JavaScript¶

支持运行原生的 JavaScript 脚本，实现自定义的业务逻辑。

配置详情¶

该算子的配置包括 General，Basic，Input/Output，和 JavaScript 的详细信息，各字段的配置如下：

General¶

名称	是否必须	描述
Name	Yes	算子名称
Description	No	算子描述
Stage Library	Yes	算子所属的库
Required Fields	No	数据必须包含的字段，如果未包含指定字段，则 record 将被过滤掉
Preconditions	No	数据必须满足的前提条件，如果不满足指定条件，则 record 将被过滤掉。例如：`${record:value('/value') > 0}`。有关 EL 语句的使用方法，参考 Expression Language。
On Record Error	Yes	对错误数据的处理方式，可选： Discard：直接丢弃 Send to Error：发送至错误中心 Stop Pipeline：停止流任务运行

Basic¶

名称

是否必须

描述

IO Mapping Method

Yes

选择数据输入点和数据输出点的对应关系。可选：

1:1：每行 Input/Output 参数配置中，一个数据输入点对应一个数据输出点
M:1：Input/Output 参数配置中，可配置任意数量的数据输入点和数据输出点

Quality Filter

No

根据数据质量过滤处理数据，只有符合质量条件的 record 才会进行此次处理

Input/Output¶

名称	是否必须	描述
Input Point	Yes	数据输入点，格式为：{模型标识}::{测点标识}，输入数据的 modelIdPath 和 pointId 必须匹配输入点，才能够进入后续计算。
Output Point	No	数据输出点，格式为：{模型标识}::{测点标识}，经过 JavaScript 脚本后的输出数据的 modelIdPath 和 pointId 必须匹配输出点，才能够作为真正的输出 record 。

JavaScript¶

名称	是否必须	描述
Script	Yes	编写自定义 JavaScript 脚本。其中 records 代表所有经过选中的点并经过质量控制后，流入的数据。

输出结果¶

运行自定义 Java 脚本后，该算子的输出结果包含在 attr 结构体中。

脚本开发指南¶

Java Script 中，若涉及 Long 类型数值的计算，如: 加减乘除/比较/…，需先使用 parseFloat() 将其转换成 Float 类型，再进行操作。示例:

record.value['time'] = parseInt(parseFloat(record.value['time'])/60000) * 60000;

/**
 * Available constants:
 *   They are to assign a type to a field with a value null.
 *   NULL_BOOLEAN, NULL_CHAR, NULL_BYTE, NULL_SHORT, NULL_INTEGER, NULL_LONG
 *   NULL_FLOATNULL_DOUBLE, NULL_DATE, NULL_DATETIME, NULL_TIME, NULL_DECIMAL
 *   NULL_BYTE_ARRAY, NULL_STRING, NULL_LIST, NULL_MAP
 *
 * Available Objects:
 *
 *  records: an array of records to process, depending on the JavaScript processor
 *           processing mode it may have 1 record or all the records in the batch.
 *
 *  state: a dict that is preserved between invocations of this script.
 *        Useful for caching bits of data e.g. counters.
 *
 *  log.<loglevel>(msg, obj...): use instead of print to send log messages to the log4j log instead of stdout.
 *                               loglevel is any log4j level: e.g. info, error, warn, trace.
 *
 *  output.write(record): writes a record to processor output
 *
 *  error.write(record, message): sends a record to error
 *
 *  sdcFunctions.getFieldNull(Record, 'field path'): Receive a constant defined above
 *                            to check if the field is typed field with value null
 *  sdcFunctions.createRecord(String recordId): Creates a new record.
 *                            Pass a recordId to uniquely identify the record and include enough information to track down the record source.
 *  sdcFunctions.createMap(boolean listMap): Create a map for use as a field in a record.
 *                            Pass true to this function to create a list map (ordered map)
 *
 *  sdcFunctions.createEvent(String type, int version): Creates a new event.
 *                            Create new empty event with standard headers.
 *  sdcFunctions.toEvent(Record): Send event to event stream
 *                            Only events created with sdcFunctions.createEvent are supported.
 *  sdcFunctions.isPreview(): Determine if pipeline is in preview mode.
 *
 *  sdcFunctions.dateTimeHelper.convertZonedDateTimeToTimestampInMilliseconds(String zonedDateTime, String
 *  dateTimeFormatterPattern): Convert zoned
 *  date time to timestamp in milliseconds.
 *  Example:
 *  var timestamp = sdcFunctions.dateTimeHelper.convertZonedDateTimeToTimestampInMilliseconds("2021-09-03
 *  05:00:00.000
 *  America/Chicago","yyy-MM-ddHH:mm:ss.SSS [VV]");
 *  //  expected timestamp =  1599127200000
 *
 *  sdcFunctions.dateTimeHelper.convertTimestampInMillisecondsToZonedDateTime(long timestamp, String
 *  dateTimeFormatterPattern, String timezoneId):
 *  Convert timestamp in milliseconds to zoned date time.
 *  Example:
 *  // var timestamp =
 *  sdcFunctions.dateTimeHelper.convertTimestampInMillisecondsToZonedDateTime(1599127200000, 'yyy-MM-dd
 *  HH:mm:ss.SSS [VV]',
 *  'America/Chicago');
 *  //  expected dateTime = '2020-09-03 05:00:00.000 America/Chicago'
 *
 * enosFunc.mcMin(Record,String pointID): Function for getting the minimum value of the measuring point;

