Archive for category Bash Scripting

Istio Observability with Go, gRPC, and Protocol Buffers-based Microservices

Posted by Gary A. Stafford in Bash Scripting, Build Automation, Client-Side Development, Cloud, Continuous Delivery, DevOps, Enterprise Software Development, GCP, Go, JavaScript, Kubernetes, Software Development on April 17, 2019

In the last two posts, Kubernetes-based Microservice Observability with Istio Service Mesh and Azure Kubernetes Service (AKS) Observability with Istio Service Mesh, we explored the observability tools which are included with Istio Service Mesh. These tools currently include Prometheus and Grafana for metric collection, monitoring, and alerting, Jaeger for distributed tracing, and Kiali for Istio service-mesh-based microservice visualization and monitoring. Combined with cloud platform-native monitoring and logging services, such as Stackdriver on GCP, CloudWatch on AWS, Azure Monitor logs on Azure, and we have a complete observability solution for modern, distributed, Cloud-based applications.

In this post, we will examine the use of Istio’s observability tools to monitor Go-based microservices that use Protocol Buffers (aka Protobuf) over gRPC (gRPC Remote Procedure Calls) and HTTP/2 for client-server communications, as opposed to the more traditional, REST-based JSON (JavaScript Object Notation) over HTTP (Hypertext Transfer Protocol). We will see how Kubernetes, Istio, Envoy, and the observability tools work seamlessly with gRPC, just as they do with JSON over HTTP, on Google Kubernetes Engine (GKE).

Technologies

gRPC

According to the gRPC project, gRPC, a CNCF incubating project, is a modern, high-performance, open-source and universal remote procedure call (RPC) framework that can run anywhere. It enables client and server applications to communicate transparently and makes it easier to build connected systems. Google, the original developer of gRPC, has used the underlying technologies and concepts in gRPC for years. The current implementation is used in several Google cloud products and Google externally facing APIs. It is also being used by Square, Netflix, CoreOS, Docker, CockroachDB, Cisco, Juniper Networks and many other organizations.

Protocol Buffers

By default, gRPC uses Protocol Buffers. According to Google, Protocol Buffers (aka Protobuf) are a language- and platform-neutral, efficient, extensible, automated mechanism for serializing structured data for use in communications protocols, data storage, and more. Protocol Buffers are 3 to 10 times smaller and 20 to 100 times faster than XML. Once you have defined your messages, you run the protocol buffer compiler for your application’s language on your .proto file to generate data access classes.

Protocol Buffers are 3 to 10 times smaller and 20 to 100 times faster than XML.

Protocol buffers currently support generated code in Java, Python, Objective-C, and C++, Dart, Go, Ruby, and C#. For this post, we have compiled for Go. You can read more about the binary wire format of Protobuf on Google’s Developers Portal.

Envoy Proxy

According to the Istio project, Istio uses an extended version of the Envoy proxy. Envoy is deployed as a sidecar to a relevant service in the same Kubernetes pod. Envoy, created by Lyft, is a high-performance proxy developed in C++ to mediate all inbound and outbound traffic for all services in the service mesh. Istio leverages Envoy’s many built-in features, including dynamic service discovery, load balancing, TLS termination, HTTP/2 and gRPC proxies, circuit-breakers, health checks, staged rollouts, fault injection, and rich metrics.

According to the post by Harvey Tuch of Google, Evolving a Protocol Buffer canonical API, Envoy proxy adopted Protocol Buffers, specifically proto3, as the canonical specification of for version 2 of Lyft’s gRPC-first API.

Reference Microservices Platform

In the last two posts, we explored Istio’s observability tools, using a RESTful microservices-based API platform written in Go and using JSON over HTTP for service to service communications. The API platform was comprised of eight Go-based microservices and one sample Angular 7, TypeScript-based front-end web client. The various services are dependent on MongoDB, and RabbitMQ for event queue-based communications. Below, the is JSON over HTTP-based platform architecture.

Below, the current Angular 7-based web client interface.

Converting to gRPC and Protocol Buffers

For this post, I have modified the eight Go microservices to use gRPC and Protocol Buffers, Google’s data interchange format. Specifically, the services use version 3 release (aka proto3) of Protocol Buffers. With gRPC, a gRPC client calls a gRPC server. Some of the platform’s services are gRPC servers, others are gRPC clients, while some act as both client and server, such as Service A, B, and E. The revised architecture is shown below.

gRPC Gateway

Assuming for the sake of this demonstration, that most consumers of the API would still expect to communicate using a RESTful JSON over HTTP API, I have added a gRPC Gateway reverse proxy to the platform. The gRPC Gateway is a gRPC to JSON reverse proxy, a common architectural pattern, which proxies communications between the JSON over HTTP-based clients and the gRPC-based microservices. A diagram from the grpc-gateway GitHub project site effectively demonstrates how the reverse proxy works.

Image courtesy: https://github.com/grpc-ecosystem/grpc-gateway

In the revised platform architecture diagram above, note the addition of the reverse proxy, which replaces Service A at the edge of the API. The proxy sits between the Angular-based Web UI and Service A. Also, note the communication method between services is now Protobuf over gRPC instead of JSON over HTTP. The use of Envoy Proxy (via Istio) is unchanged, as is the MongoDB Atlas-based databases and CloudAMQP RabbitMQ-based queue, which are still external to the Kubernetes cluster.

Alternatives to gRPC Gateway

As an alternative to the gRPC Gateway reverse proxy, we could convert the TypeScript-based Angular UI client to gRPC and Protocol Buffers, and continue to communicate directly with Service A as the edge service. However, this would limit other consumers of the API to rely on gRPC as opposed to JSON over HTTP, unless we also chose to expose two different endpoints, gRPC, and JSON over HTTP, another common pattern.

Demonstration

In this post’s demonstration, we will repeat the exact same installation process, outlined in the previous post, Kubernetes-based Microservice Observability with Istio Service Mesh. We will deploy the revised gRPC-based platform to GKE on GCP. You could just as easily follow Azure Kubernetes Service (AKS) Observability with Istio Service Mesh, and deploy the platform to AKS.

Source Code

All source code for this post is available on GitHub, contained in three projects. The Go-based microservices source code, all Kubernetes resources, and all deployment scripts are located in the k8s-istio-observe-backend project repository, in the new grpc branch.

git clone \
  --branch grpc --single-branch --depth 1 --no-tags \
  https://github.com/garystafford/k8s-istio-observe-backend.git

The Angular-based web client source code is located in the k8s-istio-observe-frontend repository on the new grpc branch. The source protocol buffers .proto file and the generated code, using the protocol buffers compiler, is located in the new pb-greeting project repository. You do not need to clone either of these projects for this post’s demonstration.

All Docker images for the services, UI, and the reverse proxy are located on Docker Hub.

Code Changes

This post is not specifically about writing Go for gRPC and Protobuf. However, to better understand the observability requirements and capabilities of these technologies, compared to JSON over HTTP, it is helpful to review some of the source code.

Service A

First, compare the source code for Service A, shown below, to the original code in the previous post. The service’s code is almost completely re-written. I relied on several references for writing the code, including, Tracing gRPC with Istio, written by Neeraj Poddar of Aspen Mesh and Distributed Tracing Infrastructure with Jaeger on Kubernetes, by Masroor Hasan.

Specifically, note the following code changes to Service A:

Import of the pb-greeting protobuf package;
Local Greeting struct replaced with pb.Greeting struct;
All services are now hosted on port 50051;
The HTTP server and all API resource handler functions are removed;
Headers, used for distributed tracing with Jaeger, have moved from HTTP request object to metadata passed in the gRPC context object;
Service A is coded as a gRPC server, which is called by the gRPC Gateway reverse proxy (gRPC client) via the Greeting function;
The primary PingHandler function, which returns the service’s Greeting, is replaced by the pb-greeting protobuf package’s Greeting function;
Service A is coded as a gRPC client, calling both Service B and Service C using the CallGrpcService function;
CORS handling is offloaded to Istio;
Logging methods are unchanged;

Source code for revised gRPC-based Service A (gist):

Greeting Protocol Buffers

Shown below is the greeting source protocol buffers .proto file. The greeting response struct, originally defined in the services, remains largely unchanged (gist). The UI client responses will look identical.

When compiled with protoc, the Go-based protocol compiler plugin, the original 27 lines of source code swells to almost 270 lines of generated data access classes that are easier to use programmatically.

# Generate gRPC stub (.pb.go)
protoc -I /usr/local/include -I. \
  -I ${GOPATH}/src \
  -I ${GOPATH}/src/github.com/grpc-ecosystem/grpc-gateway/third_party/googleapis \
  --go_out=plugins=grpc:. \
  greeting.proto

# Generate reverse-proxy (.pb.gw.go)
protoc -I /usr/local/include -I. \
  -I ${GOPATH}/src \
  -I ${GOPATH}/src/github.com/grpc-ecosystem/grpc-gateway/third_party/googleapis \
  --grpc-gateway_out=logtostderr=true:. \
  greeting.proto

# Generate swagger definitions (.swagger.json)
protoc -I /usr/local/include -I. \
  -I ${GOPATH}/src \
  -I ${GOPATH}/src/github.com/grpc-ecosystem/grpc-gateway/third_party/googleapis \
  --swagger_out=logtostderr=true:. \
  greeting.proto

Below is a small snippet of that compiled code, for reference. The compiled code is included in the pb-greeting project on GitHub and imported into each microservice and the reverse proxy (gist). We also compile a separate version for the reverse proxy to implement.

Using Swagger, we can view the greeting protocol buffers’ single RESTful API resource, exposed with an HTTP GET method. I use the Docker-based version of Swagger UI for viewing protoc generated swagger definitions.

docker run -p 8080:8080 -d --name swagger-ui \
  -e SWAGGER_JSON=/tmp/greeting.swagger.json \
  -v ${GOAPTH}/src/pb-greeting:/tmp swaggerapi/swagger-ui

The Angular UI makes an HTTP GET request to the /api/v1/greeting resource, which is transformed to gRPC and proxied to Service A, where it is handled by the Greeting function.

gRPC Gateway Reverse Proxy

As explained earlier, the gRPC Gateway reverse proxy service is completely new. Specifically, note the following code features in the gist below:

Import of the pb-greeting protobuf package;
The proxy is hosted on port 80;
Request headers, used for distributed tracing with Jaeger, are collected from the incoming HTTP request and passed to Service A in the gRPC context;
The proxy is coded as a gRPC client, which calls Service A;
Logging is largely unchanged;

The source code for the Reverse Proxy (gist):

Below, in the Stackdriver logs, we see an example of a set of HTTP request headers in the JSON payload, which are propagated upstream to gRPC-based Go services from the gRPC Gateway’s reverse proxy. Header propagation ensures the request produces a complete distributed trace across the complete service call chain.

Istio VirtualService and CORS

According to feedback in the project’s GitHub Issues, the gRPC Gateway does not directly support Cross-Origin Resource Sharing (CORS) policy. In my own experience, the gRPC Gateway cannot handle OPTIONS HTTP method requests, which must be issued by the Angular 7 web UI. Therefore, I have offloaded CORS responsibility to Istio, using the VirtualService resource’s CorsPolicy configuration. This makes CORS much easier to manage than coding CORS configuration into service code (gist):

Set-up and Installation

To deploy the microservices platform to GKE, follow the detailed instructions in part one of the post, Kubernetes-based Microservice Observability with Istio Service Mesh: Part 1, or Azure Kubernetes Service (AKS) Observability with Istio Service Mesh for AKS.

