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Choose a real-time data layer for your microservices architecture

Microservice architectures make it possible to launch new products faster, help them scale easier, and respond better to customer demands. With its multiple modern data models, fault tolerance in any scenario, multi-tenancy for isolation, and the flexibility to deploy across multiple environments, Redis enables devs and operators to optimize their data layer for a microservices architecture.

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Since Redis and microservices [removed] the constraints of our old architecture, it [increased] our speed of deployment 2x – 3x within the first year.

Paul Kurmas

Director of Strategic Product Development, Mutualink

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When we receive a telephone call, we need to make a decision on how a call should be routed, whether it should be allowed or blocked. All of that process is done via interactions with Redis and it has to be done in real time.

Alec Fenichel

Senior Software Architect, TransNexus

What is a microservices architecture?

As defined by Chris Richardson, noted microservices expert, microservices architecture is an architectural style for structuring apps as a collection of loosely coupled services that are highly maintainable and testable, independently deployable, bounded by specific business domains, and owned by small teams. A microservice architecture enables the rapid, frequent, and reliable delivery of large, complex apps. 

Why microservices matter

Microservices-based apps support strategic digital transformation & cloud migration initiatives

Microservices is an architecture style that has helped development teams create better software, faster, and minimize the costs and complexity of app modernization. As a result, microservices architectures have been adopted across all industries, for projects that justifiably can be labeled “digital transformation initiatives” as well as for more mundane but important tasks such as bootstrapping cloud deployments. 

This architecture style and its related software development culture enable microservices development teams to operate on their own release cycles, embrace end-to-end product ownership, and adopt a DevOps framework built on continuous integration/continuous delivery. The result is that enterprises can reduce time-to-market for new service development, often from projects measured in months to days. 

Microservices accelerate data tier cloud migrations. That’s because they primarily rely on cloud-native NoSQL databases. NoSQL databases are replacing on-premises relational databases that were not built for the cloud nor for independent release cycles, according to a 2021 IDC InfoBrief survey. 

In addition, some organizations cannot migrate their legacy monolith apps to cloud-native all at once. Microservices enable incremental migration of subdomains from a monolithic architecture to modern technology stacks.

A perfect solution for microservices

Performance at microservices scale

In a microservices environment, services that need to run in real-time must compensate for networking overhead. Enterprise-grade Redis delivers sub-millisecond latency for all Redis data types and models. In addition, it scales instantly and linearly to almost any throughput needed.

Designed for fault tolerance & resilience

To ensure your apps are failure resilient, Redis uses a shared-nothing cluster architecture. It is fault tolerant at all levels: with automated failover at the process level, for individual nodes, and even across infrastructure availability zones. It also includes tunable persistence and disaster recovery.

Reduce complexity with fast & flexible data models

Redis allows developers to choose the data model best suited to their performance and data access requirements for their microservices architecture and domain driven design, while retaining isolation with multi-tenant deployment on a single data platform. 

Simplify operations with native Kubernetes deployment

Redis provides a unified operational interface that reduces technology sprawl, simplifies operations, and reduces service latency. The Redis Operator for Kubernetes gives you consistent, automated deployments to reduce risk. That lets dev teams focus on innovation and business value.

Adaptable across clouds & geographies

Choose where your database should run. Redis can be deployed anywhere: on any cloud platform, on-premises, or in a multicloud or hybrid cloud architecture.

Design patterns for microservice architectures

Microservice query caching

Isolation or bounded context is an important characteristic of a microservice architecture. As part of domain-driven design, each service can have a dedicated database with its own unique data model and service level agreement performance goals. Query caching, a cache pattern commonly used to reduce microservice response times, works by deploying a Redis cache alongside each microservice to deliver data that is needed within a single business context. (That is, it serves only one microservice.) 

The Redis Smart Cache is an open source library that seamlessly adds caching to any JDBC—compliant platform, app, or microservice, improving query performance while reducing operational complexity – and you don’t need to change app code. Redis supports multiple data models that can be easily deployed multi-tenant, yet remain isolated, all without sacrificing performance.

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Caching cross-domain shared data via CQRS

Microservices need fast access to data, but that can be a challenge when dozens or hundreds of microservices try to read from the same slow disk-based database. Cross-domain data needs to be available to each microservice in real-time—and to do so without breaking the scope of its focused business context and goal.

Command Query Responsibility Segregation (CQRS) is a critical pre-fetch cache pattern within microservice architectures that decouple reads (queries) and writes (commands). This enables an app to write data to a slower disk-based SQL database, while pre-fetching and caching that data using the integrated Change Data Capture (CDC) capability in Redis for lightning-fast reads. Doing so makes that data immediately available to other microservices that need it.

