Datasets and SQL Queries

Purpose and Key Concepts

The purpose of the Dataset module in FrameworX is to facilitate efficient data exchange between the platform and various external databases and text files, thereby creating a robust data infrastructure that empowers other real-time modules to function effectively.

Dataset DBs

In order for the Dataset module to communicate with an external database, a connection must be created with certain parameters. These connections, which are created within the Datasets → DBs section of the module, are referred to as Dataset DBs.

Dataset Queries

In the context of the Dataset module, a Dataset Query refers not only to an SQL query string, but also to a Project object that has a logical name, an SQL query related to that logical name, and other parameters defined within the Datasets → Queries section. There are many ways to automatically map the results of a query execution with Tags.

Dataset Tables

A Dataset Table is a logical name that is created within a project to set up access to a specific table in a connected database. These tables are listed within the Datasets → Tables section of the module. The Tags in the real-time database can easily be mapped to columns in the tables to perform insert, update, or read operations.

Dataset Files

A Dataset File is a logical name that defines parameters for reading and writing files in ASCII, Unicode, or XML formats. To manage the Dataset Files go to Datasets → Files. 

Using Tags in the Dataset module

In the Dataset module, you can utilize Tags from your project as dynamic placeholders within SQL queries or file names. This capability facilitates real-time data manipulation within the query strings. Moreover, you can map the results of the queries to Tags, or employ Tags to populate values when inserting or modifying existing records. 

Understanding the Datasets module

Features Highlights

• SQL Query Support: Enables efficient data extraction, manipulation, and transformation from a diverse set of sources.
• Integration with External Data Sources: Provides seamless data collection and analysis from a wide range of external sources, including SQL databases and CSV files.
• Visual Query Editor: Facilitates the creation and editing of advanced SQL queries, eliminating the need for extensive SQL knowledge.
• Distributed Architecture: Supports concurrent requests and offers flexibility in processing data either on the client or server side. 
• Stored Procedures Execution: Encourages advanced data manipulation and analysis within the platform.
• Real-Time Execution:  Enables execution of SQL queries with real-time data, offering customization of parameters and timely analysis of generated data.

Data Utilization

The data retrieved from databases can be utilized in various ways throughout your project:

• In the Displays Module: Visualization tools like DataGrids can present query results on screens and dashboards, creating custom views of the data that are accessible and easy to understand for your users.
• In the Scripting Module: Custom scripts can reference query results and trigger specific actions, such as sending notifications, updating tags, or performing calculations, thereby implementing complex logic based on database data.
• In the Alarm Module: Data can be used to configure or update alarm conditions, triggers, and actions, enabling the creation of dynamic alarm systems that respond to data changes.

In a similar fashion, various modules can add data to your SQL databases. For instance, the Displays Module can log operations inputs and commands, the Scripting Module can calculate analytics, and the Alarm and Historian Module can retain long-term records and audit trails. Essentially, the Dataset module drives bi-directional real-time communication between all Project Modules and the SQL databases.

Processing Data Requests

• Datasets Data Server Service

The Datasets Data Server Service forms an integral part of the Datasets module, ensuring high performance and seamless integration while exchanging structured collections of data in real-time. It efficiently processes data requests from other modules, enabling connectivity with various components such as HMI/SCADA screens, scripting, and reporting tools.

• Default Databases Used when Running the Project

The Dataset module also serves as a data storage configuration hub for other modules. The Alarm and Historian modules, which generate events and records for long-term retention, utilize the Dataset module for defining data storage. Similarly, the Security Module can utilize user definitions from an external database defined within the Dataset module.

Data Source Virtualization

Data Source Virtualization is an advanced feature that simplifies data management across multiple data sources. It provides a unified interface for accessing, querying, and manipulating data, regardless of the underlying data storage technology. This feature allows modifications to the underlying data sources repositories without impacting the rest of the application.

• Agnostic, Standards, Centralized Management

Adhering to industry standards, the module is designed to work seamlessly with various data storage technologies. It enables centralized management of data connections and configurations, streamlining the process of integrating different data sources into your projects.

For a more detailed explanation of how the Dataset module works, please refer to the page Advanced Dataset Guide.

Configuring the Dataset module

Configuration Workflow

The typical configuration workflow for the Dataset module has the following sequence:

Establish Database Connections:

1. Navigate to Datasets → DBs to create essential database connections.
2. Gather connection details for project databases.
3. Leverage the built-in SQLite database for temporary development purposes.

