1. System Architecture Design

a. Microservice Architecture

i. Advantages of Microservices:

• Independent Development and Deployment: Microservices can be developed and deployed separately, increasing team efficiency and allowing for isolated changes.

• Technology Flexibility: The most suitable technology can be chosen for each service, for example, based on performance or development speed requirements. (Flexible choice of programming language)

• Fault Tolerance: The failure of one microservice does not affect the entire system. This feature enhances overall system reliability.

ii. Use of Load Balancer:

• API Gateway: Provides security and routing as a central access point.

• Load Balancing and Caching: Distributes requests, stores frequently used data.

• Security and Monitoring: Provides features such as JWT authentication, rate limiting, and request tracking.

With microservice architecture, we can scale our services in a more controlled and flexible manner. This architecture provides greater flexibility to our system. This situation gives us advantages in traffic management, security, and many other areas.

As shown in the image, after an incoming request, we can route requests directly to the desired endpoints of our services through a load balancer (Load Balancer L4) and a Layer 7 component such as an API Gateway. This structure ensures security with JWT while allowing our service to continue operating unaffected in case any pod crashes. Thus, we can directly provide high availability.

b. Real-Time Updates

i. Event-Driven Architecture:

• Event Producers and Consumers: Services within the system produce events (for example, a new transaction order or price update) and other relevant services consume these events.

• Loose Coupling: Services are independent from each other and interact through events rather than direct communication. This increases the flexibility of the system.

ii. Messaging System Integration:

• Use of Apache Kafka:

  • High Efficiency and Low Latency: Kafka is optimized for processing high-volume data streams with minimal delay.

  • Persistent Log Structure: Messages are stored permanently on disk, minimizing data loss risk.

  • Partitioning: Data is segmented into partitions based on specific key values, enhancing scalability.

• Use of RabbitMQ:

  • Routing and Queuing: Offers sophisticated features for directing messages to various queues and processing them.

  • Acknowledge Mechanism: Ensures reliability by confirming successful message processing.

c. Transactional Integrity

i. ACID-Compliant Databases:

• ACID:

  • Atomicity: Transactions either complete entirely or not at all.

  • Consistency: The database remains in a consistent state before and after transactions.

  • Isolation: Concurrent transactions do not interfere with each other.

  • Durability: Once a transaction is completed, data is permanently stored.

• Database Selection:

  • PostgreSQL:

    • Robust ACID Properties: Ideal for complex transactions.

    • Extensive Plugin Support: Customizable for specific needs.

  • Oracle:

    • Enterprise-Level Features: Suitable for large-scale and critical applications.

    • Advanced Security and Management Tools: Offers sophisticated security and data management capabilities.

ii. Distributed Transactions and Saga Pattern:

• Saga Pattern:

  • Management of Distributed Transactions: Used to handle long-running transactions across microservices.

  • Transaction Steps and Compensation Mechanisms: Each step defines a transaction and, if necessary, a method to reverse (compensate) this transaction.

• Implementation Approaches:

  • Choreographed Sagas:

    • Event-Driven: Services progress transactions by reacting to events.

    • Loose Coupling: Operates without a central coordinator.

      

  • Orchestrated Sagas:

    • Central Coordinator: An orchestrator service manages the transaction flow.

    • Controlled Flow: The sequence and logic of transaction steps are centrally determined.

      

d. High Availability and Scalability

i. Use of Containerization and Kubernetes:

• Containerization with Docker, Containerd, and Podman:

  • Portability: Applications and their dependencies are packaged within containers, ensuring consistent operation across different environments.

  • Resource Isolation: Each container uses its own resources, enhancing security and performance.

• Orchestration with Kubernetes:

  • Automatic Scaling (Horizontal Pod Autoscaler): Dynamically adjusts the number of pods based on system load.

  • Self-Healing: Automatically restarts pods that have crashed or are unresponsive.

  • Load Balancing: Distributes incoming traffic evenly among working pods.

• Deployment Strategies:

  • Blue/Green Deployment: New and old versions run simultaneously; traffic is redirected to the new version before the old version is removed.

    

  • Canary Deployment: The new version is initially presented to a small group of users; if no issues arise, it is rolled out to all users.

    

ii. Load Balancing and Traffic Management:

• Use of NGINX or HAProxy:

  • Reverse Proxy and Load Balancer: Balances incoming requests across servers.

  • SSL/TLS Termination: Manages secure connections and performs decryption on the load balancer.

