API Latency in Microservices

Explore API latency in the context of Microservices architecture

A microservices architecture heavily depends on the communication between services, which impacts the overall performance of the system, and often requires tools to continuously observe and monitor these services. Most microservices are designed to communicate with each other through APIs.

Latency is the time taken for data to travel from one point to another. In the context of APIs, it’s the time taken for an API request to travel from the client to the server and return the response.

When processing an API request inside a Microservice, many parameters affect the overall latency of the response. These include network latency, processing time, database access time, and any other synchronous API calls performed as a part of fulfilling the API request. Therefore, reducing API latency is critical since it directly impacts Microservices’ responsiveness and scalability.

In this article, we will explore API latency in the context of Microservices architecture and evaluate the challenges posed by latency and strategies for mitigating them.

Why do we need APIs in Microservices?

If you have a monolithic application, adopting Microservice architecture requires you to break it into smaller, independent services that can be developed, deployed, and scaled independently.

To interact with one another, some of these services need to expose APIs. These APIs are the contracts each Microservice use to communicate with one another, allowing them to share data and update state. When we standardize the interface and communication protocol (e.g. HTTP, gRPC), the underlying implementation of the service is hidden from one another. This loose coupling of services via API makes it flexible for each Microservice to scale independently without affecting others.

The significance of APIs in Microservices architecture goes beyond simple communication. APIs enable service composition, which allows businesses to create sophisticated applications by orchestrating different Microservices to fulfil business capabilities. Furthermore, each Microservice team becomes autonomous, promoting independent release lifecycles.

APIs also allow enterprises to expose their Microservices to external entities such as third-party developers and partners, stimulating innovation and expanding the reach of their services. Organizations can build ecosystems around their Microservices by providing well-documented and secure APIs. This encourages collaboration, integration, and the development of value-added applications.

The Significance of API Latency in Microservices

API latency has a wide range of impacts on Microservice design. These include:

1. Impacts the performance of other Microservices

In Microservices architecture, a collection of autonomous services work together to deliver business functionality. These services often need to communicate with each other via APIs. If there is high latency in one service, it impacts the other services that consume it, which can significantly degrade the overall system’s performance.

2. Cascading failures

High latency in a Microservice can lead to cascading failures in a Microservices architecture. If one service takes too long to respond, it could lead the requesting service to time out. And, if there is another service in the request sequence, it will timeout as well, leading to a cascading failure throughout the system.

Isolating and resolving latency issues caused by dependencies becomes problematic in the absence of extensive monitoring and observability techniques.

3. Increased resource utilization

If you look at the cascading failure example, when services timeouts, they have been waiting for more than the average time it takes to get a response. This unnecessarily increases resource utilization, potentially leading to resource exhaustion. As a result, it will reduce the capacity to serve other requests and impact the system’s scalability and increase costs.

4. Impact on Asynchronous Processes

In asynchronous operations the API calls are non-blocking. Yet, high latency can still be an issue impacting the overall performance of the system. For instance, suppose a Microservice pushes tasks to a queue to be processed later. If the latency is high, it can slow down the rate at which tasks get added to the queue, delaying the overall processing time. If this processing is time-bound and periodic, things could spill over to the next time window affecting the overall functionality of the system.

5. Impact on User Experience (UX):

This is one of the most significant impacts of high latency in Microservices and API calls. If a Microservice API is invoked by a user-triggered action and the user waits for the results, it could build up frustration resulting in a poor user experience. When the UX is poor, it can lead to user dissatisfaction, reduced user engagement, and potentially lost revenue.

6. Impacts real-time applications

In real-time applications, high latency could affect real-time requirements, leading to system failure. For example, if you build a real-time video streaming service, high latency in delivering video stream can lead to buffering, making it difficult to watch the video.

7. Scalability and load balancing

Microservice built with modern cloud-native architectures often rely on autoscaling to dynamically adjust the running instances based on the load. These autoscaling decisions are often based on metrics like CPU usage. If services wait longer due to higher latency, CPU usage may not increase, affecting the autoscale functionality. This overloads the Microservices with too many concurrent requests, leading to slow responses and timeouts.

Strategies for Improving API Latency in Microservices

The strategies mentioned below explore some of the widely used techniques for improving API latency in Microservices architecture.

1. Caching: This approach reduces API latency by storing frequently accessed data or responses, improving overall response times. It can be applied at different levels, such as Microservices, databases, or content delivery networks (CDNs), to serve cached data instead of repeating requests. This is particularly beneficial for read-heavy operations, significantly enhancing system performance.
2. Service Decomposition: Breaking down large, latency-prone services into smaller, more targeted ones can help to spread the workload and improve response times with selective scaling.
3. Performance Monitoring and Optimization: Microservices can be monitored and profiled on a regular basis to help discover latency issues. Organizations can find areas for optimization by examining metrics for performance, such as streamlining database queries, improving code efficiency, or upgrading network setups to reduce latency.

If you can’t measure it you can’t improve it. ~ Peter Drucker

Overall, the key strategy behind reducing API latency is to start monitoring these Microservices. Let’s look at how we can measure API latency by following industry best practices.

Monitoring and Diagnosing API Latency Issues in Microservices

Distributed tracing and observability

The use of distributed tracing and observability is crucial for monitoring and debugging API latency issues in microservices. Distributed tracing allows you to follow requests across different microservices, giving you a complete picture of the request-response lifecycle. Distributed tracing identifies microservices contributing to latency constraints by measuring latency at each step. It aids in identifying particular areas for improvement, allowing enterprises to enhance performance and reduce latency.

In addition to distributed tracing, observability plays a crucial role in diagnosing API latency issues. Observability encompasses collecting, analysing, and visualising various system metrics, logs, and events. By capturing and analyzing these data points, organizations can gain insights into the performance of microservices, identify patterns or anomalies, and detect potential latency-related issues. It provides a comprehensive understanding of the system behavior, enabling proactive detection and resolution of API latency problems.

Logging and error tracking

For detecting API latency issues in microservices, logging and error tracking are vital. Logging records complete information about each system operation and event, including timestamps and error messages. Organizations can identify specific locations or components where latency issues occur by monitoring logs, allowing them to focus on enhancing those parts. Error-tracking solutions complement logging by offering a centralized mechanism for real-time error capture and monitoring. This allows enterprises to quickly detect and rectify issues that cause API latency, resulting in a seamless and uninterrupted user experience.

Leveraging Helios To Perform Distributed Tracing & Observability

Helios is an effective tool for performing distributed tracing and observability in Microservices architectures. It provides complete insights into the flow of requests across separate microservices, allowing for the identification of latency bottlenecks and potential for performance optimization.

We can utilize Helios to track requests as they pass through various Microservices, obtaining a complete view of the full transactional chain. This tracing capability aids in identifying specific services or components that cause latency, allowing for targeted optimization efforts. Helios enables enterprises to effectively diagnose and address latency issues by displaying the dependencies and timing between Microservices.

Furthermore, Helios includes extensive observability features, such as collecting and analyzing telemetry data from Microservices. Metrics, logs, and events are included, enabling real-time performance monitoring and preemptive detection of latency issues. Organizations can spot patterns, abnormalities, and deviations in system behavior by exploiting the insights given by Helios, allowing for early intervention and optimization.

Conclusion

In conclusion, Microservice API latency directly impacts the overall system performance and user experience in Microservices design. Microservice API latency is impacted by factors such as weak networks, poor-performing services, and scalability bottlenecks.

Utilizing distributed tracing and observability, and monitoring is essential to identify latency issues. In addition to that, we can utilize different caching techniques to reduce latency in individual Microservices.

You can optimize performance, handle latency issues proactively, and provide a better user experience by utilizing observability solutions such as Helios.

Thank you for reading. Cheers!