Updated: Jul 6
Managing a database in a microservices architecture can be a significant challenge. In order to tackle this challenge effectively, it is crucial to establish a well-defined strategy that incorporates best practices and patterns. In this article, we will explore the tools, patterns, principles, and best practices that can be employed for data management in microservices.
It is essential to familiarize ourselves with patterns that address the issues associated with decentralizing microservices data. Each microservice should possess its own data while also requiring interaction and data sharing with other microservices. However, employing ACID transactions between distributed systems becomes problematic. Consequently, implementing queries and transactions that involve multiple microservices becomes a challenge.
Microservice Architecture Patterns and Principles
There are 5 commonly used data-related patterns, along with 1 anti-pattern:
The Database-per-Service pattern
The API Composition pattern
The CQRS pattern
The Event Sourcing pattern
The Saga pattern
The Shared Database anti-pattern
1. The Database-per-Service Pattern
The Database-per-Service Pattern is a fundamental characteristic of microservices architecture. To achieve loose coupling between services, each microservice should have its own dedicated database. Therefore, when designing the database architecture for microservices, the database-per-service pattern is almost always necessary.
During the transition from a monolithic architecture to a microservices architecture, one of the initial steps is to decompose databases. This involves breaking down the existing database into a distributed data model consisting of multiple smaller databases, each serving a specific microservice. This approach enables the design of a dedicated database for each microservice. The adoption of a database per microservice brings numerous benefits, such as facilitating rapid evolution and easy scalability of applications.
In the above architecture diagram, each microservice supports a different type of database. For instance, the product catalog microservice utilizes a NoSQL document database, specifically MongoDB. The shopping cart microservice employs a distributed cache with a simple key-value data store. On the other hand, the ordering microservice utilizes a relational database to accommodate the complex relational structural data. This segregation allows us to leverage the strengths of different databases in the appropriate context and enables independent scaling based on the load of each microservice.
Consider the below diagram where each microservice has different Database:
By separating databases, we gain the ability to select the most optimized database type for each microservice. This includes options such as relational databases, document databases (e.g., MongoDB), key-value stores, or even graph-based data stores.
Loose coupling between microservices, makes them more independent and isolated from one another.
Schema changes and updates can be made without affecting other microservices, allowing for faster evolution and deployment.
Separate databases enable individual microservices to scale independently based on their specific needs and workload, optimizing resource allocation and performance.
By using a database-per-service approach, different microservices can employ the most suitable database technology for their specific requirements, such as relational, document, key-value, or graph-based data stores.
Maintaining multiple databases adds complexity to the overall system architecture, as it requires managing and coordinating different databases, backup, and recovery mechanisms, and data synchronization.
Storing data in multiple databases can lead to data duplication and potential inconsistency, requiring additional effort to ensure data integrity and synchronization between microservices.
Coordinating transactions that span multiple microservices becomes more challenging with separate databases, as ACID transactions across distributed systems are not directly supported.
Managing multiple databases incurs additional resource overhead, including storage, maintenance, and operational costs.
2. The API Composition Patterns
The API Composition Patterns play a crucial role in retrieving data from multiple services within a distributed microservices architecture. Building upon the concepts of API Gateway patterns and practices, these patterns provide effective solutions for handling queries in microservices.
The following patterns are commonly employed in API composition:
API Gateway Pattern: The API Gateway acts as a central entry point for client requests and handles routing, authentication, and other cross-cutting concerns. It provides a unified interface for accessing multiple microservices.
Gateway Routing Pattern: This pattern focuses on routing requests from the API Gateway to the appropriate microservices based on the specific query or operation being performed. It ensures that each request reaches the relevant microservice for processing.
Gateway Aggregation Pattern: In situations where a query requires data from multiple microservices, the Gateway Aggregation pattern is utilized. The API Gateway collects the necessary data by invoking the respective microservices and combines the results into a single response, simplifying the client's interaction.
Gateway Offloading Pattern: The Gateway Offloading pattern involves offloading certain processing tasks, such as data transformation or aggregation, from the microservices to the API Gateway. This reduces the computational load on individual microservices and improves overall system performance.
When executing queries that involve invoking several microservices, the API Composition and Gateway Aggregation patterns are typically followed to combine the results effectively. By using these patterns, the microservices architecture can efficiently handle complex queries and provide a unified data response to clients.
