# Dataset Result Gate (`vittuhy/dataset-result-gate`) Actor Conditional pipeline gate. Fails if the previous actor's dataset is empty, succeeds if it has results — stopping unnecessary downstream runs before they start. - **URL**: https://apify.com/vittuhy/dataset-result-gate.md - **Developed by:** [Vít Tuhý](https://apify.com/vittuhy) (community) - **Categories:** Automation, Developer tools, Integrations - **Stats:** 1 total users, 0 monthly users, 0.0% runs succeeded, NaN 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.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.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/platform/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 Result Gate Stop downstream actors from running when there's nothing to process. This actor checks whether the previous actor's output dataset contains any results and either passes or blocks the pipeline accordingly — with zero extra cost per execution. --- ### Example use case: monitor a social media profile and only scrape if there's a new post Say you run a daily job that checks a competitor's Instagram for new posts. If they posted something, you want to enrich it — pull comments, run sentiment analysis, store it. If not, you want nothing to happen. Without a gate, your enrichment actors would run every day regardless, wasting compute and money. **With Dataset Result Gate:** ```` Instagram Profile Scraper (daily schedule) └─ on SUCCESS → Dataset Result Gate ├─ 0 new posts → STOP. Nothing else runs. └─ 1+ new posts → Comment Scraper starts → Sentiment Actor starts → ... ```` This pattern works for any scenario where a scraper may return empty results: - Price monitoring — only notify if prices changed - News scraping — only process if new articles appeared - Lead generation — only enrich if new profiles were found - Job listing monitoring — only alert if new positions are posted --- ### How it works 1. Receives the output dataset ID from the previous actor run 2. Checks the **clean item count** (actual results, excluding error records) 3. **0 clean items** → run ends as **FAILED** → downstream integrations don't fire 4. **1+ clean items** → run ends as **SUCCEEDED** → downstream integrations fire normally The actor uses `cleanItemCount` — the same number shown in the "Clean" column in Apify Console — so what you see in the UI matches what the gate acts on. --- ### Setup You need two integrations in Apify Console. No code changes required. #### Step 1 — Connect your source actor to the gate 1. Open your source actor (e.g. Instagram Profile Scraper) in Console 2. Go to the **Integrations** tab 3. Add integration: - Trigger: **Actor run succeeded** - Action: **Start actor** → select `Dataset Result Gate` 4. In the input configuration, add: ```json { "datasetId": "{{resource.defaultDatasetId}}" } ```` > `{{resource.defaultDatasetId}}` is an Apify template variable — it's automatically replaced with the source actor's output dataset ID at runtime. You don't need to fill it manually. #### Step 2 — Connect the gate to your target actor 1. Open `Dataset Result Gate` in Console 2. Go to the **Integrations** tab 3. Add integration: - Trigger: **Actor run succeeded** - Action: **Start actor** → select your target actor 4. Configure the target actor's input as needed That's it. The gate handles the conditional logic. *** ### Input | Field | Type | Required | Description | |---|---|---|---| | `datasetId` | String | Yes | ID of the dataset to check. Pass `{{resource.defaultDatasetId}}` when using via Apify Console integration. | *** ### Why not use n8n (or Zapier, Make) for this? You can build conditional logic in n8n or Make — but every execution counts against your task/operation quota. If your scraper runs 10× a day and you're checking results each time, that's 300 operations a month just for the gate logic, before any actual work happens. Dataset Result Gate runs entirely on Apify, billed only by compute time (which for a dataset metadata check is a few seconds at most). If you're already running actors on Apify, keeping the conditional logic inside the platform is simpler and cheaper than routing through an external automation tool. *** ### Notes - The actor checks `cleanItemCount`, not the total item count. If your source actor produces error records alongside real results, those won't inflate the count. - The actor has no output of its own — it exists purely to pass or block the pipeline. - Works with any source actor, not just scrapers. Any actor that writes to a dataset can be gated. # Actor input Schema ## `datasetId` (type: `string`): ID of the dataset to check. Leave empty when triggered via a native Apify Console integration — the dataset ID is picked up automatically from the previous actor run. ## Actor input object example ```json {} ``` # 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 = {}; // Run the Actor and wait for it to finish const run = await client.actor("vittuhy/dataset-result-gate").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 = {} # Run the Actor and wait for it to finish run = client.actor("vittuhy/dataset-result-gate").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 '{}' | apify call vittuhy/dataset-result-gate --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=vittuhy/dataset-result-gate", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Dataset Result Gate", "description": "Conditional pipeline gate. Fails if the previous actor's dataset is empty, succeeds if it has results — stopping unnecessary downstream runs before they start.", "version": "0.1", "x-build-id": "EW9IM6bWEMsX7e9n1" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/vittuhy~dataset-result-gate/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-vittuhy-dataset-result-gate", "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/vittuhy~dataset-result-gate/runs": { "post": { "operationId": "runs-sync-vittuhy-dataset-result-gate", "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/vittuhy~dataset-result-gate/run-sync": { "post": { "operationId": "run-sync-vittuhy-dataset-result-gate", "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", "type": "string", "description": "ID of the dataset to check. Leave empty when triggered via a native Apify Console integration — the dataset ID is picked up automatically from the previous actor run." } } }, "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 } } } } } } } } } } ```