# Dataset Flatten and Field Mapper (`eager_vitamin/apify-dataset-flatten-field-mapper`) Actor Flatten nested JSON, rename and drop fields, and coerce simple types in Apify datasets without writing transformation code. - **URL**: https://apify.com/eager\_vitamin/apify-dataset-flatten-field-mapper.md - **Developed by:** [Lau Kuan Ee](https://apify.com/eager_vitamin) (community) - **Categories:** Developer tools - **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/platform/actors/running/actors-in-store#pay-per-usage ## What's an Apify Actor? Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. 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. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js/docs.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python/docs.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/integrations/mcp.md). If your project is in a different language, use 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 Flatten and Field Mapper Turn nested scraper output into export-ready Apify dataset rows without writing JavaScript or Python. Map dot paths, flatten nested objects, drop fields, and apply conservative type coercion in one bounded run. ### Why this Actor - Works with an Apify dataset ID or inline records. - Keeps arrays intact, so a record never multiplies into surprise rows. - Runs no user-supplied code. - Continues after a record-level coercion error and emits a safe error row without the failed raw value. - Reads private datasets with the run's `APIFY_TOKEN`, which is never logged or stored. ### Input example ```json { "datasetId": "YOUR_DATASET_ID", "mappings": [ {"from": "person.name", "to": "name"}, {"from": "person.age", "to": "age"} ], "keepUnmapped": false, "coerce": {"age": "number"}, "maxItems": 10000 } ```` When mappings are present, unmapped fields are dropped by default. Set `keepUnmapped` to `true` to retain them. Set `flattenAll` to recursively flatten objects using `separator`. Supported coercions are `string`, `number`, `boolean`, and canonical `json`. Boolean parsing accepts common values such as `true`, `false`, `yes`, `no`, `1`, and `0`. ### Output The default dataset receives one row per input record. A failed coercion creates a row like: ```json { "recordType": "error", "index": 4, "errors": [{"field": "age", "target": "number", "message": "string is not a number"}] } ``` `OUTPUT.json` reports loaded, emitted, failed, dropped-field, and coercion-error counts. Runs are capped at 100,000 input records. ### Pricing proposal `$0.50` per 1,000 emitted rows plus Apify's required minimum Actor-start event. Final pricing is staged separately in the Apify Console. # Actor input Schema ## `datasetId` (type: `string`): An Apify dataset accessible to this run. Leave empty when using inline items. ## `items` (type: `array`): Optional JSON records for a quick trial. Used when Dataset ID is empty. ## `mappings` (type: `array`): Map source dot paths to output field names. ## `flattenAll` (type: `boolean`): Recursively flatten objects. Arrays remain arrays. ## `separator` (type: `string`): Character placed between nested object path segments. ## `keepUnmapped` (type: `boolean`): Keep original fields in addition to explicit mappings. ## `dropFields` (type: `array`): Exact output field names to remove after mapping. ## `coerce` (type: `object`): Map output field names to string, number, boolean, or json. ## `maxItems` (type: `integer`): Hard limit for records loaded and transformed. ## Actor input object example ```json { "items": [ { "person": { "name": "Ada", "age": "42" }, "active": "yes", "tags": [ "math", "code" ] }, { "person": { "name": "Grace", "age": "37" }, "active": "true", "tags": [ "compiler" ] } ], "mappings": [ { "from": "person.name", "to": "name" }, { "from": "person.age", "to": "age" }, { "from": "active", "to": "active" }, { "from": "tags", "to": "tags" } ], "flattenAll": false, "separator": ".", "keepUnmapped": false, "dropFields": [], "coerce": { "age": "number", "active": "boolean" }, "maxItems": 10000 } ``` # Actor output Schema ## `results` (type: `string`): No description ## `summary` (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 '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "items": [ { "person": { "name": "Ada", "age": "42" }, "active": "yes", "tags": [ "math", "code" ] }, { "person": { "name": "Grace", "age": "37" }, "active": "true", "tags": [ "compiler" ] } ], "mappings": [ { "from": "person.name", "to": "name" }, { "from": "person.age", "to": "age" }, { "from": "active", "to": "active" }, { "from": "tags", "to": "tags" } ] }; // Run the Actor and wait for it to finish const run = await client.actor("eager_vitamin/apify-dataset-flatten-field-mapper").