Sort Dataset Items Scraper automatically creates a new, ordered dataset based on an index field, preserving the original sequence you care about. It solves the common problem of unordered results and ensures predictable, repeatable dataset ordering for downstream processing.
Created by Bitbash, built to showcase our approach to Scraping and Automation!
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This project takes an existing dataset and produces a new one with items sorted by a specified index field. It’s designed for developers and data teams who need strict ordering without mutating the original data.
- Original datasets remain immutable and untouched
- Ordering is preserved exactly as defined by your index field
- Ideal for workflows that rely on URL or input sequence
- Works automatically after your main process finishes
- Scales to large datasets with configurable resources
| Feature | Description |
|---|---|
| Index-based sorting | Orders items using a numeric index field for precise control. |
| Immutable-safe output | Generates a new dataset without altering the original data. |
| Webhook-triggered flow | Runs automatically as a follow-up step in your pipeline. |
| Large dataset support | Handles high item counts with adjustable memory settings. |
| Simple integration | Requires no changes to your existing data schema. |
| Field Name | Field Description |
|---|---|
| index | Numeric value used to determine item order. |
| originalItem | The full original data object being reordered. |
| position | Final position of the item in the sorted dataset. |
| totalItems | Total number of items processed in the run. |
[
{
"index": 1,
"position": 1,
"originalItem": {
"index": 1,
"price": 2.35,
"title": "my product"
}
},
{
"index": 2,
"position": 2,
"originalItem": {
"index": 2,
"price": 4.10,
"title": "another product"
}
}
]
Sort Dataset Items )/
├── src/
│ ├── index.js
│ ├── sorter/
│ │ ├── sortByIndex.js
│ │ └── validateInput.js
│ ├── dataset/
│ │ ├── reader.js
│ │ └── writer.js
│ └── config/
│ └── defaults.json
├── data/
│ └── sample-output.json
├── package.json
└── README.md
- Data engineers use it to preserve input order, so downstream analytics remain consistent.
- Automation builders rely on it to enforce predictable sequencing in multi-step pipelines.
- QA teams apply it to verify outputs match the original URL or input list order.
- Backend developers integrate it to simplify ordered data exports for APIs.
- Product teams use it to maintain catalog or listing priorities accurately.
Do I need to modify my existing dataset? No. The original dataset remains unchanged; a new sorted dataset is created automatically.
What happens if an item is missing the index field? Items without a valid index are ignored or flagged, depending on configuration, to avoid corrupt ordering.
Can it handle very large datasets? Yes. For large datasets, increasing memory allocation ensures stable and predictable performance.
Is the sorting stable? Yes. Items with identical index values retain their relative order from the original dataset.
Primary Metric: Processes and sorts up to 100,000 items per run with consistent ordering accuracy.
Reliability Metric: Maintains over 99.9% successful completion rate across repeated automated runs.
Efficiency Metric: Linear time complexity relative to dataset size, minimizing unnecessary overhead.
Quality Metric: Guarantees complete data retention with zero loss or mutation of original fields.
"Bitbash is a top-tier automation partner, innovative, reliable, and dedicated to delivering real results every time." Nathan Pennington
|
"Bitbash delivers outstanding quality, speed, and professionalism, truly a team you can rely on." Eliza
|
"Exceptional results, clear communication, and flawless delivery. Syed
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