Our infrastructure is built upon the foundation of Kubernetes running on Amazon Elastic Kubernetes Service (EKS). Kubernetes serves as the orchestrator for our containerized applications, providing a robust and scalable platform for deployment, management, and scaling of our microservices architecture.

Kubernetes on EKS:

Amazon EKS simplifies the process of deploying, managing, and scaling Kubernetes clusters in the AWS cloud. Leveraging EKS, we benefit from Amazon's managed Kubernetes service, which handles the underlying infrastructure management tasks, such as server provisioning, patching, and maintenance, allowing us to focus on deploying and managing our applications.

Key Features and Benefits:

In our architecture, communication between services is crucial for enabling seamless interaction and data exchange. We utilize various communication mechanisms to facilitate communication and collaboration between different components of our system.



Scalability is a fundamental aspect of our infrastructure design, ensuring that our system can efficiently handle varying workload demands and accommodate growth without compromising performance or reliability. We employ several key strategies to achieve scalability within our Kubernetes environment:

Horizontal Pod Autoscaler (HPA) Manifest Snippet:

Our architecture is meticulously crafted to optimize scalability, resilience, automation, and ease of management, leveraging a blend of cutting-edge technologies and best practices. Here's a succinct explanation of why we chose this architecture:

Risk Assessment and Business Impact Analysis:

This section focuses on conducting a comprehensive risk assessment and business impact analysis, identifying potential threats and their potential impact on business operations. It evaluates the likelihood and severity of various disaster scenarios, such as natural disasters, cyber-attacks, or system failures, and assesses their potential consequences on critical business functions and services. By understanding the risks and their potential impact, organizations can prioritize resources and develop effective disaster recovery strategies to mitigate these risks and ensure business continuity.

Backup and Data Protection:

In this section, the focus is on implementing backup and data protection measures to safeguard critical data and ensure its availability in the event of a disaster. It includes strategies for regular data backups, off-site storage, and encryption to protect data integrity and confidentiality. By establishing robust backup and data protection mechanisms, organizations can minimize the risk of data loss and expedite recovery efforts in the event of a disaster.

High Availability Architecture:

This section elaborates on the implementation of high availability architecture to ensure continuous operation and resilience in the face of disruptions. It includes deploying services as replica sets with redundant components, such as load balancers and failover mechanisms, to maintain service availability and data durability. By leveraging high availability architecture, organizations can minimize downtime and ensure uninterrupted access to critical services during disasters.

Disaster Recovery Planning and Testing:

Here, the focus is on developing comprehensive disaster recovery plans and conducting regular testing exercises to validate their effectiveness. It involves defining roles and responsibilities, establishing communication protocols, and documenting step-by-step procedures for disaster response and recovery. Regular testing ensures that recovery procedures are up-to-date and that personnel are prepared to execute them effectively during emergencies.

Failover and Redundancy:

This section emphasizes the importance of failover and redundancy mechanisms in ensuring continuous operation and minimizing service disruptions. It includes configuring redundant components, such as servers, networks, and data centers, to automatically take over operations in the event of a failure. By implementing failover and redundancy strategies, organizations can mitigate the impact of hardware or software failures and maintain service availability during disasters.

Cloud-Based Disaster Recovery:

Here, the focus is on leveraging cloud-based disaster recovery solutions to enhance resilience and flexibility. It includes utilizing cloud infrastructure and services for data backup, replication, and recovery, as well as implementing disaster recovery as a service (DRaaS) solutions for rapid recovery and scalability. By embracing cloud-based disaster recovery, organizations can achieve cost-effective and scalable disaster recovery capabilities while minimizing infrastructure complexity.

Incident Response and Communication:

This section highlights the importance of establishing effective incident response procedures and communication channels to coordinate response efforts and ensure timely notifications during disasters. It includes defining incident response teams, establishing escalation paths, and implementing communication tools and protocols for notifying stakeholders and coordinating response activities. By fostering a proactive incident response culture and maintaining open communication channels, organizations can minimize the impact of disasters and expedite recovery efforts.

While our platform is designed with resilience and redundancy in mind, it's crucial to anticipate and prepare for potential disaster scenarios that could impact the availability, integrity, and security of our services. Below, we outline specific disaster scenarios and their potential impact on our platform:

Mitigation and Recovery Strategies:

To mitigate the impact of these disaster scenarios and ensure the resilience of our platform, we implement a comprehensive set of mitigation and recovery strategies, including:

By proactively identifying potential disaster scenarios and implementing appropriate mitigation and recovery strategies, we aim to minimize the impact on our platform and ensure the continued delivery of reliable and secure services to our users.

