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Node.js processor

Node.js processor

  • Version

    3.2

  • Agent restriction

    None

  • Processing type

    Row by row & Bulk

  • Multi-input step

    Not Supported

The Node.js connector allows you to run JavaScript scripts using the Node.js runtime (version 20.4). Node.js connector might look very similar to JS mapper but is much more powerfull. Although Node.js ecosystem is very large, we provide only limited yet powerfull set of imported packages. For security reasons, your JavaScript code can only access limited set of imported types or packages and cannot use dynamic imports or require statement.

Possible uses of the Multi input step include facilitating branch synchronization and enabling the acceptance of multiple inputs within integration processes. By incorporating this feature, you can streamline complex integration tasks and achieve better scalability, parallel processing, and system responsiveness. For more information, refer directly to the article dedicated to the Multi-Input Step

Provided packages

Above packages may provide more types, but Integray exposes only limited set of types. All types are covered within Node.js processor examples.

In addition to above packages and types, runtime exposes following APIs 

/* Connector logger API */
log { 
  trace: (message) => void,
  debug: (message) => void,
  info: (message) => void,
  warn: (message) => void,
  /* Calling this method will force the connector to mark statement execution as failed */
  error: (message) => void,
}

/* wait specified amount of time */
sleep: (timeout) => Promise<void>

// for example sleep for 1 second
await sleep(1000);

Configuration

Node.js statement

JavaScript statement to be executed using the Node.js service.

The output of the connector expects an array of objects that must match the output schema in structure. The defined Statement represents the body of the async function whose output is parsed and returned as the output of the connector itself. The last line of the Statement must therefore be:

return <array_of_objects>;

Example

return [
    { "hello": "Alice" }
];

Note

As the statement is async you must ensure that all Promises are awaited. Non-awaited code may not complete before your statement code.

Accessing input data

If the input schema is selected, the input data will be predefined in the inputData variable (see Predefined Variables). Input data is always in the form of an array, even if it contains a single record. Thus, individual records must be accessed via an index (e.g. inputData[0]) or via any mapping or iteration function (map(), foreach, etc. - see examples).

Predefined variables

Variable Data type Description
inputData array Input data processed by JSON.parse() into an array.
outputData array Output that can only be used when the script fails.
DataCheckpoint any Value of stored column on last row from last TaskRun.
TaskRunID number ID of the currently executing task run.
EndpointTokenID bigint When a task is executed via linked endpoint, variable contains ID of the token used for authorization (see table below).
EndpointTokenName string When a task is executed via linked endpoint, variable contains Name of the token used for authorization (see table below).
Authorization token EndpointTokenID EndpointTokenName
None null null
Primary 0 "Primary"
Secondary ID of the used seconday token Name of the used seconday token

Data checkpoint column

The data checkpoint column is a column (field), from which the platform takes the last row value after each executed task run and stores it as a Data checkpoint. The data checkpoint value can be used in the JS statements to control, which data should be processed in the next run. You can refer to the value using the predefined variable DataCheckpoint. Example of use: processing data in cycles, where every cycle processes only a subset of the entire set due to the total size. If you use e.g. record ID as a data checkpoint column, the platform will store after each cycle the last processed ID from the data subset processed by the task run. If your statement is written in a way that will evaluate the value in data checkpoint against the IDs of the records in the data set, you can ensure this way, that only not processed records will be considered in the next task run.

Input & Output Schema

Input

Data schema is optional

The connector does not expect a specific schema. The required data structure can be achieved by correct configuration. Although the selected connector doesn't require a schema generally, the individual integration task step may need to match the output data structure of the preceding task step and use a data schema selected from the repository or create a new input schema.

Output

Data schema is mandatory

The connector requires mandatory input or output data schema, which must be selected by the user from the existing data schema repository or a new one must be created. The connector will fail without structured data.

Release notes

3.2.1.

  • The primary input data is set to null if the input schema is not selected or the data is unavailable.
  • Multi input feature included - more info here

3.1.2.

  • Fixed processing sensitive errors

3.1.0

  • First release

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