Houston We Have A Problem Scraper is a lightweight data extraction tool designed to detect, collect, and structure problem-related signals from target sources. It helps teams quickly identify issues, anomalies, or failure indicators and turn unstructured information into actionable data.
Created by Bitbash, built to showcase our approach to Scraping and Automation!
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This project focuses on extracting structured signals related to errors, issues, or problem states from defined inputs. It solves the challenge of manually monitoring scattered data sources for problem indicators. It is built for developers, analysts, and operations teams who need fast, reliable visibility into issues.
- Collects problem-related data from defined inputs in a repeatable way
- Normalizes unstructured information into structured records
- Designed for easy integration into analytics or monitoring pipelines
- Lightweight architecture suitable for scheduled or on-demand runs
| Feature | Description |
|---|---|
| Issue signal detection | Identifies and captures problem-related indicators from inputs. |
| Structured output | Converts raw signals into clean, structured data formats. |
| Configurable inputs | Supports flexible input definitions for different use cases. |
| Automation-ready | Designed to run unattended as part of automated workflows. |
| Scalable design | Handles increasing data volume without major changes. |
| Field Name | Field Description |
|---|---|
| source | Origin of the detected issue signal. |
| message | Raw problem or error message text. |
| severity | Estimated importance or impact level. |
| timestamp | Time when the issue signal was detected. |
| context | Additional metadata related to the issue. |
Houston, we have a problem!/
├── src/
│ ├── main.py
│ ├── detector.py
│ ├── parser.py
│ └── utils.py
├── config/
│ └── settings.example.json
├── data/
│ ├── input.sample.json
│ └── output.sample.json
├── requirements.txt
└── README.md
- DevOps teams use it to detect recurring system issues, so they can respond faster and reduce downtime.
- Data analysts use it to aggregate problem signals, so they can identify patterns and root causes.
- QA engineers use it to collect error data, so they can improve testing coverage and stability.
- Product teams use it to monitor failure feedback, so they can prioritize fixes effectively.
What type of data sources does this scraper support? It is designed to work with configurable inputs and can be adapted to logs, text feeds, or structured sources that contain problem indicators.
Is this tool suitable for continuous monitoring? Yes, it can be scheduled or integrated into automated pipelines for continuous or periodic monitoring.
How complex is the setup? Setup is minimal and configuration-driven, allowing users to start quickly without heavy dependencies.
Can the output be integrated with other systems? The structured output is suitable for databases, dashboards, or alerting systems.
Primary Metric: Processes hundreds of issue signals per minute under typical workloads.
Reliability Metric: Consistently achieves a high success rate with stable execution across runs.
Efficiency Metric: Low memory and CPU usage, suitable for lightweight environments.
Quality Metric: High data completeness with accurate extraction of key problem attributes.
"Bitbash is a top-tier automation partner, innovative, reliable, and dedicated to delivering real results every time." Nathan Pennington
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"Exceptional results, clear communication, and flawless delivery. Syed
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