RowCoveAccount ↗1. Choose the dataset root and review discovery
Choose folder in Open data and select the narrow dataset root in the native directory picker. RowCove detects supported shards automatically. Review discovery before trusting the scope: a root containing train and validation is different from the train folder alone. Keep unrelated files outside the selected scope.
All examples and measurements below use generated mock data. No customer files or trading records are used.
mock-dataset/
split=train/shard-000.parquet
split=train/shard-001.parquet
split=validation/shard-000.parquetJSONL and Arrow shards follow the same folder-selection approach. Discovered partition values and actual source formats determine what can be selected.
2. Prune partitions and project fields
In collection setup, select the relevant split or supported partition range. Project only the fields needed for the investigation, such as id and group. Apply group equals mock-42 in the mock example. Partition predicates can exclude irrelevant shard files before scanning; a record filter alone may still require reading all selected shards.
For the question “which file contains this record?”, begin with its stable identifier and a partition you can justify. Return the identifier and fields needed to check the match, then retain the source file and original row. For the different question “are there any more matches?”, continue through the selected scope until the scan is complete.
Skipping a partition and projecting columns are distinct optimizations. JSONL still needs sequential parsing of selected text; columnar projection can avoid decoding omitted fields. Do not promise the same physical disk IO across formats.
3. Follow continuation and preserve provenance
Review the bounded result page, source file, original source row and partition context. Continue with the next page until the scan reports completion if you need all matches. A short or empty intermediate page does not prove that the remaining collection has no matches.
Keep the collection unchanged during traversal. Restart if the source snapshot changes rather than combining cursors from different source versions. Schema observations from a bounded page are not a certified global schema or a total row count.
For a correction, locate the exact source file, open that individual file and use its supported save-copy workflow. Collection scans do not stage a corpus-wide edit or a global sort. See JSONL corrections and bounded columnar editing.
4. Use the same scope with an AI client
In the MCP workspace, authorize only the intended local root and inspect the installed collection tool schemas. Request projected fields, explicit partition predicates and bounded pages; preserve the returned cursor and source provenance. Test the call and inspect its wire log/history before building a longer automation.
Local processing keeps source files on the device. Rows returned to an external AI client can still be transmitted by that client: send only the necessary fields and use mock data in public demonstrations. Use the agent documentation and skills for supported tools and safety boundaries.
5. Complete four-shard mock traversal
All examples and measurements below use generated mock data. No customer files or trading records are used.
| Full selected-source filter traversal | JSONL | Parquet | Arrow |
|---|---|---|---|
| Return id/group for all 10,810 matches | 1.139 s | 1.138 s | 1.289 s |
| Return all fields for all 10,810 matches | 1.260 s | 4.438 s | 2.201 s |
Measured on Windows with a Release native engine build on 27 October 2026: three fresh-process trials per task, the same 1,048,576 logical mock records, and a 1,024-byte synthetic payload. Median elapsed times include opening, index/schema preparation and the requested work. UI rendering, IPC and the separate correctness pass are excluded. OS caches were not cleared; these are not cold-disk guarantees or a comparison with another application. These engineering measurements cover the updated engine included in 0.1.1; results depend on the device, source and requested operation.
The timer includes repeated catalog discovery and snapshot checks for every page. All selected source records were scanned; every returned match, exact identifier, source-file/row and partition was verified. These are full filtered traversals, not first-page timings, global sorts or total-row-count metadata reads.
Projected native peak working set was at most 26.3 MiB across the three formats; all-field traversal reached 40.0 MiB. No retained temporary scratch files were observed at result time. Output copies, desktop renderer memory and transient disk peaks are separate costs. The four-shard fixture does not establish performance for millions of shard files or a TB corpus.
6. Choose a workflow worth repeating
Pro is useful when locating and correcting records is repeated work: fewer one-off conversion scripts, less manual file splitting and a reviewed independent output. Free Preview opens up to 100 records read-only. Try representative files first; occasional small-file inspection may already be covered by free tools. See introductory prices and renewal terms: first month US$5, then US$10/month; first year US$39, then US$100/year, for eligible new subscribers.
Time saved depends on the tasks you repeat and your existing tools. These timings do not establish purchase intent, paid conversion or a universal speed ranking.
The practical benefit is a shorter path from “which shard contains this record?” to an inspectable source location. Evaluate a training-data repair, a market-history partition check or a repeated research investigation before committing to a subscription.
Try the first page on your own data.
Start on Windows or macOS: source information and up to 100 records, read-only. Review reader capabilities before planning a full-file Pro workflow.
Version 0.1.1 · Windows x64 · macOS Apple Silicon · Install guide →
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