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Upgrading to v1.9

Resources​

What to know before upgrading​

dbt Labs is committed to providing backward compatibility for all versions 1.x. Any behavior changes will be accompanied by a behavior change flag to provide a migration window for existing projects. If you encounter an error upon upgrading, please let us know by opening an issue.

dbt Cloud is now versionless. If you have selected "Versionless" in dbt Cloud, you already have access to all the features, fixes, and other functionality that is included in dbt Core v1.9. For users of dbt Core, since v1.8 we recommend explicitly installing both dbt-core and dbt-<youradapter>. This may become required for a future version of dbt. For example:

python3 -m pip install dbt-core dbt-snowflake

New and changed features and functionality​

Features and functionality new in dbt v1.9.

Microbatch incremental_strategy​

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If you use a custom microbatch macro, set the require_batched_execution_for_custom_microbatch_strategy behavior flag in your dbt_project.yml to enable batched execution. If you don't have a custom microbatch macro, you don't need to set this flag as dbt will handle microbatching automatically for any model using the microbatch strategy.

Incremental models are, and have always been, a performance optimization — for datasets that are too large to be dropped and recreated from scratch every time you do a dbt run. Learn more about incremental models.

Historically, managing incremental models involved several manual steps and responsibilities, including:

  • Add a snippet of dbt code (in an is_incremental() block) that uses the already-existing table (this) as a rough bookmark, so that only new data gets processed.
  • Pick one of the strategies for smushing old and new data together (append, delete+insert, or merge).
  • If anything goes wrong, or your schema changes, you can always "full-refresh", by running the same simple query that rebuilds the whole table from scratch.

While this works for many use-cases, there’s a clear limitation with this approach: Some datasets are just too big to fit into one query.

Starting in Core 1.9, you can use the new microbatch strategy to optimize your largest datasets -- process your event data in discrete periods with their own SQL queries, rather than all at once. The benefits include:

  • Simplified query design: Write your model query for a single batch of data. dbt will use your event_time, lookback, and batch_size configurations to automatically generate the necessary filters for you, making the process more streamlined and reducing the need for you to manage these details.
  • Independent batch processing: dbt automatically breaks down the data to load into smaller batches based on the specified batch_size and processes each batch independently, improving efficiency and reducing the risk of query timeouts. If some of your batches fail, you can use dbt retry to load only the failed batches.
  • Targeted reprocessing: To load a specific batch or batches, you can use the CLI arguments --event-time-start and --event-time-end.

Currently microbatch is supported on these adapters with more to come:

  • postgres
  • snowflake
  • bigquery
  • spark

Snapshots improvements​

Beginning in dbt Core 1.9, we've streamlined snapshot configuration and added a handful of new configurations to make dbt snapshots easier to configure, run, and customize. These improvements include:

  • New snapshot specification: Snapshots can now be configured in a YAML file, which provides a cleaner and more consistent set up.
  • New snapshot_meta_column_names config: Allows you to customize the names of meta fields (for example, dbt_valid_from, dbt_valid_to, etc.) that dbt automatically adds to snapshots. This increases flexibility to tailor metadata to your needs.
  • target_schema is now optional for snapshots: When omitted, snapshots will use the schema defined for the current environment.
  • Standard schema and database configs supported: Snapshots will now be consistent with other dbt resource types. You can specify where environment-aware snapshots should be stored.
  • Warning for incorrect updated_at data type: To ensure data integrity, you'll see a warning if the updated_at field specified in the snapshot configuration is not the proper data type or timestamp.
  • Set a custom current indicator for the value of dbt_valid_to: Use the dbt_valid_to_current config to set a custom indicator for the value of dbt_valid_to in current snapshot records (like a future date). By default, this value is NULL. When configured, dbt will use the specified value instead of NULL for dbt_valid_to for current records in the snapshot table.
  • Use the hard_deletes configuration to get more control on how to handle deleted rows from the source. Supported methods are ignore (default), invalidate (replaces legacy invalidate_hard_deletes=true), and new_record. Setting hard_deletes='new_record' allows you to track hard deletes by adding a new record when row becomes "deleted" in source.

Read more about Snapshots meta fields.

state:modified improvements​

We’ve made improvements to state:modified behaviors to help reduce the risk of false positives and negatives. Read more about the state:modified behavior flag that unlocks this improvement:

  • Added environment-aware enhancements for environments where the logic purposefully differs (for example, materializing as a table in prod but a view in dev).

Managing changes to legacy behaviors​

dbt Core v1.9 has a handful of new flags for managing changes to legacy behaviors. You may opt into recently introduced changes (disabled by default), or opt out of mature changes (enabled by default), by setting True / False values, respectively, for flags in dbt_project.yml.

You can read more about each of these behavior changes in the following links:

  • (Introduced, disabled by default) state_modified_compare_more_unrendered_values. Set to True to start persisting unrendered_database and unrendered_schema configs during source parsing, and do comparison on unrendered values during state:modified checks to reduce false positives due to environment-aware logic when selecting state:modified.
  • (Introduced, disabled by default) skip_nodes_if_on_run_start_fails project config flag. If the flag is set and any on-run-start hook fails, mark all selected nodes as skipped.
    • on-run-start/end hooks are always run, regardless of whether they passed or failed last time.
  • (Introduced, disabled by default) [Redshift] restrict_direct_pg_catalog_access. If the flag is set the adapter will use the Redshift API (through the Python client) if available, or query Redshift's information_schema tables instead of using pg_ tables.
  • (Introduced, disabled by default) require_nested_cumulative_type_params. If the flag is set to True, users will receive an error instead of a warning if they're not proprly formatting cumulative metrics using the new cumulative_type_params nesting.
  • (Introduced, disabled by default) require_batched_execution_for_custom_microbatch_strategy. Set to True if you use a custom microbatch macro to enable batched execution. If you don't have a custom microbatch macro, you don't need to set this flag as dbt will handle microbatching automatically for any model using the microbatch strategy.

Adapter specific features and functionalities​

Redshift​

  • Support IAM Role auth

Snowflake​

  • Iceberg Table Format support will be available on three out-of-the-box materializations: table, incremental, dynamic tables.

Bigquery​

  • Can cancel running queries on keyboard interrupt
  • Auto-drop intermediate tables created by incremental models to save resources

Spark​

  • Support overriding the ODBC driver connection string which now enables you to provide custom connections

Quick hits​

We also made some quality-of-life improvements in Core 1.9, enabling you to:

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