Create the external MongoDB Atlas database and CloudAMQP RabbitMQ clusters;
Modify the Kubernetes resource files and bash scripts for your own environments;
Create the managed GKE or AKS cluster on GCP or Azure;
Configure and deploy Istio to the managed Kubernetes cluster, using Helm;
Create DNS records for the platform’s exposed resources;
Deploy the Go-based microservices, gRPC Gateway reverse proxy, Angular UI, and associated resources to Kubernetes cluster;
Test and troubleshoot the platform deployment;
Observe the results;

The Three Pillars

As introduced in the first post, logs, metrics, and traces are often known as the three pillars of observability. These are the external outputs of the system, which we may observe. As modern distributed systems grow ever more complex, the ability to observe those systems demands equally modern tooling that was designed with this level of complexity in mind. Traditional logging and monitoring systems often struggle with today’s hybrid and multi-cloud, polyglot language-based, event-driven, container-based and serverless, infinitely-scalable, ephemeral-compute platforms.

Tools like Istio Service Mesh attempt to solve the observability challenge by offering native integrations with several best-of-breed, open-source telemetry tools. Istio’s integrations include Jaeger for distributed tracing, Kiali for Istio service mesh-based microservice visualization and monitoring, and Prometheus and Grafana for metric collection, monitoring, and alerting. Combined with cloud platform-native monitoring and logging services, such as Stackdriver for GKE, CloudWatch for Amazon’s EKS, or Azure Monitor logs for AKS, and we have a complete observability solution for modern, distributed, Cloud-based applications.

Pillar 1: Logging

Moving from JSON over HTTP to gRPC does not require any changes to the logging configuration of the Go-based service code or Kubernetes resources.

Stackdriver with Logrus

As detailed in part two of the last post, Kubernetes-based Microservice Observability with Istio Service Mesh, our logging strategy for the eight Go-based microservices and the reverse proxy continues to be the use of Logrus, the popular structured logger for Go, and Banzai Cloud’s logrus-runtime-formatter.

If you recall, the Banzai formatter automatically tags log messages with runtime/stack information, including function name and line number; extremely helpful when troubleshooting. We are also using Logrus’ JSON formatter. Below, in the Stackdriver console, note how each log entry below has the JSON payload contained within the message with the log level, function name, lines on which the log entry originated, and the message.

Below, we see the details of a specific log entry’s JSON payload. In this case, we can see the request headers propagated from the downstream service.

Pillar 2: Metrics

Moving from JSON over HTTP to gRPC does not require any changes to the metrics configuration of the Go-based service code or Kubernetes resources.

Prometheus

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

Grafana

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 allows users to visually define alert rules for your most important metrics. Grafana will continuously evaluate rules and can send notifications.

According to Istio, the Grafana add-on is a pre-configured instance of Grafana. The Grafana Docker base image has been modified to start with both a Prometheus data source and the Istio Dashboard installed. Below, we see two of the pre-configured dashboards, the Istio Mesh Dashboard and the Istio Performance Dashboard.

Pillar 3: Traces

Moving from JSON over HTTP to gRPC did require a complete re-write of the tracing logic in the service code. In fact, I spent the majority of my time ensuring the correct headers were propagated from the Istio Ingress Gateway to the gRPC Gateway reverse proxy, to Service A in the gRPC context, and upstream to all the dependent, gRPC-based services. I am sure there are a number of optimization in my current code, regarding the correct handling of traces and how this information is propagated across the service call stack.

Jaeger

According to their website, Jaeger, inspired by Dapper and OpenZipkin, is a distributed tracing system released as open source by Uber Technologies. It 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 an excellent overview of Jaeger’s architecture and general tracing-related terminology.

Below we see the Jaeger UI Traces View. In it, we see a series of traces generated by hey, a modern load generator and benchmarking tool, and a worthy replacement for Apache Bench (ab). Unlike ab, hey supports HTTP/2. The use of hey was detailed in the previous post.

A trace, as you might recall, 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 DAGs.

Below we see the Jaeger UI Trace Detail View. The example trace contains 16 spans, which encompasses nine components – seven of the eight Go-based services, the reverse proxy, and the Istio Ingress Gateway. The trace and the spans each have timings. The root span in the trace is the Istio Ingress Gateway. In this demo, traces do not span the RabbitMQ message queues. This means you would not see a trace which includes the decoupled, message-based communications between Service D to Service F, via the RabbitMQ.

Within the Jaeger UI Trace Detail View, you also have the ability to drill into a single span, which contains additional metadata. Metadata includes the URL being called, HTTP method, response status, and several other headers.

Microservice Observability

Moving from JSON over HTTP to gRPC does not require any changes to the Kiali configuration of the Go-based service code or Kubernetes resources.

Kiali

According to their website, Kiali provides answers to the questions: What are the microservices in my Istio service mesh, and how are they connected? Kiali works with Istio, in OpenShift or Kubernetes, to visualize the service mesh topology, to provide visibility into features like circuit breakers, request rates and more. It offers insights about the mesh components at different levels, from abstract Applications to Services and Workloads.

The Graph View in the Kiali UI is a visual representation of the components running in the Istio service mesh. Below, filtering on the cluster’s dev Namespace, we should observe that Kiali has mapped all components in the platform, along with rich metadata, such as their version and communication protocols.

Using Kiali, we can confirm our service-to-service IPC protocol is now gRPC instead of the previous HTTP.

Conclusion

Although converting from JSON over HTTP to protocol buffers with gRPC required major code changes to the services, it did not impact the high-level observability we have of those services using the tools provided by Istio, including Prometheus, Grafana, Jaeger, and Kiali.

All opinions expressed in this post are my own and not necessarily the views of my current or past employers or their clients.

AKS, API, GCP, GKE, Go, Golang, Grafana, gRPC, Istio, Jaeger, Kiali, Logging, Microservices, Observability, Prometheus, Protobuf, Protocol Buffers, Service Mesh

Azure Kubernetes Service (AKS) Observability with Istio Service Mesh

Posted by Gary A. Stafford in Azure, Bash Scripting, Cloud, DevOps, Go, JavaScript, Kubernetes, Software Development on March 31, 2019

In the last two-part post, Kubernetes-based Microservice Observability with Istio Service Mesh, we deployed Istio, along with its observability tools, Prometheus, Grafana, Jaeger, and Kiali, to Google Kubernetes Engine (GKE). Following that post, I received several questions about using Istio’s observability tools with other popular managed Kubernetes platforms, primarily Azure Kubernetes Service (AKS). In most cases, including with AKS, both Istio and the observability tools are compatible.

In this short follow-up of the last post, we will replace the GKE-specific cluster setup commands, found in part one of the last post, with new commands to provision a similar AKS cluster on Azure. The new AKS cluster will run Istio 1.1.3, released 4/15/2019, alongside the latest available version of AKS (Kubernetes), 1.12.6. We will replace Google’s Stackdriver logging with Azure Monitor logs. We will retain the external MongoDB Atlas cluster and the external CloudAMQP cluster dependencies.

Previous articles about AKS include First Impressions of AKS, Azure’s New Managed Kubernetes Container Service (November 2017) and Architecting Cloud-Optimized Apps with AKS (Azure’s Managed Kubernetes), Azure Service Bus, and Cosmos DB (December 2017).

Source Code

All source code for this post is available on GitHub in two projects. The Go-based microservices source code, all Kubernetes resources, and all deployment scripts are located in the k8s-istio-observe-backend project repository.

git clone \
  --branch master --single-branch \
  --depth 1 --no-tags \
  https://github.com/garystafford/k8s-istio-observe-backend.git

The Angular UI TypeScript-based source code is located in the k8s-istio-observe-frontend repository. You will not need to clone the Angular UI project for this post’s demonstration.

Setup

This post assumes you have a Microsoft Azure account with the necessary resource providers registered, and the Azure Command-Line Interface (CLI), az, installed and available to your command shell. You will also need Helm and Istio 1.1.3 installed and configured, which is covered in the last post.

Start by logging into Azure from your command shell.

az login \
  --username {{ your_username_here }} \
  --password {{ your_password_here }}

Resource Providers

If you are new to Azure or AKS, you may need to register some additional resource providers to complete this demonstration.

az provider list --output table

If you are missing required resource providers, you will see errors similar to the one shown below. Simply activate the particular provider corresponding to the error.

Operation failed with status:'Bad Request'. 
Details: Required resource provider registrations 
Microsoft.Compute, Microsoft.Network are missing.

To register the necessary providers, use the Azure CLI or the Azure Portal UI.

az provider register --namespace Microsoft.ContainerService
az provider register --namespace Microsoft.Network
az provider register --namespace Microsoft.Compute

Resource Group

AKS requires an Azure Resource Group. According to Azure, a resource group is a container that holds related resources for an Azure solution. The resource group includes those resources that you want to manage as a group. I chose to create a new resource group associated with my closest geographic Azure Region, East US, using the Azure CLI.

az group create \
  --resource-group aks-observability-demo \
  --location eastus

Create the AKS Cluster

Before creating the GKE cluster, check for the latest versions of AKS. At the time of this post, the latest versions of AKS was 1.12.6.

az aks get-versions \
  --location eastus \
  --output table

Using the latest GKE version, create the GKE managed cluster. There are many configuration options available with the az aks create command. For this post, I am creating three worker nodes using the Azure Standard_DS3_v2 VM type, which will give us a total of 12 vCPUs and 42 GB of memory. Anything smaller and all the Pods may not be schedulable. Instead of supplying an existing SSH key, I will let Azure create a new one. You should have no need to SSH into the worker nodes. I am also enabling the monitoring add-on. According to Azure, the add-on sets up Azure Monitor for containers, announced in December 2018, which monitors the performance of workloads deployed to Kubernetes environments hosted on AKS.

time az aks create \
  --name aks-observability-demo \
  --resource-group aks-observability-demo \
  --node-count 3 \
  --node-vm-size Standard_DS3_v2 \
  --enable-addons monitoring \
  --generate-ssh-keys \
  --kubernetes-version 1.12.6

Using the time command, we observe that the cluster took approximately 5m48s to provision; I have seen times up to almost 10 minutes. AKS provisioning is not as fast as GKE, which in my experience is at least 2x-3x faster than AKS for a similarly sized cluster.

After the cluster creation completes, retrieve your AKS cluster credentials.

az aks get-credentials \
  --name aks-observability-demo \
  --resource-group aks-observability-demo \
  --overwrite-existing

Examine the Cluster

Use the following command to confirm the cluster is ready by examining the status of three worker nodes.

kubectl get nodes --output=wide

Observe that Azure currently uses Ubuntu 16.04.5 LTS for the worker node’s host operating system. If you recall, GKE offers both Ubuntu as well as a Container-Optimized OS from Google.

