Client Background

Client: A leading IT Tech firm in the USA

Industry Type: IT

Products & Services: IT Consulting, Support, IT Development

Organization Size: 300+

The Problem

To create a dashboard that visualizes log files in Kibanna

Organizations often generate massive volumes of log files from various systems and applications, which contain crucial information about system performance, errors, security events, and user activities. However, manually analyzing these log files can be time-consuming and inefficient, especially when attempting to identify patterns, anomalies, or potential issues in real time.

The challenge is to create a centralized dashboard in Kibana that can efficiently visualize log files, enabling users to monitor system health, detect anomalies, and analyze logs quickly. This solution must support real-time data updates, offer customizable visualizations, and provide users with the ability to filter and drill down into specific log events to enhance operational visibility and decision-making.

Our Solution

1. Export Log Data:

   – Export the log data from Kibana or your logging system into a file format that Python can read. Common formats include CSV, JSON, or plain text.

2. Load Log File in Python Script:

   – Use Python’s file handling capabilities to read the log file into your script. 

3. Extract Error Codes Using Regular Expressions:

   – Use regular expressions to extract error codes from each log entry. Define a pattern that matches the format of your error codes. For example.

4. Count Log Codes:

   – Count the occurrences of each error code using Python’s collections. Counter or a similar method. 

5. Export Processed Data to Kibana:

   – Export the processed data (error codes and their counts) to a format that Kibana can ingest. We  exported the data to Elasticsearch directly using the Elasticsearch Python client, or you can save it to a file (e.g., CSV) and import it into Kibana manually.

6. Visualize Data in Kibana:

   – Once the data is available in Kibana, create visualizations (e.g., bar charts, pie charts) based on the error code counts. You can also create dashboards to combine multiple visualizations and monitor the error trends over time.

Solution Architecture

Here’s a solution architecture for the workflow:

1. Log Data Export:

   – Log data is exported from Kibana or the logging system into a file format such as CSV, JSON, or plain text.

2. Python Script Execution:

   – A Python script is executed to process the exported log data.

3. Data Processing in Python:

   – The Python script reads the log file and extracts error codes using regular expressions.

   – Error codes are counted to determine their frequency.

4. Export Processed Data:

   – The processed data (error codes and their counts) is exported to a format suitable for ingestion into Kibana.

6. Ingestion into Kibana:

   – The processed data is ingested into Kibana. This can be done either directly into Elasticsearch (the backend datastore of Kibana) using the Elasticsearch Python client or by importing the data into Kibana manually.

7. Visualization in Kibana:

   – In Kibana, the ingested data is used to create visualizations such as bar charts, pie charts, or any other suitable visualization to represent the count of log error codes.

   – Dashboards can be created to combine multiple visualizations and provide a comprehensive view of the log error trends over time.

Deliverables

Kibana Dashboard

Tech Stack

  • Tools used
  •    -Elasticsearch, Logstash, or Beats (ELK stack).
  •    – Python interpreter, VSCode, Jupyter Notebook.
  •    – Python with libraries such as `re`, `collections`, and `pandas`.
  •    – `matplotlib` or `seaborn` for creating visualizations.
  •    – CSV, JSON, or other suitable formats.
  •    – Elasticsearch Python client or manual import via Kibana’s interface.
  •    – Built-in visualization and dashboarding capabilities of Kibana.
  • Language/techniques used
  • – Language: Python is primarily used for scripting and data processing due to its flexibility, rich ecosystem of libraries, and ease of use.
  •   – Regular Expressions (Regex): Utilized for pattern matching and extracting error codes from log data efficiently.
  •   – Data Manipulation: Techniques such as filtering, grouping, and counting are employed to process and analyze log data effectively.
  •   – Visualization: Matplotlib or Seaborn libraries are employed for creating visual representations of log error code counts, facilitating data interpretation and analysis.
  • Skills used
  • – Python Programming: Proficiency in Python programming language for scripting, data processing, and visualization tasks.
  • – Regular Expressions: Skill in using regular expressions to efficiently extract relevant information, such as error codes, from log data.
  • – Data Processing: Ability to manipulate and analyze log data using libraries like `re` for regular expressions and `pandas` for data manipulation.
  • – Data Visualization: Proficiency in creating visualizations using libraries such as Matplotlib or Seaborn to represent log error code counts in an understandable and insightful manner.

What are the technical Challenges Faced during Project Execution

1. Data Preprocessing:

   – Challenge: Log data often arrives in unstructured or semi-structured formats, requiring preprocessing steps such as data cleaning, parsing, and normalization. Inconsistencies in log formats across different systems can further complicate preprocessing efforts.

2. Tool Integration:

   – Challenge: Integrating different tools and technologies within the tech stack seamlessly can be challenging. For example, connecting Python scripts responsible for log data processing with Elasticsearch for data ingestion into Kibana requires careful configuration and compatibility considerations.

How the Technical Challenges were Solved

1. Data Preprocessing:

   – Solution: Develop robust preprocessing pipelines using tools like Python’s `pandas` library or scripting languages to clean and parse log data. Implement techniques such as regular expressions to extract relevant information from log entries. Utilize data wrangling techniques to handle inconsistencies and outliers effectively.

2. Tool Integration:

   – Solution: Utilize APIs, SDKs, or libraries provided by the tools to facilitate integration. Ensure compatibility between different components of the tech stack by adhering to supported versions and protocols. Leverage middleware solutions or data integration platforms to streamline communication and data flow between disparate systems. Regularly test and validate integrations to identify and address any compatibility issues proactively.

Summarize

Summarized: https://blackcoffer.com/

This project was done by the Blackcoffer Team, a Global IT Consulting firm.

Contact Details

This solution was designed and developed by Blackcoffer Team
Here are my contact details:
Firm Name: Blackcoffer Pvt. Ltd.
Firm Website: www.blackcoffer.com
Firm Address: 4/2, E-Extension, Shaym Vihar Phase 1, New Delhi 110043
Email: ajay@blackcoffer.com
Skype: asbidyarthy
WhatsApp: +91 9717367468
Telegram: @asbidyarthy

RELATED ARTICLESMORE FROM AUTHOR