensive logging pipeline for detailed analysis and auditing. This ensures we can readily track down issues and pinpoint bottlenecks.

Upgrading and migrating Kong Gateway requires a well-defined strategy to minimize downtime and ensure data integrity. I typically follow a phased approach:

• Planning and Testing: Before any upgrade, I thoroughly research the release notes and test the upgrade in a staging environment to identify potential issues. This involves replicating the production environment as closely as possible.
• Backup and Restore: A complete backup of the Kong database is crucial before any upgrade or migration. This ensures data recovery in case of unforeseen problems.
• Rolling Upgrades: For high-availability deployments, I employ rolling upgrades, updating one Kong instance at a time while maintaining the availability of the cluster. This minimizes downtime and allows for quick rollback if necessary.
• Blue/Green Deployments: In some cases, I use a blue/green deployment strategy, deploying the upgraded Kong to a new environment (green) before switching traffic from the old environment (blue). This ensures zero downtime during the upgrade.
• Post-Upgrade Verification: After the upgrade, I meticulously verify the functionality and performance of the new Kong version, checking for any regressions or unexpected behavior.

A recent migration to a newer Kong version involved a blue/green deployment strategy, ensuring seamless transition with no service disruption. Thorough testing and a well-defined rollback plan were essential components of this process.

Kong’s consumer management is a critical aspect of API security and access control. Consumers represent clients or users accessing your APIs. Kong allows you to define and manage consumers, assign them credentials, and apply specific policies. I’ve utilized Kong’s consumer management to:

• Create and Manage Consumer Credentials: I’ve assigned API keys, JWTs, and other credentials to different consumers based on their roles and access requirements.
• Implement Access Control Lists (ACLs): Using ACLs, I’ve restricted consumer access to specific APIs or routes, preventing unauthorized access.
• Apply Rate Limiting Policies: I’ve configured rate limiting policies for different consumers, ensuring fair usage and preventing abuse.
• Track Consumer Usage: Kong’s logging and monitoring features provide insights into consumer usage patterns, facilitating capacity planning and identifying potential issues.

For example, in a project with different tiers of users, Kong’s consumer management was essential for implementing granular access control, allowing each user tier to only access specific APIs, aligning with their subscription level.

Kong offers a wide variety of authentication plugins, allowing you to secure your APIs using various methods. I have extensive experience with JWT (JSON Web Tokens) and OAuth 2.0, two widely used authentication protocols. JWT provides a stateless mechanism for user authentication, where a token is generated and validated for each request. I’ve used JWT in many projects to secure APIs requiring authentication, allowing for simplified access control. The process involves generating a JWT with necessary claims and verifying it on the server-side. OAuth 2.0 is a delegated authorization framework, allowing third-party applications to access protected resources on behalf of a user. In a recent project, I integrated Kong with an OAuth 2.0 provider, enabling secure access to internal APIs. This involves setting up the OAuth 2.0 plugin in Kong, configuring it to communicate with the authorization server, and handling the access tokens within the API requests.

Kong Gateway facilitates A/B testing and canary deployments using its routing capabilities and plugins. A/B testing allows you to compare different versions of your API, while canary deployments gradually roll out a new version to a small subset of users. For A/B testing, I’d leverage Kong’s routing capabilities to split traffic between different API versions based on criteria such as headers, cookies, or weights. This enables comparing performance and user experience of the different versions. Data collected from logging and monitoring plugins are then used to analyze the results. For canary deployments, a small percentage of traffic is routed to the new version of the API, while the majority remains on the stable version. This allows for gradual rollout and early detection of potential issues, minimizing the impact of a faulty release. Kong’s health check plugins ensure that only healthy instances receive traffic, and its plugin architecture allows for custom logic to control traffic routing and monitoring.

Kong Gateway offers robust security features to protect your APIs. Think of it as a security guard for your application’s front door. It doesn’t just let anyone in; it carefully vets each request.

• Authentication: Kong integrates seamlessly with various authentication methods like OAuth 2.0, JWT (JSON Web Tokens), and API keys. You define authentication plugins, and Kong verifies the user’s identity before forwarding the request to the upstream service. For example, using the JWT plugin, you could require a valid JWT in the authorization header for every request.
• Authorization: Once authenticated, Kong can further restrict access using authorization plugins. These plugins check if the authenticated user has the necessary permissions to access the specific resource. For instance, the ACL (Access Control List) plugin allows you to define rules based on IP addresses, consumer groups, or custom criteria.
• Rate Limiting: Prevent abuse and denial-of-service attacks by limiting the number of requests from a single IP address or consumer within a specific timeframe. The rate-limiting plugin is crucial here.
• HTTPS: Ensure all communication is encrypted using HTTPS. Kong can terminate SSL/TLS at the gateway level, handling the encryption and decryption, and simplifying the security configuration for your upstream services.
• Request Transformation: Kong can modify incoming requests to enhance security. For instance, you can sanitize input data to prevent injection attacks.

In a real-world example, I secured a microservice architecture using Kong by implementing JWT authentication, rate limiting based on user roles, and HTTPS termination. This significantly reduced the security burden on the individual microservices.

API versioning is essential for maintaining backward compatibility and managing changes over time. Kong offers several ways to handle it.