Prepare Queries:

1. Access Datasets → Queries to craft queries using the Visual Query Editor or provided SQL strings.
2. Assign logical names (Dataset.Query) to identify and streamline query usage.
3. Implement a virtualization model for seamless transition to the production database.

Modify Queries for Real-time Interaction:

1. Fine-tune queries at Datasets → Queries by adding real-time parameters.
2. Example: Transform "WHERE col1 = 5" to "WHERE col1 = {{tag.Test}}".

Manage Project Tables:

1. Navigate to Datasets → Tables for direct management of database tables.
2. Connect inserted data with project Tags for efficient data handling.
3. Utilize tables directly when all necessary information resides in a single table.

Configure Stored Procedures:

1. Extend functionality at Datasets → Queries by configuring and executing Stored Procedures.
2. Utilize the same interface for defining Stored Procedures as for queries.

Exchange Data with Files:

1. Establish file-based data exchange configurations at Datasets → Files.
2. Configure interactions with plain text or XML files as needed.

Utilize Logical Objects:

1. Leverage logically named objects (Queries, Tables, Files) throughout the project.
2. Examples: Apply in script calculations, visualization displays, and other project components.

Managing DB Connections

When using SQLite databases, the Module Dataset can automatically create the Database localy if they don't already exist; for other database types, the database itslef must already exist before you set your connection.

There are four database connection already created in any new Project:

Any of them can be customized to any type of database. 

The selection of best storage location depends on all kind of factors, from internal company procedures to the volume of data and how the data shall be used. Therefore, that is decision to each Project according to its requirements.

If needed to use another database for the pre-defined connections, execute the following steps:

1. Rename or Delete the previous DB. This step is necessary, as the system would not allow to create two objects with the same name. 

2. Crate a new DB with the same name of the previous DB, with the required Database and connection strings.
   

Check the Dataset DBs guide for comprehensive details.

Dataset Queries Configuration

Users can enhance data manipulation by configuring queries with SQL statements in the Dataset module. The Visual Query Editor simplifies the process, allowing users to drag and drop tables, define relationships, and apply filters through an intuitive graphical interface. Created queries can be saved and executed seamlessly within the module.
Check the Visual SQL Query Builder page for complete information.
Check the Dataset Queries guide for comprehensive details.

Dataset Tables Configuration

Effortlessly manage data tables within the Dataset module. Configure table properties such as structure, relationships, and data types. The intuitive interface streamlines customization, ensuring efficient data handling.
Check the Dataset Tables guide for comprehensive details.

Dataset Files Configuration

Customize file interactions in the Dataset module with ease. Configure file-related properties, define formats, and establish connections. The module provides a user-friendly interface for managing various file types seamlessly.
Check the Dataset Files guide for detailed configuration instructions.

Working with the Dataset module

Runtime Execution

One of the key features of the Dataset module is the ability to execute SQL queries and retrieve data in real-time. Here are some ways to leverage the runtime execution features of the Dataset Module:

• Create SQL queries to retrieve data from external databases.
• Use query results to trigger events and actions within the platform environment.
• Configure event triggers based on specific query criteria, such as changes to a specific data point or a threshold value being exceeded.

The Dataset module can be easily integrated with other modules within the software environment. Here are some examples of how the Dataset module can be used in conjunction with other modules:

• Alarm Manager: Configure alarms based on query results to trigger notifications and actions.
• Visualization: Display query results on screens and dashboards using DataGrids and other visualization tools.
• Scripting: Use query results to trigger custom scripts and perform complex data processing and analysis.

By leveraging these integration options, users can gain greater insight and control over their data sources within the platform. With the ability to execute SQL queries and trigger actions based on query results, the Dataset module provides a powerful set of tools for working with data.

Troubleshooting and Best Practices

Common #Issues and Solutions

#Issue: Connection loss between project and database

Database Timeout Configuration: The database may have a timeout setting that automatically disconnects idle connections after a certain period of time. It's recommended to check the database's timeout setting and adjust it, if necessary, to ensure that the connection remains active overnight.

Power Settings: It's also suggested to check the computer's power settings to ensure that it's not entering sleep or hibernation mode during a certain idle moment, which could cause loss of connection to the database. Adjusting these settings to keep the computer active during these idle moments may resolve the issue.

#Issue: Database Connection Problem

In the DB configuration, there is always a "Test" button to ensure that the connection is happening correctly. When there is a problem, the return of this button is an error message, usually returned by the database provider itself. The most common errors are: invalid user, invalid password, computer without access to the database, incorrect form of authentication.

