> For the complete documentation index, see [llms.txt](https://developer.harness.io/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://developer.harness.io/feature-management-experimentation/use-fme/warehouse-native-experimentation/setup/assignment-sources.md).
# Preparing Assignment Source Tables for Warehouse Native Experimentation
To prepare an [Assignment Source](/feature-management-experimentation/use-fme/warehouse-native-experimentation/setup/configure-assignments.md) for Warehouse Native Experimentation, transform your raw exposure or impression logs into a clean, standardized table that serves as the foundation for experimentation analyses.
This page describes the required fields, recommended fields, and best practices for preparing your assignment source tables.
### Required columns
{% hint style="info" %}
These fields are mandatory. Without them, Harness FME cannot map exposures to experiment results.
{% endhint %}
Every Assignment Source table must include the following columns:
| Column | Type | Description |
| ----------------------------- | ------------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| **Unique Key** | `STRING` | Unique identifier for the unit of randomization (for example, `user_id`, `account_id`, or a custom key). Must be stable across the experiment duration. |
| **Exposure Timestamp** | `DATETIME` / `TIMESTAMP` | The time the assignment occurred (for example, when an impression was logged, a flag evaluated, or getTreatment was called). Must be provided in epoch milliseconds or UTC format (e.g. 1714953600000 or 2026-05-05T00:00:00Z).
Timestamps must be in UTC (epoch milliseconds or UTC datetime) and align with the format used in Metric Sources for accurate joins and analysis.
|
| **Treatment (Variant Group)** | `STRING` | The assigned experiment variant (for example, `control`, `treatment_a`, `variant_1`). |
### Recommended columns
While not required, the following fields make debugging, filtering, and governance more efficient.
| Column | Type | Description |
| ------------------------ | -------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| **Experiment ID / Name** | `STRING` | Helps differentiate exposures when multiple experiments are logged in the same raw table. |
| **Targeting Rule** | `STRING` | Indicates which targeting rule or condition led to the assignment. Useful for audit and debugging. If you are using FME feature flag impressions, filter by a single targeting rule to ensure the experiment analyzes the intended population. |
| **Environment ID** | `STRING` | Allows filtering by environment (for example, `production`, `staging`). When configuring an assignment source in FME, you can map column values to a matching Harness environment or hard-code a single environment. When creating an experiment, it must be scoped to one environment. |
| **Traffic Type** | `STRING` | Distinguishes the unit type (for example, `user`, `account`, `anonymous visitor`). When configuring an assignment source, you can map column values or hard-code the environment. Each experiment must be scoped to one traffic type. |
### Common raw table schemas
Most organizations log impressions or exposures from feature flag evaluations, SDKs, or event pipelines. Below are common raw schemas and how to normalize them.
#### Feature Flag Evaluation Logs
| **Example Raw Schema** | **Transformations** |
| ----------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| user\_id
flag\_name
treatment
impression\_time
environment
rule\_id
| • Map flag\_name values → experiment\_id (if multiple flags correspond to the same experiment).
• Cast evaluation\_time to TIMESTAMP.
• Deduplicate on (user\_id, experiment\_id) by keeping the earliest exposure.
|
#### A/B Test Impression Logs
| **Example Raw Schema** | **Transformations** |
| --------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| experiment\_id
user\_id
bucket or arm
impression\_time
| • Standardize bucket → treatment.
• Standardize impression\_time → exposure\_timestamp.
• Deduplicate to keep only the first exposure per user per experiment.
|
#### Event Logging Pipelines (Custom Analytics Events)
| **Example Raw Schema** | **Transformations** |
| ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| event\_name
event\_time
properties.experiment\_id
properties.variant
properties.user\_id
| • Flatten nested fields (JSON → explicit columns).
• Filter to only event\_name = 'experiment\_exposure'.
• Standardize column names to match required schema.
|
### Prepare your assignment table
Follow these best practices for preparing your assignment table in your data warehouse.
* **De-duplication**: Keep only the earliest exposure per user per experiment. For example:
```sql
QUALIFY ROW_NUMBER() OVER (
PARTITION BY user_id, experiment_id
ORDER BY exposure_timestamp ASC
) = 1
```
* **Consistent Variant Labels**: Standardize variant naming (`control`, `treatment`, `variant_1`) across experiments. Avoid null or empty strings; default to `control` if needed.
* **Timestamp Format**: All exposure timestamps must be in epoch milliseconds or UTC (ISO 8601).
* **Use UTC Consistently**: Store all exposure and event timestamps in UTC to ensure consistent comparisons across regions.
* **Stable Identifiers**: Use the same user or account key across Assignment Source and Metric Source tables. If your system logs multiple IDs (for example, `cookie_id` and `user_id`), choose the most stable one.
* **Environment Separation**: If raw tables mix environments (for example, `staging` and `production`), add an `environment_id` column and filter accordingly. This prevents accidental inclusion of test data in production environments.
* **Partitioning and Indexing**: Partition large tables by `DATE(exposure_timestamp)` to optimize query performance. Cluster or index by `experiment_id` and `user_id` for faster lookups.
### Example prepared table schema
| Column | Type | Example |
| -------------------- | ----------- | ---------------------- |
| `user_id` | `STRING` | `abc123` |
| `experiment_id` | `STRING` | `checkout_flow_v2` |
| `treatment` | `STRING` | `control` |
| `exposure_timestamp` | `TIMESTAMP` | `2025-03-14T12:45:00Z` |
| `environment_id` | `STRING` | `prod` |
| `traffic_type` | `STRING` | `user` |
Once your Assignment Source tables are prepared and validated, see [Setting Up an Assignment Source](/feature-management-experimentation/use-fme/warehouse-native-experimentation/setup/index.md) to connect them in Harness FME.