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Event-Driven Log Processing Architecture for an Enterprise E-Commerce Application

Description

The Event-Driven Log Processing Architecture is designed for an Enterprise E-Commerce Application to automatically collect, process, and analyze application logs. The system detects new logs generated by activities such as user login, product browsing, order placement, and payment processing. The logs are automatically processed to identify errors, warnings, failed transactions, and important application events. The solution uses a serverless and event-driven cloud architecture, reducing the need to manage continuously running servers.

Aim

The aim is to develop a serverless automated log-processing system that can process e-commerce application logs in an event-driven manner and help administrators identify application errors and important events quickly.

Objectives

01 Automatically collect and store e-commerce application logs.
02 Detect new log data and trigger processing automatically.
03 Validate and parse incoming log records.
04 Identify errors, warnings, and important events.
05 Store processed log information for analysis.
06 Provide monitoring of log-processing activities.
07 Build a scalable serverless architecture without managing application servers.

Application Workflow

01

Stage 1 – Customer Registration and Login

Process

Customer creates an account or logs into the e-commerce application.

Tools
Keycloak
Implementation

The customer provides login credentials. Keycloak authenticates the customer and provides an access token after successful authentication. The same authentication is used for accessing other application functions.

02

Stage 2 – Product Browsing

Process

Customer searches and browses available products.

Tools
Kong Gateway PostgreSQL
Implementation

The authenticated customer sends a product request through Kong Gateway. The application retrieves product information such as product name, price, category, and availability from PostgreSQL and displays it to the customer.

03

Stage 3 – Shopping Cart Management

Process

Customer adds products to the shopping cart and updates quantities.

Tools
Kong Gateway Redis
Implementation

The authenticated customer adds selected products to the cart. Redis stores the active cart information so that cart details can be retrieved and updated quickly.

04

Stage 4 – Order Placement

Process

Customer confirms the cart and places an order.

Tools
Kong Gateway PostgreSQL
Implementation

The application retrieves the cart information and creates an order containing customer ID, products, quantity, price, and order status. The order information is stored in PostgreSQL.

05

Stage 5 – Payment Processing

Process

Customer makes payment for the placed order.

Tools
Kong Gateway
Implementation

The authenticated customer submits the payment request. The application sends the request to the payment service and receives the payment result. The payment and order status are updated based on the result.

06

Stage 6 – Order Confirmation

Process

The application confirms the order after successful payment.

Tools
PostgreSQL
Implementation

The application updates the order status in PostgreSQL to indicate that the payment was successful and the order has been confirmed. The confirmation is then displayed to the customer.

07

Stage 7 – Order Tracking

Process

Customer checks the current status of the order.

Tools
Kong Gateway PostgreSQL
Implementation

The authenticated customer requests the order status. The application retrieves the latest order information from PostgreSQL and displays the current status.

Cloud Infrastructure and Tools

Cloud Data Storage Cloud S3

Stores the raw application logs generated by the Enterprise E-Commerce Application before and during the log-processing workflow.

Event Trigger Cloud EventBridge

Detects new log-related events and automatically starts the log-processing workflow.

Serverless Workflow Orchestration Cloud Step Functions

Controls the sequence of the log-processing workflow, including validation, parsing, filtering, and storage.

Serverless Compute Cloud Lambda

Executes the required log-processing functions, such as log validation, parsing, filtering, and error identification.

Log Search and Analysis Cloud OpenSearch Service

Stores and indexes processed logs so administrators can search and analyze application errors, warnings, and events.

Authentication Tool Keycloak

Provides authentication and access control for customers and administrators of the e-commerce application.

API Gateway Tool Kong Gateway

Handles application API requests and routes them to the appropriate e-commerce services.

Application Database PostgreSQL

Stores structured e-commerce information such as customer, product, cart, and order data.

Cache Tool Redis

Stores frequently accessed and temporary application information, particularly active shopping-cart data, for faster access.

Log Collection Tool Fluent Bit

Collects logs generated by the e-commerce application and forwards them to the log-processing pipeline.

Metrics Collection Tool Prometheus

Collects metrics from the application and log-processing workflow, such as log-processing status, error counts, and processing time.

Monitoring Dashboard Grafana

Displays Prometheus metrics through dashboards to monitor application health and log-processing performance.

Infrastructure as Code Tool OpenTofu

Creates and manages the required Cloud infrastructure and configuration using code.

Implementation Process

01
Step 1 – Analyze Application and Log Requirements
  • Identify the e-commerce application activities that need to be monitored.
  • Define important events such as login, product search, cart updates, orders, and payments.
  • Define the log information and errors that need to be detected.
  • Define the required monitoring metrics and dashboards.
02
Step 2 – Set Up the E-Commerce Application
  • Configure Keycloak for customer and administrator authentication.
  • Configure Kong Gateway to handle application API requests.
  • Configure PostgreSQL to store customer, product, cart, and order information.
  • Configure Redis for active shopping-cart and frequently accessed data.
  • Enable application logging for important customer and system activities.
03
Step 3 – Configure Log Collection and Storage
  • Install and configure Fluent Bit to collect application logs.
  • Configure Fluent Bit to forward the generated logs to Cloud S3.
  • Organize logs based on application service, date, and log type.
  • Verify that logs are being received and stored correctly.
04
Step 4 – Build the Serverless Log-Processing Workflow
  • Configure Cloud EventBridge to detect new log events.
  • Use Cloud Step Functions to define the processing sequence.
  • Use Cloud Lambda for log validation, parsing, filtering, and error identification.
  • Configure retry and error-handling mechanisms for failed processing stages.
05
Step 5 – Configure Log Analysis and Monitoring
  • Send processed logs to Cloud OpenSearch Service.
  • Create indexes for application errors, warnings, and important events.
  • Configure Prometheus to collect application and processing metrics.
  • Create Grafana dashboards to display errors, processing status, and performance.

Proposed Solution

The proposed solution uses an event-driven serverless architecture to process logs generated by an Enterprise E-Commerce Application. Fluent Bit collects application logs and stores them in Cloud S3. Cloud EventBridge detects new log events and starts Cloud Step Functions, which coordinates the processing through Cloud Lambda. The processed logs are stored and indexed in Cloud OpenSearch Service for searching and analysis. Prometheus collects application and processing metrics, while Grafana provides monitoring dashboards. Open-source tools such as Keycloak, Kong Gateway, PostgreSQL, and Redis support the e-commerce application.

Benefits

Automated Processing: Logs are processed automatically without manual intervention.
Scalability: Handles varying volumes of e-commerce application logs.
Real-Time Monitoring: Quickly identifies application errors and failures.
Centralized Analysis: Provides a single place to search and analyze logs.
Improved Troubleshooting: Helps administrators identify issues faster.
Cost Efficient: Serverless services run based on processing requirements.

Challenges

High Log Volume: Large applications can generate huge amounts of logs.
Data Quality: Logs may have inconsistent formats or missing information.
Processing Failures: Failures in one stage can affect subsequent processing.
Security: Logs may contain sensitive application information.
Serverless Limits: Lambda has execution and resource limitations.
Monitoring Complexity: Multiple services require proper monitoring and error handling.
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