Import
CSV, TSV, JSON arrays, JSON objects, and objects containing a data array.
SOFTWARE / DATA TOOL
A browser-based tool for inspecting, searching, editing, analyzing, and benchmarking structured datasets.
Data Workbench began as a Data Structures and Algorithms course project and was later redesigned independently into a more general structured-data exploration tool.
The current application can load real CSV, TSV, and JSON datasets, normalize their structure, search and sort records, calculate numeric summaries, modify data, export CSV files, and compare different lookup strategies.
CSV, TSV, JSON arrays, JSON objects, and objects containing a data array.
Search globally or by column, sort fields, and browse records through pagination.
Add, modify, and delete records directly inside the current dataset.
Automatically calculate mean, median, minimum, and maximum for numeric fields.
Compare linear search, a Map index, and sorted-array binary search.
Export the current edited dataset back to CSV.
Duplicate CSV headers are retained safely, blank headers are ignored, and malformed extra fields do not silently expand the schema.
JSON schemas are generated from the union of keys found across the loaded records.
A field is classified as numeric when at least 80% of its populated values parse as numbers.
The current benchmark measures repeated exact-match lookups while separating preprocessing cost from lookup cost.
| Strategy | Build | Search | Total |
|---|---|---|---|
| Linear Array | 0 ms | 439.1 ms | 439.1 ms |
| Map Index | 1.0 ms | 0.2 ms | 1.1 ms |
| Sorted + Binary | 844.2 ms | 162.4 ms | 1003.3 ms |
Binary search reduced lookup time compared with repeated linear scanning, but constructing the sorted representation introduced a substantial upfront cost.
For this workload, the Map index provided the lowest combined build and lookup time. The experiment demonstrates why data-structure selection depends on the complete workload, not lookup complexity alone.
CURRENT BUILD