Fixing Bottlenecks in Your Microservices App Flows

Significance of Bottleneck Analysis in Microservices

Bottleneck analysis has become a significant part of microservices development for many reasons. Such as:

1. Identify and Isolate Performance Issues

Conducting a bottleneck analysis allows the developer to pinpoint specific areas where the application is experiencing performance issues. This process involves identifying the application's slow-performing components and evaluating its reasons. Metrics such as response time, error rate, and throughput can be used to identify and isolate the bottlenecks to improve the application's overall performance.

2. Optimize Resource Utilization

When a service utilizes too many resources, such as memory, CPU time, or I/O, it can degrade the performance of other services creating a bottleneck. Bottleneck analysis can help to identify these resource-heavy services and to optimize resource utilization. Optimizing resource utilization can involve rewriting code to optimize resource utilization, scaling services, and changing infrastructure to improve the application's overall performance.

3. Improve the User Experience

Slow and resource-heavy applications tend to impact the user experience negatively, which can result in a higher churn rate and eventually lead to a loss of business. This can be avoided by doing a bottleneck analysis to identify the performance and resource bottlenecks early and optimize them for an improved user experience.

4. Enhanced Scalability

Bottleneck analysis can enhance scalability in multiple ways.

• Efficient Resource Allocation: Identifying resource bottlenecks will lead to optimized resource allocation, improved performance, and higher application throughput.
• Improved Load Balancing: Bottleneck analysis enables developers to identify underutilized and overutilized services, allowing them to implement better load-balancing strategies and improve the applications' overall performance.
• Optimal Scaling: Bottleneck analysis can also help identify which services need to be scaled up and which services need to be scaled down and find the optimal point for scaling in each service.

5. Reduce Cost

Improving resource utilization and optimized scaling will reduce costs on infrastructure and similar operations costs, and the application will be able to handle a larger load with fewer resources.

Overall, it is crucial to conduct bottleneck analysis when implementing software to identify and fix bottlenecks, improve performance, resource utilization, and user experience, and reduce costs.

Challenges in Identifying Bottlenecks

Identifying and fixing bottlenecks in an application has become a crucial part of software development. However, modern distributed applications span across many services, and one task can involve multiple services, processes, and threads. Hence, there can be many places where a bottleneck can occur, and finding these congestion points can be challenging.

The importance of observability in modern distributed systems has increased due to the difficulty of locating and identifying these bottlenecks. Therefore, frameworks that provide standardized protocols and tools for collecting telemetry data, such as OpenTelemetry, have gained popularity. Using these tools to collect telemetry data can help when performing bottleneck analysis in complex applications.

Helios is a tool built upon OTel standards that can help developers maintain observability in the application with the ability of end-to-end tracing. Helios can provide end-to-end tracing even in complex scenarios such as microservices applications. By adding Helios in all services and with the telemetry data collected, bottlenecks can be easily traced and pinpointed to the exact service with the provided dashboards.

Using E2E Trace Visualization to Identify and Optimize Bottlenecks in a Microservices Application

To demonstrate E2E trace visualization, let's consider an example of three microservices: the user service, payment service, and order service.

When a user is placing an order, the order service will fetch user details from the user service and create a payment using the payment service. After that, the order service will place an order for the user.

Let's assume that when performing this operation, the order service is running some inefficient database queries, creating a bottleneck for the application. Due to the reduced user experience and complaints, this congestion point needs to be identified and fixed with a bottleneck analysis. Let's use Helios as the telemetry tool in this scenario to identify the bottleneck.

Step 1: Identify Bottlenecks

To get a better understanding of where the bottleneck is, take a look at traces of recent requests and try to figure out which endpoints are slowing down the application. Here we will be using the Helios E2E trace visualization.

The above image shows the dashboard with time spent on the most recent requests. And by switching services and API endpoints, you can easily identify how much time each request has taken.

In this case, it is clear that the orders endpoint has taken more time than it should and is reducing the entire application's performance. Since this is a microservices-based application, a bottleneck can occur in many places. Therefore to identify exactly where the bottleneck is occurring, Helios provides a visualization for each trace which can be revealed by clicking on one of the requests span duration bars.

The time spent on each service is shown in the image above, and it is clear that the order service is causing the bottleneck.

Step 2: Bottleneck Analysis

Once the bottleneck location is identified, it is crucial to identify the root cause to address and resolve performance issues effectively. There can be many reasons for bottlenecks, such as, 

• Inefficient Algorithms or Code That Cause Delays in Processing Requests: Poor programming practices and improper use of data structures and algorithms can cause delays in processing requests, reducing the application's performance and user experience. Using optimized and efficient code, algorithms, and data structures can avoid such bottlenecks,
• Poorly Designed Database Schemas and Queries: Poorly designed database schemas and queries can cause significant delays when performing database operations. Especially in a microservices environment, there can be complex interactions with databases involving multiple services, which can further increase the delays. Therefore database schemas and queries should be optimized as per the application requirement.
• Overloaded Network or Infrastructure Resources: Insufficient bandwidth, limited network resources, and insufficient infrastructure resources can cause the network or infrastructure to fail when faced with a large volume of requests. Microservices must be designed to cope with peak request loads, and appropriate scaling strategies can be used to handle high request loads.
• Slow or Unavailable External Services: The unavailability or slowness of third-party dependencies of an application can also cause bottlenecks. Heavy traffic, service maintenance, network issues, or any other external issues can cause the dependencies to become slow or unavailable.
• Interference or Contention Among Microservices Competing for Shared Resources: When microservices interact with each other, two services may be needed to utilize the same resource or to have data access simultaneously, causing delays or system failures. You can fix such congestion points by using scheduling techniques, caching mechanisms, or changing the architecture to prevent multiple services' from accessing the data at the same resource.

Various reasons can cause bottlenecks in microservices, and only a few most common root causes are mentioned above.

To further localize the bottleneck location OTel defines a mechanism called manual instrumentation which lets the developer wrap any part of suspicious code as a separate span where it can be identified as a separate block. This enables the developers to check the time spent on each function to easily locate the bottleneck.

With the custom span implemented wrapping the database query function, it is visible that the bottleneck is in the query implementation. The query needs to be analyzed and optimized to fix the bottleneck.

Step 3: Evaluating the Solution

Once the solution is implemented, we can check the E2E trace visualization provided by Helios once again and verify that the bottleneck has been fixed, as shown in the image below.

Since this was a straightforward application created for demonstrations, the bottleneck was simple to find and fix. But in a real-world complex application, identifying and fixing a bottleneck might involve many changes. But the use of applications such as Helios can make it much easier to identify and fix bottlenecks in your application. The sample application used for the example is uploaded here.

Conclusion

With modern distributed systems becoming increasingly complex, effective tools and techniques must be used to identify application bottlenecks. By using distributed tracing solutions like OpenTelemetry and Helios, developers can effectively identify and fix application bottlenecks, ultimately improving user experience and the business's revenue.

I hope you have found this article helpful, and thank you for reading it!