Schema Evolution

Telemetry is designed to be a robust and flexible tool for ingesting and analyzing data, accommodating changes in your data structure over time through a process known as schema evolution. This feature allows you to extend your data schemas without disrupting existing queries or requiring extensive rework of your data pipelines. However, certain constraints must be observed to ensure smooth schema evolution, particularly regarding data type enforcement. This guide outlines how schema evolution works in Telemetry, providing examples of valid and invalid schema changes.

What is Schema Evolution?

Schema evolution refers to the ability to modify the structure of your data schema over time while maintaining compatibility with existing data. In a dynamic environment where data structures can change as new features are added or business needs evolve, schema evolution allows for these changes to be incorporated without requiring a complete overhaul of your database.

Valid Schema Evolution Scenarios

Here are some common examples of valid schema evolution scenarios that Telemetry supports:

1. Adding New Fields

   You can add new fields to your existing schema without affecting existing data or queries. For example, if you initially stored user data with only name and email fields, you could later add a phone_number field:

   // Original schema
   {
     "name": "John Doe",
     "email": "[e-mail]"
   }
   
   // Evolved schema
   {
     "name": "John Doe",
     "email": "[e-mail]",
     "phone_number": "555-1234"
   }

   In this case, the new phone_number field can be added without any disruption, and your existing queries will continue to function as expected.

2. Adding Nested Structures

   You can also evolve your schema by adding nested structures. For instance, if you initially had a flat structure but later needed to include additional details, such as an address, you could add a nested JSON object:

   // Original schema
   {
     "name": "John Doe",
     "email": "[e-mail]"
   }
   
   // Evolved schema with nested structure
   {
     "name": "John Doe",
     "email": "[e-mail]",
     "address": {
       "street": "123 Main St",
       "city": "Anytown",
       "zip_code": "12345"
     }
   }

   This change is backward-compatible, allowing you to introduce more complexity into your data model without disrupting existing processes.

Invalid Schema Evolution Scenarios

While Telemetry allows for many flexible schema changes, certain modifications can lead to issues with data ingestion. Below are examples of schema changes that are considered invalid:

1. Changing Field Types

   Changing the data type of an existing field is not supported. For instance, if a field was originally defined as an integer, you cannot change it to a string without causing ingestion failures. Consider the following scenario:

   // Original schema with integer field
   {
     "user_id": 123,
     "name": "John Doe"
   }
   
   // Invalid schema evolution (changing int to string)
   {
     "user_id": "123", // This change will cause errors
     "name": "John Doe"
   }

   Attempting to send data with user_id as a string when it was initially defined as an integer will result in Telemetry rejecting the data. This is because Telemetry enforces data types to maintain consistency and ensure the reliability of SQL queries.

2. Removing Fields

   Removing a field from your schema can lead to issues if there are existing queries or data pipelines that depend on that field. For example:

   // Original schema
   {
     "user_id": 123,
     "name": "John Doe",
     "email": "[e-mail]"
   }
   
   // Invalid schema evolution (removing a field)
   {
     "user_id": 123,
     "name": "John Doe"
     // 'email' field removed
   }

   Removing the email field would break any existing queries or data processes that rely on this field, leading to potential data loss or query failures.

Best Practices for Schema Evolution

To make the most of schema evolution in Telemetry, consider the following best practices:

• Plan for Evolution: When designing your schema, anticipate future changes. Use nested structures where appropriate to allow for growth.

• Avoid Type Changes: If you anticipate that a field's data type might need to change, consider using a new field name rather than modifying the existing field.

• Test Changes in a Staging Environment: Before deploying schema changes in production, test them in a staging environment to ensure they don't disrupt existing processes.

• Document Schema Changes: Maintain thorough documentation of your schema and any changes made over time. This will help in debugging and understanding the evolution of your data model.

Schema evolution is a powerful feature in Telemetry that allows you to adapt your data structures over time without disrupting your workflows. By following best practices and understanding the limits of what can and cannot be changed, you can maintain a flexible yet consistent data schema, enabling you to harness the full potential of Telemetry for your data analysis needs.