Examples

Reading tag and timestamp values from a MySQL database

In this example, SQL Client is used to connect to a MySQL database located in the same machine in order to extract the following data:

• Step 1: Configuration of the connection.

• Step 2: Configuration of the transaction.

The Query script for requesting the required columns from the data base:

$.output = "SELECT id, temperature, humidity, status, errorCode, ts FROM test_compact";

The Parser script iterates through the Dataset returned from the query, starting by iterating all rows (one row per sensor) and then iterating through the columns for each individual row (one column per signal):

for(const row of $.input.rows()){
  //Iterate through the columns of the Dataset for each row
  for(const col of $.input.columns()){
    //Get the name of the current column
    const column = col.getName();
    $.logger.debug("Column is %s", column);
    //Ignore id and ts columns
    if(column === "id" || column === "ts") continue;
    const tagData = {};
    //Set the destination tag using the format rowid_columnName
    tagData.tag = "sensor"+row.getValue("id")+"_"+column;
    //Get the value of the current column at this row
    tagData.value = row.getValue(column);
    //Set quality to 192
    tagData.quality = 192;
    //Set the ts to the value of the ts column
    tagData.ts = row.getValue("ts");
    $.logger.debug("Tag data is %j", tagData);
    //Push the tag data object to the output array
    $.output.push(tagData);
  }
}

• Step 3: Tag configuration settings for linking the data received from the database to tags.

Inserting historical data into a database

This example explains how to use a HistoricalTransaction to insert N3uron tag events into a database.

The database in this example is MySQL and as such, a syntax that is compatible with MySQL is used.

• Step 1: The target table should have the following schema:

• Step 2: The following screenshot shows the HistoryTransaction configuration:

• Step 3: The following data serialization script is used: 

const ds = new Dataset();
//Add all columns that are used to the Dataset
ds.addColumn("tag", "NVARCHAR");
ds.addColumn("number_value", "DOUBLE");
ds.addColumn("string_value", "NVARCHAR");
ds.addColumn("bool_value", "TINY_INT");
ds.addColumn("quality", "TINY_INT");
ds.addColumn("ts", "DATETIME");
for (const event of $.input) {
  const row = {
    //Tag is escaped with the string separator for MySQL
    tag: "'"+event.tag+"'",
    string_value: null,
    number_value: null,
    bool_value: null,
    quality: event.quality,
    ts: event.ts
  }
  //Depending on the type of the event, set the relevant field
  switch (typeof event.value) {
    case "number":
      row.number_value = event.value;
      break;
    case "string":
      //Add string quotes since string values need to be quoted in the query
      row.string_value = sprintf("'%s'", event.value);
      break;
    case "boolean":
      row.bool_value = event.value;
      break;
  }
  //Add the row to the Dataset
  ds.addRow(row);
}

//If the output row count is 0, return empty Dataset
if (ds.getRowCount() === 0) $.output = new Dataset();
else $.output = ds;

• Step 4: The next query builder is used:

//If the input Datasets has no rows, return an empty string to abort the query
if($.input.getRowCount() === 0){
  $.output = ""
} else {
  $.output += _insertData();
}

function _insertData(){
  const values = [];
  //Iterate through all the rows in the input Dataset
  for(const row of $.input){
    //String variable that holds the values to insert for each row
    let rowValues = "(";
    rowValues += row.getValue("tag")+",";
    rowValues += row.getValue("string_value")+",";
    rowValues += row.getValue("number_value")+",";
    rowValues += row.getValue("bool_value")+",";
    rowValues += row.getValue("quality")+",";
    //Timestamp is created using the MySQL function FROM_UNIXTIME(epoch).
    rowValues += sprintf("FROM_UNIXTIME(%d)", row.getValue("ts").getTime()/1000);
    rowValues += ")";
    //Push the row values into the values array
    values.push(rowValues);
  }
  //Return the query
  const query = sprintf("INSERT INTO n3_history (tag, string_value, number_value, bool_value, quality, ts) VALUES "+ values.join(",\r\n"));
  return query;
}

This query will execute in event mode (it only sends new tag events) and the trigger will be a buffer size trigger, which means that the transaction triggers whenever there are 500 events or more in the buffer.

Creating INSERT queries from parameters

This example deals with a common scenario where several tag values need to be periodically inserted into the database. Although a History Transaction is usually a more efficient way for this task, we will be using a Standard Transaction to illustrate how to work with Parameters. The database connection is assumed to be already created and working.