• URI Versioning: The simplest approach. Include the version number in the API’s URI, for example: /v1/users and /v2/users. Kong routes requests based on the URI.
• Header Versioning: Use a custom header (e.g., X-API-Version) to specify the API version. A plugin would extract this header and route the request accordingly.
• Query Parameter Versioning: Similar to header versioning, but the version is specified as a query parameter (e.g., /users?version=2). A plugin needs to be configured to read and handle the parameter.
• Custom Plugin: For more complex scenarios, you might develop a custom plugin to handle versioning based on specific criteria or business logic.

For instance, in a project I worked on, we used URI versioning initially and transitioned to a header-based approach for better flexibility as we released new API versions. This allowed us to gracefully deprecate older versions without breaking existing clients.

Kong’s flexibility allows for diverse upstream service configurations. Think of the upstream as the actual service your API calls.

• Directly Specifying the URL: This is the most basic method. You provide the target service’s URL in the Kong configuration.
• Using a Load Balancer: Kong can act as a load balancer, distributing requests across multiple instances of your upstream service for high availability and scalability. You define the individual instances in the configuration, and Kong manages the distribution.
• Using DNS: Configure Kong to resolve the upstream service’s address using DNS. This allows for dynamic scaling and easier management of your service instances.
• Using a Service Discovery System: Integrate with service discovery solutions like Consul or etcd. Kong automatically detects and updates its configuration as instances of your service register and deregister themselves. This provides seamless dynamic updating.

In practice, I’ve used all of these methods depending on the project’s needs. For a simple service, direct URL configuration was sufficient. But for critical services, a load balancer behind a DNS entry or a service discovery system proved essential for resilience and scalability.

Kong’s request transformation plugins are powerful tools allowing you to modify requests before they reach your upstream service and responses before they are sent to the client. Think of them as translators ensuring proper communication.

• Modifying Headers: Add, remove, or modify HTTP headers. This is useful for things like adding security headers or forwarding authentication tokens.
• Modifying the Request Body: Transform the JSON or XML payload of the request, perhaps to sanitize input, reformat data, or remove unnecessary fields.
• Adding or Removing Query Parameters: Adjust the query parameters based on different rules. For example, adding a tracing ID to each request.

In a recent project, I used a custom request transformation plugin to normalize incoming requests from different clients. Each client sent data in slightly different formats, and the plugin ensured consistent data for the backend service. This standardized our API’s input, significantly reducing development time and maintenance overhead.

Creating a custom plugin for Kong involves writing code in Lua. Kong’s plugin architecture is well-documented, and you’ll leverage its API to interact with requests and responses. The process generally involves these steps:

• Define the Plugin’s Functionality: Clearly define what your plugin should do. What are its inputs and outputs? What problem will it solve?
• Write the Lua Code: Implement the plugin’s logic using Kong’s plugin API. This will involve handling events like request handling and response modification. You’ll register handlers for the relevant events.
• Package and Deploy the Plugin: Package your Lua code as a Kong plugin and deploy it. This may involve using Kong’s CLI or other deployment mechanisms.
• Test and Debug: Thoroughly test your plugin to ensure it functions correctly under various conditions.

For example, I once built a custom plugin to integrate with a specific logging system. The plugin intercepted all requests and responses and forwarded log entries to our centralized logging infrastructure. This improved the observability of our API traffic. The Lua code leveraged Kong’s logging capabilities and made API calls to our logging service.

Kong’s rate-limiting plugin makes it simple to limit requests based on IP addresses. It’s designed to prevent abuse and protect your backend services.

You configure the plugin to specify the limits: how many requests are allowed from a single IP address within a given timeframe (e.g., 100 requests per minute).

When a request exceeds the defined limits, Kong automatically returns a 429 (Too Many Requests) response. This protects your backend services from being overwhelmed.

Example Configuration (Simplified): You’d define a rate-limiting plugin with a policy specifying the IP address as the key and the limits. Kong would then automatically track the requests and apply the limits.

This is just a basic example. The actual configuration could be more complex, allowing for different limits based on various factors or even using Redis as a backend store for the limit counters.

Troubleshooting slow API responses with Kong involves a systematic approach. Imagine it like diagnosing a car problem; you need to check different parts.

• Check Kong’s Logs: Examine Kong’s logs for errors, warnings, or slowdowns. This often provides clues about the source of the issue.
• Monitor Kong’s Metrics: Kong provides metrics on request latency, error rates, and other vital statistics. Look for anomalies or spikes in these metrics. Tools like Prometheus and Grafana can be extremely helpful here.
• Analyze Upstream Service Performance: If the slowdown isn’t within Kong itself, the problem might lie in your upstream service. Check its logs, performance metrics, and resource utilization.
• Test with a Simple Request: Send a simple request directly to the upstream service to determine if the latency is from Kong or the service itself.
• Examine Plugins: Ensure your Kong plugins aren’t causing the slowdown. Temporarily disable plugins to isolate the problem if necessary.
• Check Network Connectivity: Make sure there are no network issues hindering communication between Kong and the upstream service.

In a recent incident, we discovered that a poorly configured plugin was causing significant latency. By isolating the plugin, we quickly resolved the problem. Using Kong’s logs and monitoring tools was key to identifying the root cause.