Kubernetes Dashboard

Unlike GKE, there is no custom AKS dashboard. Therefore, we will use the Kubernetes Web UI (dashboard), which is installed by default with AKS, unlike GKE. According to Azure, to make full use of the dashboard, since the AKS cluster uses RBAC, a ClusterRoleBinding must be created before you can correctly access the dashboard.

kubectl create clusterrolebinding kubernetes-dashboard \
  --clusterrole=cluster-admin \
  --serviceaccount=kube-system:kubernetes-dashboard

Next, we must create a proxy tunnel on local port 8001 to the dashboard running on the AKS cluster. This CLI command creates a proxy between your local system and the Kubernetes API and opens your web browser to the Kubernetes dashboard.

az aks browse \
  --name aks-observability-demo \
  --resource-group aks-observability-demo

Although you should use the Azure CLI, PowerShell, or SDK for all your AKS configuration tasks, using the dashboard for monitoring the cluster and the resources running on it, is invaluable.

The Kubernetes dashboard also provides access to raw container logs. Azure Monitor provides the ability to construct complex log queries, but for quick troubleshooting, you may just want to see the raw logs a specific container is outputting, from the dashboard.

Azure Portal

Logging into the Azure Portal, we can observe the AKS cluster, within the new Resource Group.

In addition to the Azure Resource Group we created, there will be a second Resource Group created automatically during the creation of the AKS cluster. This group contains all the resources that compose the AKS cluster. These resources include the three worker node VM instances, and their corresponding storage disks and NICs. The group also includes a network security group, route table, virtual network, and an availability set.

Deploy Istio

From this point on, the process to deploy Istio Service Mesh and the Go-based microservices platform follows the previous post and use the exact same scripts. After modifying the Kubernetes resource files, to deploy Istio, use the bash script, part4_install_istio.sh. I have added a few more pauses in the script to account for the apparently slower response times from AKS as opposed to GKE. It definitely takes longer to spin up the Istio resources on AKS than on GKE, which can result in errors if you do not pause between each stage of the deployment process.

Using the Kubernetes dashboard, we can view the Istio resources running in the istio-system Namespace, as shown below. Confirm that all resource Pods are running and healthy before deploying the Go-based microservices platform.

Deploy the Platform

Deploy the Go-based microservices platform, using bash deploy script, part5a_deploy_resources.sh.

The script deploys two replicas (Pods) of each of the eight microservices, Service-A through Service-H, and the Angular UI, to the dev and test Namespaces, for a total of 36 Pods. Each Pod will have the Istio sidecar proxy (Envoy Proxy) injected into it, alongside the microservice or UI.

Azure Load Balancer

If we return to the Resource Group created automatically when the AKS cluster was created, we will now see two additional resources. There is now an Azure Load Balancer and Public IP Address.

Similar to the GKE cluster in the last post, when the Istio Ingress Gateway is deployed as part of the platform, it is materialized as an Azure Load Balancer. The front-end of the load balancer is the new public IP address. The back-end of the load-balancer is a pool containing the three AKS worker node VMs. The load balancer is associated with a set of rules and health probes.

DNS

I have associated the new Azure public IP address, connected with the front-end of the load balancer, with the four subdomains I am using to represent the UI and the edge service, Service-A, in both Namespaces. If Azure is your primary Cloud provider, then Azure DNS is a good choice to manage your domain’s DNS records. For this demo, you will require your own domain.

Testing the Platform

With everything deployed, test the platform is responding and generate HTTP traffic for the observability tools to record. Similar to last time, I have chosen hey, a modern load generator and benchmarking tool, and a worthy replacement for Apache Bench (ab). Unlike ab, hey supports HTTP/2. Below, I am running hey directly from Azure Cloud Shell. The tool is simulating 10 concurrent users, generating a total of 500 HTTP GET requests to Service A.

# quick setup from Azure Shell using Bash
go get -u github.com/rakyll/hey
cd go/src/github.com/rakyll/hey/
go build
  
./hey -n 500 -c 10 -h2 http://api.dev.example-api.com/api/ping

We had 100% success with all 500 calls resulting in an HTTP 200 OK success status response code. Based on the results, we can observe the platform was capable of approximately 4 requests/second, with an average response time of 2.48 seconds and a mean time of 2.80 seconds. Almost all of that time was the result of waiting for the response, as the details indicate.

Logging

In this post, we have replaced GCP’s Stackdriver logging with Azure Monitor logs. According to Microsoft, Azure Monitor maximizes the availability and performance of applications by delivering a comprehensive solution for collecting, analyzing, and acting on telemetry from Cloud and on-premises environments. In my opinion, Stackdriver is a superior solution for searching and correlating the logs of distributed applications running on Kubernetes. I find the interface and query language of Stackdriver easier and more intuitive than Azure Monitor, which although powerful, requires substantial query knowledge to obtain meaningful results. For example, here is a query to view the log entries from the services in the dev Namespace, within the last day.

let startTimestamp = ago(1d);
KubePodInventory
| where TimeGenerated > startTimestamp
| where ClusterName =~ "aks-observability-demo"
| where Namespace == "dev"
| where Name contains "service-"
| distinct ContainerID
| join
(
    ContainerLog
    | where TimeGenerated > startTimestamp
)
on ContainerID
| project LogEntrySource, LogEntry, TimeGenerated, Name
| order by TimeGenerated desc
| render table

Below, we see the Logs interface with the search query and log entry results.

Below, we see a detailed view of a single log entry from Service A.

Observability Tools

The previous post goes into greater detail on the features of each of the observability tools provided by Istio, including Prometheus, Grafana, Jaeger, and Kiali.

We can use the exact same kubectl port-forward commands to connect to the tools on AKS as we did on GKE. According to Google, Kubernetes port forwarding allows using a resource name, such as a service name, to select a matching pod to port forward to since Kubernetes v1.10. We forward a local port to a port on the tool’s pod.

# Grafana
kubectl port-forward -n istio-system \
  $(kubectl get pod -n istio-system -l app=grafana \
  -o jsonpath='{.items[0].metadata.name}') 3000:3000 &
  
# Prometheus
kubectl -n istio-system port-forward \
  $(kubectl -n istio-system get pod -l app=prometheus \
  -o jsonpath='{.items[0].metadata.name}') 9090:9090 &
  
# Jaeger
kubectl port-forward -n istio-system \
$(kubectl get pod -n istio-system -l app=jaeger \
-o jsonpath='{.items[0].metadata.name}') 16686:16686 &
  
# Kiali
kubectl -n istio-system port-forward \
  $(kubectl -n istio-system get pod -l app=kiali \
  -o jsonpath='{.items[0].metadata.name}') 20001:20001 &

Prometheus and Grafana

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 users to visually define alert rules for your most important metrics. Grafana will continuously evaluate rules and can send notifications.

According to Istio, the Grafana add-on is a pre-configured instance of Grafana. The Grafana Docker base image has been modified to start with both a Prometheus data source and the Istio Dashboard installed. Below, we see one of the pre-configured dashboards, the Istio Service Dashboard.

Jaeger

According to their website, Jaeger, inspired by Dapper and OpenZipkin, is a distributed tracing system released as open source by Uber Technologies. It 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 good overview of Jaeger’s architecture and general tracing-related terminology.

Below, we see a typical, distributed trace of the services, starting ingress gateway and passing across the upstream service dependencies.

Kaili

There is a common Kubernetes Secret that controls access to the Kiali API and UI. The default login is admin, the password is 1f2d1e2e67df.

Below, we see a detailed view of our platform, running in the dev namespace, on AKS.

Delete AKS Cluster

Once you are finished with this demo, use the following two commands to tear down the AKS cluster and remove the cluster context from your local configuration.

time az aks delete \
  --name aks-observability-demo \
  --resource-group aks-observability-demo \
  --yes

kubectl config delete-context aks-observability-demo

Conclusion

In this brief, follow-up post, we have explored how the current set of observability tools, part of the latest version of Istio Service Mesh, integrates with Azure Kubernetes Service (AKS).

All opinions expressed in this post are my own and not necessarily the views of my current or past employers or their clients.

AKS, Azure, Azure Kubernetes Service, Cloud, Grafana, Infrastructure, Isio, Jaeger, k8s, Kiali, Logging, Monitoring, Observability, Prometheus, Tracing

Getting Started with Red Hat Ansible for Google Cloud Platform

Posted by Gary A. Stafford in Bash Scripting, Build Automation, DevOps, GCP on January 30, 2019

In this post, we will explore the use of Ansible, the open source community project sponsored by Red Hat, for automating the provisioning, configuration, deployment, and testing of resources on the Google Cloud Platform (GCP). We will start by using Ansible to configure and deploy applications to existing GCP compute resources. We will then expand our use of Ansible to provision and configure GCP compute resources using the Ansible/GCP native integration with GCP modules.

Red Hat Ansible

ansible Ansible, purchased by Red Hat in October 2015, seamlessly provides workflow orchestration with configuration management, provisioning, and application deployment in a single platform. Unlike similar tools, Ansible’s workflow automation is agentless, relying on Secure Shell (SSH) and Windows Remote Management (WinRM). Ansible has published a whitepaper on The Benefits of Agentless Architecture.

According to G2 Crowd, Ansible is a clear leader in the Configuration Management Software category, ranked right behind GitLab. Some of Ansible’s main competitors in the category includes GitLab, AWS Config, Puppet, Chef, Codenvy, HashiCorp Terraform, Octopus Deploy, and TeamCity. There are dozens of published articles, comparing Ansible to Puppet, Chef, SaltStack, and more recently, Terraform.

Google Compute Engine

According to Google, Google Compute Engine (GCE) delivers virtual machines (VMs) running in Google’s data centers and on their worldwide fiber network. Compute Engine’s tooling and workflow support enables scaling from single instances to global, load-balanced cloud computing.

Comparable products to GCE in the IaaS category include Amazon Elastic Compute Cloud (EC2), Azure Virtual Machines, IBM Cloud Virtual Servers, and Oracle Compute Cloud Service.

Apache HTTP Server

According to Apache, the Apache HTTP Server (“httpd”) is an open-source HTTP server for modern operating systems including Linux and Windows. The Apache HTTP Server provides a secure, efficient, and extensible server that provides HTTP services in sync with the current HTTP standards. The Apache HTTP Server was launched in 1995 and it has been the most popular web server on the Internet since 1996. We will deploy Apache HTTP Server to GCE VMs, using Ansible.

Demonstration

In this post, we will demonstrate two different workflows with Ansible on GCP. First, we will use Ansible to configure and deploy the Apache HTTP Server to an existing GCE instance.

Provision and configure a GCE VM instance, disk, firewall rule, and external IP, using the Google Cloud (gcloud) CLI tool;
Deploy and configure the Apache HTTP Server and associated packages, using an Ansible Playbook containing an httpd Ansible Role;
Manually test the GCP resources and Apache HTTP Server;
Clean up the GCP resources using the gcloud CLI tool;

In the second workflow, we will use Ansible to provision and configure the GCP resources, as well as deploy the Apache HTTP Server the new GCE VM.

Provision and configure a VM instance, disk, VPC global network, subnetwork, firewall rules, and external IP address, using an Ansible Playbook containing an Ansible Role, as opposed to the gcloud CLI tool;
Deploy and configure the Apache HTTP Server and associated packages, using an Ansible Playbook containing an httpd Ansible Role;
Test the GCP resources and Apache HTTP Server using role-based test tasks;
Clean up all the GCP resources using an Ansible Playbook containing an Ansible Role;

Source Code

The source code for this post may be found on the master branch of the ansible-gcp-demo GitHub repository.

git clone --branch master --single-branch --depth 1 --no-tags \
  https://github.com/garystafford/ansible-gcp-demo.git

The project has the following file structure.