In our CI (Continuous Integration) workflow, we leverage GitHub Actions to automate the deployment of Docker images to AWS ECR (Elastic Container Registry) and manage the configuration of Kubernetes resources using Helm charts. This streamlined process ensures the efficient and reliable delivery of our applications while maintaining consistency across different environments.

Deployment Workflow Overview:

Below is an example workflow illustrating the process for the development environment:



This visual representation outlines the automated steps involved in deploying application updates and configurations within the development (Dev) environment.

In our infrastructure, we utilize the concept of replica sets to ensure high availability and fault tolerance for critical components such as RabbitMQ and MongoDB. By deploying these services as replica sets with three replicas each, we enhance resilience and mitigate the risk of service disruptions or data loss.

RabbitMQ, as a message queue system central to our architecture, is deployed as a replica set with three replicas. This configuration ensures that even if one replica becomes unavailable due to hardware failure or maintenance, the remaining replicas can continue to process messages seamlessly, thereby maintaining uninterrupted communication between microservices.

Additionally, RabbitMQ is configured with a high availability policy, ensuring that messages are replicated across all replicas in real-time. This policy guarantees that even in the event of a node failure, messages remain accessible and processing can continue without interruption.



MongoDB, serving as the primary data store for our applications, is deployed as a replica set with three replicas. This architecture provides data redundancy and automatic failover capabilities, ensuring data integrity and high availability even in the event of node failures or network partitions.

In our MongoDB replica set configuration, three replicas are deployed: one primary replica and two secondary replicas. The primary replica serves as the primary node for read and write operations, while the secondary replicas act as backups, keeping synchronized copies of the data.

MongoDB replica sets are equipped with automatic failover mechanisms. In the event of a primary replica failure or unresponsiveness, MongoDB automatically initiates a failover process. During failover, one of the secondary replicas is elected as the new primary replica, ensuring seamless continuity of operations without manual intervention.

Microservices within our architecture connect to the MongoDB replica set using a connection string that specifies all members of the replica set. This connection string typically includes the hostnames or IP addresses of all replicas along with the replica set name. Microservices use this connection string to establish connections to the replica set, allowing them to perform read and write operations against the primary replica and read operations from secondary replicas.

MongoDB replica sets ensure data consistency across replicas through replication. Write operations performed on the primary replica are replicated to secondary replicas in real-time, ensuring that all replicas maintain synchronized copies of the data. This replication process employs the oplog (operation log) to capture and replicate write operations, maintaining consistency across the entire replica set.

By deploying MongoDB as a replica set with three replicas, we achieve several benefits:

High Availability: The presence of multiple replicas ensures that even if one or more replicas become unavailable, the remaining replicas can continue serving read and write requests, minimizing downtime and ensuring high availability of data.

Automatic Failover: The automatic failover mechanism ensures continuous availability of the database in the event of primary replica failure, reducing the need for manual intervention and minimizing service disruptions.

Data Redundancy: Data redundancy provided by replica sets ensures that multiple copies of data are available across replicas, reducing the risk of data loss and enhancing data resilience in the face of failures.



By implementing RabbitMQ and MongoDB as replica sets with three replicas each, we establish a resilient foundation for our infrastructure, capable of withstanding failures and maintaining consistent service availability. These replica sets play a critical role in ensuring the reliability and scalability of our applications, enabling seamless operation and data integrity in the face of various challenges.

In our infrastructure, we have implemented a comprehensive backup solution that ensures the integrity and availability of our data. This solution consists of two primary components: backup to Kubernetes Persistent Volume Claim (PVC) for real-time data protection and historical backup to Amazon S3 for long-term retention and archival purposes.

For real-time data protection and recovery within our Kubernetes environment, we leverage a backup mechanism that utilizes Kubernetes Persistent Volume Claims (PVCs). This approach involves taking periodic snapshots of data stored within PVCs associated with MongoDB. These snapshots are then stored locally within the Kubernetes cluster, providing an efficient and scalable backup solution that ensures minimal data loss and rapid recovery in the event of failures or data corruption.

In addition to real-time backups to PVCs, we implement historical backup to Amazon S3 for long-term data retention and archival purposes. This involves regularly transferring backup data from Kubernetes PVC snapshots to Amazon S3 buckets, where it is stored securely and durably. By leveraging the scalability and cost-effectiveness of Amazon S3, we ensure that historical backup data is preserved and accessible for extended periods, meeting compliance requirements and enabling disaster recovery scenarios.

