Analyze JSON

Before you can parse, transform, migrate or document a JSON payload, you need to know what is actually in it. Opening an unfamiliar file and scrolling is slow, and guesses about depth, size or schema shape lead to broken code. A quick, quantified profile answers the questions that matter: how big is it, how deep does it nest, what types dominate, are there duplicate keys, and where are the hot spots.

Analyze JSON walks the whole document in your browser and produces a complete structural report — root type, size, depth, node and key counts, type distribution, duplicates, empty containers, top keys and the deepest paths. Understand any JSON in seconds, without uploading it anywhere.

• Full structural profile: Root type, size in characters and bytes, node count and key count.
• Depth analysis: Maximum nesting depth plus the exact deepest paths in the document.
• Type distribution: Objects, arrays, strings, numbers, booleans and nulls, counted separately.
• Duplicate detection: Key names that repeat across the document, with occurrence counts.
• Top keys table: The most frequent key names at a glance.
• Quality flags: Empty objects, empty arrays, empty strings and non-ASCII values are surfaced.
• Read-only: Input is never modified, only measured.
• Private: All processing happens in your browser; nothing is uploaded.

1. Paste your JSON into the input pane.
2. Wait for the live analysis — the report updates as you type.
3. Read the summary line for root type, size and node count.
4. Check depth and type distribution to understand the structure.
5. Scan duplicates and top keys to spot schema patterns and anomalies.

Example 1 — Integration contract review. A developer receives a new vendor payload. The analyzer shows 11 levels of nesting and a deepest path of data.items[3].meta.history[0].values — immediately explaining why the previous flattening attempt failed.

Example 2 — Size triage. A support team investigates a slow endpoint. The analyzer shows the response is 8.4 MB with 62% of nodes being strings, pointing to verbose fields that could be trimmed before optimizing anything else.

Example 3 — Schema consistency. A data team loads log exports. The duplicate-keys report shows created_at appearing under 6 different spellings across objects — a schema drift they can now fix systematically.

Example 4 — Documentation. An architect profiles a legacy config file and uses the top-keys table and deepest paths to write accurate documentation and JSON-schema validation rules.

• Understand before you code: Depth, size and types inform parsing and transformation choices.
• Find anomalies fast: Duplicates, empty containers and non-ASCII values are flagged automatically.
• No guessing: Precise counts replace scrolling and eyeballing.
• Zero data movement: The document stays in your browser.
• Instant feedback: Live analysis updates with every keystroke.