.
├── LICENSE
├── README.md
├── _unused
│   ├── httpd_playbook.yml
├── ansible
│   ├── ansible.cfg
│   ├── group_vars
│   │   └── webservers.yml
│   ├── inventories
│   │   ├── hosts
│   │   └── webservers_gcp.yml
│   ├── playbooks
│   │   ├── 10_webserver_infra.yml
│   │   └── 20_webserver_config.yml
│   ├── roles
│   │   ├── gcpweb
│   │   └── httpd
│   └── site.yml
├── part0_source_creds.sh
├── part1_create_vm.sh
└── part2_clean_up.sh

Source code samples in this post are displayed as GitHub Gists which may not display correctly on all mobile and social media browsers, such as LinkedIn.

Setup New GCP Project

For this demonstration, I have created a new GCP Project containing a new service account and public SSH key. The project’s service account will be used the gcloud CLI tool and Ansible to access and provision compute resources within the project. The SSH key will be used by both tools to SSH into GCE VM within the project. Start by creating a new GCP Project.

Add a new service account to the project on the IAM & admin ⇒ Service accounts tab.

Grant the new service account permission to the ‘Compute Admin’ Role, within the project, using the Role drop-down menu. The principle of least privilege (PoLP) suggests we should limit the service account’s permissions to only the role(s) necessary to provision the required compute resources.

Create a private key for the service account, on the IAM & admin ⇒ Service accounts tab. This private key is different than the SSH key will add to the project, next. This private key contains the credentials for the service account.

Choose the JSON key type.

Download the private key JSON file and place it in a safe location, accessible to Ansible. Be careful not to check this file into source control. Again, this file contains the service account’s credentials used to programmatically access GCP and administer compute resources.

We should now have a service account, associated with the new GCP project, with permissions to the ‘Compute Admin’ role, and a private key which has been downloaded and accessible to Ansible. Note the Email address of the service account, in my case, ansible@ansible-gce-demo.iam.gserviceaccount.com; you will need to reference this later in your configuration.

screen_shot_2019-01-23_at_10_14_50_am

Next, create an SSH public/private key pair. The SSH key will be used to programmatically access the GCE VM. Creating a separate key pair allows you to limit its use to just the new GCP project. If compromised, the key pair is easily deleted and replaced in the GCP project and in the Ansible configuration. On a Mac, you can use the following commands to create a new key pair and copy the public key to the clipboard.

ssh-keygen -t rsa -b 4096 -C "ansible"
cat ~/.ssh/ansible.pub | pbcopy

Add your new public key clipboard contents to the project, on the Compute Engine ⇒ Metadata ⇒ SSH Keys tab. Adding the key here means it is usable by any VM in the project unless you explicitly block this option when provisioning a new VM and configure a key specifically for that VM.

Note the name, ansible, associated with the key, you will need to reference this later in your configuration.

Setup Ansible

Although this post is not a primer on Ansible, I will cover a few setup steps I have done to prepare for this demo. On my Mac, I am running Python 3.7, pip 18.1, and Ansible 2.7.6. With Python and pip installed, the easiest way to install Ansible in Mac or Linux is using pip.

pip install ansible

You will also need to install two additional packages in order to gather information about GCP-based hosts using GCE Dynamic Inventory, explained later in the post.

pip install requests google-auth

Ansible Configuration

I created a simple Ansible ansible.cfg file for this project, located in the /ansible/inventories/ sub-directory. The Ansible configuration file contains the location of the project’s roles and inventory, which is explained later. The file also contains two configuration items associated with an SSH key pair, which we just created. If your key is named differently or in a different location, update the file (gist).

Ansible has a complete example of a configuration file parameters on GitHub.

Ansible Environment Variables

To decouple our specific GCP project’s credentials from the Ansible playbooks and roles, Ansible recommends setting those required module parameters as environment variables, as opposed to including them in the playbooks. Additionally, I have set the GCP project name as an environment variable, in order to also decouple it from the playbooks. To set those environment variables, source the part0_source_creds.sh script in the project’s root directory, using the source command (gist).

source ./part0_source_creds.sh

GCP CLI/Ansible Hybrid Workflow

Oftentimes, enterprises employ a mix of DevOps tooling to provision, configure, and deploy to compute resources. In this first workflow, we will use Ansible to configure and deploy a web server to an existing GCE VM, created in advance with the gcloud CLI tool.

Create GCP Resources

First, use the gcloud CLI tool to create a GCE VM and associated resources, including an external IP address and firewall rule for port 80 (HTTP). For simplicity, we will use the existing GCP default Virtual Private Cloud (VPC) network and the default us-east1 subnetwork. Execute the part1_create_vm.sh script in the project’s root directory. The default network should already have port 22 (SSH) open on the firewall. Note the SERVICE_ACCOUNT variable, in the script, is the service account email found on the IAM & admin ⇒ Service accounts tab, shown in the previous section (gist).

The output from the script should look similar to the following. Note the external IP address associated with the VM, you will need to reference this later in the post.

Using the gcloud CLI tool or Google Cloud Console, we should be able to view our newly provisioned resources on GCP. First, our new GCE VM, using the Compute Engine ⇒ VM instances ⇒ Details tab.

Next, examine the Network interface details tab. Here we see details about the network and subnetwork our VM is running within. We see the internal and external IP addresses of the VM. We also see the firewall rules, including our new rule, allowing TCP ingress traffic on port 80.

Lastly, examine the new firewall rule, which will allow TCP traffic on port 80 from any IP address to our VM, located in the default network. Note the other, pre-existing rules controlling access to the default network.

The final GCP architecture looks as follows.

GCE Dynamic Inventory

Two core concepts in Ansible are hosts and inventory. We need an inventory of the hosts on which to run our Ansible playbooks. If we had long-lived hosts, often referred to as ‘pets’, who had long-lived static IP addresses or DNS entries, then we could manually add the hosts to a static hosts file, similar to the example below.

[webservers]
34.73.171.5
34.73.170.97
34.73.172.153
 
[dbservers]
db1.example.com
db2.example.com

However, given the ephemeral nature of the cloud, where hosts (often referred to as ‘cattle’), IP addresses, and even DNS entries are often short-lived, we will use the Ansible concept of Dynamic Inventory.

If you recall we pip installed two packages, requests and google-auth, during our Ansible setup for use with GCE Dynamic Inventory. According to Ansible, the best way to interact with your GCE VM hosts is to use the gcp_compute inventory plugin. The plugin allows Ansible to dynamically query GCE for the nodes that can be managed. With the gcp_compute inventory plugin, we can also selectively classify the hosts we find into Groups. We will then run playbooks, containing roles, on a group or groups of hosts.

To demonstrate how to dynamically find the new GCE host, and add it to a group, execute the following command, using the Ansible Inventory CLI.

ansible-inventory --graph -i inventories/webservers_gcp.yml

The command calls the webservers_gcp.yml file, which contains logic necessary to associate the GCE hosts with the webservers host group. Ansible’s current documentation is pretty sparse on this subject. Thanks to Matthieu Remy for his great post, How to Use Ansible GCP Compute Inventory Plugin. For this demo, we are only looking for hosts in us-east1-b, which have ‘web-’ in their name. (gist).

The output from the command should look similar to the following. We should observe our new VM, as indicated by its external IP address, is assigned to the part of the webservers group. We will use the power of Dynamic Inventory to apply a playlist to all the hosts within the webservers group.

We can also view details about hosts by modifying the inventory command.

ansible-inventory --list -i inventories/webservers_gcp.yml --yaml

The output from the command should look similar to the following. This particular example was run against an earlier host, with a different external IP address.

Apache HTTP Server Playbook

For our first taste of Ansible on GCP, we will run an Ansible Playbook to install and configure the Apache HTTP Server on the new CentOS-based VM. According to Ansible, Playbooks, which are YAML-based, can declare configurations, they can also orchestrate steps of any manual ordered process, even as different steps must bounce back and forth between sets of machines in particular orders. They can launch tasks synchronously or asynchronously. Playbooks are used to orchestrate tasks, as opposed to using Ansible’s ad-hoc task execution mode.

A playbook can be ‘monolithic’ in nature, containing all the required Variables, Tasks, and Handlers, to achieve the desired outcome. If we wrote a single playbook to deploy and configure our Apache HTTP Server, it might look like the httpd_playbook.yml, playbook, below (gist).

We could run this playbook with the following command to deploy the Apache HTTP Server, but we won’t. Instead, next, we will run a playbook that applies the httpd role.

ansible-playbook \
  -i inventories/webservers_gcp.yml \
  playbooks/httpd_playbook.yml

Ansible Roles

According to Ansible, Roles are ways of automatically loading certain vars_files, tasks, and handlers based on a known file structure. Grouping content by roles also allows easy sharing of roles with other users. The usage of roles is preferred as it provides a nice organizational system.

The httpd role is identical in functionality to the httpd_playbook.yml, used in the first workflow. However, the primary parts of the playbook have been decomposed into individual resource files, as described by Ansible. This structure is created using the Ansible Galaxy CLI. Ansible Galaxy is Ansible’s official hub for sharing Ansible content.

ansible-galaxy init httpd

This ansible-galaxy command creates the following structure. I added the files and Jinja2 template, afterward.

.
├── README.md
├── defaults
│   └── main.yml
├── files
│   ├── info.php
│   └── server-status.conf
├── handlers
│   └── main.yml
├── meta
│   └── main.yml
├── tasks
│   └── main.yml
├── templates
│   └── index.html.j2
├── tests
│   ├── inventory
│   └── test.yml
└── vars
    └── main.yml

Within the httpd role:

Variables are stored in the defaults/main.yml file;
Tasks are stored in the tasks/main.yml file;
Handles are stored in the handlers/main.yml file;
Files are stored in the files/ sub-directory;
Jinja2 templates are stored in the templates/ sub-directory;
Test are stored in the tests/ sub-directory;
Other sub-directories and files contain metadata about the role;

To apply the httpd role, we will run the 20_webserver_config.yml playbook. Compare this playbook, below, with the previous, monolithic httpd_playbook.yml playbook. All of the logic has now been decomposed across the httpd role’s separate backing files (gist).

We can start by running our playbook using Ansible’s Check Mode (“Dry Run”). When ansible-playbook is run with --check, Ansible will not make any actual changes to the remote systems. According to Ansible, Check mode is just a simulation, and if you have steps that use conditionals that depend on the results of prior commands, it may be less useful for you. However, it is great for one-node-at-time basic configuration management use cases. Execute the following command using Check mode.

ansible-playbook \
  -i inventories/webservers_gcp.yml \
  playbooks/20_webserver_config.yml --check

The output from the command should look similar to the following. It shows that if we execute the actual command, we should expect seven changes to occur.

If everything looks good, then run the same command without using Check mode.

ansible-playbook \
  -i inventories/webservers_gcp.yml \
  playbooks/20_webserver_config.yml

The output from the command should look similar to the following. Note the number of items changed, seven, is identical to the results of using Check mode, above.

If we were to execute the command using Check mode for a second time, we should observe zero changed items. This means the last command successfully applied all changes and no new changes are present in the playbook.

