Overcoming Identity Column Limitations in Microsoft Fabric Warehouse: Exploring Alternative Architectural Approaches

Imagine a scenario where you are managing a large dataset in Microsoft Fabric’s warehouse, and you need to uniquely identify each record with an incrementing identity column. This sounds like a straightforward task, right? After all, identity columns are a standard feature in most databases. However, in Microsoft Fabric warehouse, you encounter a limitation: adding an identity column to a table is not possible. [Link]

The Problem: No Identity Column

To illustrate, let's say you're managing a warehouse table that tracks customer orders. Naturally, you want each order to have a unique identifier, which increments with each new record—OrderID 1, 2, 3, and so on. Typically, you would rely on an identity column for this task, but in Microsoft Fabric warehouse, this feature is unavailable.

The absence of this capability might initially seem like a major roadblock. You might ask:

How can I ensure that each row in my table gets a unique, sequential identifier without using an identity column?

Here’s where we pivot to a workaround that utilizes the broader capabilities of Microsoft Fabric, specifically Dataflows Gen2, to achieve the same goal using a low-code/no-code method.

The Workaround: Using Dataflows Gen2

Instead of directly applying an identity column, we can work around this limitation by leveraging Microsoft Fabric’s Dataflows Gen2. Dataflows Gen2 enables you to manipulate data through a series of transformation steps, including the ability to calculate the next available ID value. The process hinges on identifying the maximum value of the existing ID column, and then adding a fixed value to generate the next unique identifier.

Let’s break this down with an example.

Step-by-Step Example

So, you're working with a table that tracks customer orders, where each order needs a unique ID. Initially, your table might look like this:

Now, let's say new records are about to be inserted into this table. You need to determine the next available ID value—OrderID 4 in this case—without the help of an identity column.

Here’s how you can do it with Dataflows Gen2:

1. Extract the maximum ID value: You first need to identify the current maximum value in the OrderID column. In this example, the maximum OrderID is 3.

2. Add a fixed increment: Once you know the maximum ID, you can add a fixed value (typically 1) to generate the next identifier. So, the next OrderID will be 3 + 1 = 4.

This can be done by adding a Custom step after the previous step (Calculated maximum) and setting the below code as its formula:

if #"Calculated maximum" + 1 = null then 1 else #"Calculated maximum" + 1        

3. Apply this logic as a transformation step: In Dataflows Gen2, you create a transformation step that automatically extracts the maximum value from the ID column and adds the fixed increment. Each time a new row is added, the next ID will be calculated accordingly.

To satisfy this goal, firstly, a new Index Column is added to the table which contains new records and secondly, replacing the starting value of index (in this case, 0) with the step 2 query name (LastOrderID). And finally, index column is renamed to OrderID.

Here’s what the table would look like after adding the next few orders:

But What About Large-Scale Data?

What if my table contains millions of rows? How does this workaround perform under such conditions?

This is a valid concern. In environments with very large datasets, calculating the maximum value of a column could introduce some performance overhead. However, since Dataflows Gen2 allows for incremental data processing, you can design your transformations to handle such cases efficiently. For instance, you could partition the data into chunks and perform the maximum-value calculation on smaller subsets of the data at a time.

Instead of delegating data processing and computational tasks to the data storage layer, which is primarily designed to handle high volumes of concurrent read requests and optimize query performance, the calculation of the maximum value for a given column should be efficiently managed during the ELT (Extract, Load, Transform) process. The ELT process is specifically responsible for ingesting, transforming, and loading data into the data warehouse in a single operation, making it the appropriate stage for such computations.

This method works because even though Microsoft Fabric warehouse doesn’t support native identity columns, its integration with other tools like Dataflows Gen2 allows for creative problem-solving. By breaking down the task into manageable steps—identifying the maximum value and incrementing it—you can simulate the behavior of an identity column without needing direct support for it.

While the inability to directly add an identity column might initially seem limiting, the broad functionality of Microsoft Fabric provides alternative pathways to achieve the same goal, ensuring that your data remains well-structured and manageable.

The reason behind the lack of support for adding identity columns

The reason behind the lack of support for adding identity columns in Microsoft Fabric warehouse primarily stems from the architecture and design philosophy of the platform. Microsoft Fabric is built with modern data workloads in mind, particularly those focused on large-scale, distributed data processing, often involving cloud-native approaches. Several factors contribute to this design choice:

1. Distributed Architecture

Microsoft Fabric warehouse is designed to operate in a distributed environment where data is partitioned across multiple nodes for scalability and performance. Identity columns, which require sequential and unique values for each row, can be difficult to maintain in such a distributed setup. Ensuring that each node in the distributed system assigns unique, sequential numbers without any overlap requires complex coordination between nodes, which can introduce performance bottlenecks.

Why is generating sequential IDs in a distributed environment challenging?

In distributed systems, multiple nodes may attempt to insert records simultaneously. Ensuring that each node can generate a unique, sequential ID would require constant communication between nodes to track the next available number. This synchronization would slow down the system, affecting the performance of data writes, which is contrary to the high-throughput goals of modern data warehouses.

2. Focus on Scalable Data Processing

Microsoft Fabric is designed to handle large-scale, high-velocity data. Adding an identity column, especially one that increments in sequence, can be resource-intensive and could affect the performance of data loading operations. The overhead involved in generating and maintaining sequential IDs at such scale could slow down the processing of massive datasets, which is one of the key use cases for Microsoft Fabric.

3. Emphasis on ELT Pipelines

The architecture of Microsoft Fabric focuses heavily on Extract, Load, and Transform (ELT) processes. In modern data workflows, it’s common to load raw data into the warehouse and perform transformations later as needed. Identity columns, which are typically generated during data insertion, don’t always align well with ELT workflows, where transformations (like generating unique IDs) are handled after the data is loaded. By using tools like Dataflows Gen2 to generate identifiers, Microsoft Fabric promotes a more flexible and scalable approach to data transformation.

4. Alternative Approaches

Microsoft encourages users to adopt alternative methods for generating unique identifiers. By using techniques like leveraging existing tools (e.g., Dataflows Gen2 or surrogate keys), users can generate IDs in a way that better aligns with the platform’s distributed and cloud-native architecture. These methods offer more flexibility and allow for better scalability compared to traditional identity columns.

5. Cloud-Native and Distributed Database Trends

Many cloud-native data platforms, such as Microsoft Fabric, follow a trend where traditional features like identity columns are either discouraged or implemented differently. Instead of relying on auto-incrementing identity columns, cloud-native platforms often recommend using GUIDs (Globally Unique Identifiers) or generating IDs via transformation steps, as these approaches are more suited to distributed data environments.

Why prefer GUIDs or calculated IDs in cloud platforms?

GUIDs, for instance, can be generated without needing a central authority, which eliminates the performance hit of synchronizing between nodes in a distributed system. Though they are not sequential, they are guaranteed to be unique and scale better across distributed environments. Similarly, calculating IDs post-load, as with Dataflows Gen2, avoids the synchronization problems inherent in sequential ID generation.

The decision to not directly support identity columns in Microsoft Fabric Warehouse, as an example, highlights a limitation that underscores the architectural differences between Microsoft Fabric and other data platforms, such as Azure Synapse Analytics. Designing and developing data solutions with Microsoft Fabric requires a fresh approach and architectural perspective, as this challenge stems from the platform's focus on scalability, performance, and alignment with modern data practices. By avoiding sequential identity columns, Microsoft Fabric can provide more efficient, distributed data processing while encouraging users to leverage other tools and methods for generating unique identifiers.