Kubernetes-based Microservice Observability with Istio Service Mesh: Part 2 of 2

In part two of this two-part post, we will continue to explore the set of popular open-source observability tools that are easily integrated with the Istio service mesh. While these tools are not a part of Istio, they are essential to making the most of Istio’s observability features. The tools include Jaeger and Zipkin for distributed transaction monitoring, Prometheus for metrics collection and alerting, Grafana for metrics querying, visualization, and alerting, and Kiali for overall observability and management of Istio. We will round out the toolset with the addition of Fluent Bit for log processing and aggregation. We will observe a distributed, microservices-based reference application platform deployed to an Amazon Elastic Kubernetes Service (Amazon EKS) cluster using these tools. The platform, running on EKS, will use Amazon DocumentDB as a persistent data store and Amazon MQ to exchange messages.

Kiali Management Console showing reference application platform


The O’Reilly book, Distributed Systems Observability, by Cindy Sridharan, describes The Three Pillars of Observability in Chapter 4: “Logs, metrics, and traces are often known as the three pillars of observability. While plainly having access to logs, metrics, and traces doesn’t necessarily make systems more observable, these are powerful tools that, if understood well, can unlock the ability to build better systems.

Reference Application Platform

To demonstrate Istio’s observability tools, we deployed a reference application platform to EKS on AWS. I have developed the application platform to demonstrate different Kubernetes platforms, such as EKS, GKE, AKS, and concepts such as service mesh, API management, observability, DevOps, and Chaos Engineering. The platform comprises a backend containing eight Go-based microservices, labeled generically as Service A — Service H, one Angular 12 TypeScript-based frontend UI, four MongoDB databases, and one RabbitMQ message queue. The platform and all its source code are open-sourced on GitHub.

Reference Application Platform’s Angular-based UI

The reference application platform is designed to generate HTTP-based service-to-service, TCP-based service-to-database, and TCP-based service-to-queue-to-service IPC (inter-process communication). For example, Service A calls Service B and Service C; Service B calls Service D and Service E; Service D produces a message to a RabbitMQ queue, which Service F consumes message off on the RabbitMQ queue, and writes to MongoDB, and so on. The platform’s distributed service communications can be observed using Istio’s observability tools when the system is deployed to a Kubernetes cluster running the Istio service mesh.

High-level architecture of Reference Application Platform

Part Two

In part one of the post, we configured and deployed the reference application platform to an Amazon EKS development-grade cluster on AWS. The reference application, running on EKS, communicates with two external systems, Amazon DocumentDB (with MongoDB compatibility) and Amazon MQ.

Deployed Reference Application Platform as seen from Argo CD

In part two of the post, we will explore each of the observability tools we installed in greater detail. We will understand how each tool contributes to the three pillars of observability: logs, metrics, and traces.

Logs, metrics, and traces are often known as the three pillars of observability.
 — Cindy Sridharan

Pillar One: Logs

To paraphrase Jay Kreps on the LinkedIn Engineering Blog, a log is an append-only, totally-ordered sequence of records ordered by time. The ordering of records defines a notion of “time” since entries to the left are defined to be older than entries to the right. Logs are a historical record of events that happened in the past. Logs have been around almost as long as computers and are at the heart of many distributed data systems and real-time application architectures.

Go-based Microservice Logging

An effective logging strategy starts with what you log, when you log, and how you log. As part of our logging strategy, the eight Go-based microservices use Logrus, a popular structured logger for Go first released in 2014. The microservices also implement Banzai Cloud’s logrus-runtime-formatter. There is an excellent article on the formatter, Golang runtime Logrus Formatter. These two logging packages give us greater control over what you log, when you log, and how you log information about our microservices. The recommended configuration of the packages is minimal.

func init() {
formatter := runtime.Formatter{ChildFormatter: &log.JSONFormatter{}}
formatter.Line = true
level, err := log.ParseLevel(logLevel)
if err != nil {

Logrus provides several advantages over Go’s simple logging package, log. For example, log entries are not only for Fatal errors, nor should all verbose log entries be output in a Production environment. The post’s microservices are taking advantage of Logrus’ ability to log at seven levels: Trace, Debug, Info, Warning, Error, Fatal, and Panic. I have also variabilized the log level, allowing it to be easily changed in the Kubernetes Deployment resource at deploy-time.

