Posts Tagged ‘DistributedSystems’
Efficient Inter-Service Communication with Feign and Spring Cloud in Multi-Instance Microservices
In a world where systems are becoming increasingly distributed and cloud-native, microservices have emerged as the de facto architecture. But as we scale
microservices horizontally—running multiple instances for each service—one of the biggest challenges becomes inter-service communication.
How do we ensure that our services talk to each other reliably, efficiently, and in a way that’s resilient to failures?
Welcome to the world of Feign and Spring Cloud.
The Challenge: Multi-Instance Microservices
Imagine you have a user-service that needs to talk to an order-service, and your order-service runs 5 instances behind a
service registry like Eureka. Hardcoding URLs? That’s brittle. Manual load balancing? Not scalable.
You need:
- Service discovery to dynamically resolve where to send the request
- Load balancing across instances
- Resilience for timeouts, retries, and fallbacks
- Clean, maintainable code that developers love
The Solution: Feign + Spring Cloud
OpenFeign is a declarative web client. Think of it as a smart HTTP client where you only define interfaces — no more boilerplate REST calls.
When combined with Spring Cloud, Feign becomes a first-class citizen in a dynamic, scalable microservices ecosystem.
✅ Features at a Glance:
- Declarative REST client
- Automatic service discovery (Eureka, Consul)
- Client-side load balancing (Spring Cloud LoadBalancer)
- Integration with Resilience4j for circuit breaking
- Easy integration with Spring Boot config and observability tools
Step-by-Step Setup
1. Add Dependencies
[xml][/xml]
If using Eureka:
[xml][/xml]
2. Enable Feign Clients
In your main Spring Boot application class:
[java]@SpringBootApplication
@EnableFeignClients
public <span>class <span>UserServiceApplication { … }
[/java]
3. Define Your Feign Interface
[java]
@FeignClient(name = "order-service")
public interface OrderClient { @GetMapping("/orders/{id}")
OrderDTO getOrder(@PathVariable("id") Long id); }
[/java]
Spring will automatically:
- Register this as a bean
- Resolve order-service from Eureka
- Load-balance across all its instances
4. Add Resilience with Fallbacks
You can configure a fallback to handle failures gracefully:
[java]
@FeignClient(name = "order-service", fallback = OrderClientFallback.class)
public interface OrderClient {
@GetMapping("/orders/{id}") OrderDTO getOrder(@PathVariable Long id);
}[/java]
The fallback:
[java]
@Component
public class OrderClientFallback implements OrderClient {
@Override public OrderDTO getOrder(Long id) {
return new OrderDTO(id, "Fallback Order", LocalDate.now());
}
}[/java]
⚙️ Configuration Tweaks
Customize Feign timeouts in application.yml:
feign:
client:
config:
default:
connectTimeout:3000
readTimeout:500
[/yml]
Enable retry:
[xml]
feign:
client:
config:
default:
retryer:
maxAttempts: 3
period: 1000
maxPeriod: 2000
[/xml]
What Happens Behind the Scenes?
When user-service calls order-service:
- Spring Cloud uses Eureka to resolve all instances of order-service.
- Spring Cloud LoadBalancer picks an instance using round-robin (or your chosen strategy).
- Feign sends the HTTP request to that instance.
- If it fails, Resilience4j (or your fallback) handles it gracefully.
Observability & Debugging
Use Spring Boot Actuator to expose Feign metrics:
[xml]
<dependency>
<groupId>org.springframework.boot</groupId>
<artifactId>spring-boot-starter-actuator</artifactId>
</dependency[/xml]
And tools like Spring Cloud Sleuth + Zipkin for distributed tracing across Feign calls.
Beyond the Basics
To go even further:
- Integrate with Spring Cloud Gateway for API routing and external access.
- Use Spring Cloud Config Server to centralize configuration across environments.
- Secure Feign calls with OAuth2 via Spring Security and OpenID Connect.
✨ Final Thoughts
Using Feign with Spring Cloud transforms service-to-service communication from a tedious, error-prone task into a clean, scalable, and cloud-native solution.