Testing the Results

There are a number of methods and tools we could use to test the deployments of the Apache HTTP Server and server tools. First, we can use an ad-hoc ansible CLI command to confirm the httpd process is running on the VM, by calling systemctl. The systemctl application is used to introspect and control the state of the systemd system and service manager, running on the CentOS-based VM.

ansible webservers \
  -i inventories/webservers_gcp.yml \
  -a "systemctl status httpd"

The output from the command should look similar to the following. We see the Apache HTTP Server service details. We also see it being stopped and started as required by the tasks and handler in the role.

We can also check that the home page and PHP info documents, we deployed as part of the playbook, are in the correct location on the VM.

ansible webservers \
  -i inventories/webservers_gcp.yml \
  -a "ls -al /var/www/html"

The output from the command should look similar to the following. We see the two documents we deployed are in the root of the website directory.

Next, view our website’s home page by pointing your web browser to the external IP address we created earlier and associated with the VM, on port 80 (HTTP). We should observe the variable value in the playbook, ‘Hello Ansible on GCP!’, was injected into the Jinja2 template file, index.html.j2, and the page deployed correctly to the VM.

If you recall from the httpd role, we had a task to deploy the server status configuration file. This configuration file exposes the /server-status endpoint, as shown below. The status page shows the internal and the external IP addresses assigned to the VM. It also shows the current version of Apache HTTP Server and PHP, server uptime, traffic, load, CPU usage, number of requests, number of running processes, and so forth.

screen_shot_2019-01-27_at_10_14_39_am

Testing with Apache Bench

Apache Bench (ab) is the Apache HTTP server benchmarking tool. We can use Apache Bench locally, to generate CPU, memory, file, and network I/O loads on the VM. For example, using the following command, we can generate 100K requests to the server-status page, simulating 100 concurrent users.

ab -kc 100 -n 100000 http://your_vms_external_ip/server-status

The output from the command should look similar to the following. Observe this command successfully resulted in a sustained load on the web server for approximately 17.5 minutes.

Using the Compute Engine ⇒ VM instances ⇒ Monitoring tab, we see the corresponding Apache Bench CPU, memory, file, and network load on the VM, starting at about 10:03 AM, soon after running the playbook to install Apache HTTP Server.

Destroy GCP Resources

After exploring the results of our workflow, tear down the existing GCE resources before we continue to the next workflow. To delete resources, execute the part2_clean_up.sh script in the project’s root directory (gist).

The output from the script should look similar to the following.

Ansible Workflow

In the second workflow, we will provision and configure the GCP resources, and deploy Apache HTTP Server to the new GCE VM using Ansible. We will be using the same Project, Region, and Zone as the previous example. However this time, we will create a new global VPC network instead of using the default network as before, a new subnetwork instead of using the default subnetwork as before, and a new firewall with ingress rules to open ports 22 and 80. Lastly, will create an external IP address and assign it to the VM.

Provision GCP Resources

Instead of using the gcloud CLI tool, we will use Ansible to provision the GCP resources. To accomplish this, I have created one playbook, 10_webserver_infra.yml, with one role, gcpweb, but two sets of tasks, one to create the GCE resources, create.yml, and one to delete the GCP resources, delete.yml. This is a typical Ansible playbook pattern. The standard file directory structure of the role looks as follows, similar to the httpd role.

.
├── README.md
├── defaults
│   └── main.yml
├── files
├── handlers
│   └── main.yml
├── meta
│   └── main.yml
├── tasks
│   ├── create.yml
│   ├── delete.yml
│   └── main.yml
├── templates
├── tests
│   ├── inventory
│   └── test.yml
└── vars
    └── main.yml

To provision the GCE resources, we run the 10_webserver_infra.yml playbook (gist).

This playbook runs the gcpweb role. The role’s default main.yml task file imports two other sets of tasks, one for create and one for delete. Each set of tasks have a corresponding tag associated with them (gist).

By calling the playbook and passing the ‘create’ tag, the role will run apply the associated set of create tasks. Tags are a powerful construct in Ansible. Execute the following command, passing the create tag.

ansible-playbook -t create playbooks/10_webserver_infra.yml

In the case of this playbook, the Check mode, used earlier, would fail here. If you recall, this feature is not designed to work with playbooks that have steps that use conditionals that depend on the results of prior commands, such as with this playbook.

The create.yml file contains six tasks, which leverage Ansible GCP Modules. The tasks create a global VPC network, subnetwork in the us-east1 Region, firewall and rules, external IP address, disk, and VM instance (gist).

If your interested in what is actually happening during the execution of the playbook, add the verbose option (-v or -vv) to the above command. This can be very helpful in learning Ansible.

The output from the command should look similar to the following. Note the changes applied to localhost. Since no GCE VM host(s) exist on GCP until the resources are provisioned, we reference localhost. The entire process took less than two minutes to create a global VPC network, subnetwork, firewall rules, VM, attached disk, and assign a public IP address.

All GCP resources are now provisioned and configured. Below, we see the new GCE VM created by Ansible.

Below, we see the new GCE VM’s network interface details console page, showing details about the VM, NIC, internal and external IP addresses, network, subnetwork, and ingress firewall rules.

Below, we see the VPC details showing each of the automatically-created regional subnets, and our new ‘ansible-subnet’, in the us-east1 region, and spanning 14 IP addresses in the 172.16.0.0/28 CIDR (Classless Inter-Domain Routing) block.

To deploy and configure Apache HTTP Server, run the httpd role exactly the same way we did in the first workflow.

ansible-playbook \
  -i inventories/webservers_gcp.yml \
  playbooks/20_webserver_config.yml

Role-based Testing

In the first workflow, we manually tested our results using a number of ad-hoc commands and by viewing web pages in our browser. These methods of testing do not lend themselves to DevOps automation. A more effective strategy is writing tests, which are part of the role, and maybe run each time the role is applied, as part of a CI/CD pipeline. Each role in this project contains a few simple tests to confirm the success of the tasks in the role. First, run the gcpweb role’s tests with the following command.

ansible-playbook \
  -i inventories/webservers_gcp.yml \
  roles/gcpweb/tests/test.yml

The playbook gathers facts about the GCE hosts in the host group and runs a total of five test tasks against those hosts. The tasks confirm the host’s timezone, vCPU count, OS type, OS major version, and hostname, using the facts gathered (gist).

The output from the command should look similar to the following. Observe that all five tasks ran successfully.

Next, run the the httpd role’s tests.

ansible-playbook \
  -i inventories/webservers_gcp.yml \
  roles/httpd/tests/test.yml

Similarly, the output from the command should look similar to the following. The playbook runs four test tasks this time. The tasks confirm both files are present, the home page is accessible, and that the server-status page displays properly. Below, we all four ran successfully.

Making a Playbook Change

To observe what happens if we apply a change to a playbook, let’s change the greeting variable value in the /roles/httpd/defaults/main.yml file in the httpd role. Recall, the original home page looked as follows.

Change the greeting variable value and re-run the playbook, using the same command.

ansible-playbook \
  -i inventories/webservers_gcp.yml \
  playbooks/20_webserver_config.yml

The output from the command should look similar to the following. As expected, we should observe that only one task, deploying the home page, was changed.

Viewing the home page again, or by modifying the associated test task, we should observe the new value is injected into the Jinja2 template file, index.html.j2, and the new page deployed correctly.

Destroy GCP Resources with Ansible

Once you are finished, you can destroy all the GCP resources by calling the 10_webserver_infra.yml playbook and passing the delete tag, the role will run apply the associated set of delete tasks.

ansible-playbook -t delete playbooks/10_webserver_infra.yml

With Ansible, we delete GCP resources by changing the state from present to absent. The playbook will delete the resources in a particular order, to avoid dependency conflicts, such as trying to delete the network before the VM. Note we do not have to explicitly delete the disk since, if you recall, we provisioned the VM instance with the disks.auto_delete=true option (gist).

The output from the command should look similar to the following. We see the VM instance, attached disk, firewall, rules, external IP address, subnetwork, and finally, the network, each being deleted.

Conclusion

In this post, we saw how easy it is to get started with Ansible on the Google Cloud Platform. Using Ansible’s 300+ cloud modules, provisioning, configuring, deploying to, and testing a wide range of GCP, Azure, and AWS resources are easy, repeatable, and completely automatable.

All opinions expressed in this post are my own and not necessarily the views of my current or past employers or their clients.

Ansible, GCE, GCP, Google Cloud Platform, Google Compute Engine, IaC, Infrastructure, Playbook, Red Hat

1 Comment

Automating Multi-Environment Kubernetes Virtual Clusters with Google Cloud DNS, Auth0, and Istio 1.0

Posted by Gary A. Stafford in Bash Scripting, Build Automation, Cloud, DevOps, Enterprise Software Development, GCP, Java Development, Kubernetes, Software Development on January 19, 2019

Kubernetes supports multiple virtual clusters within the same physical cluster. These virtual clusters are called Namespaces. Namespaces are a way to divide cluster resources between multiple users. Many enterprises use Namespaces to divide the same physical Kubernetes cluster into different virtual software development environments as part of their overall Software Development Lifecycle (SDLC). This practice is commonly used in ‘lower environments’ or ‘non-prod’ (not Production) environments. These environments commonly include Continous Integration and Delivery (CI/CD), Development, Integration, Testing/Quality Assurance (QA), User Acceptance Testing (UAT), Staging, Demo, and Hotfix. Namespaces provide a basic form of what is referred to as soft multi-tenancy.

Generally, the security boundaries and performance requirements between non-prod environments, within the same enterprise, are less restrictive than Production or Disaster Recovery (DR) environments. This allows for multi-tenant environments, while Production and DR are normally single-tenant environments. In order to approximate the performance characteristics of Production, the Performance Testing environment is also often isolated to a single-tenant. A typical enterprise would minimally have a non-prod, performance, production, and DR environment.

Using Namespaces to create virtual separation on the same physical Kubernetes cluster provides enterprises with more efficient use of virtual compute resources, reduces Cloud costs, eases the management burden, and often expedites and simplifies the release process.

Demonstration

In this post, we will re-examine the topic of virtual clusters, similar to the recent post, Managing Applications Across Multiple Kubernetes Environments with Istio: Part 1 and Part 2. We will focus specifically on automating the creation of the virtual clusters on GKE with Istio 1.0, managing the Google Cloud DNS records associated with the cluster’s environments, and enabling both HTTPS and token-based OAuth access to each environment. We will use the Storefront API for our demonstration, featured in the previous three posts, including Building a Microservices Platform with Confluent Cloud, MongoDB Atlas, Istio, and Google Kubernetes Engine.

Source Code

The source code for this post may be found on the gke branch of the storefront-kafka-docker GitHub repository.

git clone --branch gke --single-branch --depth 1 --no-tags \
  https://github.com/garystafford/storefront-kafka-docker.git

Source code samples in this post are displayed as GitHub Gists, which may not display correctly on all mobile and social media browsers, such as LinkedIn.

This project contains all the code to deploy and configure the GKE cluster and Kubernetes resources.

To follow along, you will need to register your own domain, arrange for an Auth0, or alternative, authentication and authorization service, and obtain an SSL/TLS certificate.

