Home - News ( United States | United Kingdom | Italy | Germany ) - Football scores

Showing content from https://docs.aws.amazon.com/step-functions/latest/dg/example-csv-parse-dist-map.html below:

How Step Functions parses input CSV files

Step Functions parses text delimited files based on the following rules:

• The delimiter that separates fields is specified by CSVDelimiter in ReaderConfig. The delimiter defaults to COMMA.

• Newlines are a delimiter that separates records.

• Fields are treated as strings. For data type conversions, use the States.StringToJson intrinsic function in ItemSelector (Map).

• Double quotation marks (" ") are not required to enclose strings. However, strings that are enclosed by double quotation marks can contain commas and newlines without acting as record delimiters.

• You can preserve double quotes by repeating them.

• If the number of fields in a row is less than the number of fields in the header, Step Functions provides empty strings for the missing values.

• If the number of fields in a row is more than the number of fields in the header, Step Functions skips the additional fields.

Say that you have provided a CSV file named myCSVInput.csv that contains one row as input. Then, you've stored this file in an Amazon S3 bucket that's named amzn-s3-demo-bucket. The CSV file is as follows.

abc,123,"This string contains commas, a double quotation marks (""), and a newline (
)",{""MyKey"":""MyValue""},"[1,2,3]"

The following state machine reads this CSV file and uses ItemSelector (Map) to convert the data types of some of the fields.

{
  "StartAt": "Map",
  "States": {
    "Map": {
      "Type": "Map",
      "ItemProcessor": {
        "ProcessorConfig": {
          "Mode": "DISTRIBUTED",
          "ExecutionType": "STANDARD"
        },
        "StartAt": "Pass",
        "States": {
          "Pass": {
            "Type": "Pass",
            "End": true
          }
        }
      },
      "End": true,
      "Label": "Map",
      "MaxConcurrency": 1000,
      "ItemReader": {
        "Resource": "arn:aws:states:::s3:getObject",
        "ReaderConfig": {
          "InputType": "CSV",
          "CSVHeaderLocation": "GIVEN",
          "CSVHeaders": [
            "MyLetters",
            "MyNumbers",
            "MyString",
            "MyObject",
            "MyArray"
          ]
        },
        "Parameters": {
          "Bucket": "amzn-s3-demo-bucket",
          "Key": "myCSVInput.csv"
        }
      },
      "ItemSelector": {
        "MyLetters.$": "$$.Map.Item.Value.MyLetters",
        "MyNumbers.$": "States.StringToJson($$.Map.Item.Value.MyNumbers)",
        "MyString.$": "$$.Map.Item.Value.MyString",
        "MyObject.$": "States.StringToJson($$.Map.Item.Value.MyObject)",
        "MyArray.$": "States.StringToJson($$.Map.Item.Value.MyArray)"
      }
    }
  }
}

When you run this state machine, it produces the following output.

[
  {
    "MyNumbers": 123,
    "MyObject": {
      "MyKey": "MyValue"
    },
    "MyString": "This string contains commas, a double quote (\"), and a newline (\n)",
    "MyLetters": "abc",
    "MyArray": [
      1,
      2,
      3
    ]
  }
]

RetroSearch is an open source project built by @garambo | Open a GitHub Issue

Search and Browse the WWW like it's 1997 | Search results from DuckDuckGo

HTML: 3.2 | Encoding: UTF-8 | Version: 0.7.4