The microservices also take advantage of Banzai Cloud’s logrus-runtime-formatter. The Banzai formatter automatically tags log messages with runtime and stack information, including function name and line number; extremely helpful when troubleshooting. I am also using Logrus’ JSON formatter.

Service A log entries in CloudWatch Insights

In 2020, Logus entered maintenance mode. The author, Simon Eskildsen (Principal Engineer at Shopify), stated they will not be introducing new features. This does not mean Logrus is dead. With over 18,000 GitHub Stars, Logrus will continue to be maintained for security, bug fixes, and performance. The author states that many fantastic alternatives to Logus now exist, such as Zerolog, Zap, and Apex.

Client-side Angular UI Logging

Likewise, I have enhanced the logging of the Angular UI using NGX Logger. NGX Logger is a simple logging module for angular (currently supports Angular 6+). It allows “pretty print” to the console and allows log messages to be POSTed to a URL for server-side logging. For this demo, the UI will only log to the web browser’s console. Similar to Logrus, NGX Logger supports multiple log levels: Trace, Debug, Info, Warning, Error, Fatal, and Off. However, instead of just outputting messages, NGX Logger allows us to output properly formatted log entries to the browser’s console.

The level of logs output is configured to be dependent on the environment, Production or not Production. Below is an example of the log output from the Angular UI in Chrome. Since the UI’s Docker Image was built with the Production configuration, the log level is set to INFO. You would not want to expose potentially sensitive information in verbose log output to our end-users in Production.

Controlling logging levels is accomplished by adding the following ternary operator to the app.module.ts file.

imports: [
level: !environment.production ?
NgxLoggerLevel.DEBUG : NgxLoggerLevel.INFO,
serverLogLevel: NgxLoggerLevel.INFO

Platform Logs

Based on the platform built, configured, and deployed in , you now have access logs from multiple sources.

  1. Amazon DocumentDB: Amazon CloudWatch Audit and Profiler logs;
  2. Amazon MQ: Amazon CloudWatch logs;
  3. Amazon EKS: API server, Audit, Authenticator, Controller manager, and Scheduler CloudWatch logs;
  4. Kubernetes Dashboard: Individual EKS Pod and Replica Set logs;
  5. Kiali: Individual EKS Pod and Container logs;
  6. Fluent Bit: EKS performance, host, dataplane, and application CloudWatch logs;

Fluent Bit

According to a recent AWS Blog post, Fluent Bit Integration in CloudWatch Container Insights for EKS, Fluent Bit is an open-source, multi-platform log processor and forwarder that allows you to collect data and logs from different sources and unify and send them to different destinations, including CloudWatch Logs. Fluent Bit is also fully compatible with Docker and Kubernetes environments. Using the newly launched Fluent Bit DaemonSet, you can send container logs from your EKS clusters to CloudWatch logs for logs storage and analytics.

With Fluent Bit, deployed in part one, the EKS cluster’s performance, host, dataplane, and application logs will also be available in Amazon CloudWatch.

Within the application log groups, you have access to the individual log streams for each reference application’s components.

Within each CloudWatch log stream, you can view individual log entries.

CloudWatch Logs Insights enables you to interactively search and analyze your log data in Amazon CloudWatch Logs. You can perform queries to help you more efficiently and effectively respond to operational issues. If an issue occurs, you can use CloudWatch Logs Insights to identify potential causes and validate deployed fixes.

