As the number of models in a project grows, you may start to notice performance issues, especially when running dbt compile and dbt run. This article provides a series of strategies to optimize large DBT projects and ensure they run smoothly and efficiently.
Reduce Model Complexity
Complex SQL transformations can slow down the performance of your DBT project. Always strive to break down complicated transformations into simpler steps. This approach not only speeds up the model execution but also makes your code more understandable and maintainable.
Leverage Incremental Models
DBT allows you to create incremental models, which only process new or updated records since the last run, instead of reprocessing the entire dataset. If your models are processing a considerable amount of data, leveraging incremental models can save a significant amount of processing time and resources.
{{
config(
materialized='incremental'
)
}}
SELECT *
FROM {{ source('raw_data', 'large_table') }}
WHERE timestamp_col > (SELECT MAX(timestamp_col) FROM {{ this }})
This example only processes records from the source table that are newer than the most recent record in the target table. The result is a significant reduction in the data processed and, therefore, improved performance.
Use Database Optimizations
DBT works on top of existing databases, so you can often leverage the optimization techniques provided by the underlying database. This could be as simple as creating indices on frequently filtered or joined columns, partitioning large tables, or using other database-specific performance optimization techniques.
Exploit Parallel Execution
DBT supports parallel execution of models, which can considerably speed up the total execution time. By setting the threads configuration parameter in your profiles.yml file, you can define how many models DBT should execute in parallel.
my_project:
target: dev
outputs:
dev:
type: postgres
threads: 4
host: localhost
DBT will execute up to four models concurrently. Note that the optimal number of threads will depend on the capacity of your database.
Organize Models into Folders
DBT runs models in a specific order based on the dependency graph. By organizing models into folders and using the –models option with dbt run, you can control the order in which models are compiled and run. Running independent models first can make the overall execution faster.
Enable Caching
DBT can cache results during a run to speed up the process in future runs. By setting persist_docs in the dbt_project.yml file, you can persist the intermediate data to your database.
models:
my_project:
+persist_docs:
relation: true
Use Minimal Seed Files
Seed files are great for small amounts of static data. However, if you’re using large seed files, consider loading that data directly into your database using an ETL tool. Smaller seed files mean faster compile times.
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