Whether you’re scaling services across zones or deploying in Kubernetes, Feign ensures your services communicate intelligently and resiliently.
[DevoxxBE2024] A Kafka Producer’s Request: Or, There and Back Again by Danica Fine
Danica Fine, a developer advocate at Confluent, took Devoxx Belgium 2024 attendees on a captivating journey through the lifecycle of a Kafka producer’s request. Her talk demystified the complex process of getting data into Apache Kafka, often treated as a black box by developers. Using a Hobbit-themed example, Danica traced a producer.send() call from client to broker and back, detailing configurations and metrics that impact performance and reliability. By breaking down serialization, partitioning, batching, and broker-side processing, she equipped developers with tools to debug issues and optimize workflows, making Kafka less intimidating and more approachable.
Preparing the Journey: Serialization and Partitioning
Danica began with a simple schema for tracking Hobbit whereabouts, stored in a topic with six partitions and a replication factor of three. The first step in producing data is serialization, converting objects into bytes for brokers, controlled by key and value serializers. Misconfigurations here can lead to errors, so monitoring serialization metrics is crucial. Next, partitioning determines which partition receives the data. The default partitioner uses a key’s hash or sticky partitioning for keyless records to distribute data evenly. Configurations like partitioner.class, partitioner.ignore.keys, and partitioner.adaptive.partitioning.enable allow fine-tuning, with adaptive partitioning favoring faster brokers to avoid hot partitions, especially in high-throughput scenarios like financial services.
Batching for Efficiency
To optimize throughput, Kafka groups records into batches before sending them to brokers. Danica explained key configurations: batch.size (default 16KB) sets the maximum batch size, while linger.ms (default 0) controls how long to wait to fill a batch. Setting linger.ms above zero introduces latency but reduces broker load by sending fewer requests. buffer.memory (default 32MB) allocates space for batches, and misconfigurations can cause memory issues. Metrics like batch-size-avg, records-per-request-avg, and buffer-available-bytes help monitor batching efficiency, ensuring optimal throughput without overwhelming the client.
Sending the Request: Configurations and Metrics
Once batched, data is sent via a produce request over TCP, with configurations like max.request.size (default 1MB) limiting batch volume and acks determining how many replicas must acknowledge the write. Setting acks to “all” ensures high durability but increases latency, while acks=1 or 0 prioritizes speed. enable.idempotence and transactional.id prevent duplicates, with transactions ensuring consistency across sessions. Metrics like request-rate, requests-in-flight, and request-latency-avg provide visibility into request performance, helping developers identify bottlenecks or overloaded brokers.
Broker-Side Processing: From Socket to Disk
On the broker, requests enter the socket receive buffer, then are processed by network threads (default 3) and added to the request queue. IO threads (default 8) validate data with a cyclic redundancy check and write it to the page cache, later flushing to disk. Configurations like num.network.threads, num.io.threads, and queued.max.requests control thread and queue sizes, with metrics like network-processor-avg-idle-percent and request-handler-avg-idle-percent indicating thread utilization. Data is stored in a commit log with log, index, and snapshot files, supporting efficient retrieval and idempotency. The log.flush.rate and local-time-ms metrics ensure durable storage.
Replication and Response: Completing the Journey
Unfinished requests await replication in a “purgatory” data structure, with follower brokers fetching updates every 500ms (often faster). The remote-time-ms metric tracks replication duration, critical for acks=all. Once replicated, the broker builds a response, handled by network threads and queued in the response queue. Metrics like response-queue-time-ms and total-time-ms measure the full request lifecycle. Danica emphasized that understanding these stages empowers developers to collaborate with operators, tweaking configurations like default.replication.factor or topic-level settings to optimize performance.
Empowering Developers with Kafka Knowledge
Danica concluded by encouraging developers to move beyond treating Kafka as a black box. By mastering configurations and monitoring metrics, they can proactively address issues, from serialization errors to replication delays. Her talk highlighted resources like Confluent Developer for guides and courses on Kafka internals. This knowledge not only simplifies debugging but also fosters better collaboration with operators, ensuring robust, efficient data pipelines.