CloudWatch Logs Insights supports CloudWatch Logs Insights query syntax, a query language you can use to perform queries on your log groups. Each query can include one or more query commands separated by Unix-style pipe characters (|). For example:

fields @timestamp, @message
| filter kubernetes.container_name = "service-f"
and @message like "error"
| sort @timestamp desc
| limit 20

Pillar Two: Metrics

For metrics, we will examine CloudWatch Container Insights, Prometheus, and Grafana. Prometheus and Grafana are industry-leading tools you installed as part of the Istio deployment.


Prometheus is an open-source systems monitoring and alerting toolkit originally built at SoundCloud circa 2012. Prometheus joined the Cloud Native Computing Foundation (CNCF) in 2016 as the second project hosted after Kubernetes.

According to Istio, the Prometheus addon is a Prometheus server that comes preconfigured to scrape Istio endpoints to collect metrics. You can use Prometheus with Istio to record metrics that track the health of Istio and applications within the service mesh. You can visualize metrics using tools like Grafana and Kiali. The Istio Prometheus addon is intended for demonstration only and is not tuned for performance or security.

The istioctl dashboardcommand provides access to all of the Istio web UIs. With the EKS cluster running, Istio installed, and the reference application platform deployed, access Prometheus using the istioctl dashboard prometheus command from your terminal. You must be logged into AWS from your terminal to connect to Prometheus successfully. If you are not logged in to AWS, you will often see the following error: Error: not able to locate <tool_name> pod: Unauthorized. Since we used the non-production demonstration versions of the Istio Addons, there is no authentication and authorization required to access Prometheus.

According to Prometheus, users select and aggregate time-series data in real-time using a functional query language called PromQL (Prometheus Query Language). The result of an expression can either be shown as a graph, viewed as tabular data in Prometheus’s expression browser, or consumed by external systems through Prometheus’ HTTP API. The expression browser includes a drop-down menu with all available metrics as a starting point for building queries. Shown below are a few PromQL examples that were developed as part of writing this post.

up{alpha_eksctl_io_cluster_name="istio-observe-demo", job="kubernetes-nodes"}
sum by (pod) (rate(container_network_transmit_packets_total{stack="reference-app",namespace="dev",pod=~"service-.*"}[5m]))
sum by (instance) (istio_requests_total{source_app="istio-ingressgateway",connection_security_policy="mutual_tls",response_code="200"})
sum by (response_code) (istio_requests_total{source_app="istio-ingressgateway",connection_security_policy="mutual_tls",response_code!~"200|0"})

Prometheus APIs

Prometheus has both an HTTP API and a Management API. There are many useful endpoints in addition to the Prometheus UI, available at http://localhost:9090/graph. For example, the Prometheus HTTP API endpoint that lists all the command-line configuration flags is available at http://localhost:9090/api/v1/status/flags. The endpoint that lists all the available Prometheus metrics is available at http://localhost:9090/api/v1/label/__name__/values; a total of 951 metrics in this demonstration!

The Prometheus endpoint that lists many available metrics with HELP and TYPE to explain their function is found at http://localhost:9090/metrics.

Understanding Metrics

In addition to these endpoints, the standard service level metrics exported by Istio and available via Prometheus are found in the Istio Standard Metrics documentation. An explanation of many of the metrics available via Prometheus are also found in the cAdvisor README on their GitHub site. As mentioned in this AWS Blog Post, the cAdvisor metrics are also available from the command line using the following commands:

export NODE=$(kubectl get nodes | sed -n '2 p') | awk {'print $1'}
kubectl get --raw "/api/v1/nodes/${NODE}/proxy/metrics/cadvisor"

Observing Metrics

Below is an example graph of the backend microservice containers deployed to EKS. The graph PromQL expression returns the amount of working set memory, including recently accessed memory, dirty memory, and kernel memory (container_memory_working_set_bytes), summed by pod, in megabytes (MB). There was no load on the services during the period displayed.

sum by (pod) (container_memory_working_set_bytes{image=~"registry.hub.docker.com/garystafford/.*"}) / (1024^2)

The container_memory_working_set_bytes metric is the same metric used by the kubectl top command (not container_memory_usage_bytes).

