LogSphere

LogSphere is a scalable, distributed log collection and analysis system designed to support large-scale web services by centralizing log data from multiple servers worldwide. This repository provides a robust solution for real-time monitoring, error detection, and alerting to ensure high service reliability and efficient operations.

Project Overview

As web services scale, the volume of log data grows exponentially. LogSphere enables centralized log aggregation, storage, and real-time analysis, allowing operations teams to quickly detect errors and monitor system performance.

Key features include:

• Log Collection: Centralized log collection from distributed servers
• Central Storage: Partitioned and replicated storage for data reliability
• Real-Time Data Analysis and Alerts: Automated error detection and alerts
• Fault Recovery: Safe recovery and minimal data loss in case of failure

System Architecture

The architecture comprises the following main components:

1. Log Collection

   • Each server streams log data to Apache Kafka.
   • Logs are organized by topic, e.g., "error logs" and "user activity logs."

2. Central Data Storage

   • Cassandra or MongoDB stores log data in a distributed manner.
   • Each log entry includes fields like timestamp, log_level, message, and source.
   • Data replication ensures reliability.

3. Real-Time Data Analysis and Alerts

   • Apache Flink processes log data in real time.
   • Alerts are triggered on critical log levels, and warning alerts are sent for threshold breaches (e.g., more than 100 error logs within a minute).

4. Data Integrity and Transaction Management

   • Transactions are used for critical data to maintain integrity.
   • ACID transactions ensure that modifications are consistent.

5. Fault Recovery and Data Reliability

   • Write-Ahead Logging protects against data loss during failures.
   • Logs are backed up in HDFS for long-term storage and analysis.

Getting Started

Prerequisites

• Docker
• Kafka
• Cassandra or MongoDB
• Flink
• HDFS (optional for backup)

Installation

Usage

Log Streaming: Use the Kafka producer to send logs from each server to the Kafka cluster.

Example Log Structure

Logs are structured with essential fields for analysis:

• timestamp: When the log entry was generated
• log_level: Severity level (e.g., INFO, WARN, ERROR)
• message: Log message details
• source: Origin of the log (e.g., server name)

Expected Outcomes

• Real-Time Monitoring: Quickly detect errors and respond in real time.
• Data Reliability: Minimize log data loss during failures.
• Scalability: Flexible system scaling as log data grows.
• Operational Efficiency: Insight into system performance and user activity.

License

This project is licensed under the MIT License. See the LICENSE file for details.