ItemReader (Map)

A Distributed Map state can accept a JSON input passed from a previous step in the workflow. This input must either be an array, or must contain an array within a specific node. To select a node that contains the array, you can use the ItemsPath (Map, JSONPath only) field.

To process individual items in the array, the Distributed Map state starts a child workflow execution for each array item. The following tabs show examples of the input passed to the Map state and the corresponding input to a child workflow execution.

A Distributed Map state can iterate over the objects that are stored in an Amazon S3 bucket. When the workflow execution reaches the Map state, Step Functions invokes the ListObjectsV2 API action, which returns an array of the Amazon S3 object metadata. In this array, each item contains data, such as ETag and Key, for the data stored in the bucket.

To process individual items in the array, the Distributed Map state starts a child workflow execution. For example, suppose that your Amazon S3 bucket contains 100 images. Then, the array returned after invoking the ListObjectsV2 API action contains 100 items. The Distributed Map state then starts 100 child workflow executions to process each array item.

The following tabs show examples of the ItemReader field syntax and the input passed to a child workflow execution for this dataset.

A Distributed Map state can accept a JSON file that's stored in an Amazon S3 bucket as a dataset. The JSON file must contain an array.

When the workflow execution reaches the Map state, Step Functions invokes the GetObject API action to fetch the specified JSON file. The Map state then iterates over each item in the array and starts a child workflow execution for each item. For example, if your JSON file contains 1000 array items, the Map state starts 1000 child workflow executions.

The following tabs show examples of the ItemReader field syntax and the input passed to a child workflow execution for this dataset.

For this example, imagine you have a JSON file named factcheck.json. You've stored this file within a prefix named jsonDataset in an Amazon S3 bucket. The following is an example of the JSON dataset.

[
  {
    "verdict": "true",
    "statement_date": "6/11/2008",
    "statement_source": "speech"
  },
  {
    "verdict": "false",
    "statement_date": "6/7/2022",
    "statement_source": "television"
  },
  {
    "verdict": "mostly-true",
    "statement_date": "5/18/2016",
    "statement_source": "news"
  },
  ...
]

A Distributed Map state can accept a CSV file that's stored in an Amazon S3 bucket as a dataset. If you use a CSV file as your dataset, you need to specify a CSV column header. For information about how to specify a CSV header, see Contents of the ItemReader field.

Step Functions parses CSV files based on the following rules:

For more information about how Step Functions parses a CSV file, see Example of parsing an input CSV file.

When the workflow execution reaches the Map state, Step Functions invokes the GetObject API action to fetch the specified CSV file. The Map state then iterates over each row in the CSV file and starts a child workflow execution to process the items in each row. For example, suppose that you provide a CSV file that contains 100 rows as input. Then, the interpreter passes each row to the Map state. The Map state processes items in serial order, starting after the header row.

The following tabs show examples of the ItemReader field syntax and the input passed to a child workflow execution for this dataset.

A Distributed Map state can accept an Amazon S3 inventory manifest file that's stored in an Amazon S3 bucket as a dataset.

When the workflow execution reaches the Map state, Step Functions invokes the GetObject API action to fetch the specified Amazon S3 inventory manifest file. The Map state then iterates over the objects in the inventory to return an array of Amazon S3 inventory object metadata.

The following is an example of an inventory file in CSV format. This file includes the objects named csvDataset and imageDataset, which are stored in an Amazon S3 bucket that's named amzn-s3-demo-source-bucket.

"amzn-s3-demo-source-bucket","csvDataset/","0","2022-11-16T00:27:19.000Z"
"amzn-s3-demo-source-bucket","csvDataset/titles.csv","3399671","2022-11-16T00:29:32.000Z"
"amzn-s3-demo-source-bucket","imageDataset/","0","2022-11-15T20:00:44.000Z"
"amzn-s3-demo-source-bucket","imageDataset/n02085620_10074.jpg","27034","2022-11-15T20:02:16.000Z"
...

The following example of an inventory manifest file shows the CSV headers for the inventory object metadata.

{
  "sourceBucket" : "amzn-s3-demo-source-bucket",
  "destinationBucket" : "arn:aws:s3:::amzn-s3-demo-inventory",
  "version" : "2016-11-30",
  "creationTimestamp" : "1668560400000",
  "fileFormat" : "CSV",
  "fileSchema" : "Bucket, Key, Size, LastModifiedDate",
  "files" : [ {
    "key" : "amzn-s3-demo-bucket/destination-prefix/data/20e55de8-9c21-45d4-99b9-46c732000228.csv.gz",
    "size" : 7300,
    "MD5checksum" : "a7ff4a1d4164c3cd55851055ec8f6b20"
  } ]
}

The following tabs show examples of the ItemReader field syntax and the input passed to a child workflow execution for this dataset.