With API composition, clients can retrieve all the necessary data with a single request, reducing the complexity of client-side development and improving overall efficiency.
By combining multiple API calls into a single request, the API composition pattern helps minimize network round trips and reduces the overhead associated with making multiple requests.
Aggregating data from multiple microservices into a single response can enhance performance by reducing latency and improving overall system responsiveness.
API composition allows developers to specify and customize the data they need, enabling efficient retrieval of only the required information and avoiding unnecessary data transfer.
Implementing API composition requires additional effort to design and maintain the logic for combining and aggregating data from different microservices, which can add complexity to the system.
API composition can introduce a degree of coupling between microservices, as changes or updates to one service's API may impact the composition logic and require corresponding adjustments.
In cases where the composed data involves a large volume of information or requires heavy computations, the API composition pattern may introduce performance bottlenecks if not designed and optimized carefully.
API composition is typically better suited for retrieving static or pre-aggregated data, and it may not be ideal for real-time or dynamic data scenarios that require immediate updates or real-time synchronization.
3. The CQRS Pattern
The Command Query Responsibility Segregation (CQRS) pattern involves separating the database for commands (write operations) from the database for queries (read operations) in order to optimize the performance of querying multiple microservices.
CQRS follows a write-less, read-more approach, meaning that it focuses on the efficient handling of read operations while minimizing the impact of write operations. This pattern is particularly useful when there are distinct operational behaviors between write and read operations.
By segregating the databases for commands and queries, CQRS allows each database to be optimized for its specific purpose. The command database can prioritize transactional consistency and write performance, while the query database can be optimized for fast and scalable reads.
CQRS allows for separate optimization of read and write operations, enabling faster and more efficient querying of data. This can result in improved system performance and scalability.
By segregating the databases for commands and queries, CQRS allows for independent scaling of read and write operations. This means that the system can be scaled according to the specific demands of each type of operation.
With CQRS, read operations can be optimized for responsiveness and user experience, as the query database can be tailored to deliver data quickly and efficiently.
Since the command and query databases are separate, it allows for greater flexibility in designing and evolving the data model for each type of operation. This enables better alignment with the specific requirements of commands and queries.
Implementing CQRS introduces additional complexity to the system architecture. It requires managing and synchronizing data between the command and query databases, which can add overhead and potential points of failure.
Keeping the command and query databases in sync requires careful consideration and implementation. Ensuring data consistency and avoiding data divergence between the two databases can be challenging and may require additional mechanisms.
CQRS introduces the need for separate code paths and logic for commands and queries. This can increase development and maintenance efforts, as developers need to handle the intricacies of both the command and query sides separately.
With separate databases for commands and queries, achieving immediate consistency across the system becomes more complex. The system may rely on eventual consistency, which means that there might be a slight delay in propagating updates from the command database to the query database.
Overall, if your system exhibits a significant disparity between write and read operations and requires efficient querying across multiple microservices, the CQRS pattern can be beneficial in optimizing performance and scalability for these operations.
4. The Event Sourcing Pattern
The Event Sourcing pattern involves capturing and storing events as a sequence in databases. It provides the ability to accumulate events and reconstruct the state of an application at any given point in time by replaying those events.
In Event Sourcing, instead of storing the current state of an entity, the system maintains a log of events that have occurred over time. Each event represents a specific action or change in the system. These events are stored in a sequence, forming a historical record of what has happened in the application.
By storing events in this manner, the system can rebuild or rehydrate the state of an entity by replaying the events from the beginning. This allows for a complete audit trail and provides the ability to query the state of the system at any specific point in time.
The Event Sourcing pattern works well in conjunction with other patterns such as CQRS (Command Query Responsibility Segregation) and Saga patterns. When used with CQRS, events serve as the source of truth for updating the query database and building materialized views. In the Saga pattern, events play a crucial role in coordinating and managing long-running transactions across multiple microservices.
The benefits of Event Sourcing include:
The pattern enables the ability to trace and understand how the state of an application has evolved over time by replaying the events.
By replaying events, it becomes possible to query the state of the application at any specific point in time, enabling historical analysis and reporting.
