title: “Design Document for Project 3: Multithreaded Log Analyzer” layout: page hide: true —-

  1. Title and Overview

Project Name: Multithreaded Log Analyzer Objective: Develop a C++ application to parse and analyze large macOS system logs using multithreading to achieve optimal performance. The application will provide insights into warnings, errors, and performance bottlenecks found in system logs.

  1. Goals and Non-Goals

Goals: • Parse large macOS system logs efficiently. • Process log chunks concurrently using multithreading. • Provide actionable insights, including most frequent warnings/errors. • Implement a command-line interface for user interaction.

Non-Goals: • Development of a GUI for visual representation of results. • Compatibility with non-macOS systems (focus is macOS-specific logs).

  1. Design Overview • Input: Logs fetched using macOS log show command or a pre-existing .log file. • Output: Summary of insights (e.g., frequency of warnings/errors, timestamp-based analysis). • Architecture: • Log Reader: Parses raw logs into structured data. • Log Processor: Splits logs into chunks for parallel processing. • Aggregator: Collects and summarizes results from all threads. • CLI: Provides an interface for specifying log source and analysis criteria.

  2. System Design

4.1 Components • Log Reader: • Reads logs from a file or executes the log show command. • Normalizes logs into a structured format (e.g., JSON or key-value pairs). • Log Processor: • Divides logs into chunks for parallel processing. • Processes each chunk to extract relevant data like error types and timestamps. • Thread Pool: • Manages threads to avoid spawning excessive threads and ensures resource efficiency. • Aggregator: • Consolidates results from each thread. • Outputs insights such as frequency distributions and anomaly detection. • CLI Interface: • Allows users to specify log files, date ranges, and keywords for analysis.

4.2 Workflow 1. Input Handling: • User specifies a log file or directs the tool to run log show. 2. Preprocessing: • Log Reader normalizes logs. • Log Processor splits logs into chunks for analysis. 3. Processing: • Threads process chunks concurrently. • Intermediate results are pushed to a shared result store. 4. Aggregation: • The Aggregator compiles results into the final summary. 5. Output: • Insights are displayed in the CLI.

  1. API and Data Structures

5.1 Core Data Structures • LogEntry:

struct LogEntry { std::string timestamp; std::string level; // INFO, WARN, ERROR std::string message; };

•	ResultSummary:

struct ResultSummary { std::unordered_map<std::string, int> frequencyByLevel; std::vector<std::string> frequentErrors; std::vector<std::string> anomalies; };

5.2 Interfaces • LogReader:

std::vector parseLogs(const std::string& logSource);

•	LogProcessor:

ResultSummary processLogs(const std::vector& logs);

•	Aggregator:

ResultSummary aggregateResults(const std::vector& partialResults);

  1. Multithreading Strategy • Thread Pool: Use a thread pool to manage worker threads and avoid overloading the system. • Mutex for Aggregation: Use std::mutex to protect shared data structures during result aggregation. • Chunk Distribution: Logs are divided into equal-sized chunks for efficient load balancing.

  2. Testing • Unit Testing: Test each component individually (LogReader, LogProcessor). • Performance Testing: Benchmark single-threaded vs. multithreaded processing on large logs. • Edge Cases: • Empty logs. • Logs with unusual characters or formatting. • Extremely large logs.