JSON Transformer

Overview

Transforms and validates JSON data using jq (CLI), Python (json, jsonpath-ng, pandas.json_normalize), and JSON Schema (jsonschema library). Covers common patterns: flattening nested objects/arrays, extracting specific fields, array of objects → table, schema validation, streaming large JSON files, and producing clean output for databases or downstream processing.

When to Use This Skill

• Normalizing complex API responses (nested, inconsistent).
• Converting JSON logs or documents into tabular form for analysis.
• Validating incoming JSON payloads against a contract.
• The user provides JSON files or describes JSON transformation needs.

Prerequisites

• jq installed for CLI transformations (highly recommended).
• Python with pandas, jsonschema, jsonpath-ng (optional but powerful).
• The JSON data (file, API response, or stdin).

Steps

1. Inspect the JSON:

   • jq '.' file.json | head -100 or Python json.load.
   • Identify the shape (object, array of objects, deeply nested).

2. Choose tool by use case:

   • Simple reshaping / extraction: jq.
   • To pandas DataFrame: pandas.json_normalize.
   • Validation: JSON Schema + jsonschema.
   • Streaming large files: ijson (Python) or jq with --stream.

3. Common jq patterns (provide ready-to-use one-liners):

   • Extract array: .items[]
   • Flatten nested: .user | {id, name, "address.city": .address.city}
   • Filter + map.
   • Group by.

4. pandas.json_normalize:

   • pd.json_normalize(data, record_path=['results'], meta=['query']).
   • errors='ignore' for inconsistent structures.

5. JSON Schema validation:

   • Write a schema (draft 2020-12 or 2019-09).
   • Validate in Python or with ajv (JS) / check-jsonschema (CLI).
   • Produce clear error messages with path to the bad field.

6. Streaming large JSON:

   • For NDJSON (newline-delimited): process line by line.
   • For large arrays: use ijson.items(f, 'item').

7. Output:

   • jq one-liners or script.
   • Python function transform_json(input_path) -> pd.DataFrame or list[dict].
   • JSON Schema file.
   • Example of before/after shape.

Examples

• Normalize a complex GitHub API search response (nested user + repo objects) into a flat table using both jq and pandas.json_normalize.
• Validate incoming webhook payloads against a strict schema and quarantine bad ones.
• Stream-process a 500MB NDJSON log file and extract only error events.

Edge Cases & Error Handling

• Inconsistent nesting: Use errors='ignore' or try/except per record + dead letter.
• Huge files: Never load the entire thing into memory.
• Unicode / escaping issues: Ensure UTF-8 throughout.

Verification

1. Run the transformation on sample input — output shape matches expectation.
2. Validate a good and a deliberately bad JSON — errors are clear and point to the bad field.
3. For large files: memory usage stays reasonable (monitor with htop or similar).
4. Re-run produces deterministic output.
5. Success: JSON is turned into the exact shape needed for the next step, with validation protecting downstream systems.

References

• jq Manual
• pandas.json_normalize
• JSON Schema
• ijson
• jsonpath-ng