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Archiving Process Overview

The Historian module process to archive data is composed of three steps:

1. An event triggers the request to archive a group of values. You configure two types of events (Trigger or Tag Change) when creating a Historian TableHistorianTable.

2. The Historian archives the values in the Archive Storage Location after the trigger. You can use SQL databases or a Tag Provider when configuring the Archive Storage Location.

3. If you enable the Store and Forward feature, the system executes the data synchronization. This option stores data in a local database if the configured database is unavailable and sends it to the target when it becomes available.

In the following sections, you find additional details regarding each step.

Triggering Events

In the platform, two possible actions can initiate the archiving process. You can configure a Trigger based on a Tag or always save the Tags' value changes using the Save on Change option.

Trigger

You have three options to define as triggers in the Historian module:

• A Tag value.
• A Tag property.
• Any object from the runtime namespace, such as Server.minute.

Whenever there's a change in the object's value, it creates an archive request event.

Triggers are limited to Tags falling under the domain of the Server or objects situated in server-side namespaces to ensure compatibility with the Historian process. This restriction exists because the Historian process operates exclusively on the Server computer.

You can choose one Trigger for each Historian Table HistorianTable. When the trigger happens, all current values of Tags and objects connected to that Historian Table will be archived, regardless of whether or not they have a new value.

Save On Change

When creating or editing a Historian Table HistorianTable, you set the Save on Change OnChange option as the Trigger.

When you enable the Save on ChangeSaveOnChange, the Historian module continuously verifies all Tags connected to each Historian TableHistorianTable. As a Tag changes its value, the archive request event is generated. Only the Tag whose value changed will be archived.

Archiving Data

After the archive request is created, the platform system will check how the data will be stored depending on the Archive Location StorageLocation of the current Historian TableHistorianTable. You configure this option when creating the Historian TableHistorianTable.

Archiving the data will differ if you use a SQL database or TagProvider as a Historian.

Archiving to SQL database (Tag Historian)

The Datasets module has a pre-defined object named Tag Historian TagHistorian. By default, a SqlLite database is used, but you can choose other databases. Access the Historian Tables HistorianTables to learn how to do it.

When archiving to the SQL database defined by the TagHistorian object, you can choose between the Standard and Normalized table schemas.

Standard Tables

If you use Standard standard tables, both Trigger and Tag Change events result in a single additional row in the database. Each column in the table corresponds to a Tag in the Historian Table HistorianTable group, ensuring that all tags receive an entry, even if only one Tag has a new value.

The row's timestamp is determined by the Trigger object when the archive event is triggered or by the Tag timestamp that initiated the archive request in the case of On Tag Change OnTagChange events. All tags listed in the associated Historian Table HistorianTable are stored, independent of whether they have new values, sharing a single timestamp as defined earlier. In the case of On Tag Change OnTagChange events involving multiple tag value changes, a single row is inserted with all tags in the group, utilizing the timestamp of the Tag that triggered the event.

Standard

Tables Schema

The following table describes all existing columns from a Standard SQL Table containing the following columns:

You can usually assign up to 200 tags to each historian tableHistorianTable. However, the exact number can vary depending on how many columns your target database can accommodate. As a best practice, define tags in the same table if they have similar storing rates and process dynamics.

Normalized Tables

Normalized tables archive data only after an On Tag Change events. If you check the Normalized feature when creating or editing the Historian TableHistorianTable, the Trigger option is disabled. 

Normalized tables store In this table schema, each one has only the TimeStamp of Tag, the ID of the Tag, and the Value of Tag that generated the archived event.

Normalized Tables Schema

The system automatically creates four more tables:

• TableName_BIT
• TableName_FLOAT
• TableName_NTEXT
• TableName_REAL

The following table describes the schemas used by the created tables.

Archiving Externally using a TagProvider

When archiving data externally using a TagProvider, the external system defines the schemas. It determines the structural organization, naming conventions, and other specific settings.

You need to specify the Protocol to add a new Archive Storage Location using a TagProvider. The Protocol is an intermediary between the solution you build with FramewroX platform and the external data historian systems. They interpret and translate data formats, protocols, and other communication specifics to ensure seamless data archiving and retrieval. Currently, the platform provides three protocol options to connect using TagProviders:

• CanaryLabs: A robust data historian system that's optimized for real-time data collection and analytics. When archiving to CanaryLabs, the data is stored in a highly compressed format that facilitates faster retrieval and analytics.

• InfluxDB: An open-source time-series database designed for high availability and real-time analytics. InfluxDB is particularly useful when working with large sets of time-series data where timely data retrieval is of the essence.

• GE Proficy: A comprehensive platform that provides real-time data collection and advanced analytics capabilities. GE Proficy is a scalable system that integrates and analyzes vast amounts of industrial data.

You can use the Store and Forward feature when configuring a new Archive Location StorageLocation using TagProvider.

Using Store and Forward

The Store and Forward feature ensures you will not lose data if the system can't connect with the external database.

When you define an Archive Locasion StorageLocation using a Tag Provider TagProvider and disable Store and Forward, the archive requests events are sent directly to the external database as the events occur, independent of an existing working connection. A built-in protection exists for SQL-Dataset-Tag Historian targets with Normalized tables, buffering new rows and including them in the database every 5 five seconds.

Store and Forward

pProcess

When the Historian module receives an archive request, it'll try to store the data in the Archive Storage Location. If unsuccessful, it stores the data in a locally created SQLite database. After an unsuccessful attempt, the Historian module will attempt to copy data from the local SQLite database (rows inserted when the Target database was inaccessible) to the Target Database every 5 seconds, in maximum blocks of 250 rows.

Historian tables HistorianTables are verified within a 4-second window. If not all tables are processed in time, the verification continues in the next 5-second cycle. If the copy process to the Archive Location StorageLocation succeeds, meaning the connection was reestablished, the copied rows are removed from the temporary SQLite cache. If the temporary SQLite database is empty after the process, it is deleted.

In applications with a high volume of data and several tables to be synchronized, the data availability in the Archive Location StorageLocation (external database) may take some time. The synchronization velocity depends on the insertion performance of the main database and the local databases' insertion performance (SQLite). In most applications, the Store and Forward synchronization process takes up to 1 second per table.

Due to the possible synchronization restrictions, it's essential to take the following points when deciding the database system to be used in your solution:

• For large projects with significant data volumes, it's recommended to use robust databases like SQL Server or Oracle for better performance.
• SQLite has a 10GB limit and limited performance and is suitable for smaller data models. The Keep a Local Copy feature works well for projects not requiring immediate synchronization, especially if the main database experiences occasional unavailability due to other projects or third-party software usage.

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