Serverless migrations constraints

One of the biggest misconceptions about migrating from traditional server based architecture to serverless is just a matter of changing the method of computation. In reality, serverless migrations require careful planning and execution. Organisations need to consider a number of factors, including:

• Application architecture: Not all applications are well-suited for serverless. Applications with complex dependencies or high-performance requirements may not be able to run effectively in a serverless environment.
  

For example, Serverless architectures are not well-suited for long-running processes for several reasons:

• Ephemeral compute instances: Serverless functions are typically executed on ephemeral compute instances that are spun up and down on demand. This means that there is no guarantee that a given function will continue to run for a long period of time. If the process requires a long execution time, it may be terminated before it completes.
  

In order to overcome the challenge of ephemeral compute, development teams will need to work in conjunction with business stakeholders to re-design the current application into smaller and independent micro-services. This will ensure that each micro-service can be executed quickly and reliably within the constraints of the serverless environment.

Additionally, business processes may need to be re-aligned to accommodate the new workflow requirements of a serverless architecture. For example, processes that rely on long-running batch jobs may need to be broken down into smaller, incremental tasks that can be executed more quickly and efficiently.

Challenges with Data Management in Serverless Architectures

Due to the characteristics of serverless architectures being stateless, the reliance on managed services for data storage and processing, creates a set of challenges for data management.

• Statelessness and Data Persistence: Serverless functions operate on an ephemeral basis; meaning it doesn't retain data from previous invocations. This lack of data persistence requires the use of external data stores to maintain data across function invocations. While this approach has flexibility, it also introduces additional complexity in managing data consistency and durability.
  

• Inadequacy of Traditional RDBMS: Traditional relational database management systems (RDBMS), with their centralized architecture and locking mechanisms, are not suited for the high concurrency and scale demands of serverless applications. The high volume of concurrent requests generated by serverless functions can overload a centralized RDBMS, which becomes a bottleneck within a system. Additionally, the locking mechanisms used by RDBMS to ensure data integrity can become an issue within a highly concurrent environment, potentially causing data inconsistencies or deadlocks.
  

• Data Consistency and Eventual Consistency: Maintaining data consistency in a serverless environment can be challenging due to the distributed nature of data storage and processing. The reliance on external data stores and the asynchronous nature of many serverless operations can lead to eventual consistency models, where data may not be immediately consistent across all components of the system. This can introduce challenges for applications that require strong data consistency guarantees.
  

Addressing these data management challenges will require a careful consideration of data storage options, data modeling strategies, and data access patterns that are optimized for the serverless paradigm. It also requires a shift in mindset from traditional RDBMS-centric data management approaches to more flexible and distributed data management strategies that can leverage the scalability and agility of serverless architectures.

Organisations may need to consider alternative data management solutions that are designed for serverless architectures. Solutions like:

• NoSQL databases: NoSQL databases are designed to handle high concurrency and scale. They do not use traditional locking mechanisms, which can help to avoid deadlocks.
  

• Serverless databases: Serverless databases are managed database services that are designed to scale automatically. They eliminate the need for organizations to manage the underlying infrastructure, making it easier to migrate legacy data stores to serverless architectures.
  

• In-memory databases: In-memory databases store data in memory, which can significantly improve performance. Which is a good option for applications that require fast read and write operations.
  

• Database Proxy: Consider the use case of database proxy if in the case that migration to a non-relational datastore is un-realistic. A database proxy can help to reduce the number of concurrent connections to the database by pooling connections and routing requests to the database in an efficient manner. This can help to improve performance and scalability.
  

Culture and Process Uplift

In addition to these technical challenges, organisations also need to consider the cultural and organisational changes that are required for a successful serverless migration. Serverless requires different ways of working on application development, deployment, production support and organisations need to be prepared to make the necessary changes to their processes and workflows in order to cater for the new architectural change.

Summary

Migrating to a serverless architecture can be complex and challenging, it is a transformation process that requires careful planning, strategic adjustment and collaboration across teams that includes from re-designing applications into microservices to adapting new data management systems and strategies and re-aligning business processes.

Organisations who are looking to embark on the journey of migrating to serverless should tackle various technical, cultural and operations challenges with thorough preparation and fostering a culture of adaptability. In order to unlock the benefits the scalability, agility and cost-efficiency benefits of serverless architecture brings.