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 '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = { "items": [ { "person": { "name": "Ada", "age": "42", }, "active": "yes", "tags": [ "math", "code", ], }, { "person": { "name": "Grace", "age": "37", }, "active": "true", "tags": ["compiler"], }, ], "mappings": [ { "from": "person.name", "to": "name", }, { "from": "person.age", "to": "age", }, { "from": "active", "to": "active", }, { "from": "tags", "to": "tags", }, ], } # Run the Actor and wait for it to finish run = client.actor("eager_vitamin/apify-dataset-flatten-field-mapper").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "items": [ { "person": { "name": "Ada", "age": "42" }, "active": "yes", "tags": [ "math", "code" ] }, { "person": { "name": "Grace", "age": "37" }, "active": "true", "tags": [ "compiler" ] } ], "mappings": [ { "from": "person.name", "to": "name" }, { "from": "person.age", "to": "age" }, { "from": "active", "to": "active" }, { "from": "tags", "to": "tags" } ] }' | apify call eager_vitamin/apify-dataset-flatten-field-mapper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=eager_vitamin/apify-dataset-flatten-field-mapper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Dataset Flatten and Field Mapper", "description": "Flatten nested JSON, rename and drop fields, and coerce simple types in Apify datasets without writing transformation code.", "version": "0.1", "x-build-id": "hfa1qAwBgcier4u9u" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/eager_vitamin~apify-dataset-flatten-field-mapper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-eager_vitamin-apify-dataset-flatten-field-mapper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/eager_vitamin~apify-dataset-flatten-field-mapper/runs": { "post": { "operationId": "runs-sync-eager_vitamin-apify-dataset-flatten-field-mapper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/eager_vitamin~apify-dataset-flatten-field-mapper/run-sync": { "post": { "operationId": "run-sync-eager_vitamin-apify-dataset-flatten-field-mapper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "properties": { "datasetId": { "title": "Dataset ID or name", "type": "string", "description": "An Apify dataset accessible to this run. Leave empty when using inline items." }, "items": { "title": "Inline records", "type": "array", "description": "Optional JSON records for a quick trial. Used when Dataset ID is empty.", "items": { "type": "object" } }, "mappings": { "title": "Field mappings", "type": "array", "description": "Map source dot paths to output field names.", "items": { "type": "object", "required": [ "from", "to" ], "properties": { "from": { "type": "string", "title": "Source dot path", "description": "Dot path to read from each input record.", "editor": "textfield" }, "to": { "type": "string", "title": "Output field", "description": "Field name to write in each output record.", "editor": "textfield" } } } }, "flattenAll": { "title": "Flatten all nested objects", "type": "boolean", "description": "Recursively flatten objects. Arrays remain arrays.", "default": false }, "separator": { "title": "Flattened field separator", "type": "string", "description": "Character placed between nested object path segments.", "default": "." }, "keepUnmapped": { "title": "Keep unmapped fields", "type": "boolean", "description": "Keep original fields in addition to explicit mappings.", "default": false }, "dropFields": { "title": "Output fields to drop", "type": "array", "description": "Exact output field names to remove after mapping.", "items": { "type": "string" }, "default": [] }, "coerce": { "title": "Type coercions", "type": "object", "description": "Map output field names to string, number, boolean, or json.", "default": { "age": "number", "active": "boolean" }, "additionalProperties": true }, "maxItems": { "title": "Maximum records", "minimum": 1, "maximum": 100000, "type": "integer", "description": "Hard limit for records loaded and transformed.", "default": 10000 } } }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } } } ```