Comprehensive Observability via Distributed Tracing on Node.js8

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The benefits of Node.js to develop real-time applications at scale are very well known. As Node.js architectures get more and more complex, visualization of your microservice-based architecture is crucial. However, the visualization of microservices is incredibly complex given the scale and the transactions across them. You not only need to visualize your Node.js applications but also analyze the health, flow, and performance of applications to have a complete observability solution. In this talk, we'll go over the challenges of scaling your Node.js applications and tools (such as distributed tracing) available to you to scale with confidence.

Chinmay Gaikwad
Chinmay Gaikwad
8 min
24 Jun, 2021

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Video Summary and Transcription

Welcome to the session on comprehensive observability via distributed tracing on Node.js. We'll explore the challenges of microservices and troubleshoot distributed applications using an example. Correlation is the missing piece in troubleshooting distributed applications. Distributed tracing helps pinpoint issues that logging or metrics may miss, reducing mean time to resolution. It provides visualization of microservices architecture, actionable data, and enables code optimization.

1. Introduction to Observability

Short description:

Welcome to the session on comprehensive observability via distributed tracing on Node.js. In this session, we'll look at the new challenges in microservices, troubleshoot distributed applications using an example, and build a sustainable observability strategy for your company. Microservices have great benefits but also bring new challenges such as observability. Traditional monitoring systems make it hard to know what's happening under the hood.

Hello, everyone. Welcome to the session on comprehensive observability via distributed tracing on Node.js. I'm the host for the session. I'm Chinmay Gaikwad. I'm a technical evangelist at Epsigon.

Let's get started with the session. In this session, we'll look at the new challenges in microservices, specifically focusing on observability. We'll also look at how to troubleshoot distributed applications using an example, and finally we'll look at how to build a sustainable observability strategy for your company.

So let's start with the challenges on microservices. We know microservices have great benefits including scalability, speed of development, decreased system administration time, but microservices have also brought about new challenges such as observability in microservices. Using traditional monitoring systems, it can be nearly impossible to know what is going on under the hood. We'll explore this into much details in the upcoming slides.

2. Troubleshooting Distributed Applications

Short description:

Let's start with metrics, which are a great way to identify issues. Logs tell us why something went wrong, but they are not sufficient in a microservices-based environment. The traditional way of debugging involves looking at metrics, then logs, but it lacks context. Correlation is the missing piece in troubleshooting distributed applications.

First, let's see how to troubleshoot distributed applications. So we know the three pillars of observability are logs and traces. We'll deep dive into tracing a bit later. Let's start with metrics. Metrics are a great way for opps to figure out if something has gone wrong. Some examples of metrics include CPU usage, memory usage. We also have business level metrics such as bounce rates, revenue, click through rate, etc.

Logs on the other hand, tell us why something went wrong. So for this session, let us consider an example of a virtual shop. As you can see, the SAP server authenticates requests using Auth0, and then pushes them onto the Kafka Stream. A Java container pulls the stream and updates a DynamoDB table. Let's say there was a situation where users complained about OAuth that was sent but not handled. Where would you start?

Traditional monitoring solutions come at the expense of higher resource utilization because they have multiple high-heavyweight agents. And they also have the ability to only collect host metrics or are purely metric-driven. Metrics, as we have seen, really only let us know that something is broken, but not when or why. Context is absolutely critical in today's environments. Using the traditional way, first you look at Kafka metrics. You don't see anything abnormal here, so maybe look at the DynamoDB metrics next. We see some spikes here, so that's pretty interesting. So for debugging this, you need more data. And more data means logs. But are logs really sufficient in a microservices-based environment? Let's look into it.

We all know what logs look like. Personally, I have a love-hate relationship with logs. I love the fact that they are available, but I hate digging through them. I've sat myself digging through hundreds or even thousands of lines of logs hoping to spot that outlier. What if I knew the exact path that request is taking through individual services and components? Logs are good to debug on the list, but they don't really work as a starting point in a highly distributed system. So in a workshop example, if you're very lucky, you'll be able to spot the problem, but it might take a very long time. So let's recap of what are the things that are missing here. It essentially boils down to correlation.

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