Links:
[PHPForumParis2021] Chasing Unicorns: The Limits of the CAP Theorem – Lætitia Avrot
Lætitia Avrot, a PostgreSQL contributor and database consultant at EnterpriseDB, delivered a compelling presentation at Forum PHP 2021, demystifying the CAP theorem and its implications for distributed systems. With a nod to Ireland’s mythical unicorns, Lætitia used humor and technical expertise to explore the trade-offs between consistency, availability, and partition tolerance. Her talk provided practical guidance for designing resilient database architectures. This post covers four key themes: understanding the CAP theorem, practical database design, managing latency, and realistic expectations.
Understanding the CAP Theorem
Lætitia Avrot opened with a clear explanation of the CAP theorem, which states that a distributed system can only guarantee two of three properties: consistency, availability, and partition tolerance. She emphasized that chasing a “unicorn” system achieving all three is futile. Drawing on her work with PostgreSQL, Lætitia illustrated how the theorem shapes database design, using real-world scenarios to highlight the trade-offs developers must navigate in distributed environments.
Practical Database Design
Focusing on practical applications, Lætitia outlined strategies for designing PostgreSQL-based systems. She described architectures using logical replication, connection pooling with HAProxy, and standby nodes to balance consistency and availability. By tailoring designs to acceptable data loss and downtime thresholds, developers can create robust systems without overengineering. Lætitia’s approach, informed by her experience at EnterpriseDB, ensures that solutions align with business needs rather than pursuing unattainable perfection.
Managing Latency
Addressing audience questions, Lætitia tackled the challenge of latency in distributed systems. She explained that latency is primarily network-driven, not hardware-dependent, and achieving sub-100ms latency between nodes is difficult. By measuring acceptable latency thresholds and using tools like logical replication, developers can optimize performance. Lætitia’s insights underscored the importance of realistic metrics, reminding attendees that most organizations don’t need Google-scale infrastructure.
Realistic Expectations
Concluding her talk, Lætitia urged developers to set pragmatic goals, quoting her colleague: “Unicorns are more mythical than the battle of China.” She emphasized that robust systems require backups, testing, and clear definitions of acceptable data loss and downtime. By avoiding overcomplexity and focusing on practical trade-offs, developers can build reliable architectures that meet real-world demands, leveraging PostgreSQL’s strengths for scalable, resilient solutions.
Links:
[KotlinConf2018] Implementing Raft with Coroutines and Ktor: Andrii Rodionov’s Distributed Systems Approach
Lecturer
Andrii Rodionov, a Ph.D. in computer science, is an associate professor at National Technical University and a software engineer at Wix. He leads JUG UA, organizes JavaDay UA, and co-organizes Kyiv Kotlin events. Relevant links: Wix Engineering Blog (publications); LinkedIn Profile (professional page).
Abstract
This article analyzes Andrii Rodionov’s implementation of the Raft consensus protocol using Kotlin coroutines and Ktor. Set in distributed systems, it examines leader election, log replication, and fault tolerance. The analysis highlights innovations in asynchronous communication, with implications for scalable, fault-tolerant key-value stores.
Introduction and Context
Andrii Rodionov presented at KotlinConf 2018 on implementing Raft, a consensus protocol used in systems like Docker Swarm. Distributed systems face consensus challenges; Raft ensures agreement via leader election and log replication. Rodionov’s in-memory key-value store demo leveraged Kotlin’s coroutines and Ktor for lightweight networking, set against the need for robust, asynchronous distributed architectures.
Methodological Approaches to Raft Implementation
Rodionov used coroutines for non-blocking node communication, with async for leader election and channel for log replication. Ktor handled HTTP-based node interactions, replacing heavier JavaNet. The demo showcased a cluster tolerating node failures: Servers transition from follower to candidate to leader, propagating logs via POST requests. Timeouts triggered elections, ensuring fault tolerance.
Analysis of Innovations and Features
Coroutines innovate Raft’s asynchronous tasks, simplifying state machines compared to Java’s thread-heavy approaches. Ktor’s fast startup and lightweight routing outperform JavaNet, enabling efficient cluster communication. The demo’s fault tolerance—handling node crashes—demonstrates robustness. Limitations include coroutine complexity for novices and Ktor’s relative immaturity versus established frameworks.
Implications and Consequences
Rodionov’s implementation implies easier development of distributed systems, with coroutines reducing concurrency boilerplate. Ktor’s efficiency suits production clusters. Consequences include broader Kotlin adoption in systems like Consul, though mastering coroutines requires investment. The demo’s open-source nature invites community enhancements.
Conclusion
Rodionov’s Raft implementation showcases Kotlin’s strengths in distributed systems, offering a scalable, fault-tolerant model for modern consensus-driven applications.
Links
- Lecture video: https://www.youtube.com/watch?v=pNFmreSEXic
- Lecturer’s X/Twitter: @andriirodionov
- Lecturer’s LinkedIn: Andrii Rodionov
- Organization’s X/Twitter: @WixEng
- Organization’s LinkedIn: Wix
[ScalaDaysNewYork2016] Finagle Under the Hood: Unraveling Twitter’s RPC System
At Scala Days New York 2016, Vladimir Kostyukov, a member of Twitter’s Finagle team, provided an in-depth exploration of Finagle, a scalable and extensible RPC system written in Scala. Vladimir elucidated how Finagle simplifies the complexities of distributed systems, offering a functional programming model that enhances developer productivity while managing intricate backend operations like load balancing and circuit breaking.
The Essence of Finagle
Vladimir Kostyukov introduced Finagle as a robust RPC system used extensively at Twitter and other organizations. Unlike traditional application frameworks, Finagle focuses on facilitating communication between services, abstracting complexities such as connection pooling and load balancing. Vladimir highlighted its protocol-agnostic nature, supporting protocols like HTTP/2 and Twitter’s custom Mux, which enables efficient multiplexing. This flexibility allows developers to build services in Scala or Java, seamlessly integrating Finagle into diverse tech stacks.
Client-Server Dynamics
Delving into Finagle’s internals, Vladimir described its client-server model, where services are treated as composable functions. When a client sends a request, Finagle’s stack—comprising modules for connection pooling, load balancing, and failure handling—processes it efficiently. On the server side, incoming requests are routed through similar modules, ensuring resilience and scalability. Vladimir emphasized the “power of two choices” load balancing algorithm, which selects the least-loaded node from two randomly chosen servers, achieving near-optimal load distribution in constant time.
Advanced Features and Ecosystem
Vladimir showcased Finagle’s advanced capabilities, such as streaming support for large payloads and integration with tools like Zipkin for tracing and Twitter Server for monitoring. Libraries like Finatra and Featherbed further enhance Finagle’s utility, enabling developers to build RESTful APIs and type-safe HTTP clients. These features make Finagle a powerful choice for handling high-throughput systems, as demonstrated by its widespread adoption at Twitter for managing massive data flows.
Community and Future Prospects
Encouraging community engagement, Vladimir invited developers to contribute to Finagle’s open-source repository on GitHub. He discussed ongoing efforts to support HTTP/2 and improve performance, underscoring Finagle’s evolution toward a utopian RPC system. By offering a welcoming environment for pull requests and feedback, Vladimir emphasized the collaborative spirit driving Finagle’s development, ensuring it remains a cornerstone of scalable service architectures.
Links:
[ScalaDaysNewYork2016] The Zen of Akka: Mastering Asynchronous Design
At Scala Days New York 2016, Konrad Malawski, a key member of the Akka team at Lightbend, delivered a profound exploration of the principles guiding the effective use of Akka, a toolkit for building concurrent and distributed systems. Konrad’s presentation, inspired by the philosophical lens of “The Tao of Programming,” offered practical insights into designing applications with Akka, emphasizing the shift from synchronous to asynchronous paradigms to achieve robust, scalable architectures.