> kubectl top pod -n dev --containers=true --use-protocol-buffer
POD                          NAME          CPU(cores)   MEMORY(bytes)
service-a-546fbd558d-28jlm service-a 1m 6Mi
service-a-546fbd558d-2lcsg service-a 1m 6Mi
service-b-545c85df9-dl9h8 service-b 1m 6Mi
service-b-545c85df9-q99xm service-b 1m 5Mi
service-c-58996574-58wd8 service-c 1m 7Mi
service-c-58996574-6q7n4 service-c 1m 7Mi
service-d-867796bb47-87ps5 service-d 1m 6Mi
service-d-867796bb47-fh6wl service-d 1m 6Mi

In another Prometheus example, the PromQL query expression returns the per-second rate of CPU resources measured in CPU units (1 CPU = 1 AWS vCPU), as measured over the last 5 minutes, per time series in the range vector, summed by the pod. During this period, the backend services were under a consistent, simulated load of 25 concurrent users using hey. The four Service D pods were consuming the most CPU units during this time period.

sum by (pod) (rate(container_cpu_usage_seconds_total{image=~"registry.hub.docker.com/garystafford/.*"}[5m])) * 1000

The container_cpu_usage_seconds_total metric is the same metric used by the kubectl top command. The above PromQL expression multiplies the query results by 1,000 to match the results from kubectl top, shown below.

> kubectl top pod -n dev --containers=true --use-protocol-buffer
POD                          NAME          CPU(cores)   MEMORY(bytes)
service-a-546fbd558d-28jlm service-a 25m 9Mi
service-a-546fbd558d-2lcsg service-a 27m 8Mi
service-b-545c85df9-dl9h8 service-b 29m 11Mi
service-b-545c85df9-q99xm service-b 23m 8Mi
service-c-58996574-c8hkn service-c 62m 9Mi
service-c-58996574-kx895 service-c 55m 8Mi
service-d-867796bb47-87ps5 service-d 285m 12Mi
service-d-867796bb47-9ln7p service-d 226m 11Mi


Prometheus also exposes container resource limits. For example, the memory limits set on the reference platform’s backend services, displayed in megabytes (MB), using the container_spec_memory_limit_bytes metric. When viewed alongside the real-time resources consumed by the services, these metrics are useful to properly configure and monitor Kubernetes management features such as the Horizontal Pod Autoscaler.

sum by (container) (container_spec_memory_limit_bytes{image=~"registry.hub.docker.com/garystafford/.*"}) / (1024^2) / count by (container) (container_spec_memory_limit_bytes{image=~"registry.hub.docker.com/garystafford/.*"})

Or, memory limits by Pod:

sum by (pod) (container_spec_memory_limit_bytes{image=~"registry.hub.docker.com/garystafford/.*"}) / (1024^2)

Cluster Metrics

Prometheus also contains metrics about Istio components, Kubernetes components, and the EKS cluster. For example, the total memory in gigabytes (GB) of each m5.large EC2 worker nodes in the istio-observe-demo EKS cluster’s managed-ng-1 Managed Node Group.

machine_memory_bytes{alpha_eksctl_io_cluster_name="istio-observe-demo", alpha_eksctl_io_nodegroup_name="managed-ng-1"} / (1024^3)

For total physical cores, use the machine_cpu_physical_core metric, and for vCPU cores use the machine_cpu_cores metric.


Grafana describes itself as the leading open-source software for time-series analytics. According to Grafana Labs, Grafana allows you to query, visualize, alert on, and understand your metrics no matter where they are stored. You can easily create, explore, and share visually rich, data-driven dashboards. Grafana also allows users to visually define alert rules for their most important metrics. Grafana will continuously evaluate rules and can send notifications.

If you deployed Grafana using the Istio addons process demonstrated in part one of the post, access Grafana similar to the other tools:

istioctl dashboard grafana

According to Istio, Grafana is an open-source monitoring solution used to configure dashboards for Istio. You can use Grafana to monitor the health of Istio and applications within the service mesh. While you can build your own dashboards, Istio offers a set of preconfigured dashboards for all of the most important metrics for the mesh and the control plane. The preconfigured dashboards use Prometheus as the data source.

Below is an example of the Istio Mesh Dashboard, filtered to show the eight backend services workloads running in the dev namespace. During this period, the backend services were under a consistent simulated load of approximately 20 concurrent users using hey. You can observe the p50, p90, and p99 latency of requests to these workloads.

Dashboards are built from Panels, the basic visualization building blocks in Grafana. Each panel has a query editor specific to the data source (Prometheus in this case) selected. The query editor allows you to write your (PromQL) query. Below is the PromQL expression query responsible for the p50 latency Panel displayed in the Istio Mesh Dashboard.

 label_join((histogram_quantile(0.50, sum(rate(istio_request_duration_milliseconds_bucket{reporter="source"}[1m])) by (le, destination_workload, destination_workload_namespace)) / 1000) or histogram_quantile(0.50, sum(rate(istio_request_duration_seconds_bucket{reporter="source"}[1m])) by (le, destination_workload, destination_workload_namespace)), "destination_workload_var", ".", "destination_workload", "destination_workload_namespace")

Below is an example of the Outbound Workloads section of the Istio Workload Dashboard. The complete dashboard contains three sections: General, Inbound Workloads, and Outbound Workloads. Here we have filtered the on reference platform’s backend services in the dev namespace.

Here is a different view of the Istio Workload Dashboard, the dashboard’s Inbound Workloads section filtered to a single workload, Service A, the backend’s edge service. Service A accepts incoming traffic from the Istio Ingress Gateway as shown in the dashboard’s panels.

Grafana provides the ability to Explore a Panel. Explore strips away the dashboard and panel options so that you can focus on the query. It helps you iterate until you have a working query and then think about building a dashboard. Below is an example of the Panel showing the egress TCP traffic, based on the istio_tcp_sent_bytes_total metric, for Service F. Service F consumes messages off on the RabbitMQ queue (Amazon MQ) and writes messages to MongoDB (DocumentDB).

You can monitor the resource usage of Istio with the Performance Dashboard.

Additional Dashboards

Grafana provides a site containing official and community-built dashboards, including the above-mentioned Istio dashboards. Importing dashboards into your Grafana instance is as simple as copying the dashboard URL or the ID provided from the Grafana dashboard site and pasting it into the dashboard import option of your Grafana instance. Be aware that not every Kubernetes dashboard in Grafan’s site is compatible with your specific version of Kubernetes, Istio, or EKS, nor relies on Prometheus as a data source. As a result, you might have to test and tweak imported dashboards to get them working.

Below is an example of an imported community dashboard, Kubernetes cluster monitoring (via Prometheus) by Instrumentisto Team (dashboard ID 315).


An effective observability strategy must include more than just the ability to visualize results. An effective strategy must also detect anomalies and notify (alert) the appropriate resources or directly resolve incidents. Grafana, like Prometheus, is capable of alerting and notification. You visually define alert rules for your critical metrics. Grafana will continuously evaluate metrics against the rules and send notifications when pre-defined thresholds are breached.

Prometheus supports multiple popular notification channels, including PagerDuty, HipChat, Email, Kafka, and Slack. Below is an example of a Prometheus notification channel that sends alert notifications to a Slack support channel.

Below is an example of an alert based on an arbitrarily high CPU usage of 300 milliCPUs (m). When the CPU usage of a single pod goes above that value for more than 3 minutes, an alert is sent. The high CPU usage could be caused by the Horizontal Pod Autoscaler not functioning, or the HPA has reached its maxReplicas limit, or there are not enough resources available within the cluster to schedule additional pods.

Triggered by the alert, Prometheus sends detailed notifications to the designated Slack channel.

Amazon CloudWatch Container Insights

Lastly in the category of Metrics, Amazon CloudWatch Container Insights collects, aggregates, and summarizes metrics and logs from your containerized applications and microservices. CloudWatch alarms can be set on metrics that Container Insights collects. Container Insights is available for Amazon Elastic Container Service (Amazon ECS) including Fargate, Amazon EKS, and Kubernetes platforms on Amazon EC2.

In Amazon EKS, Container Insights uses a containerized version of the CloudWatch agent to discover all running containers in a cluster. It then collects performance data at every layer of the performance stack. Container Insights collects data as performance log events using the embedded metric format. These performance log events are entries that use a structured JSON schema that enables high-cardinality data to be ingested and stored at scale.

In part one of the post, we also installed CloudWatch Container Insights monitoring for Prometheus, which automates the discovery of Prometheus metrics from containerized systems and workloads.

Below is an example of a basic Performance Monitoring CloudWatch Container Insights Dashboard. The dashboard is filtered to the dev namespace of the EKS cluster, where the reference application platform is running. During this period, the backend services were put under a simulated load using hey. As the load on the application increases, observe the Number of Pods increases from 19 to 34 pods, based on the Deployment resources and HPA configurations. There is also an Alert, shown on the right of the screen. An alarm was triggered for an arbitrarily high level of network transmission activity.

Next is an example of Container Insights’ Container Map view in Memory mode. You see a visual representation of the dev namespace, with each of the backend service’s Service and Deployment resources shown.

There is a warning icon indicating an Alarm on the cluster was triggered.

Lastly, CloudWatch Insights allows you to jump from the CloudWatch Insights to the CloudWatch Log Insights console. CloudWatch Insights will also write the CloudWatch Insights query for you. Below, we went from the Service D container metrics view in the CloudWatch Insights Performance Monitoring console directly to the CloudWatch Log Insights console with a query, ready to run.

Pillar 3: Traces

According to the Open Tracing website, distributed tracing, also called distributed request tracing, is used to profile and monitor applications, especially those built using a microservices architecture. Distributed tracing helps pinpoint where failures occur and what causes poor performance.

According to Istio, header propagation may be accomplished through client libraries, such as Zipkin or Jaeger. It may also be accomplished manually, referred to as trace context propagation, documented in the Distributed Tracing Task. Istio proxies can automatically send spans. Applications need to propagate the appropriate HTTP headers so that when the proxies send span information, the spans can be correlated correctly into a single trace. To accomplish this, an application needs to collect and propagate the following headers from the incoming request to any outgoing requests.

  • x-request-id
  • x-b3-traceid
  • x-b3-spanid
  • x-b3-parentspanid
  • x-b3-sampled
  • x-b3-flags
  • x-ot-span-context

The x-b3 headers originated as part of the Zipkin project. The B3 portion of the header is named for the original name of Zipkin, BigBrotherBird. Passing these headers across service calls is known as B3 propagation. According to Zipkin, these attributes are propagated in-process and eventually downstream (often via HTTP headers) to ensure all activity originating from the same root are collected together.

To demonstrate distributed tracing with Jaeger and Zipkin, Service A, Service B, and Service E have been modified to pass the b3 headers. These are the three services that make HTTP requests to other upstream services. The following code has been added to propagate the headers from one service to the next. The Istio sidecar proxy (Envoy) generates the first headers. It is critical to only propagate the headers that are present in the downstream request and have a value, as the code below does. Propagating an empty header will break the distributed tracing.

incomingHeaders := []string{
for _, header := range incomingHeaders {
if r.Header.Get(header) != "" {
req.Header.Add(header, r.Header.Get(header))

Below, the highlighted section of the response payload from a call to Service A’s /api/request-echo endpoint reveals the b3 headers originating from the Istio proxy and passed to Service A.


According to their website, Jaeger, inspired by Dapper and OpenZipkin, is a distributed tracing system released as open source by Uber Technologies. Jaeger is used for monitoring and troubleshooting microservices-based distributed systems, including distributed context propagation, distributed transaction monitoring, root cause analysis, service dependency analysis, and performance and latency optimization. The Jaeger website contains a helpful overview of Jaeger’s architecture and general tracing-related terminology.

If you deployed Jaeger using the Istio addons process demonstrated in part one of the post, access Jaeger similar to the other tools:

istioctl dashboard jaeger

Below is an example of the Jaeger UI’s Search view, displaying the results of a search for the Istio Ingress Gateway service over a period of time. We see a timeline of traces across the top with a list of trace results below. As discussed on the Jaeger website, a trace is composed of spans. A span represents a logical unit of work in Jaeger that has an operation name. A trace is an execution path through the system and can be thought of as a directed acyclic graph (DAG) of spans. If you have worked with systems like Apache Spark, you are probably already familiar with the concept of DAGs.

Below is a detailed view of a single trace in Jaeger’s Trace Timeline mode. The 14 spans encompass eight of the reference platform’s components: seven of the eight backend services and the Istio Ingress Gateway. The spans each have individual timings, with an overall trace time of 160 ms. The root span in the trace is the Istio Ingress Gateway. The Angular UI, loaded in the end user’s web browser, calls Service A via the Istio Ingress Gateway. From there, we see the expected flow of our service-to-service IPC. Service A calls Services B and Service C. Service B calls Service E, which calls Service G and Service H.

In this demonstration, traces are not instrumented to span the RabbitMQ message queue nor MongoDB. This means you would not see a trace that includes a call from Service D to Service F via the RabbitMQ.

The visualization of the trace’s timeline demonstrates the synchronous nature of the reference platform’s service-to-service IPC instead of the asynchronous nature of the decoupled communications using the RabbitMQ messaging queue. Note how Service A waits for each service in its call chain to respond before returning its response to the requester.

Within Jaeger’s Trace Timeline view, you have the ability to drill into a single span, which contains additional metadata. The span’s metadata includes the API endpoint URL being called, HTTP method, response status, and several other headers.

Jaeger also has an experimental Trace Graph mode, which displays a graph view of the same trace.

Jaeger also includes a Compare Trace feature and two Dependencies views: Force-Directed Graph and DAG. I find both views rather primitive compared to Kiali. Lacking access to Kiali, the views are marginally useful as a dependency graph.


Zipkin is a distributed tracing system, which helps gather timing data needed to troubleshoot latency problems in service architectures. According to a 2012 post on Twitter’s Engineering Blog, Zipkin started as a project during Twitter’s first Hack Week. During that week, they implemented a basic version of the Google Dapper paper for Thrift.

Zipkin and Jaeger are very similar in terms of capabilities. I have chosen to focus on Jaeger in this post as I prefer it over Zipkin. If you want to try Zipkin instead of Jaeger, you can use the following commands to remove Jaeger and install Zipkin from the Istio addons extras directory. In part one of the post, we did not install Zipkin by default when we deployed the Istio addons. Be aware that running both tools at the same time in the same Kubernetes cluster will cause unpredictable tracing results.

kubectl delete -f https://raw.githubusercontent.com/istio/istio/release-1.10/samples/addons/jaeger.yaml
kubectl apply -f https://raw.githubusercontent.com/istio/istio/release-1.10/samples/addons/extras/zipkin.yaml

Access Zipkin similar to the other observability tools:

istioctl dashboard zipkin

Below is an example of a distributed trace visualized in Zipkin’s UI, containing 14 spans. This is very similar to the trace visualized in Jaeger, shown above. The spans encompass eight of the reference platform’s components: seven of the eight backend services and the Istio Ingress Gateway. The spans each have individual timings, with an overall trace time of 154 ms.

Zipkin can also visualize a dependency graph based on the distributed trace. Below is an example of a traffic simulation over a two-minute period, showing network traffic flowing between the reference platform’s components, illustrated as a dependency graph.

