# Batch Runner (`useful-tools/batch-runner`) Actor

Actor for starting several Runs from single Input. It also has capability to merge the Dataset items from the created batch.

- **URL**: https://apify.com/useful-tools/batch-runner.md
- **Developed by:** [Useful tools](https://apify.com/useful-tools) (community)
- **Categories:** Automation, Developer tools
- **Features:** Full permissions
- **Stats:** 64 total users, 7 monthly users, 99.5% runs succeeded, 8 bookmarks
- **User rating**: 5.00 out of 5 stars

## 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?

An Actor is a serverless cloud program that runs on the Apify platform. It has two run modes.
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.

Apify vocabulary and the platform model are defined once, in the agent quickstart at https://apify.com/agents.md.

## 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.

Do not guess an integration path. Every one of them is in the agent quickstart at https://apify.com/agents.md: the Apify MCP server, Agent Skills with the Apify CLI, the JavaScript and Python clients, the REST API, and the account-free path for an agent with no human to sign in. It also carries the rule on stating cost before the first paid run.

For examples already wired to this Actor's own input schema, see the [API](#api) section below.

Each client library has reference documentation the quickstart does not restate: [JavaScript/TypeScript](https://docs.apify.com/api/client/js/docs.md) (`npm install apify-client`) and [Python](https://docs.apify.com/api/client/python/docs.md) (`pip install apify-client`).

# README

### Batch runner

Actor for managing running multiple runs based on single input. Optionally it can also merge the default Datasets from the run
and Provide results in its own default Dataset.

Also allows to overcome issue of exceeding Apify Platform limits (max Memory usage) by starting new Runs only when it is possible.

#### Input example

```json
{
    "batch": [
        {
            "runId": "UuwtsB8GJ4JpsqTdh"
        },
        {
            "actorId": "useful-tools/wait-and-finish",
            "input": {
                "minWaitTime": 30,
                "dataset": {
                    "items": [
                        {
                            "key": "value1"
                        }
                    ]
                }
            },
            "timeout": 300,
            "build": "latest",
            "memoryMb": 256
        },
        {
            "actorId": "useful-tools/wait-and-finish",
            "input": {
                "fail": "dsa"
            }
        },
        {
            "actorId": "useful-tools/wait-and-finish",
            "input": {
                "dataset": {
                    "items": [
                        {
                            "key": "value1"
                        },
                        {
                            "key": "value2"
                        }
                    ]
                }
            }
        }
    ],
    "mergeDatasets": true,
    "failOnAnyRunFails": false
}
```

# Actor input Schema

## `batch` (type: `array`):

Configurations for individual runs

## `mergeDatasets` (type: `boolean`):

If checked all the datasets from the Batch would be merged and imported into Batch Runner's default Dataset.

## `doNotImportDuplicities` (type: `boolean`):

If checked all the Dataset items from all Runs will be deduplicated.

## `failOnAnyRunFails` (type: `boolean`):

If checked this Run will fails in case, that any of Run from Batch fails.

## `maxConcurrency` (type: `integer`):

Run would be started in parallel only if this maxConcurrency limit is not exceeded. (default: 25 - 25 parallel Runs)

## `maxMemoryMb` (type: `integer`):

Run would be started in parallel only if this Memory limit is not exceeded. (default: 0 - no limit)

## `tableReport` (type: `boolean`):

If checked te state will be represented by table in the log

## Actor input object example

```json
{
  "batch": [
    {
      "actorId": "useful-tools/wait-and-finish",
      "input": {
        "minWaitTime": 30,
        "dataset": {
          "items": [
            {
              "key": "value1"
            }
          ]
        }
      }
    },
    {
      "actorId": "useful-tools/wait-and-finish",
      "input": {
        "dataset": {
          "items": [
            {
              "key": "value1"
            },
            {
              "key": "value2"
            }
          ]
        }
      }
    }
  ],
  "mergeDatasets": false,
  "doNotImportDuplicities": false,
  "failOnAnyRunFails": false,
  "maxConcurrency": 25,
  "maxMemoryMb": 32768,
  "tableReport": true
}
```

# 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 = {
    "batch": [
        {
            "actorId": "useful-tools/wait-and-finish",
            "input": {
                "minWaitTime": 30,
                "dataset": {
                    "items": [
                        {
                            "key": "value1"
                        }
                    ]
                }
            }
        },
        {
            "actorId": "useful-tools/wait-and-finish",
            "input": {
                "dataset": {
                    "items": [
                        {
                            "key": "value1"
                        },
                        {
                            "key": "value2"
                        }
                    ]
                }
            }
        }
    ],
    "maxConcurrency": 25,
    "maxMemoryMb": 32768
};

// Run the Actor and wait for it to finish
const run = await client.actor("useful-tools/batch-runner").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 = {
    "batch": [
        {
            "actorId": "useful-tools/wait-and-finish",
            "input": {
                "minWaitTime": 30,
                "dataset": { "items": [{ "key": "value1" }] },
            },
        },
        {
            "actorId": "useful-tools/wait-and-finish",
            "input": { "dataset": { "items": [
                        { "key": "value1" },
                        { "key": "value2" },
                    ] } },
        },
    ],
    "maxConcurrency": 25,
    "maxMemoryMb": 32768,
}

# Run the Actor and wait for it to finish
run = client.actor("useful-tools/batch-runner").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 '{
  "batch": [
    {
      "actorId": "useful-tools/wait-and-finish",
      "input": {
        "minWaitTime": 30,
        "dataset": {
          "items": [
            {
              "key": "value1"
            }
          ]
        }
      }
    },
    {
      "actorId": "useful-tools/wait-and-finish",
      "input": {
        "dataset": {
          "items": [
            {
              "key": "value1"
            },
            {
              "key": "value2"
            }
          ]
        }
      }
    }
  ],
  "maxConcurrency": 25,
  "maxMemoryMb": 32768
}' |
apify call useful-tools/batch-runner --silent --output-dataset

```

## MCP server setup

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

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/xmQmTAUl9yccufpWL/builds/twobw1hmnUBFfkJn5/openapi.json