SSL/TLS Wildcard Certificate

In the recent post, Securing Your Istio Ingress Gateway with HTTPS, we examined how to create and apply an SSL/TLS certificate to our GKE cluster, to secure communications. Although we are only creating a non-prod cluster, it is more and more common to use SSL/TLS everywhere, especially in the Cloud. For this post, I have registered a single wildcard certificate, *.api.storefront-demo.com. This certificate will cover the three second-level subdomains associated with the virtual clusters: dev.api.storefront-demo.com, test.api.storefront-demo.com, and uat.api.storefront-demo.com. Setting the environment name, such as dev.*, as the second-level subdomain of my storefront-demo domain, following the first level api.* subdomain, makes the use of a wildcard certificate much easier.

As shown below, my wildcard certificate contains the Subject Name and Subject Alternative Name (SAN) of *.api.storefront-demo.com. For Production, api.storefront-demo.com, I prefer to use a separate certificate.

Create GKE Cluster

With your certificate in hand, create the non-prod Kubernetes cluster. Below, the script creates a minimally-sized, three-node, multi-zone GKE cluster, running on GCP, with Kubernetes Engine cluster version 1.11.5-gke.5 and Istio on GKE version 1.0.3-gke.0. I have enabled the master authorized networks option to secure my GKE cluster master endpoint. For the demo, you can add your own IP address CIDR on line 9 (i.e. 1.2.3.4/32), or remove lines 30 – 31 to remove the restriction (gist).

Lines 16–39: Create a 3-node, multi-zone GKE cluster with Istio;
Line 48: Creates three non-prod Namespaces: dev, test, and uat;
Lines 51–53: Enable Istio automatic sidecar injection within each Namespace;

If successful, the results should look similar to the output, below.

The cluster will contain a pool of three minimally-sized VMs, the Kubernetes nodes.

Deploying Resources

The Istio Gateway and three ServiceEntry resources are the primary resources responsible for routing the traffic from the ingress router to the Services, within the multiple Namespaces. Both of these resource types are new to Istio 1.0 (gist).

Lines 9–16: Port config that only accepts HTTPS traffic on port 443 using TLS;
Lines 18–20: The three subdomains being routed to the non-prod GKE cluster;
Lines 28, 63, 98: The three subdomains being routed to the non-prod GKE cluster;
Lines 39, 47, 65, 74, 82, 90, 109, 117, 125: Routing to FQDN of Storefront API Services within the three Namespaces;

Next, deploy the Istio and Kubernetes resources to the new GKE cluster. For the sake of brevity, we will deploy the same number of instances and the same version of each the three Storefront API services (Accounts, Orders, Fulfillment) to each of the three non-prod environments (dev, test, uat). In reality, you would have varying numbers of instances of each service, and each environment would contain progressive versions of each service, as part of the SDLC of each microservice (gist).

Lines 13–14: Deploy the SSL/TLS certificate and the private key;
Line 17: Deploy the Istio Gateway and three ServiceEntry resources;
Lines 20–22: Deploy the Istio Authentication Policy resources each Namespace;
Lines 26–37: Deploy the same set of resources to the dev, test, and uat Namespaces;

The deployed Storefront API Services should look as follows.

Google Cloud DNS

Next, we need to enable DNS access to the GKE cluster using Google Cloud DNS. According to Google, Cloud DNS is a scalable, reliable and managed authoritative Domain Name System (DNS) service running on the same infrastructure as Google. It has low latency, high availability, and is a cost-effective way to make your applications and services available to your users.

Whenever a new GKE cluster is created, a new Network Load Balancer is also created. By default, the load balancer’s front-end is an external IP address.

Using a forwarding rule, traffic directed at the external IP address is redirected to the load balancer’s back-end. The load balancer’s back-end is comprised of three VM instances, which are the three Kubernete nodes in the GKE cluster.

If you are following along with this post’s demonstration, we will assume you have a domain registered and configured with Google Cloud DNS. I am using the storefront-demo.com domain, which I have used in the last three posts to demonstrate Istio and GKE.

Google Cloud DNS has a fully functional web console, part of the Google Cloud Console. However, using the Cloud DNS web console is impractical in a DevOps CI/CD workflow, where Kubernetes clusters, Namespaces, and Workloads are ephemeral. Therefore we will use the following script. Within the script, we reset the IP address associated with the A records for each non-prod subdomains associated with storefront-demo.com domain (gist).

Lines 23–25: Find the previous load balancer’s front-end IP address;
Lines 27–29: Find the new load balancer’s front-end IP address;
Line 35: Start the Cloud DNS transaction;
Lines 37–47: Add the DNS record changes to the transaction;
Line 49: Execute the Cloud DNS transaction;

The outcome of the script is shown below. Note how changes are executed as part of a transaction, by automatically creating a transaction.yaml file. The file contains the six DNS changes, three additions and three deletions. The command executes the transaction and then deletes the transaction.yaml file.

> sh ./part3_set_cloud_dns.sh

Old LB IP Address: 35.193.208.115
New LB IP Address: 35.238.196.231

Transaction started [transaction.yaml].

dev.api.storefront-demo.com.
Record removal appended to transaction at [transaction.yaml].
Record addition appended to transaction at [transaction.yaml].

test.api.storefront-demo.com.
Record removal appended to transaction at [transaction.yaml].
Record addition appended to transaction at [transaction.yaml].

uat.api.storefront-demo.com.
Record removal appended to transaction at [transaction.yaml].
Record addition appended to transaction at [transaction.yaml].

Executed transaction [transaction.yaml] for managed-zone [storefront-demo-com-zone].
Created [https://www.googleapis.com/dns/v1/projects/gke-confluent-atlas/managedZones/storefront-demo-com-zone/changes/53].

ID  START_TIME                STATUS
55  2019-01-16T04:54:14.984Z  pending

Based on my own domain and cluster details, the transaction.yaml file looks as follows. Again, note the six DNS changes, three additions, followed by three deletions (gist).

Confirm DNS Changes

Use the dig command to confirm the DNS records are now correct and that DNS propagation has occurred. The IP address returned by dig should be the external IP address assigned to the front-end of the Google Cloud Load Balancer.

> dig dev.api.storefront-demo.com +short
35.238.196.231

Or, all the three records.

echo \
  "dev.api.storefront-demo.com\n" \
  "test.api.storefront-demo.com\n" \
  "uat.api.storefront-demo.com" \
  > records.txt | dig -f records.txt +short

35.238.196.231
35.238.196.231
35.238.196.231

Optionally, more verbosely by removing the +short option.

> dig +nocmd dev.api.storefront-demo.com

;; Got answer:
;; ->>HEADER<<- opcode: QUERY, status: NOERROR, id: 30763
;; flags: qr rd ra; QUERY: 1, ANSWER: 1, AUTHORITY: 0, ADDITIONAL: 1

;; OPT PSEUDOSECTION:
; EDNS: version: 0, flags:; udp: 512
;; QUESTION SECTION:
;dev.api.storefront-demo.com.   IN  A

;; ANSWER SECTION:
dev.api.storefront-demo.com. 299 IN A   35.238.196.231

;; Query time: 27 msec
;; SERVER: 8.8.8.8#53(8.8.8.8)
;; WHEN: Wed Jan 16 18:00:49 EST 2019
;; MSG SIZE  rcvd: 72

The resulting records in the Google Cloud DNS management console should look as follows.

JWT-based Authentication

As discussed in the previous post, Istio End-User Authentication for Kubernetes using JSON Web Tokens (JWT) and Auth0, it is typical to limit restrict access to the Kubernetes cluster, Namespaces within the cluster, or Services running within Namespaces to end-users, whether they are humans or other applications. In that previous post, we saw an example of applying a machine-to-machine (M2M) Istio Authentication Policy to only the uat Namespace. This scenario is common when you want to control access to resources in non-production environments, such as UAT, to outside test teams, accessing the uat Namespace through an external application. To simulate this scenario, we will apply the following Istio Authentication Policy to the uat Namespace. (gist).

For the dev and test Namespaces, we will apply an additional, different Istio Authentication Policy. This policy will protect against the possibility of dev and test M2M API consumers interfering with uat M2M API consumers and vice-versa. Below is the dev and test version of the Policy (gist).

Testing Authentication

Using Postman, with the ‘Bearer Token’ type authentication method, as detailed in the previous post, a call a Storefront API resource in the uat Namespace should succeed. This also confirms DNS and HTTPS are working properly.

The dev and test Namespaces require different authentication. Trying to use no Authentication, or authenticating as a UAT API consumer, will result in a 401 Unauthorized HTTP status, along with the Origin authentication failed. error message.

Conclusion

In this brief post, we demonstrated how to create a GKE cluster with Istio 1.0.x, containing three virtual clusters, or Namespaces. Each Namespace represents an environment, which is part of an application’s SDLC. We enforced HTTP over TLS (HTTPS) using a wildcard SSL/TLS certificate. We also enforced end-user authentication using JWT-based OAuth 2.0 with Auth0. Lastly, we provided user-friendly DNS routing to each environment, using Google Cloud DNS. Short of a fully managed API Gateway, like Apigee, and automating the execution of the scripts with Jenkins or Spinnaker, this cluster is ready to provide a functional path to Production for developing our Storefront API.

All opinions expressed in this post are my own and not necessarily the views of my current or past employers or their clients.

Auth0, Authentication, Authorization, GCP, GKE, Google Cloud Platform, Istio, Istio 1.0, Jason Web Token, JWT, SSL, TLS

1 Comment

Istio End-User Authentication for Kubernetes using JSON Web Tokens (JWT) and Auth0

Posted by Gary A. Stafford in Bash Scripting, Cloud, DevOps, Enterprise Software Development, GCP, Kubernetes, Software Development on January 6, 2019

In the recent post, Building a Microservices Platform with Confluent Cloud, MongoDB Atlas, Istio, and Google Kubernetes Engine, we built and deployed a microservice-based, cloud-native API to Google Kubernetes Engine, with Istio 1.0.x, on Google Cloud Platform. For brevity, we intentionally omitted a few key features required to operationalize and secure the API. These missing features included HTTPS, user authentication, request quotas, request throttling, and the integration of a full lifecycle API management tool, like Google Apigee.

In a follow-up post, Securing Your Istio Ingress Gateway with HTTPS, we disabled HTTP access to the API running on the GKE cluster. We then enabled bidirectional encryption of communications between a client and GKE cluster with HTTPS.

In this post, we will further enhance the security of the Storefront Demo API by enabling Istio end-user authentication using JSON Web Token-based credentials. Using JSON Web Tokens (JWT), pronounced ‘jot’, will allow Istio to authenticate end-users calling the Storefront Demo API. We will use Auth0, an Authentication-as-a-Service provider, to generate JWT tokens for registered Storefront Demo API consumers, and to validate JWT tokens from Istio, as part of an OAuth 2.0 token-based authorization flow.

istio-gke-auth

JSON Web Tokens

Token-based authentication, according to Auth0, works by ensuring that each request to a server is accompanied by a signed token which the server verifies for authenticity and only then responds to the request. JWT, according to JWT.io, is an open standard (RFC 7519) that defines a compact and self-contained way for securely transmitting information between parties as a JSON object. This information can be verified and trusted because it is digitally signed. Other common token types include Simple Web Tokens (SWT) and Security Assertion Markup Language Tokens (SAML).

JWTs can be signed using a secret with the Hash-based Message Authentication Code (HMAC) algorithm, or a public/private key pair using Rivest–Shamir–Adleman (RSA) or Elliptic Curve Digital Signature Algorithm (ECDSA). Authorization is the most common scenario for using JWT. Within the token payload, you can easily specify user roles and permissions as well as resources that the user can access.