 * enosFunc.mcMax(Record,String pointID): Function for getting the maximum value of the measuring point;

 * enosFunc.mcSum(Record,String pointID): Function for getting the sum of the values of the measuring point;

 * enosFunc.mcMean(Record,String pointID): Function for getting the average of the values of the measuring point;

 * enosFunc.mcCov(Record,String pointID): Function for getting the dispersion rate of the values of the measuring point;

 * enosFunc.input(Record,String pointID): Function for getting the value of the measuring point (supported data types are int、long、short、decimal、boolean、float、double);
 *
 * Available Record Header Variables:n *
 *  record.attributes: a map of record header attributes.
 *
 *  record.<header name>: get the value of 'header name'.
 */

// Sample JavaScript code

function generateOutput(record, outputPointId, outputValue) {
    var output_record = sdcFunctions.createRecord(true);
    output_record.value = record.value;
    output_record.value['pointId'] = outputPointId;
    output_record.value['value'] = outputValue;
    output_record.value['attr'] = record.value['attr'];
    output_record.value['assetId']=record.value['assetId']
    output_record.value['modelIdPath']=record.value['modelIdPath']
    output.write(output_record);
}

for(var i = 0; i < records.length; i++) {
  try {
    // Change record root field value to a STRING value
    //records[i].value = 'Hello ' + i;


    // Change record root field value to a MAP value and create an entry
    //records[i].value = { V : 'Hello' };

    // Access a MAP entry
    //records[i].value.X = records[i].value['V'] + ' World';

    // Modify a MAP entry
    //records[i].value.V = 5;

    // Create an ARRAY entry
    //records[i].value.A = ['Element 1', 'Element 2'];

    // Access a Array entry
    //records[i].value.B = records[i].value['A'][0];

    // Modify an existing ARRAY entry
    //records[i].value.A[0] = 100;

    // Assign a integer type to a field and value null
    // records[i].value.null_int = NULL_INTEGER

    // Check if the field is NULL_INTEGER. If so, assign a value
    // if(sdcFunctions.getFieldNull(records[i], '/null_int') == NULL_INTEGER)
    //    records[i].value.null_int = 123

    // Create a new record with map field
    // var newRecord = sdcFunctions.createRecord(records[i].sourceId + ':newRecordId');
    // newRecord.value = {'field1' : 'val1', 'field2' : 'val2'};
    // output.write(newRecord);
    // Create a new map and add it to the original record
    // var newMap = sdcFunctions.createMap(true);
    // newMap['key'] = 'value';
    // records[i].value['b'] = newMap;

    //Applies if the source uses WHOLE_FILE as data format
    //var input_stream = record.value['fileRef'].getInputStream();
    //try {
      //input_stream.read(); //Process the input stream
    //} finally{
      //input_stream.close()
    //}

    // Modify a header attribute entry
    // records[i].attributes['name'] = records[i].attributes['first_name'] + ' ' + records[i].attributes['last_name']    //

    // Get a record header with field names ex. get sourceId and errorCode
    // var sourceId = records[i].sourceId
    // var errorCode = ''
    // if(records[i].errorCode) {
    //     errorCode = records[i].errorCode
    // }

    // var record = records[i];
    // var targetPoint = 'pointId';
    // var minValue = enosFunc.mcMin(record,targetPoint);
    // var maxValue = enosFunc.mcMax(record,targetPoint);
    // var sumValue = enosFunc.mcSum(record,targetPoint);
    // var avgValue = enosFunc.mcMean(record,targetPoint);
    // var covValue = enosFunc.mcCov(record,targetPoint);
    // var inputValue = enosFunc.input(record,targetPoint);


    // Write record to processor output
    output.write(records[i]);
    }

     catch (e) {

      // trace the exception
      error.trace(e);

      // Send record to error
      error.write(records[i], e);
     }
}