Columnar Nulls Schema (JSON)
JSON description of nullable columns in the null-heavy columnar fixtures.
{
"fields": [
{
"name": "id",
"nullable": true
},
{
"name": "name",
"nullable": true
},
{
"name": "score",
"nullable": true
},
{
"name": "note",
"nullable": true
}
],
"source": "nulls-heavy.parquet"
}
Specifications
- Documents
- nulls-heavy.parquet
Testing contract
Expected to pass- Scenario
- Exercise Columnar Nulls Schema (JSON) in its columnar workflow. JSON description of nullable columns in the null-heavy columnar fixtures.
- Expected result
- top-level keys are fields, source; array lengths: fields=4. Declared feature checks: documents=nulls-heavy.parquet.
What is a .json file?
JSON (JavaScript Object Notation) is a lightweight, text-based data-interchange format representing objects, arrays, strings, numbers, booleans, and null. It is language-independent, human-readable, and the dominant format for web APIs and configuration. It requires a single well-formed root value.
How to use this file
Use an example JSON file to test parsers and serializers, schema validation, Unicode and number-precision handling, and API request or response processing.
How to use this file for testing
“Columnar Nulls Schema (JSON)” is a deterministic Novus Examples fixture for Data import, Conversion testing, JSON parsing. Realistic faker-generated datasets with documented schemas for testing import and ETL flows.
Documented properties for this file: JSON · 308 bytes. Compare results against paired or grouped companions on this page when present (clean↔damaged, searchable↔scanned, or format twins) so scores stay reproducible across runs.
Download the file once, keep the path stable in CI or local scripts, and treat the spec table as the contract: dimensions, seeds, field lists, and roles are intentional. Corrupt or invalid samples are labelled as such, expect parsers to fail loudly rather than silently accept them.
Data fixtures document their exact quirks (delimiters, encodings, null handling, schema, and row counts) in the spec table. Point your parser or importer at the file and assert it handles the documented edge cases; clean and deliberately-messy siblings make before/after diffs straightforward.
Code examples
import json
with open("nulls-schema.json") as f:
data = json.load(f)
print(type(data), len(data))Related files
- jsonlOSV Vulnerability Records (JSON Lines)All four SAMPLE advisories as newline-delimited OSV records — the bulk shape an advisory database is loaded from, where each line must parse independently. Advisory identifiers use the invented NOVUS-SAMPLE namespace with SAMPLE-CVE aliases; no identifier here refers to a published CVE, GHSA or OSV record, and no package named exists.
- featherFeather — Dictionary-encoded ColumnFeather file with dictionary-encoded strings — Arrow IPC edge-case fixture.
- parquetParquet — Decimal as String ColumnAmounts stored as strings in Parquet — common ingestion edge case for ETL parsers.
- parquetParquet — Dictionary-encoded ColumnParquet with dictionary-encoded string column — tests dictionary page decoding.
- parquetParquet — Duplicate Key ColumnParquet with repeated key values — tests join/aggregation edge cases.
- parquetParquet — Empty TableEmpty Parquet file with schema but no rows — edge case for readers.
Generated by generation/data_wave_f.py. Free for any use, no attribution required, license.