    

• Service Mesh (e.g., Istio):

  • Managing Microservice Communication: Routes and controls traffic between services.

  • Security and Policies: Provides communication policies, security features, and monitoring capabilities.

e. Cloud Infrastructure and Distribution

i. Multi-Region and Data Center Usage:

• Geographical Redundancy:

  • High Availability: Service interruptions are minimized by using data centers in different geographical regions.

  • Disaster Recovery: If one region fails, another continues to provide service.

• Data Replication:

  • Synchronous and Asynchronous Replication: Critical data is copied to databases in other regions either in real-time or at specific intervals.

ii. Infrastructure as Code (IaC):

• Using Terraform:

  • Defining Cloud Resources with Code: Resources such as servers, networks, and databases are defined and managed as code.

  • Version Control: Infrastructure code is stored in source control systems, enabling tracking and rollback of changes.

Summary

To ensure real-time updates, transactional integrity, and high availability, I would design the system architecture as a microservice architecture. Using Containerd for containerization and Kubernetes for orchestration, I would make the services independent and scalable. For real-time data flow, I would implement an Event-Driven Architecture with messaging systems like Apache Kafka or RabbitMQ, enabling me to process high-volume messages with low latency. To maintain transactional integrity, I would use ACID-compliant databases (such as PostgreSQL, Oracle) and implement patterns like the Saga Pattern to manage distributed transactions. For high availability, I would deploy across multiple regions on AWS, Azure, or Google Cloud Platform, manage traffic using load balancing with NGINX or HAProxy, and ensure uninterrupted service operation.

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2. Processing Sequential Updates and Preventing Data Loss/Delay

a. Sequential Data Processing

i. Apache Kafka's Sequential Processing Features:

• Partitioning and Ordered Logs:

  • Partition Key: Data is assigned to partitions using a specific key (e.g., user ID or transaction ID).

  • Sequence Guarantee: Messages within the same partition are consumed in the order they were produced.

• Consumer Groups:

  • Scalability: Multiple consumers share the load across partitions, enabling scalable consumption.

  • Offset Management: Consumers record the offset of the last message they've read, allowing them to resume from where they left off.

b. Preventing Data Loss

i. Data Replication:

• Replication Factor in Kafka:

  • High Availability: Each partition is replicated across different brokers with a specific replication factor.

  • Leader and Follower Brokers: For each partition, there is one leader and one or more followers. When the leader broker becomes unavailable, one of the followers takes over leadership.

ii. Acknowledgment (ACK) Mechanisms:

• On the Producer Side:

  • acks=all Setting: The producer waits for confirmation that the message has been written to all replicas.

  • Retry Mechanism: If message sending fails, the producer automatically retries.

• On the Consumer Side:

  • Manual Acknowledgment: The consumer manually updates the offset after successfully processing the message.

  • At-Least-Once Processing Semantics: Messages are guaranteed to be processed at least once, but there is a possibility of the same message being processed more than once.

c. Minimizing Delay

i. Servers Close to Users (Edge Computing):

• Use of CDN:

  • Distribution of Static Content: Latency is reduced by serving content from the nearest point to users.

• Edge Servers:

  • Moving Processing Power to the Edge: Some processing loads are performed on servers close to users.

d. Idempotent Operations

i. Idempotent Design Principles:

• Management of Repeated Requests:

  • Unique Request Identifier: Each request is assigned a unique identifier to detect duplicates.

  • State Check: The system checks whether the operation has been performed before, and skips it if necessary.

• Consistency of Operation Results:

  • Stateless Services: Services not storing state information ensure operations are independent of each other.

  • Cases Where State Storage is Necessary: When state information is required, it is stored in a database or distributed cache.

Summary

To process sequential updates and prevent delay or data loss, I would prefer Apache Kafka. Kafka's sequential and persistent log structure ensures messages are processed in the correct order through partitions and offsets. To prevent data loss, I would increase the replication factor in Kafka, ensuring messages are stored on multiple brokers. By designing idempotent producers and consumers, I prevent repeated messages from affecting the system. To minimize delays, I would use low-latency network infrastructure, position servers close to users, or implement edge computing solutions. I would guarantee reliable message processing through acknowledge mechanisms and error management strategies.

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3. Monitoring and Real-Time Intervention System

a. Monitoring and Visualization

i. Prometheus (Layers 1-6)

• Metric Collection:

  • Pull Model: Prometheus fetches metrics from target services at regular intervals.