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API gateway caching for global data

Microservices apps can cache globally accessed data at the API gateway level to distribute and speed up data that is accessed by all services. Typically this would be session data (such as user ID and preferences) and authentication data (tokens, authorization status, permissions). This enables frequently needed data available in real time to all services. The result? Reducing app latency without breaking the bounds of each microservice business context.

Rate limiting, in which one meters the number of API requests in a certain timespan, can also be implemented at the API gateway using Redis. This can prevent overloading the system and prevents DDoS attacks.

Asynchronous messaging for inter-service communication 

Microservices must communicate state, events, and data with one another without breaking isolation, and they have to stay decoupled. A common solution is to bring a publish—subscribe messaging broker into the architecture—that is, to make inter-service communication event-driven and eventually consistent–and to treat every message between microservices as an event.

Redis Streams is an immutable time-ordered log data structure that enables a service (producer) to publish asynchronous messages to which multiple consumers can subscribe. It can be configured to handle different delivery guarantees, support consumer groups, and to apply other features that are comparable to Apache Kafka topic partitions. Even better, Redis Streams helps to create reporting, analytics, auditing, and forensic analysis on the backend.

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Redis features for microservice architecture

Active-Active replication

A microservices architecture has many connected services, yet it faces the same performance demands as monolithic apps. To minimize latency, data should reside as close to the services as possible. You also need to ensure databases are consistent with one another in the event of failures or conflicting updates. Redis can be deployed as an Active-Active, conflict-free replicated database to handle updates from multiple local installations of your services without compromising latency or data consistency and providing continuity in the event of failures.

Multiple data models

Redis provides multiple data structures (hashes, strings, Streams, lists, etc.) and models including JSON, search, time-series, and graph that let you choose the data model best suited for your microservice domain, performance, and data-access requirements. And it’s all in a single data platform.

Multi-tenant databases

Within a microservices architecture database design, a single Redis cluster can provide databases to many different services, each with its own isolated instance, tuned for the given workload. Each database instance is deployed, scaled, and modeled independently of the others, while leveraging the same cluster environment, isolating data between services without increasing operational complexity.

Flexible across clouds

Microservices provide a great deal of technology flexibility, and choosing where you want to run your database should be no exception. Redis can be deployed anywhere: on any cloud platform, on-premises, or in a multicloud or hybrid-cloud architecture. It is also available on Kubernetes, Pivotal Kubernetes Service (PKS), and Red Hat OpenShift.

Native Kubernetes container orchestration and management

Containers are closely aligned with and help enterprises implement microservice apps. Kubernetes is the de facto standard platform for container deployment, scheduling, and orchestration. Redis is the top database technology running on containers, with over two billion Docker hub launches. Redis Operator for Kubernetes provides: automatic scalability, persistent storage volumes, simplified database endpoint management, and zero downtime rolling upgrades. It is available on multiple Kubernetes platforms and cloud managed services, including RedHat OpenShift, VMware Tanzu Kubernetes Grid (formerly Enterprise PKS), upstream Kubernetes, and Azure Kubernetes Service (AKS), Google Kubernetes Engine (GKE), or Amazon Elastic Kubernetes Service (EKS).

FAQ

  • What are microservices?
    • Microservices architecture (often shortened to microservices) refers to an architectural style for developing applications. Microservices allow a large application to be separated into smaller independent parts, with each part having its own realm of responsibility. To serve a single user request, a microservices-based application can call on many internal microservices to compose its response.
  • What is the difference between monolithic architecture and microservices architecture?
    • In a monolithic architecture, processes are tightly coupled and run as a single deployable artifact. While this is relatively simple to begin with, scaling up or modifying one part of your microservice application requires updating the entire service, resulting in inefficient scalability and increased complexity as your codebase grows in size. 
      • Microservices architecture involves a collection of loosely coupled services that can be independently updated and scaled by smaller teams. Because individual services are easier to build, deploy, and manage than a single monolithic application, microservices enable more frequent deployments, data store autonomy, and increased flexibility.
      • Organizations are transitioning their entire applications to microservices architecture in order to drastically decrease time to market, more easily adopt new technologies, and respond faster to customer needs.
  • What is Kubernetes?
    • Kubernetes, also known as k8s, is an open-source orchestration system for automating deployment, scaling, and management of containerized applications, typically used as part of microservice and cloud native architectures. 
  • What are Docker containers?
    • Docker containers images are lightweight, standalone, executable packages of software that includes everything needed to run an application.
  • What is an API gateway?
    • An API gateway is a software application for api management that sits between a client and a set of backend microservices. The API Gateway serves as a reverse proxy to accept API calls from the client application, forwarding this traffic to the appropriate service.