#Issue: Error accessing the Database Table

Once the connection is established, the Table configuration is specific to a table. In the "Table" combobox, the list of available tables automatically appears. It is possible, via script, to change which table will be accessed. However, care must be taken that the table exists and that the configuration is done using the correct name. The same care must be taken when Queries are used, as it is the user's responsibility to type the correct table name, as well as the syntax of the separators.

#Error in the syntax of the Query

It is the user's responsibility to type the correct SQLStatement of a query using the QueryBuilder. Table name, column, values, all can generate an error if used incorrectly. For example: comparing different types may not return the expected result, strings in general should be in single quotes. The separators and clauses available can vary between databases, for example:

SQLServer

SELECT TOP 10 * FROM table WHERE column = value

SQLite

SELECT * FROM table WHERE column = value LIMIT 10;

Oracle

SELECT * FROM table WHERE column = value AND ROWNUM <= 10;

or new Oracle version

SELECT * FROM table WHERE column = value FETCH FIRST 10 ROWS ONLY;

IBM DB2

SELECT * FROM table WHERE column = value FETCH FIRST 10 ROWS ONLY;

#ServerIP without TWebServer running on the remote machine

In some cases, the computer may not have access to the database. In this case, it is possible to create a gateway, routing the commands to be executed on the computer that has access to the database. The ServerIP field should be configured with the IP and port (<IP>:<port>), pointing to this computer that has access permission. This computer must have the software with the TWebServer running installed. It will automatically perform this gateway service and send the commands to the database.

#DataTable returned NULL

When the return of a select or query is null, some error has occurred. Some common errors include: connection failure with the database, table not found, Dataset module is not running, incorrect query syntax. Check the return of the method using WithStatus when using a synchronous method or use the LastStatus and LastStatusMessage property when using asynchronous mode.

#DataTable returned with 0 rows

When this happens, in general, there is a connection with the database and the table name is correct. In this case, the search condition is usually wrong, or the table is really empty. Check if the column names are correct and if the separators and clauses are valid.

#Dataset module is down

Although the TServer is responsible for forwarding requests to the database, the management and communication with the TServer is done by the Dataset module, as well as the treatment of responses. Therefore, if you are having basic problems with access and execution of database access, the first thing to check is whether the module is set up to run and is actually running.

#Very high response time

Sometimes, it may seem that access to the database is not being made, but what might actually be happening is that some accesses can return a very large amount of data, or the database may be overloaded, or with a bad configuration, causing it to have a low performance. Or even, the network itself can be overloaded and slow, and all these factors can impact the response time. In these cases, it is important to execute the Query directly in the database environment to be sure that the problem is not on the side of the database. Do this and check how long the database itself takes to execute the query. It is also worth checking the volume of data exchanged to have an idea of ??the related side effects.

#Update of a table with the wrong schema (select before update)

The Dataset module uses ADO technology, and many things are resolved at the level of this API. When we are going to perform an Update on a table, the schema of the table and controls in the .Net DataTable type are used. Therefore, if you are going to perform an update passing a Tag or .net DataTable object as a parameter, it is important that this object respects the schema of the destination Table in the database. Normally, a Select command must have been given at some point to obtain the correct schema used by the bank. After this, it is easy to add, remove, and modify values in this DataTable and update it back to the physical table in the database.

#Where condition CaseSensitive

Case sensitivity in a WHERE clause depends on the database and the configuration of the database you are using. For example, in MySQL, queries are case-insensitive by default, which means 'abc' and 'ABC' would be considered equal. However, this can be changed with specific database settings. In SQL Server, case sensitivity is also determined by the database configuration. In PostgreSQL, queries are case-sensitive by default, so 'abc' and 'ABC' would be considered different. Therefore, it really depends on the specific database and the settings of that database. If you need to ensure case-insensitivity in a query, you can use functions like UPPER() or LOWER() to convert all values to upper or lower case before comparison. For example:

SELECT * FROM table WHERE LOWER(column) = LOWER(value);

This query will return records where the column matches the value, regardless of capitalization.

Best Practices and #Recommendations

#recommendation: Error accessing the Database Table

Dataset Runtime Attributes

The Dataset namespace  exposes properties and methods of the .NET objects used by the Dataset module execution.

For more information on namespaces and objects, go to Objects and Attributes.

Dataset.Query.QueryName.CursorIndex + 1

Dataset.DB.DatabaseName.OpenStatusMessage

Dataset.DB.ProviderName.ConnectionString

This section describes only some commonly used properties, for the full list properties and methods, go to the Namespaces Reference.

In this section: