Microservices - Minidev News

In planning for the future of a software development project, it’s vital to have a firm understanding of the underlying architecture that will shape the project’s trajectory. Specifically, business owners need to decide on an architecture that ensures agility, scalability, and performance. Among the front-runners for scalable solutions are two distinctive models: serverless and microservices.

The Microservices Model – A Brief Overview

The microservices architecture is, essentially, a decentralized application that has been partitioned into several modules, often referred to as services. Each of these services is tasked with executing a specific function. Generally, these services are specialized to perform a singular task exceptionally well.

The Serverless Model – A Quick Summary

Serverless architecture, as its name implies, eschews the need for your company to maintain its server space. Instead, a third-party vendor handles all the internal operations – storage, security, computing capabilities, among others – which could lead to significant cost reductions.

Key Differences: Serverless vs Microservices

Understanding the fundamental differences between these two architectures requires a detailed examination of several factors, such as functions, IT operations, cost, and runtime.

Functions

While both models have their unique sets of functions, the serverless model employs short-term functions that run based on specific conditions. Conversely, a microservices function is broader, often handling related processes simultaneously.

IT Operations

In the microservices model, the development team undertakes monitoring, deployment, support, and maintenance, whereas serverless architecture relies heavily on third-party vendors.

Cost

The cost of developing microservices tends to be higher initially, primarily because it demands several teams and meticulous planning. On the other hand, serverless architecture, with its shared resources and the absence of hardware specialists, is typically cheaper.

Runtime

Serverless functions have limited runtime, contingent on the provider’s specific limitations. Alternatively, microservices aren’t limited by runtime, storage space, or RAM, making them more suitable for complex long-term operations.

Combining Serverless and Microservices Architectures

The rapid evolution in technology has presented development teams with the opportunity to leverage both the serverless and microservice architectures concurrently. By hosting a set of event-driven services (microservices) on a third-party infrastructure (serverless), companies can significantly enhance their scalability, flexibility, and cost-efficiency.

Advantages of Adopting Serverless Architecture

Serverless architectures offer several benefits, including easy deployment, reduced cost, heightened scalability and flexibility, decreased latency, and Function as a Service (FaaS). It also frees companies from potentially risky investment in excessive server spaces by offering access to vast computing resources owned by large technology corporations.

Advantages of Adopting Microservices Architecture

Adopting a microservices architecture comes with its share of advantages, such as the decentralization of tasks, reduced risks, and most notably, scalability. This model aligns well with modern management methodologies like Agile and DevOps and fosters a culture of responsibility and ownership among small development teams.

Frequently Asked Questions

Is serverless architecture the future?

Serverless architecture is indeed seen as the future of cloud computing and software development models. It provides an automated solution to managing cloud computing resources, eliminating the need for users to estimate the required resources for their workload.
What is the next trend in microservices?

Trends such as serverless architecture, Cloud adoption, and DevOps are expected to shape the microservices industry’s future.

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Tags: #Serverless, #Microservices, #SoftwareDevelopment, #CloudComputing

Reference Link

Apache Kafka is a revered name in the realm of distributed event store and stream-processing platforms. It is highly recognized for its robust functionality in handling voluminous data at a compelling speed. To further augment Kafka’s capabilities, there’s Kafka Streams – intricately designed to simplify the creation of data pipelines and conduct high-level operations like aggregation and joining.

In this blog, we will dive deep into understanding the nuances of working with Kafka while building Kestra and leveraging its strengths in stream processing, navigating through its limitations, and customizing it to suit our specific requirements.

Why Apache Kafka?

Faced with the challenge of choosing a persistent queue for our application without any additional dependencies, we crossed paths with numerous candidates like RabbitMQ, Apache Pulsar, Redis, etc. However, Apache Kafka was the one that stood out, efficiently catering to all our project needs.

One major advantage of Kafka is that it allows us to embed the application directly within our Java application, removing the need for managing a separate platform, quite literally taking microservices to the next level.

Working with Kafka Topics

Kafka comes with its own set of constraints as it isn’t a database. It may seem confusing at first to use the same Kafka topic for source and destination.

Consider this example of a topology, which has the topic as the source, some branching logic, and two separate processes writing to the same destination. Here, the risk of overwriting the previous value becomes evident, ultimately resulting in data loss.

The Custom Joiner for Kafka Streams

To combat this issue, we came up with a customized joiner for Kafka Streams. This alternative was designed to process the executions and split the microservice into multiple topics such as:

A topic with the executions (multiple tasks)
A topic with task results

Our custom joiner needed to manually create a store, incorporate merge function, and get the last value. This ensured that regardless of the number of task results entering in parallel, the execution state is always the latest version.

Dealing with Distributed Workload Between Multiple Backends

In our application, Kestra, a scheduler with scheduled execution or long-polling mechanism (detecting files on S3 or SFTP) looks up all flows. To avoid a single point of failure on this service, we needed to split the flows between all instances of schedulers.

We did it by relying on Kafka’s consumer groups that handled complexities of a distributed system for us. Kafka undertakes all the heavy parts of the distributed systems. In case of a thousand flows, every consumer will have ~500 flows, thanks to Kafka’s phenomenal handling of:

Heartbeat to detect consumer failure
Notifications for rebalancing
Ensuring exactly-once semantic for a topic

Monitoring and Debugging

While working with Kafka streams, monitoring and debugging can be a real challenge due to the lag in streams. To alleviate this, we chose to deal with only one topic at a specific time.

This approach helped us minimize network transit and group all streams by source topics.

Throughout this process, we learned some notable tips that helped us navigate our challenges. We were able to adapt our code efficiently to Kafka and make it work well for our use case.

In the end, the experiences and learnings derived from working closely with Apache Kafka and Kestra have been immensely rewarding. If you’re interested in our work and want to learn more, you can find us on GitHub, Twitter, or join our discussions on Slack.

Message us if you found this article helpful or if you have any questions about Apache Kafka.

Tags: #ApacheKafka #Kestra #StreamProcessing #Microservices