Dagster having a daily partitioned asset depend on the previous partition

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31-08-2024

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Leveraging Past Partitions in Dagster: Building Dynamic Dependencies

When working with partitioned assets in Dagster, you might encounter scenarios where a downstream asset needs to access data from the previous partition of an upstream asset. This allows for a more dynamic workflow, enabling analysis or computations that consider both current and historical data. This article explores how to achieve this dependency pattern, drawing inspiration from a Stack Overflow question by user [username] (link to original question).

The Challenge:

Imagine you have two daily partitioned assets: my_asset_a and my_asset_b. my_asset_a runs at 4 pm and my_asset_b at 6 pm. Your goal is to make my_asset_a depend on the previous day's partition of my_asset_b. This means if today is 2024-07-08, you want my_asset_a to access the output from my_asset_b generated on 2024-07-07.

Solution:

To achieve this, we can leverage Dagster's flexibility in defining asset dependencies. Here's a breakdown of the solution:

1. Defining Partitions:

   • We first need to define the daily partition definition. The Stack Overflow code snippet provides a starting point:

     from dagster import daily_partitions_def
     

2. Asset Definitions:

   • We create two assets, my_asset_a and my_asset_b.

   • my_asset_b is straightforward, simply taking the context.partition_key as input and performing its operation.

   • my_asset_a requires a slightly more nuanced approach. Instead of directly depending on my_asset_b, we utilize the DagsterContext to access the previous partition key:

     from dagster import AssetIn, AssetKey
     
     @asset(partitions_def=daily_partitions_def)
     def my_asset_a(context, my_asset_b):
         dt = context.partition_key
         previous_dt = context.get_previous_partition_key(AssetKey("my_asset_b"))
         return run_a(dt, my_asset_b, my_asset_b.get_partition(previous_dt))
     
     @asset(partitions_def=daily_partitions_def)
     def my_asset_b(context):
         dt = context.partition_key
         return run_b(dt)
     

   • Explanation:

     • context.get_previous_partition_key(AssetKey("my_asset_b")) retrieves the previous partition key for my_asset_b.
     • We then use this previous partition key to access the output of my_asset_b from the previous day: my_asset_b.get_partition(previous_dt).
     • run_a is your function that takes the current partition key (dt) and the outputs from my_asset_b for both the current and previous partitions.

Benefits and Considerations:

• Dynamic Data Access: This approach gives you the flexibility to utilize data from both the current and previous partitions, making your assets more context-aware.
• Avoiding Data Duplication: You avoid replicating the previous day's data within the current partition, keeping your data storage efficient.
• Error Handling: Be sure to handle scenarios where the previous partition data might be unavailable, such as on the first day of running the pipeline.

Conclusion:

By combining Dagster's powerful partition definitions with careful dependency management, we can create sophisticated workflows that effectively leverage data from past partitions. This flexibility unlocks a world of possibilities for building dynamic and responsive data pipelines. Remember to always consider error handling and data availability when implementing this pattern.