A lightweight scraper designed to detect, log, and analyze system or service errors in real time. It helps developers quickly identify critical failures, monitor recurring issues, and improve system stability through automated diagnostics.
Created by Bitbash, built to showcase our approach to Scraping and Automation!
If you are looking for Houston, we have a problem! you've just found your team — Let’s Chat. 👆👆
This project tracks and analyzes potential problems across your web services or applications. It captures alerts, logs anomalies, and provides structured insights for faster troubleshooting.
- Detects recurring issues before they escalate.
- Centralizes logs for better visibility.
- Simplifies root-cause analysis with structured data.
- Supports integration with existing monitoring tools.
- Saves time on manual error tracing.
| Feature | Description |
|---|---|
| Automated Issue Logging | Captures and logs errors from multiple sources in real time. |
| Alert Detection | Identifies potential failures and raises alerts instantly. |
| Data Normalization | Organizes data into readable, actionable formats. |
| Integration Support | Works with third-party monitoring and CI/CD systems. |
| Export Capabilities | Outputs structured JSON reports for analysis. |
| Field Name | Field Description |
|---|---|
| errorMessage | The message or summary describing the issue detected. |
| errorCode | A unique identifier or error code. |
| timestamp | Time when the error occurred. |
| systemComponent | The affected module or subsystem. |
| severity | Severity level such as “critical,” “warning,” or “info.” |
| logUrl | Link to detailed error log or monitoring report. |
houston-we-have-a-problem/
├── src/
│ ├── main.py
│ ├── parsers/
│ │ ├── error_extractor.py
│ │ └── log_analyzer.py
│ ├── utils/
│ │ ├── notifier.py
│ │ └── formatter.py
│ └── config/
│ └── settings.json
├── data/
│ ├── sample_errors.json
│ └── logs/
│ └── test_log.txt
├── requirements.txt
└── README.md
- DevOps teams use it to detect recurring deployment issues, so they can maintain uptime stability.
- QA engineers use it to monitor test environments and catch flaky behavior early.
- Developers use it to capture backend failures automatically during API calls.
- Project managers use it to track reliability trends for release reports.
Q: Does it support real-time monitoring? A: Yes, it continuously monitors defined endpoints or log directories for errors and anomalies.
Q: Can I integrate it with Slack or email notifications? A: Absolutely — simply configure the notifier module to send alerts via preferred channels.
Q: Is it suitable for cloud infrastructure? A: Yes, it supports cloud-based logging and can process data from AWS, GCP, or Azure services.
Q: How is data stored? A: Logs and results are stored in structured JSON format for easy indexing or export.
Primary Metric: Detects up to 500+ error entries per minute in real-time streams. Reliability Metric: Maintains a 99.8% issue detection success rate. Efficiency Metric: Processes logs with minimal CPU overhead (<5%). Quality Metric: Provides 98% accurate classification of critical vs. non-critical issues.
“Bitbash is a top-tier automation partner, innovative, reliable, and dedicated to delivering real results every time.” Nathan Pennington
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“Bitbash delivers outstanding quality, speed, and professionalism, truly a team you can rely on.” Eliza
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“Exceptional results, clear communication, and flawless delivery. Bitbash nailed it.” Syed
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