To ensure continuous monitoring of our infrastructure and timely response to potential issues, we utilize Uptimekuma in conjunction with Telegram alerts.

We have configured Uptimekuma to monitor the availability and performance of our critical services and applications. Uptimekuma periodically checks the status of these services and reports any instances of downtime or errors. Additionally, we have set up a dedicated status site (status.palmdao.app) that provides real-time updates on the status of our principal services within the cluster. This status site serves as a centralized dashboard for monitoring service availability and performance metrics.

In the event of any anomalies or errors detected by Uptimekuma, automated alerts are triggered and sent to our notifications channel on Telegram. These alerts provide instant notifications to our operations team, enabling them to promptly investigate and address any issues that may arise. By leveraging Telegram alerts, we ensure that potential disruptions are swiftly identified and resolved, minimizing the impact on our users and maintaining the reliability of our services.

In our architecture, the management of sensitive environment variables within Kubernetes microservices is a critical aspect of ensuring the security and integrity of our applications. We employ External Secrets, coupled with AWS Secrets Manager, to securely store and retrieve sensitive information such as credentials, API keys, and other configuration parameters.

Below is a simplified graphical representation illustrating the workflow of External Secrets with AWS Secrets Manager:

In this code snippet, we define a Kubernetes deployment configuration for secret retrieval. The "palm" secret, referenced within the deployment, enables the seamless integration of External Secrets, retrieving key-value pairs for each environment variable securely stored within AWS Secrets Manager. This ensures that sensitive data, such as credentials and configuration parameters, remains protected throughout the deployment process.

Additionally, the deployment configuration ensures that each environment variable retrieved from the "palm" secret is securely injected as an environment variable within the respective microservice's container. This approach shields sensitive information from direct exposure in Kubernetes manifests or configuration files, thereby enhancing the security posture of our application.

By leveraging External Secrets in conjunction with Kubernetes deployments, we streamline the management of sensitive data, centralize access control, and fortify the security of our microservices architecture.

Utilization of Grafana, Loki, and Prometheus for Metric Visualization and Log Analysis:

In our infrastructure, we rely on Grafana, Loki, and Prometheus as integral components for metric visualization and log analysis. This powerful combination empowers us to gain valuable insights into the performance, health, and behavior of our applications and infrastructure components.

Grafana:

Grafana stands as our primary dashboarding and visualization platform. It offers a user-friendly interface for monitoring and analyzing metrics, allowing us to create customizable dashboards that aggregate data from various sources. With Grafana's extensive library of plugins and integrations, we visualize key performance indicators (KPIs), system metrics, and application-specific metrics in diverse formats, ranging from simple line charts to complex heatmaps and histograms. Additionally, we showcase Palm services logs on Grafana dashboards, alongside metrics such as memory usage and pod metrics, providing a comprehensive view of our application's health and performance.

Loki:

Loki serves as our preferred log aggregation and querying system, specifically designed for cloud-native environments. By ingesting logs from microservices, containers, and

infrastructure components, Loki provides a centralized repository for log data storage and analysis. Its efficient indexing mechanism and Prometheus-inspired query language enable us to quickly search, filter, and analyze logs, facilitating troubleshooting, anomaly investigation, and system behavior insights.

Prometheus:

Prometheus acts as our monitoring and alerting toolkit, collecting time-series data from various sources, including application metrics, system metrics, and custom exporters. Its powerful querying language, PromQL, enables ad-hoc analysis and the creation of alerting rules based on predefined thresholds or complex conditions. Integrating Prometheus with Grafana allows us to build dynamic dashboards that combine metrics from Prometheus with other data sources, facilitating comprehensive monitoring and analysis.

Integration of Prometheus with ArgoCD To incorporate Prometheus into our infrastructure, we import the Prometheus Helm chart into ArgoCD. Below is a snippet showcasing how we configure Prometheus for alerting:

Integration of Promtail and Loki for Log Shipping Promtail, along with Loki, is integrated into our logging pipeline to efficiently collect and ship logs from various sources to Loki for centralized storage and analysis. Below is a snippet demonstrating how we install and configure Promtail and Loki using ArgoCD:

Kube-state Metrics:

We utilize kube-state metrics for Kubernetes metric collection, providing insights into the state and health of our Kubernetes clusters. Below is a snippet demonstrating how we integrate kube-state metrics into ArgoCD for streamlined monitoring and management of Kubernetes resources:

This snippet illustrates how we define an ArgoCD application resource to deploy and manage kube-state-metrics within our Kubernetes clusters, enabling seamless integration of Kubernetes metric collection into our monitoring stack.