Embracing the Messaging Paradigm
Konrad Malawski began by underscoring the centrality of messaging in Akka’s actor model. Drawing from Alan Kay’s vision of object-oriented programming, Konrad explained that actors encapsulate state and communicate solely through messages, mirroring real-world computing interactions. This approach fosters loose coupling, both spatially and temporally, allowing components to operate independently. A single actor, Konrad noted, is limited in utility, but when multiple actors collaborate—such as delegating tasks to specialized actors like a “yellow specialist”—powerful patterns like worker pools and sharding emerge. These patterns enable efficient workload distribution, aligning perfectly with the distributed nature of modern systems.
Structuring Actor Systems for Clarity
A common pitfall for newcomers to Akka, Konrad observed, is creating unstructured systems with actors communicating chaotically. To counter this, he advocated for hierarchical actor systems using context.actorOf to spawn child actors, ensuring a clear supervisory structure. This hierarchy not only organizes actors but also enhances fault tolerance through supervision, where parent actors manage failures of their children. Konrad cautioned against actor selection—directly addressing actors by path—as it leads to brittle designs akin to “stealing a TV from a stranger’s house.” Instead, actors should be introduced through proper references, fostering maintainable and predictable interactions.
Balancing Power and Constraints
Konrad emphasized the philosophy of “constraints liberate, liberties constrain,” a principle echoed across Scala conferences. Akka actors, being highly flexible, can perform a wide range of tasks, but this power can overwhelm developers. He contrasted actors with more constrained abstractions like futures, which handle single values, and Akka Streams, which enforce a static data flow. These constraints enable optimizations, such as transparent backpressure in streams, which are harder to implement in the dynamic actor model. However, actors excel in distributed settings, where messaging simplifies scaling across nodes, making Akka a versatile choice for complex systems.
Community and Future Directions
Konrad highlighted the vibrant Akka community, encouraging contributions through platforms like GitHub and Gitter. He noted ongoing developments, such as Akka Typed, an experimental API that enhances type safety in actor interactions. By sharing resources like the Reactive Streams TCK and community-driven initiatives, Konrad underscored Lightbend’s commitment to evolving Akka collaboratively. His call to action was clear: engage with the community, experiment with new features, and contribute to shaping Akka’s future, ensuring it remains a cornerstone of reactive programming.
Links:
[DevoxxFR2015] Advanced Streaming with Apache Kafka
Jonathan Winandy and Alexis Guéganno, co-founder and operations director at Valwin, respectively, presented a deep dive into advanced Apache Kafka streaming techniques at Devoxx France 2015. With expertise in distributed systems and data warehousing, they explored how Kafka enables flexible, high-performance real-time streaming beyond basic JSON payloads.
Foundations of Streaming
Jonathan opened with a concise overview of streaming, emphasizing Kafka’s role in real-time distributed systems. He explained how Kafka’s topic-based architecture supports high-throughput data pipelines. Their session moved beyond introductory concepts, focusing on advanced writing, modeling, and querying techniques to ensure robust, future-proof streaming solutions.
This foundation, Jonathan noted, sets the stage for scalability.
Advanced Modeling and Querying
Alexis detailed Kafka’s ability to handle structured data, moving past schemaless JSON. They showcased techniques for defining schemas and optimizing queries, improving performance and maintainability. Q&A revealed their use of a five-node cluster for fault tolerance, sufficient for basic journaling but scalable to hundreds for larger workloads.
These methods, Alexis highlighted, enhance data reliability.
Managing Kafka Clusters
Jonathan addressed cluster management, noting that five nodes ensure fault tolerance, while larger clusters handle extensive partitioning. They discussed load balancing and lag management, critical for high-volume environments. The session also covered Kafka’s integration with databases, enabling real-time data synchronization.
This scalability, Jonathan concluded, supports diverse use cases.
Community Engagement and Resources
The duo encouraged engagement through Scala.IO, where Jonathan organizes, and shared Valwin’s expertise in data solutions. Their insights into cluster sizing and health monitoring, particularly in regulated sectors like healthcare, underscored Kafka’s versatility.
This session equips developers for advanced streaming challenges.