README.md

Log ingestion & analysis

Efficiently manage and query vast log data volumes with a scalable Log Ingestor and Query Interface, featuring real-time ingestion, advanced filtering, and a user-friendly interface.

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Table of Contents

1. About The Project
   • Built With
2. Getting Started
   • Prerequisites
   • Installation & usage
3. API Documentation
   • Ingestion Routes
   • Query Routes
4. Additional Information
5. Contributing
6. License
7. Contact

About The Project

This is an efficient implementation of a log ingestion & analysis solution. It utilises Golang & Elastic DB to efficiently ingest, store & analyse logs. It can accept logs from two sources, Kafka queue or direct HTTP API requests. It also provides a web UI for analysis of logs using different filters & search queries.

Features

• Text-search across all fields
• Regular expression search on all fields
• Filters on specific fields
• Search in given time range
• Combination of all filters & search
• HTTP endpoint for posting logs
• Kafka queue for streamlined Log processing
• Ingestion Buffer & Batch processing
• Efficient search queries leveraging Elastic DB
• Scalable & Efficient processing using sharding provided by Elastic DB

Built With

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Getting Started

Prerequisites

• Python
• Node JS & NPM
• Golang
• Kafka
• Elastic Search

Installation & usage

• Clone the repo

  git clone https://github.com/dyte-submissions/november-2023-hiring-OmkarPh
  cd november-2023-hiring-OmkarPh/

• Setup Log ingestion server

  1. Go to log-server directory

       cd log-server/

  2. Install golang dependencies

     go mod download

  3. Install Python dependencies

     pip install -r requirements.txt

  4. Start the ingestion server

     cd cmd
     GIN_MODE=release go run .

     The server should now be running on http://localhost:3000.

  5. Simulate huge amount of sample logs simultaneously Configure LOGS_LENGTH in log-server/tests/performance_test.py. Default value: 3000

     python tests/performance_test.py

• Setup Web UI

  1. Go to frontend directory

       cd frontend/

  2. Install NPM dependencies

     npm install

  3. Start the React app

     npm start

  4. View the app here - http://localhost:3006

• Simulate Log Publishers (Optional)

  1. Start zookeepr

     zookeeper-server-start //path-to-kafka-config/zoo.cfg

  2. Start Kafka

     kafka-server-start /path-to-kafka-config/server.properties

  3. Start publisher script Go to log-producers directory

     cd log-server/log-producers

     Start a producer to simulate service using --topic option in different shells. Configured topics: auth,database,email,payment,server,services,

     # Example:
     python producer.py --topic payment
     python producer.py --topic auth

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API Documentation

Ingestion Routes

1. New Log Ingestion

• Endpoint: POST /

• Description: Ingests a new log entry into the system.

  Request Example:

  {
    "level": "error",
    "message": "Failed to connect to DB",
    "resourceId": "server-1234",
    "timestamp": "2023-09-15T08:00:00Z",
    "traceId": "abc-xyz-123",
    "spanId": "span-456",
    "commit": "5e5342f",
    "metadata": {
      "parentResourceId": "server-0987"
    }
  }

  Response Example:

  {
    "status": "success"
  }

2. Count Logs

• Endpoint: GET /logs-count

• Description: Retrieves the count of logs stored in Elasticsearch.

  Response Example:

  {
    "count": 5286
  }

Query Routes

1. Search Logs

• Endpoint: POST /search-logs

• Description: Searches for logs based on specified parameters. All the filter params, search text & time range are optional.

  Request Example:

  {
    "text": "email",
    "regexText": "jkl-*",
    "filters": [
      {
        "columnName": "level",
        "filterValues": ["error", "warning"]
      },
      {
        "columnName": "resourceId",
        "filterValues": ["user-123"]
      },
      {
        "columnName": "metadata.parentResourceId",
        "filterValues": ["9876", "1234"]
      },
      ... Other columns
    ],
    "timeRange": {
      "startTime": "2023-11-19T00:00:00Z",
      "endTime": "2023-11-19T23:59:59Z"
    }
  }

  Response Example:

  {
    "hits": {
      "total": 5,
      "hits": [
        {
          "_id": "1",
          "_source": {
            "level": "error",
            "message": "Database connection error",
            "resourceId": "user-123"
            // ... (other log fields)
          }
        }
        // Additional log entries
      ]
    }
  }

Additional Information

• Elasticsearch Index Name: log-ingestor
• Server Port: :3000
• CORS Configuration: Allows all origins (*) and supports HTTP methods: GET, POST, PUT, DELETE.
• Concurrency Configuration:
  • Buffered log channel with a default capacity of 5000 logs. (Can be changed via logsBufferSize)
  • Maximum concurrent log processing workers: 20.

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Future improvements / Flaws

• Kibana integration for better visual analysis
• Persistent TCP or Web socket connection between servers (log producer) and log ingestor for lower latency
• Redundant disk buffer for reliability
• Alert system to notify administrators or users about critical log events in real-time.

Contributing

Contributions are what make the open source community such an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated.

If you have a suggestion that would make this better, please fork the repo and create a pull request. You can also simply open an issue with the tag "enhancement". Don't forget to give the project a star! Thanks again!

1. Fork the Project
2. Create your Feature Branch (git checkout -b feature/AmazingFeature)
3. Commit your Changes (git commit -m 'Add some AmazingFeature')
4. Push to the Branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

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License

Distributed under the MIT License. See LICENSE.txt for more information.

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Contact

Omkar Phansopkar - @omkarphansopkar - [email protected]

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