Kiali: Microservice Observability

According to their website, Kiali is a management console for an Istio-based service mesh. It provides dashboards, observability, and lets you operate your mesh with robust configuration and validation capabilities. It shows the structure of a service mesh by inferring traffic topology and displaying the mesh’s health. Kiali provides detailed metrics, powerful validation, Grafana access, and strong integration for distributed tracing with Jaeger.

If you deployed Kaili using the Istio addons process demonstrated in part one of the post, access Kiali similar to the other tools:

istioctl dashboard kaili

For improved security, I optionally chose to install the latest version of Kaili using the customizable install mentioned in Istio’s documentation. Using Kiali’s Install via Kiali Server Helm Chart option adds token-based authentication, similar to the Kubernetes Dashboard.

Logging into Kiali, we see the Overview tab, which provides a global view of all namespaces within the Istio service mesh and the number of applications within each namespace.

The Graph tab in the Kiali UI represents the components running in the Istio service mesh. Below, filtering on the cluster’s dev Namespace, we can observe that Kiali has mapped 8 applications (Workloads), 10 services, and 22 edges (a graph term). Specifically, we see the Istio Ingres Proxy at the edge of the service mesh, the Angular UI and eight backend services all with their respective Envoy proxy sidecars that are taking traffic (Service F did not take any direct traffic from another service in this example), the external DocumentDB egress point, and the external Amazon MQ egress point. Finally, note how service-to-service traffic flows, with Istio, from the service to its sidecar proxy, to the other service’s sidecar proxy, and finally to the service.

Below is a similar view of the service mesh, but this time, there are failures between the Istio Ingress Gateway and Service A, shown in red. We can also observe overall metrics for the HTTP traffic, such as the request per second inbound and outbound, total requests, success and error rates, and HTTP status codes.

Kiali allows you to zoom in and focus on a single component in the graph and its individual metrics.

Kiali can also display average request times and other metrics for each edge in the graph (communication between two components). Kaili can even show those metrics over a given period of time, using Kiali’s Replay feature, shown below.

Focusing on the external DocumentDB cluster, Kiali also allows us to view TCP traffic between the four services within the service mesh that connect to the external cluster.

The Applications tab lists all the applications, their namespace, and labels.

You can drill into an individual component on both the Applications and Workloads tabs and view additional details. Details include the overall health, Pods, and Istio Config status. Below is an overview of the Service A workload in the dev Namespace.

The Workloads detailed view also includes inbound and outbound metrics. Below is an example of the outbound request volume, duration, throughput, and size metrics, for Service A in the dev Namespace.

Kiali also gives you access to the individual pod’s container logs. Although log access is not as user-friendly as other log sources discussed previously, having logs available alongside metrics (integration with Grafana), traces (integration with Jaeger), and mesh visualization, all in Kiali, can be very effective as a single source for observability.

Kiali also has an Istio Config tab. The Istio Config tab displays a list of all of the available Istio configuration objects that exist in the user’s environment.

You can use Kiali to configure and manage the Istio service mesh and its installed resources. Using Kiali, you can actually modify the deployed resources, similar to using the kubectl edit command.

Oftentimes, I find Kiali to be my first stop when troubleshooting platform issues. Once I identify the specific components or communication paths having issues, I can query the CloudWatch logs and Prometheus metrics through the Grafana dashboard.


In this two-part post, we explored a set of popular open-source observability tools, easily integrated with the Istio service mesh. These tools included Jaeger and Zipkin for distributed transaction monitoring, Prometheus for metrics collection and alerting, Grafana for metrics querying, visualization, and alerting, and Kiali for overall observability and management of Istio. We rounded out the toolset with the addition of Fluent Bit for log processing and forwarding to Amazon CloudWatch Container Insights. Using these tools, we successfully observed a microservices-based, distributed reference application platform deployed to Amazon EKS.

This blog represents my own viewpoints and not of my employer, Amazon Web Services (AWS). All product names, logos, and brands are the property of their respective owners.

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  1. Kubernetes-based Microservice Observability with Istio Service Mesh: Part 1 of 2 | Programmatic Ponderings

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