  • Exporters: Specialized metric collectors are used for applications and systems (e.g., Node Exporter, JMX Exporter).

• Time Series Data Storage:

  • Labeling: Metrics are enriched with labels, enabling detailed querying capabilities.

ii. APM Tools (Layer 7)

• OpenTelemetry or paid APM tools can be used.

  • Solutions such as Jaeger, Signoz (CE), Datadog, Elastic APM, and Zipkin are available.

• With APM, we can perform detailed monitoring on applications and detect application-related errors faster and more easily. Additionally, we can set up effective notification systems using tools like Alert Manager.

iii. Visualization with Grafana:

• Dashboard Creation:

  • Pre-built Panels: Pre-designed panels are available for basic metrics such as CPU usage, memory consumption, and network traffic.

  • Custom Panels: Custom panels and graphs can be designed to meet business needs.

• Alert System Integration:

  • Alert Rules: Automatic alerts are triggered when specified metric thresholds are exceeded.

b. Centralized Log Management

i. ELK Centralized Logging:

• Elasticsearch:

  • Centralized Log Storage: Log data is stored for full-text search and analysis.

  • High Scalability: Designed to process large amounts of data.

  • Architectural Design: Master node, data node, and coordinator node architecture must be carefully planned.

    

• Logstash:

  • Data Processing and Transformation: Log data is collected from various sources, processed, and forwarded to Elasticsearch.

• Kibana:

  • Visualization and Analysis: Presents log data in graphs and tables.

• Log Standards and Formats:

  • JSON Format: Structured log data is easier to process and analyze.

  • Log Levels: Priorities of logs are determined using levels such as ERROR, WARN, INFO, DEBUG.

• Log Rotation and Retention Policies:

  • Disk Space Management: Old logs are deleted or archived after a certain period.

c. Alert and Alarm Mechanisms

i. Alertmanager:

• Alert Rule Management:

  • Silencing: Temporarily silencing specific alerts.

  • Grouping: Combining similar alerts into a single notification.

• Integration:

  • Slack, Teams, Discord, Webhook etc.

  • SMS, Call (OpsGenie, Zenduty etc.)

d. Anomaly Detection and Automated Response

i. Anomaly Detection with Machine Learning:

• Anomaly Algorithms:

  • Time Series Analysis: Modeling normal behavior in metrics and detecting deviations.

  • Unsupervised Learning: Used for detecting previously undefined anomalies.

• Tools and Platforms:

  • ElastAlert: Creates alerts based on Elasticsearch data.

  • Azure Monitor or AWS CloudWatch Anomaly Detection: Anomaly detection services offered by cloud providers.

  • Custom application can be created based on your specific traffic.

ii. Automatic Scaling and Restart:

• Automatic Intervention Mechanisms:

  • Kubernetes HPA (Horizontal Pod Autoscaler), KEDA, Karpenter

  • Self-Healing: Kubernetes automatically restarts or reschedules problematic containers.

• Runbook Automation:

  • Automatic Workflows: Specific actions are automatically performed when certain events are triggered.

e. Event Management and Improvement

i. Incident Response Plans:

• Incident Identification and Prioritization:

  • Severity Levels: Classification of incidents based on their severity (e.g., Level1, Level2, Level3).

  • Action Plans: Defined response steps for each level.

• Team Responsibilities:

  • Task Distribution: Determining who will respond to which types of incidents.

  • Communication Protocols: Defining how communication will be managed during and after an incident.

ii. Post-Mortem Analyses:

• Root Cause Analysis:

  • Causes of the Incident: Detailed analysis of technical and operational causes.

  • Preventive Measures: Actions to be taken to prevent similar issues in the future.

• Documentation and Sharing:

  • Report Preparation: A report is created detailing the incident, its impacts, and solutions.

  • Information Sharing: Shared within the team and, if necessary, across the organization.

    

Summary

To detect and respond to delays, failures, or anomalies in real-time, I would implement a comprehensive monitoring system. I'd use Prometheus to collect system metrics and Grafana for visualization, while employing Jaeger or Zipkin to monitor inter-microservice calls. For centralized log collection and analysis, I'd utilize the ELK Stack (Elasticsearch, Logstash, Kibana). Automatic alerts based on predefined metric thresholds would be set up using Alertmanager or PagerDuty, with notifications sent to teams via Slack, email, or SMS. To identify unusual situations, I'd apply machine learning algorithms for anomaly detection. Quick responses to issues would be facilitated by Kubernetes' automatic scaling and self-healing capabilities.