The syntax used in this example is compatible with MySQL. If the destination database is different, the syntax might require small changes within the query.

• Step 1: Configuration of the transaction.

• Step 2: Script used to create the query.

const TABLE_NAME = "n3_data";

$.output = insert();

function insert(){
  const query = sprintf("INSERT INTO %s (%s) VALUES (%s)", TABLE_NAME, getColumns().join(","), getValues().join(","));
  return query;
}

function getColumns(){
  const columns = [];
  //Iterate through all the parameters
  for(const p of Object.keys($.parameter)){
    //Use the parameter name as column name, and escape it using the escape character `
    columns.push(sprintf("`%s`", p));
  }
  return columns;
}

function getValues(){
  const values = [];
  //Iterate through all the parameters
  for(const value of Object.values($.parameter)){
    //Cast the value into the appropiate type and push it into the values array
    values.push(cast(value));
  }
  return values;
}
function cast(value){
  //If the value is null, the string NULL is returned
  if(value === null){
      return "NULL";
  }
  switch (typeof value){
    case "number":
      //If the value is a number, return it without changing
      return value;
    case "string":
      //If the value is a string, convert it to a MySQL string using '
      return "'"+value+"'";
    case "boolean":
      //If the value is a boolean, convert it to a 0 or 1 number
      return Number(value);
    default:
    //If the type is none of the above, throw an error since 
    //only the above types are supported in this example
      throw new Error("Unknown type for value: "+typeof value);
  }
}

Synchronizing data (from N3uron to a database)

In this example, data from N3uron is going to be synchronized with the database, so that both N3uron and the database contain the same data. The tags that will be synchronized come from an external device (whether this be a PLC, a Modbus device, or an OPC UA Server) and are set up as parameters in the transaction.

This example uses a Standard Transaction since this is the ideal transaction for this scenario.

The destination database in this example is MySQL and as such, the syntax for MySQL is used. 

• Step 1: The database destination table has the following schema:

• Step 2: Configuration settings for StandardTransaction:

• Step 3: Script used to create the query: 

$.output = sprintf("UPDATE `n3_db_sync` SET `setpoint`=%f, `status`='%s', `is_running`=%d",
  $.parameter.setpoint.value,
  $.parameter.status.value,
  $.parameter.is_running.value);

This query will be executed every 5 seconds and will update the "n3_db_sync" table with the current values in N3uron. This table has a single row, which contains the values that were synchronized between N3uron and the database.

Synchronizing data (from the database to N3uron)

This example is analogous to the previous one, except that the data flows in the opposite direction, from a database to N3uron tags. 

The destination tags in this example are not SQL Client source tags, instead, they are tags whose sources are other modules, such as Modbus, Siemens S7, etc. Like in the previous example we will use a Standard Transaction. It is important to note that these tags must have Read/Write permissions, otherwise, the write command will not succeed.

The destination database is MySQL, so the MySQL syntax is being used.

• Step 1: The destination table has the following schema:

• Step 2: The following StandardTransaction configuration is used:

• Step 3: The query script is as follows:

$.output = "SELECT `product1_demand`, `product2_demand`, `product1_label`, `product2_label`, `line1_active`, `line2_active`, `line3_active`, `line4_active` FROM db_n3_sync";

• Step 4: The parser script is as follows:

$.output.push({tag:"/PLC/product1_demand",value:$.input.getValue(0, "product1_demand")});
$.output.push({tag:"/PLC/product2_demand",value:$.input.getValue(0, "product2_demand")});
$.output.push({tag:"/PLC/product1_label",value:$.input.getValue(0, "product1_label")});
$.output.push({tag:"/PLC/product2_label",value:$.input.getValue(0, "product2_label")});
$.output.push({tag:"/PLC/line1_active", value: $.input.getValue(0, "line1_active")});
$.output.push({tag:"/PLC/line2_active", value: $.input.getValue(0, "line2_active")});
$.output.push({tag:"/PLC/line3_active", value: $.input.getValue(0, "line3_active")});
$.output.push({tag:"/PLC/line4_active", value: $.input.getValue(0, "line4_active")});

This transaction will be executed every 5 seconds and will write all data obtained in the first row of the query to the specified tags. 

