Using Microbatch for Incremental Models

Explores the microbatch method for incremental models, designed for processing very large datasets in smaller batches for improved reliability and performance.

The microbatch method processes incremental updates in smaller batches, designed specifically for very large time-series datasets. It's particularly valuable when dealing with datasets that are too large to process in a single operation.

The microbatch method is currently in Beta. Features and syntax may change in future releases.

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When to Use Microbatch Method

• Very large time-series datasets

• When reliability is crucial

• When processing needs to be more resilient

• When database resource limits are a concern

• For tables with billions of rows

• When single-transaction operations time out

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Advantages and Trade-offs

Advantages	Trade-offs
More efficient for large datasets	More complex setup
Better error handling	Requires careful batch size tuning
Improved resilience	Only available on certain platforms
Reduced memory usage	Slightly more overhead than single-batch operations
Can work around query timeouts	Still in beta status

Example Implementation

{{
  config(
    materialized='incremental',
    incremental_strategy='microbatch',
    unique_key='id',
    incremental_predicates=[
      "DBT_INTERNAL_DEST.event_time > dateadd(day, -7, current_date)"
    ]
  )
}}

SELECT 
    id,
    event_type,
    event_time,
    user_id,
    properties
FROM {{ ref('stg_events') }}

{% if is_incremental() %}
    WHERE event_time > (SELECT MAX(event_time) FROM {{ this }})
{% endif %}

In this example:

• Updates are processed in smaller batches

• incremental_predicates limits the scope of each operation

• The overall process is more resilient to timeouts and failures

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How It Works

When you run an incremental model with the microbatch method, dbt:

1. Breaks the update into smaller chunks (batches)

2. Processes each batch in a separate transaction

3. Continues with remaining batches even if some fail

4. Reports overall success or partial failure

This approach is similar to the merge method, but with added reliability for very large datasets.

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Configuration Options

The microbatch method has several unique configuration options:

{{
  config(
    materialized='incremental',
    incremental_strategy='microbatch',
    unique_key='id',
    
    -- Microbatch-specific options
    microbatch_size=5000,  -- Number of rows per batch
    microbatch_limit=250,  -- Maximum number of batches
    incremental_predicates=[
      "DBT_INTERNAL_DEST.created_at > dateadd(day, -7, current_date)"
    ]
  )
}}

Option	Description	Default
microbatch_size	Number of rows per batch	10000
microbatch_limit	Maximum number of batches to process	500
incremental_predicates	Conditions to limit the scope of each batch	None

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Best Practices for Microbatch Method

1. Tune Batch Size - Find the optimal batch size that balances performance and reliability

2. Use Incremental Predicates - Always use predicates to limit the scope of the operation

3. Monitor Partial Failures - Set up alerting for when some batches fail but others succeed

4. Consider Recovery Strategies - Have plans for reprocessing failed batches

The microbatch method is still evolving but offers a promising solution for handling very large incremental models where other methods might time out or consume too many resources. It's particularly valuable for high-volume event data or IoT datasets where individual transactions might exceed database limitations.