# Dataset JSON Flattener (`starshaped_bullsnake/dataset-json-flattener`) Actor

Flatten nested JSON objects into flat key/value rows using configurable dot notation, from inline JSON or an Apify Dataset.

- **URL**: https://apify.com/starshaped\_bullsnake/dataset-json-flattener.md
- **Developed by:** [Starshape Tools](https://apify.com/starshaped_bullsnake) (community)
- **Categories:**
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

Pay per usage

This Actor is paid per platform usage. The Actor is free to use, and you only pay for the Apify platform usage, which gets cheaper the higher subscription plan you have.

Learn more: https://docs.apify.com/actors/running/actors-in-store.md#pay-per-usage

## What's an Apify Actor?

Actors are web data automations that power AI and operations. They run on the Apify platform to scrape websites, process data, connect APIs, and automate workflows.
In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours,
and optionally produces a well-defined JSON output, datasets with results, or files in key-value store.
In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server.
Actors are written with capital "A".

## How to integrate an Actor?

If asked about integration, you help developers integrate Actors into their projects.
You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready.
The best way to integrate Actors is as follows.

- **AI agents and MCP clients** — the [Apify MCP server](https://docs.apify.com/integrations/mcp.md) at `https://mcp.apify.com` (remote, streamable HTTP, OAuth on first use).
- **Agentic workflows and local Actor development** — [Agent Skills](https://apify.com/.well-known/agent-skills/index.json) with the [Apify CLI](https://docs.apify.com/cli/docs.md): `npm install -g apify-cli`, then `apify login`.
- **JavaScript/TypeScript projects** — the official [JS/TS client](https://docs.apify.com/api/client/js/docs.md): `npm install apify-client`.
- **Python projects** — the official [Python client](https://docs.apify.com/api/client/python/docs.md): `pip install apify-client`.
- **Any other language** — the [REST API](https://docs.apify.com/api/v2.md).

For usage examples, see the [API](#api) section below.

For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt).

# README

## Dataset JSON Flattener

Flatten nested JSON objects into flat key/value rows and write them to the run's default Apify Dataset.

The Actor has no external API or credential requirements. Its default inline input runs immediately and produces a non-empty Dataset.

### What it does

- Reads rows from inline JSON or an optional Apify Dataset
- Recursively flattens nested plain objects
- Uses `.` as the default separator, with a configurable alternative
- Leaves primitives, arrays, and `null` unchanged
- Keeps empty nested objects as empty objects
- Writes every flattened row to the default Dataset

### Example

Input row:

```json
{
  "id": "A001",
  "user": {
    "name": "Alice",
    "address": {
      "city": "Tokyo"
    }
  }
}
```

Output row:

```json
{
  "id": "A001",
  "user.name": "Alice",
  "user.address.city": "Tokyo"
}
```

### Input sources

Run with the default input to flatten the two included inline rows without authentication. To process an existing Dataset instead, select it in **Source Dataset**; that picker requests READ permission only.

When a source Dataset is selected, it takes precedence over inline rows.

### Custom separator

Set **Key separator** to another non-empty string, such as `_`, to produce keys like `user_address_city`.

### Notes

- Arrays are retained as arrays and are not flattened.
- Key collisions are resolved in traversal order. For example, an existing `user.name` key may be overwritten by a nested `user.name` path.
- The Actor uses Apify storage only and does not call external APIs.

# Actor input Schema

## `sourceDatasetId` (type: `string`):

Optional Apify Dataset to flatten. If empty, the inline rows below are used.

## `rows` (type: `array`):

JSON rows to flatten when no Source Dataset is selected.

## `separator` (type: `string`):

Text placed between nested key segments.

## Actor input object example

```json
{
  "rows": [
    {
      "id": "A001",
      "user": {
        "name": "Alice",
        "address": {
          "city": "Tokyo"
        }
      }
    },
    {
      "id": "A002",
      "user": {
        "name": "Bob",
        "address": {
          "city": "Osaka"
        }
      },
      "tags": [
        "customer",
        "active"
      ]
    }
  ],
  "separator": "."
}
```

# Actor output Schema

## `dataset` (type: `string`):

No description

# API

You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup.

## JavaScript example

```javascript
import { ApifyClient } from 'apify-client';

// Initialize the ApifyClient with your Apify API token
// Replace the '<YOUR_API_TOKEN>' with your token
const client = new ApifyClient({
    token: '<YOUR_API_TOKEN>',
});

// Prepare Actor input
const input = {};

// Run the Actor and wait for it to finish
const run = await client.actor("starshaped_bullsnake/dataset-json-flattener").call(input);

// Fetch and print Actor results from the run's dataset (if any)
console.log('Results from dataset');
console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`);
const { items } = await client.dataset(run.defaultDatasetId).listItems();
items.forEach((item) => {
    console.dir(item);
});

// 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs

```

## Python example

```python
from apify_client import ApifyClient

# Initialize the ApifyClient with your Apify API token
# Replace '<YOUR_API_TOKEN>' with your token.
client = ApifyClient("<YOUR_API_TOKEN>")

# Prepare the Actor input
run_input = {}

# Run the Actor and wait for it to finish
run = client.actor("starshaped_bullsnake/dataset-json-flattener").call(run_input=run_input)

# Fetch and print Actor results from the run's dataset (if there are any)
print(f"💾 Check your data here: https://console.apify.com/storage/datasets/{run.default_dataset_id}")
for item in client.dataset(run.default_dataset_id).iterate_items():
    print(item)

# 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start

```

## CLI example

```bash
echo '{}' |
apify call starshaped_bullsnake/dataset-json-flattener --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,starshaped_bullsnake/dataset-json-flattener"
        }
    }
}

```

The hosted server signs you in with OAuth on first connect, so no API token belongs in this config. Clients without OAuth support can send an `Authorization: Bearer <APIFY_API_TOKEN>` header instead, using a token from API & Integrations in Apify Console (https://console.apify.com/settings/integrations).

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/actors/kE7e2tbkAukrjoXcZ/builds/Xop3gGBUC6eLxk7FG/openapi.json