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    MySQL as a Source
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    MySQL as a Source

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    Article summary

    MySQL is an open-source relational database management system. It is widely used for storing, managing, and retrieving structured data, making it a popular choice for web applications, content management systems, and various other software solutions.

    Authorize the Connection to MySQL

    In order to connect MySQL as a data source, you will need to first authorize the connection to your MySQL.

    Create a MySQL Data Source

    1. On the Sources page, click on the Create Source button and select the connector from the list.
    2. From the drop-down menu, choose your account.
      Didn't find your account?

      Click on Add new Account at the bottom of the drop-down and follow the on-screen prompts. You can also go to the Authorizers tab and click on Add New Service.

    3. Name your data source, choose your MySQL server and fill in your SQL statement.
    4. Configure your snapshotting preferences. Choose your sync frequency or the exact synchronization time under Show advanced settings.
      DATADDO TIP

      If you need to load historical data, please refer to the Data Backfilling article.

    5. Preview your data by clicking on the Test Data button in the top right corner. You can adjust the date range for a more specific time frame.
    6. Click on Save and congratulations, your new data source is ready!
    WARNING

    Dataddo relies on your query statements to accurately retrieve the right data. By choosing specific statements, you guide Dataddo's actions on your data.

    Every command in your query statement directly affects your database. Special attention is required when using commands likeDELETE or UPDATE, which are more impactful than a simple SELECT.

    SQL Statement Examples

    1. Extract all data from the clients table:
      SELECT * FROM clients
    2. Extract the name, email, company columns from the clients table:
      SELECT name,email,company FROM clients
    3. Extract data from specific rows (rows 240,000 to 800,000) from all columns from the clients table:
      SELECT * FROM clients  
LIMIT 2400000,  
800000
    4. Extract all data from columns for a specific country from the clients table:
      SELECT * FROM clients WHERE countryCode = 'USA'
    5. Extract all data from all columns if a job title contains a specific position from the clients table:
      SELECT * FROM clients WHERE jobTitle = '%Manager%' OR company= 'Dataddo'

    Limitations

    Only Append is Possible

    As neither delete or update operations are perfomed during data delivery, data is inserted in append-only mode.

    In certain cases, the append-only solution might have drawbacks due to the growing size of the database. The best solution is to define an AFTER INSERT trigger that will delete historical data as the retention period can be customized. In the example below, the trigger deletes all the rows meeting the insert_date < DATE_SUB(NOW(), INTERVAL 1 DAY) conditions.

    DELIMITER $$  
   
CREATE TRIGGER trigger_name  
    AFTER INSERT  
    ON table_name  
BEGIN  
    DELETE FROM table_name WHERE insert_date < DATE_SUB(NOW(), INTERVAL 1 DAY)  
END$$      
   
DELIMITER ;

    Troubleshooting

    Context Deadline Exceeded Error

    ERROR CODE

    rpc error: code = DeadlineExceeded desc = context deadline exceeded

    This issue may be caused by extracting data over an extended timeframe. Use WHERE or LIMIT clauses in your SQL query to manage the size and scope of the data extraction.

    1. Use WHERE to specify the date range.
      SELECT * FROM your_table
WHERE date_column BETWEEN '202X-01-01' AND '202X-01-31';
    2. Use LIMIT to specify the specify the maximum number of records to return.
      SELECT * FROM your_table
LIMIT 1000;
    3. Combine WHERE and LIMIT for more precise control. The query in this example will return the first 1000 records where the date is after January 1, 202X.
      SELECT * FROM your_table
WHERE date_column > '202X-01-01'
LIMIT 1000;

    Related Articles

    Now that you have successfully created a data source, see how you can connect your data to a dashboarding app or a data storage.

    Sending Data to Dashboarding Apps

    Sending Data to Data Storages

    Other Resources

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