A registered API consumer makes an initial request to the Authorization server, in which they exchange some form of credentials for a token. The JWT is associated with a set of specific user roles and permissions. Each subsequent request will include the token, allowing the user to access authorized routes, services, and resources that are permitted with that token.

Auth0

To use JWTs for end-user authentication with Istio, we need a way to authenticate credentials associated with specific users and exchange those credentials for a JWT. Further, we need a way to validate the JWTs from Istio. To meet these requirements, we will use Auth0. Auth0 provides a universal authentication and authorization platform for web, mobile, and legacy applications. According to G2 Crowd, competitors to Auth0 in the Customer Identity and Access Management (CIAM) Software category include Okta, Microsoft Azure Active Directory (AD) and AD B2C, Salesforce Platform: Identity, OneLogin, Idaptive, IBM Cloud Identity Service, and Bitium.

Auth0 currently offers four pricing plans: Free, Developer, Developer Pro, and Enterprise. Subscriptions to plans are on a monthly or discounted yearly basis. For this demo’s limited requirements, you need only use Auth0’s Free Plan.

Client Credentials Grant

The OAuth 2.0 protocol defines four flows, or grants types, to get an Access Token, depending on the application architecture and the type of end-user. We will be simulating a third-party, external application that needs to consume the Storefront API, using the Client Credentials grant type. According to Auth0, The Client Credentials Grant, defined in The OAuth 2.0 Authorization Framework RFC 6749, section 4.4, allows an application to request an Access Token using its Client Id and Client Secret. It is used for non-interactive applications, such as a CLI, a daemon, or a Service running on your backend, where the token is issued to the application itself, instead of an end user.

jwt-istio-authorize-flow

With Auth0, we need to create two types of entities, an Auth0 API and an Auth0 Application. First, we define an Auth0 API, which represents the Storefront API we are securing. Second, we define an Auth0 Application, a consumer of our API. The Application is associated with the API. This association allows the Application (consumer of the API) to authenticate with Auth0 and receive a JWT. Note there is no direct integration between Auth0 and Istio or the Storefront API. We are facilitating a decoupled, mutual trust relationship between Auth0, Istio, and the registered end-user application consuming the API.

Start by creating a new Auth0 API, the ‘Storefront Demo API’. For this demo, I used my domain’s URL as the Identifier. For use with Istio, choose RS256 (RSA Signature with SHA-256), an asymmetric algorithm that uses a public/private key pair, as opposed to the HS256 symmetric algorithm. With RS256, Auth0 will use the same private key to both create the signature and to validate it. Auth0 has published a good post on the use of RS256 vs. HS256 algorithms.

Scopes

Auth0 allows granular access control to your API through the use of Scopes. The permissions represented by the Access Token in OAuth 2.0 terms are known as scopes, According to Auth0. The scope parameter allows the application to express the desired scope of the access request. The scope parameter can also be used by the authorization server in the response to indicate which scopes were actually granted.

Although it is necessary to define and assign at least one scope to our Auth0 Application, we will not actually be using those scopes to control fine-grain authorization to resources within the Storefront API. In this demo, if an end-user is authenticated, they will be authorized to access all Storefront API resources.

Machine to Machine Applications

Next, define a new Auth0 Machine to Machine (M2M) Application, ‘Storefront Demo API Consumer 1’.

Next, authorize the new M2M Application to request access to the new Storefront Demo API. Again, we are not using scopes, but at least one scope is required, or you will not be able to authenticate, later.

Each M2M Application has a unique Client ID and Client Secret, which are used to authenticate with the Auth0 server and retrieve a JWT.

Multiple M2M Applications may be authorized to request access to APIs.

In the Endpoints tab of the Advanced Application Settings, there are a series of OAuth URLs. To authorize our new M2M Application to consume the Storefront Demo API, we need the ‘OAuth Authorization URL’.

Testing Auth0

To test the Auth0 JWT-based authentication and authorization workflow, I prefer to use Postman. Conveniently, Auth0 provides a Postman Collection with all the HTTP request you will need, already built. Use the Client Credentials POST request. The grant_type header value will always be client_credentials. You will need to supply the Auth0 Application’s Client ID and Client Secret as the client_id and client_secret header values. The audience header value will be the API Identifier you used to create the Auth0 API earlier.

If the HTTP request is successful, you should receive a JWT access_token in response, which will allow us to authenticate with the Storefront API, later. Note the scopes you defined with Auth0 are also part of the response, along with the token’s TTL.

jwt.io Debugger

For now, test the JWT using the jwt.io Debugger page. If everything is working correctly, the JWT should be successfully validated.

Istio Authentication Policy

To enable Istio end-user authentication using JWT with Auth0, we add an Istio Policy authentication resource to the existing set of deployed resources. You have a few choices for end-user authentication, such as:

Applied globally, to all Services across all Namespaces via the Istio Ingress Gateway;
Applied locally, to all Services within a specific Namespace (i.e. uat);
Applied locally, to a single Service or Services within a specific Namespace (i.e prod.accounts);

In reality, since you would likely have more than one registered consumer of the API, with different roles, you would have more than one Authentication Policy applied the cluster.

For this demo, we will enable global end-user authentication to the Storefront API, using JWTs, at the Istio Ingress Gateway. To create an Istio Authentication Policy resource, we use the Istio Authentication API version authentication.istio.io/v1alpha1(gist).

The single audiences YAML map value is the same Audience header value you used in your earlier Postman request, which was the API Identifier you used to create the Auth0 Storefront Demo API earlier. The issuer YAML scalar value is Auth0 M2M Application’s Domain value, found in the ‘Storefront Demo API Consumer 1’ Settings tab. The jwksUri YAML scalar value is the JSON Web Key Set URL value, found in the Endpoints tab of the Advanced Application Settings.

The JSON Web Key Set URL is a publicly accessible endpoint. This endpoint will be accessed by Istio to obtain the public key used to authenticate the JWT.

Assuming you have already have deployed the Storefront API to the GKE cluster, simply apply the new Istio Policy. We should now have end-user authentication enabled on the Istio Ingress Gateway using JSON Web Tokens.

kubectl apply -f ./resources/other/ingressgateway-jwt-policy.yaml

Finer-grain Authentication

If you need finer-grain authentication of resources, alternately, you can apply an Istio Authentication Policy across a Namespace and to a specific Service or Services. Below, we see an example of applying a Policy to only the uat Namespace. This scenario is common when you want to control access to resources in non-production environments, such as UAT, to outside test teams or a select set of external beta-testers. According to Istio, to apply Namespace-wide end-user authentication, across a single Namespace, it is necessary to name the Policy, default (gist).

Below, we see an even finer-grain Policy example, scoped to just the accounts Service within just the prod Namespace. This scenario is common when you have an API consumer whose role only requires access to a portion of the API. For example, a marketing application might only require access to the accounts Service, but not the orders or fulfillment Services (gist).

Test Authentication

To test end-user authentication, first, call any valid Storefront Demo API endpoint, without supplying a JWT for authorization. You should receive a ‘401 Unauthorized’ HTTP response code, along with an Origin authentication failed. message in the response body. This means the Storefront Demo API is now inaccessible unless the API consumer supplies a JWT, which can be successfully validated by Istio.

Next, add authorization to the Postman request by selecting the ‘Bearer Token’ type authentication method. Copy and paste the JWT (access_token) you received earlier from the Client Credentials request. This will add an Authorization request header. In curl, the request header would look as follows (gist).

Make the request with Postman. If the Istio Policy is applied correctly, the request should now receive a successful response from the Storefront API. A successful response indicates that Istio successfully validated the JWT, located in the Authorization header, against the Auth0 Authorization Server. Istio then allows the user, the ‘Storefront Demo API Consumer 1’ application, access to all Storefront API resources.

Troubleshooting

Istio has several pages of online documentation on troubleshooting authentication issues. One of the first places to look for errors, if your end-user authentication is not working, but the JWT is valid, is the Istio Pilot logs. The core component used for traffic management in Istio, Pilot, manages and configures all the Envoy proxy instances deployed in a particular Istio service mesh. Pilot distributes authentication policies, like our new end-user authentication policy, and secure naming information to the proxies.

Below, in Google Stackdriver Logging, we see typical log entries indicating the Pilot was unable to retrieve the JWT public key (recall we are using RS256 public/private key pair asymmetric algorithm). This particular error was due to a typo in the Istio Policy authentication resource YAML file.

Below we see an Istio Mixer log entry containing details of a Postman request to the Accounts Storefront service /accounts/customers/summary endpoint. According to Istio, Mixer is the Istio component responsible for providing policy controls and telemetry collection. Note the apiClaims section of the textPayload of the log entry, corresponds to the Payload Segment of the JWT passed in this request. The log entry clearly shows that the JWT was decoded and validated by Istio, before forwarding the request to the Accounts Service.

Conclusion

In this brief post, we added end-user authentication to our Storefront Demo API, running on GKE with Istio. Although still not Production-ready, we have secured the Storefront API with both HTTPS client-server encryption and JSON Web Token-based authorization. Next steps would be to add mutual TLS (mTLS) and a fully-managed API Gateway in front of the Storefront API GKE cluster, to provide advanced API features, like caching, quotas and rate limits.

All opinions expressed in this post are my own and not necessarily the views of my current or past employers or their clients.

API, Auth0, Authentication, GCP, GKE, Google Cloud Platform, Istio, JSON Web Token, JWT, Kubernetes, OAuth, Security

3 Comments

Securing Your Istio Ingress Gateway with HTTPS

Posted by Gary A. Stafford in Bash Scripting, Cloud, Enterprise Software Development, GCP, Software Development on January 3, 2019

In the last post, Building a Microservices Platform with Confluent Cloud, MongoDB Atlas, Istio, and Google Kubernetes Engine, we built and deployed a microservice-based, cloud-native API to Google Kubernetes Engine (GKE), with Istio 1.0, on Google Cloud Platform (GCP). For brevity, we neglected a few key API features, required in Production, including HTTPS, OAuth for authentication, request quotas, request throttling, and the integration of a full lifecycle API management tool, like Google Apigee.

In this brief post, we will revisit the previous post’s project. We will disable HTTP, and secure the GKE cluster with HTTPS, using simple TLS, as opposed to mutual TLS authentication (mTLS). This post assumes you have created the GKE cluster and deployed the Storefront API and its associated resources, as explained in the previous post.

What is HTTPS?

According to Wikipedia, Hypertext Transfer Protocol Secure (HTTPS) is an extension of the Hypertext Transfer Protocol (HTTP) for securing communications over a computer network. In HTTPS, the communication protocol is encrypted using Transport Layer Security (TLS), or, formerly, its predecessor, Secure Sockets Layer (SSL). The protocol is therefore also often referred to as HTTP over TLS, or HTTP over SSL.

Further, according to Wikipedia, the principal motivation for HTTPS is authentication of the accessed website and protection of the privacy and integrity of the exchanged data while in transit. It protects against man-in-the-middle attacks. The bidirectional encryption of communications between a client and server provides a reasonable assurance that one is communicating without interference by attackers with the website that one intended to communicate with, as opposed to an impostor.

Public Key Infrastructure

According to Comodo, both the TLS and SSL protocols use what is known as an asymmetric Public Key Infrastructure (PKI) system. An asymmetric system uses two keys to encrypt communications, a public key and a private key. Anything encrypted with the public key can only be decrypted by the private key and vice-versa.

