Dataform: Shared Config Issues And Solutions

Dec 5, 2025 by Alex Johnson 45 views

Dataform: Shared JavaScript Config Issues and Solutions

Are you wrestling with inconsistent configurations in your Dataform projects? You're not alone! Many users encounter issues when attempting to share settings across multiple SQLX files using a centralized JavaScript configuration file. This article dives into a common problem: shared JavaScript config files that appear to only apply to the first action within a Dataform workflow. We'll explore the issue, provide a minimal reproduction scenario, analyze the root cause, and suggest potential solutions and best practices to ensure consistent configurations across your Dataform models.

The Problem: Shared Configs Not Applying Consistently

The core of the problem lies in how Dataform processes and applies configurations defined in shared JavaScript files. When you attempt to share configurations, such as partitioning settings for BigQuery tables, it's expected that these settings apply consistently across all tables that utilize the shared config. However, as the user described in the original issue, this isn't always the case. The first table defined seems to pick up the configurations correctly, but subsequent tables might revert to default settings or ignore the shared configurations altogether. This inconsistency can lead to data integrity issues, performance problems, and a general lack of trust in your data pipelines. Understanding why this happens and how to fix it is crucial for anyone using Dataform to manage data transformations and build reliable data warehouses.

Imagine you're building a data pipeline and want to partition several tables by a specific date field. You create a shared configuration in a configs.js file, expecting all tables to inherit these settings. But, when you run your Dataform workflow, you discover that only the first table is properly partitioned. The remaining tables are created without partitioning, leading to potential performance bottlenecks and making it more difficult to query your data efficiently. This is precisely the kind of problem we're addressing here. The primary goal is to provide a clear understanding of the issue and offer practical solutions for ensuring consistent configuration application across all your Dataform models.

Detailed Breakdown of the Issue

Let's break down the scenario that highlights the problem. The user's goal is to share configuration properties via a centralized JavaScript file, specifically configs.js. They define settings like table types, schemas, and BigQuery-specific options, such as partitionBy and requirePartitionFilter. They then spread these configurations into each SQLX file using the spread operator (...). When running the Dataform workflow with a full refresh, only the first table (foo) is correctly partitioned, while the second table (bar) is not. This behavior suggests that Dataform might be caching or reusing the configuration from the first file and not re-evaluating it for subsequent files. The result is a failure to apply shared settings consistently. This inconsistency leads to difficulties in maintaining and scaling your data pipelines. The user's experience points to the need for a more reliable way to share and apply configurations across various Dataform models.

Reproduction Scenario

To understand the issue better, let's look at the minimal reproduction steps provided by the user. This setup helps to isolate the problem and provides a clear demonstration of the behavior.

Step-by-Step Guide to Reproduce the Issue

Shared Configuration (includes/configs.js): The user starts by creating a shared configuration file named configs.js. Inside this file, they define a cleanTable configuration object. This object specifies the table type as incremental, the schema as clean, and BigQuery-specific settings for partitioning by DATE(event_ts) and enforcing a partition filter (requirePartitionFilter: true). This configuration is exported to be used in other files.
```
const cleanTable = {
  type: "incremental",
  schema: "clean",
  bigquery: {
    partitionBy: "DATE(event_ts)",
    requirePartitionFilter: true,
  },
};
module.exports = { cleanTable };
```
SQLX Model Definitions: The user then defines two SQLX model files: foo.sqlx and bar.sqlx, both located in the definitions/clean directory. Each of these files aims to create a table named foo and bar respectively, using the configurations defined in configs.js. They use the spread operator (...) to merge the shared cleanTable configuration with the file-specific configurations, such as the table name.

definitions/clean/foo.sqlx
```
config {
  ...configs.cleanTable,
  name: "foo"
}
select 1 as event_ts;
```
definitions/clean/bar.sqlx
```
config {
  ...configs.cleanTable,
  name: "bar"
}
select 1 as event_ts;
```
Running the Dataform Workflow: Finally, the user runs the Dataform workflow using the command dataform run --full-refresh. This command triggers Dataform to execute all defined models and create the tables in BigQuery.

Expected vs. Actual Behavior

Expected Behavior: Both clean.foo and clean.bar should be created as partitioned tables with the requirePartitionFilter setting enabled. This ensures that both tables are configured identically, inheriting the shared settings from configs.js.
Actual Behavior: Only the clean.foo table is created as a partitioned table. The clean.bar table is created without partitioning and the requirePartitionFilter option is not applied. This discrepancy confirms the issue of inconsistent application of shared configurations.

This detailed reproduction scenario clearly demonstrates the problem and provides a basis for further investigation and the development of effective solutions.

Root Cause Analysis

The inconsistent application of shared configurations is often related to how Dataform processes and caches JavaScript files. Dataform might evaluate the configs.js file only once and then cache the resulting configuration objects. When the configuration is referenced in multiple SQLX files, Dataform might reuse the cached object instead of re-evaluating it for each file. This behavior can lead to the shared configurations not being applied consistently.

