Introduction: Time travel is a feature in Snowflake that allows users to query data as it appeared at a specific point in time. This feature is incredibly powerful and allows users to recover from mistakes, perform audits, and recover lost data.
How Time Travel Works: Snowflake uses a feature called micro-partitions to store data. Each micro-partition is a self-contained, immutable block of data that contains a snapshot of the data at a specific point in time. When a user updates or deletes data, Snowflake creates a new micro-partition with the updated data, leaving the original micro-partition intact. This allows users to query data as it appeared at any point in time, even if the data has been updated or deleted.
Using Time Travel: To use time travel in Snowflake, you can specify a point in time by using the TIMESTAMP or SYSTEM_TIME functions. The TIMESTAMP function allows you to specify a specific timestamp, while the SYSTEM_TIME function allows you to specify a number of minutes or hours in the past.
For example, let's say we have a table called "orders" that contains information about customer orders. We can use the following query to see the state of the data as it existed 30 minutes ago:
SELECT * FROM orders AS OF SYSTEM_TIME - INTERVAL '30' MINUTE;
This query will return the data as it existed 30 minutes ago, even if the data has been updated or deleted since then.
Time Travel Variations: There are two types of time travel in Snowflake: "Point-in-time" and "Time travel with Time-Travel Clone."
Point-in-time time travel allows you to query data as it appeared at a specific point in time. This is the type of time travel we have been discussing so far.
Time travel with Time-Travel Clone allows you to create a clone of a table as it appeared at a specific point in time. This is useful when you need to perform analysis on data as it existed in the past, without affecting the current data.
Conclusion: Time travel is an incredibly powerful feature in Snowflake that allows users to query data as it appeared at any point in time. By using micro-partitions to store data, Snowflake ensures that users can always access historical data, even if it has been updated or deleted. This feature is essential for performing audits, recovering lost data, and recovering from mistakes.