Data Transformation: Converting Stringified JSON into Tag Data

In this example, a SQL client connects to a local MySQL database to extract specific data. The script iterates through rows and focuses on particular 'System' values. Once this data is selected, it is organized into individual JSON objects. These objects are then converted into strings before being passed to tags of string type. These tags can be further used, for example, by publishing their values through an MQTT client.

• Step 1: The source table has the following schema:

• Step 2: The source table has the following data:

• Step 3: The query script is as follows: 
  

  // TABLE_NAME => name of the target table of the database. For tables within a schema the name would be "schema.table_name"
  const TABLE_NAME = "mes";
  
  // WHERE => clause used in the SELECT query. It may be left blank when no WHERE clause is required.
  const WHERE = "";
  
  //COLUMNS => Array of the column names to retrieve from the table. Using "*" wildcard retrieves all columns from the table.
  const COLUMNS = [
    "PO",
    "System",
    "SystemPO",
    "DATE_FORMAT(POPlannedStartTime, '%Y-%m-%d %H:%i:%s') AS POPlannedStartTime",
    "DATE_FORMAT(POPlannedEndTime, '%Y-%m-%d %H:%i:%s') AS POPlannedEndTime",
    "Product"
    ];
  
  // The output can be generated using the select() function to retrieve data from the database.
  $.output = select();
  
  // This function returns a SELECT query used to retrieve data from the database
  function select(){
    let query = sprintf("SELECT %s FROM %s", COLUMNS.join(","), TABLE_NAME);
    // WHERE is added to the query if not blank.
    if(WHERE !== ""){
        query += " WHERE " + WHERE;
    }
    return query;
  }
  

• Step 4: The parser script is as follows:
  

  // INDEX_COLUMN => Column containing the tagpath (or alias) of the target tag.
  //    - If present, the tagpath (or alias) will be INDEX_COLUMN_VALUE.CURRENT_COLUMN_NAME.
  //    - If empty (""), the tagpath (or alias) will be the current column name directly.
  const INDEX_COLUMN = "System";
  
  // QUALITY_COLUMN = Column containing the quality of the tag. If empty (""), Good quality (192) will be used.
  const QUALITY_COLUMN = "";
  
  // DATE_COLUMN => Column containing the timestamp in DATETIME or equivalent type such as TIMESTAMP for PostgreSQL.
  //    If empty (""), NOW() will be used.
  const DATE_COLUMN = "";
  
  const systemValues = ['R112', 'R113', 'R114', 'R115'];
  const filteredData = {};
  
  for (const row of $.input.rows()) {
    const systemValue = row.getValue('System');
  
    if (systemValues.includes(systemValue)) {
      if (!filteredData[systemValue]) {
        filteredData[systemValue] = [];
      }
  
      const rowObject = {};
      
      for (const column of $.input.columns()) {
        const columnName = column.getName();
        const columnValue = row.getValue(columnName);
        $.logger.debug("%s: %s", columnName, columnValue);
        if (columnValue !== null && columnName !== 'System') {
          rowObject[columnName] = columnValue;
        }
      }
      filteredData[systemValue].push(rowObject);
    }
  }
  
  // Store the accumulated JSON for 'R112' and 'R113' in separate tags
  for (const system in filteredData) {
    if (Object.hasOwnProperty.call(filteredData, system)) {
      const jsonString = JSON.stringify(filteredData[system]);
      $.output.push({ tag: `${system}`, value: jsonString, quality: 192 });
    }
  }

  
  
• Step 5: Tag configuration settings for linking the data received from the database to tags.
  
  

• Step 5: Real-time Tag data visualization.

• Step 6: The internal structure of the tags.
  
  JavaScriptJavaScript

  [
    {
      PO: 12002,
      SystemPO: 12002001,
      POPlannedStartTime: "2023-12-28 10:00:00",
      POPlannedEndTime: "2023-12-28 11:00:00",
      Product: "Product_02",
    },
    {
      PO: 12003,
      SystemPO: 12003001,
      POPlannedStartTime: "2023-12-28 11:00:00",
      POPlannedEndTime: "2023-12-28 12:00:00",
      Product: "Product_02",
    },
    {
      PO: 12004,
      SystemPO: 12004001,
      POPlannedStartTime: "2023-12-28 12:00:00",
      POPlannedEndTime: "2023-12-28 13:00:00",
      Product: "Product_02",
    },
  ];
  

  
  

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