Event Sourcing can provide scalability and performance advantages as events are append-only and can be processed asynchronously.
However, there are considerations to keep in mind:
Implementing Event Sourcing introduces additional complexity in terms of event handling, event storage, and managing eventual consistency.
Storing events as a log can lead to increased storage requirements compared to traditional state-based approaches.
Managing changes to event schemas can be challenging, especially when older events need to be replayed and transformed to accommodate schema updates.
The Event Sourcing pattern is a powerful technique for capturing and managing the history of events in an application. It provides benefits such as audibility, historical querying, and compatibility with other patterns like CQRS and Saga. However, it should be adopted after careful consideration of the complexity it introduces and the specific requirements of the system.
5. The Saga Pattern
Transaction management can be challenging in microservices architectures due to the distributed nature of the services. To address this challenge and maintain data consistency when implementing transactions across multiple microservices, the SAGA pattern is commonly employed.
The SAGA pattern offers two different approaches
Choreography: In the choreography approach, microservices exchange events to collaborate and coordinate the transaction without relying on a centralized controller. Each microservice communicates with others through events, and collectively they achieve the desired transactional outcome. This decentralized coordination promotes loose coupling between microservices and allows them to make decisions based on local events.
Orchestration: In the orchestration approach, there is a centralized controller, often referred to as the orchestrator, that coordinates and manages the transactional flow. The orchestrator initiates and directs the sequence of steps across multiple microservices to execute the transaction. It takes responsibility for maintaining the transaction state and making decisions based on the responses received from the microservices.
Both approaches aim to ensure data consistency and integrity in a distributed transactional context. The choice between choreography and orchestration depends on factors such as the complexity of the transactional flow, the level of coordination required, and the overall architecture and design preferences.
The SAGA pattern provides a means to implement transactional behavior in microservices architectures, addressing the challenges of maintaining data consistency across distributed systems. Whether through choreography or orchestration, the pattern helps ensure that the transactional flow across multiple microservices is coordinated and executed correctly, thereby maintaining data integrity and achieving the desired transactional outcomes.
6. The Shared Database Anti-Pattern
If you deviate from the Database-per-Service pattern and opt for a Shared Database among multiple microservices, it is considered an anti-pattern, and it is advised to avoid such approaches.
While it is possible to create a single shared database and have each service access data using local ACID transactions, this approach goes against the nature of microservices and can lead to significant problems in the long run. Ultimately, you may end up developing a few large monolithic applications instead of true microservices.
By using a shared database, you lose the benefits of microservices such as loose coupling and service independence. Additionally, a shared database introduces a single point of failure that can potentially block or impact multiple microservices simultaneously.
To fully leverage the advantages offered by microservices, it is recommended to adhere to the Database-per-Service pattern. This approach allows each microservice to have its own dedicated database, promoting autonomy, scalability, and resilience while maintaining loose coupling between services.
The microservices architecture allows for the utilization of diverse data storage technologies for different services, known as polyglot persistence. This means that each development team can choose the persistence technology that best suits the specific needs and requirements of their service.
Polyglot persistence, as highlighted by Martin Fowler in his informative article, acknowledges that adopting multiple persistence technologies comes with a cost but asserts that the benefits outweigh the drawbacks.
When relational databases are used inappropriately, they can impede application development. It is important to understand the usage requirements of each microservice. For instance, if a microservice only needs to retrieve page elements by ID, has no need for transactions, and does not require database sharing, it may not be meaningful to employ a relational database.
In such cases, a key-value NoSQL database may be a more suitable choice. Key-value databases are better aligned with the requirements of simple lookups by ID and can provide the necessary performance and scalability without the complexity and overhead of a relational database.
Polyglot persistence allows microservices to leverage the most appropriate and effective data storage technologies for their specific needs. By selecting the right persistence technology for each microservice, developers can optimize performance, scalability, and maintainability, ultimately enhancing the overall effectiveness of the microservice architecture.
Microservices Architecture Patterns and Principles offer valuable guidelines and approaches for designing and implementing microservices-based systems. By understanding and applying these patterns and principles, developers can create scalable, resilient, and loosely coupled microservices architectures that optimize data management, performance, and maintainability. It's important to evaluate the specific needs of the system and make informed choices based on the trade-offs involved in implementing these patterns and principles.