Potential Reasons for the Inconsistency

Caching Mechanisms: Dataform may have a caching mechanism to optimize performance. This can involve caching the results of the configs.js file, which means that the shared configuration is only processed once. Subsequent SQLX files might then use the cached version, missing any potential updates or changes in the configuration.
Scope and Evaluation Order: The order in which Dataform evaluates the files can also play a role. If Dataform processes the configs.js file and the first SQLX file before processing the others, the subsequent files may not receive the updated configuration, leading to inconsistencies.
Object References: The spread operator (...) creates shallow copies of the objects. If the shared configuration object is modified after being used in the first SQLX file, the changes might not be reflected in the subsequent files because they are referencing the original cached object.

Understanding these underlying mechanisms is crucial for developing effective workarounds and solutions.

Solutions and Best Practices

To address the issue of inconsistent configuration application, here are several solutions and best practices you can implement. These strategies aim to ensure that shared configurations are applied consistently across all your Dataform models.

Solution 1: Function-Based Configuration

One effective solution is to define your shared configurations as functions rather than simple objects. This approach ensures that a new configuration object is created and evaluated for each SQLX file.

// includes/configs.js
function cleanTableConfig() {
  return {
    type: "incremental",
    schema: "clean",
    bigquery: {
      partitionBy: "DATE(event_ts)",
      requirePartitionFilter: true,
    },
  };
}

module.exports = { cleanTableConfig };

// definitions/clean/foo.sqlx
config {
  ...configs.cleanTableConfig(),
  name: "foo"
}
select 1 as event_ts;

// definitions/clean/bar.sqlx
config {
  ...configs.cleanTableConfig(),
  name: "bar"
}
select 1 as event_ts;

By using a function, each SQLX file calls the function, which returns a new configuration object. This approach bypasses the caching issue, ensuring that each file gets a fresh configuration. This method is especially useful when configurations need to be dynamically determined or when you want to avoid potential issues related to cached object references.

Solution 2: Deep Copying (If Necessary)

If you need to use objects, you can use a deep copy strategy to ensure that each SQLX file receives a unique copy of the configuration object. This can be achieved using libraries like lodash or writing your own deep copy function. However, use this approach with caution, as it can add complexity.

// includes/configs.js
const _ = require('lodash'); // install lodash: npm install lodash
const cleanTable = {
  type: "incremental",
  schema: "clean",
  bigquery: {
    partitionBy: "DATE(event_ts)",
    requirePartitionFilter: true,
  },
};

module.exports = {
  cleanTable: () => _.cloneDeep(cleanTable)
};

// definitions/clean/foo.sqlx
config {
  ...configs.cleanTable(),
  name: "foo"
}
select 1 as event_ts;

// definitions/clean/bar.sqlx
config {
  ...configs.cleanTable(),
  name: "bar"
}
select 1 as event_ts;

Using _.cloneDeep(cleanTable) creates a completely independent copy of the object, which avoids any reference issues. This ensures that modifications in one SQLX file do not affect the configurations in other files. Deep copying is a more robust solution, but it might have performance implications if used excessively.

Solution 3: Dataform Package Management

For more complex configurations, consider using Dataform's package management features. This allows you to create reusable modules that manage configurations and can be imported into your project. Packages provide a structured way to share and version configurations across multiple projects.

Best Practices for Consistent Configuration

Use Functions: Favor functions over simple objects for shared configurations. This ensures that each SQLX file receives a fresh configuration object.
Avoid Mutable Shared Objects: If you must use objects, avoid modifying the shared object after it has been used in a SQLX file.
Test Thoroughly: Always test your configurations after making changes, especially when sharing configurations across multiple files. This helps catch inconsistencies early.
Keep Configurations Simple: Simplify your shared configurations as much as possible to reduce complexity and potential issues.

By following these solutions and best practices, you can effectively manage shared configurations in Dataform and avoid the pitfalls of inconsistent application.

Conclusion

Inconsistent application of shared configurations is a common challenge in Dataform projects. By understanding the root cause, implementing the solutions, and following the best practices outlined in this article, you can ensure that your configurations are applied consistently across all your Dataform models. This leads to more reliable and efficient data pipelines.

Remember to choose the solution that best fits your project's needs, considering factors like complexity and performance. Using functions for shared configurations is often the most straightforward and reliable approach, preventing caching issues and ensuring that each SQLX file receives a fresh configuration. Furthermore, by keeping your configurations clean, well-tested, and well-documented, you can create a more maintainable and scalable data infrastructure.

For further reading and more in-depth information about Dataform and BigQuery configurations, check out the Dataform documentation and the BigQuery documentation. These resources provide comprehensive information, tutorials, and examples that can help you master Dataform and build robust data pipelines.