Again, according to Wikipedia, a PKI is an arrangement that binds public keys with respective identities of entities, like people and organizations. The binding is established through a process of registration and issuance of certificates at and by a certificate authority (CA).

SSL/TLS Digital Certificate

Again, according to Comodo, when you request an HTTPS connection to a webpage, the website will initially send its SSL certificate to your browser. This certificate contains the public key needed to begin the secure session. Based on this initial exchange, your browser and the website then initiate the SSL handshake (actually, TLS handshake). The handshake involves the generation of shared secrets to establish a uniquely secure connection between yourself and the website. When a trusted SSL digital certificate is used during an HTTPS connection, users will see the padlock icon in the browser’s address bar.

Registered Domain

In order to secure an SSL Digital Certificate, required to enable HTTPS with the GKE cluster, we must first have a registered domain name. For the last post, and this post, I am using my own personal domain, storefront-demo.com. The domain’s primary A record (‘@’) and all sub-domain A records, such as api.dev, are all resolve to the external IP address on the front-end of the GCP load balancer.

For DNS hosting, I happen to be using Azure DNS to host the domain, storefront-demo.com. All DNS hosting services basically work the same way, whether you chose Azure, AWS, GCP, or another third party provider.

Let’s Encrypt

If you have used Let’s Encrypt before, then you know how easy it is to get free SSL/TLS Certificates. Let’s Encrypt is the first free, automated, and open certificate authority (CA) brought to you by the non-profit Internet Security Research Group (ISRG).

According to Let’s Encrypt, to enable HTTPS on your website, you need to get a certificate from a Certificate Authority (CA); Let’s Encrypt is a CA. In order to get a certificate for your website’s domain from Let’s Encrypt, you have to demonstrate control over the domain. With Let’s Encrypt, you do this using software that uses the ACME protocol, which typically runs on your web host. If you have generated certificates with Let’s Encrypt, you also know the domain validation by installing the Certbot ACME client can be a bit daunting, depending on your level of access and technical expertise.

SSL For Free

This is where SSL For Free comes in. SSL For Free acts as a proxy of sorts to Let’s Encrypt. SSL For Free generates certificates using their ACME server by using domain validation. Private Keys are generated in your browser and never transmitted.

SSL For Free offers three domain validation methods:

Automatic FTP Verification: Enter FTP information to automatically verify the domain;
Manual Verification: Upload verification files manually to your domain to verify ownership;
Manual Verification (DNS): Add TXT records to your DNS server;

Using the third domain validation method, manual verification using DNS, is extremely easy, if you have access to your domain’s DNS recordset.

SSL For Free provides TXT records for each domain you are adding to the certificate. Below, I am adding a single domain to the certificate.

Add the TXT records to your domain’s recordset. Shown below is an example of a single TXT record that has been to my recordset using the Azure DNS service.

SSL For Free then uses the TXT record to validate your domain is actually yours.

With the TXT record in place and validation successful, you can download a ZIPped package containing the certificate, private key, and CA bundle. The CA bundle containing the end-entity root and intermediate certificates.

Decoding PEM Encoded SSL Certificate

Using a tool like SSL Shopper’s Certificate Decoder, we can decode our Privacy-Enhanced Mail (PEM) encoded SSL certificates and view all of the certificate’s information. Decoding the information contained in my certificate.crt, I see the following.

Certificate Information:
Common Name: api.dev.storefront-demo.com
Subject Alternative Names: api.dev.storefront-demo.com
Valid From: December 26, 2018
Valid To: March 26, 2019
Issuer: Let's Encrypt Authority X3, Let's Encrypt
Serial Number: 03a5ec86bf79de65fb679ee7741ba07df1e4

Decoding the information contained in my ca_bundle.crt, I see the following.

Certificate Information:
Common Name: Let's Encrypt Authority X3
Organization: Let's Encrypt
Country: US
Valid From: March 17, 2016
Valid To: March 17, 2021
Issuer: DST Root CA X3, Digital Signature Trust Co.
Serial Number: 0a0141420000015385736a0b85eca708

The Let’s Encrypt intermediate certificate is also cross-signed by another certificate authority, IdenTrust, whose root is already trusted in all major browsers. IdenTrust cross-signs the Let’s Encrypt intermediate certificate using their DST Root CA X3. Thus, the Issuer, shown above.

Configure Istio Ingress Gateway

Unzip the sslforfree.zip package and place the individual files in a location you have access to from the command line.

unzip -l ~/Downloads/sslforfree.zip
Archive:  /Users/garystafford/Downloads/sslforfree.zip
  Length      Date    Time    Name
---------  ---------- -----   ----
     1943  12-26-2018 18:35   certificate.crt
     1707  12-26-2018 18:35   private.key
     1646  12-26-2018 18:35   ca_bundle.crt
---------                     -------
     5296                     3 files

Following the process outlined in the Istio documentation, Securing Gateways with HTTPS, run the following command. This will place the istio-ingressgateway-certs Secret in the istio-system namespace, on the GKE cluster.

kubectl create -n istio-system secret tls istio-ingressgateway-certs \
  --key path_to_files/sslforfree/private.key \
  --cert path_to_files/sslforfree/certificate.crt

Modify the existing Istio Gateway from the previous project, istio-gateway.yaml. Remove the HTTP port configuration item and replace with the HTTPS protocol item (gist). Redeploy the Istio Gateway to the GKE cluster.

By deploying the new istio-ingressgateway-certs Secret and redeploying the Gateway, the certificate and private key were deployed to the /etc/istio/ingressgateway-certs/ directory of the istio-proxy container, running on the istio-ingressgateway Pod. To confirm both the certificate and private key were deployed correctly, run the following command.

kubectl exec -it -n istio-system \
  $(kubectl -n istio-system get pods \
    -l istio=ingressgateway \
    -o jsonpath='{.items[0].metadata.name}') \
  -- ls -l /etc/istio/ingressgateway-certs/

lrwxrwxrwx 1 root root 14 Jan  2 17:53 tls.crt -> ..data/tls.crt
lrwxrwxrwx 1 root root 14 Jan  2 17:53 tls.key -> ..data/tls.key

That’s it. We should now have simple TLS enabled on the Istio Gateway, providing bidirectional encryption of communications between a client (Storefront API consumer) and server (Storefront API running on the GKE cluster). Users accessing the API will now have to use HTTPS.

Confirm HTTPS is Working

After completing the deployment, as outlined in the previous post, test the Storefront API by using HTTP, first. Since we removed the HTTP port item configuration in the Istio Gateway, the HTTP request should fail with a connection refused error. Insecure traffic is no longer allowed by the Storefront API.

Now try switching from HTTP to HTTPS. The page should be displayed and the black lock icon should appear in the browser’s address bar. Clicking on the lock icon, we will see the SSL certificate, used by the GKE cluster is valid.

By clicking on the valid certificate indicator, we may observe more details about the SSL certificate, used to secure the Storefront API. Observe the certificate is issued by Let’s Encrypt Authority X3. It is valid for 90 days from its time of issuance. Let’s Encrypt only issues certificates with a 90-day lifetime. Observe the public key uses SHA-256 with RSA (Rivest–Shamir–Adleman) encryption.

In Chrome, we can also use the Developer Tools Security tab to inspect the certificate. The certificate is recognized as valid and trusted. Also important, note the connection to this Storefront API is encrypted and authenticated using TLS 1.2 (a strong protocol), ECDHE_RSA with X25519 (a strong key exchange), and AES_128_GCM (a strong cipher). According to How’s My SSL?, TLS 1.2 is the latest version of TLS. The TLS 1.2 protocol provides access to advanced cipher suites that support elliptical curve cryptography and AEAD block cipher modes. TLS 1.2 is an improvement on previous TLS 1.1, 1.0, and SSLv3 or earlier.

Lastly, the best way to really understand what is happening with HTTPS, the Storefront API, and Istio, is verbosely curl an API endpoint.

curl -Iv https://api.dev.storefront-demo.com/accounts/

Using the above curl command, we can see exactly how the client successfully verifies the server, negotiates a secure HTTP/2 connection (HTTP/2 over TLS 1.2), and makes a request (gist).

Line 3: DNS resolution of the URL to the external IP address of the GCP load-balancer
Line 3: HTTPS traffic is routed to TCP port 443
Lines 4 – 5: Application-Layer Protocol Negotiation (ALPN) starts to occur with the server
Lines 7 – 9: Certificate to verify located
Lines 10 – 20: TLS handshake is performed and is successful using TLS 1.2 protocol
Line 20: CHACHA is the stream cipher and POLY1305 is the authenticator in the Transport Layer Security (TLS) 1.2 protocol ChaCha20-Poly1305 Cipher Suite
Lines 22 – 27: SSL certificate details
Line 28: Certificate verified
Lines 29 – 38: Establishing HTTP/2 connection with the server
Lines 33 – 36: Request headers
Lines 39 – 46: Response headers containing the expected 204 HTTP return code

Mutual TLS

Istio also supports mutual authentication using the TLS protocol, known as mutual TLS authentication (mTLS), between external clients and the gateway, as outlined in the Istio 1.0 documentation. According to Wikipedia, mutual authentication or two-way authentication refers to two parties authenticating each other at the same time. Mutual authentication a default mode of authentication in some protocols (IKE, SSH), but optional in TLS.

Again, according to Wikipedia, by default, TLS only proves the identity of the server to the client using X.509 certificates. The authentication of the client to the server is left to the application layer. TLS also offers client-to-server authentication using client-side X.509 authentication. As it requires provisioning of the certificates to the clients and involves less user-friendly experience, it is rarely used in end-user applications. Mutual TLS is much more widespread in B2B applications, where a limited number of programmatic clients are connecting to specific web services. The operational burden is limited and security requirements are usually much higher as compared to consumer environments.

This form of mutual authentication would be beneficial if we had external applications or other services outside our GKE cluster, consuming our API. Using mTLS, we could further enhance the security of those types of interactions.

All opinions expressed in this post are my own and not necessarily the views of my current or past employers or their clients.

GCP, GKE, Google, HTTPS, Istio, Istio 1.0, Kubernetes, Security, TLS

4 Comments

Building a Microservices Platform with Confluent Cloud, MongoDB Atlas, Istio, and Google Kubernetes Engine

Posted by Gary A. Stafford in Bash Scripting, Cloud, DevOps, Enterprise Software Development, GCP, Java Development, Python, Software Development on December 28, 2018

Leading SaaS providers have sufficiently matured the integration capabilities of their product offerings to a point where it is now reasonable for enterprises to architect multi-vendor, single- and multi-cloud Production platforms, without re-engineering existing cloud-native applications. In previous posts, we have integrated other SaaS products, including as MongoDB Atlas fully-managed MongoDB-as-a-service, ElephantSQL fully-manage PostgreSQL-as-a-service, and CloudAMQP RabbitMQ-as-a-service, into cloud-native applications on Azure, AWS, GCP, and PCF.

In this post, we will build and deploy an existing, Spring Framework, microservice-based, cloud-native API to Google Kubernetes Engine (GKE), replete with Istio 1.0, on Google Cloud Platform (GCP). The API will rely on Confluent Cloud to provide a fully-managed, Kafka-based messaging-as-a-service (MaaS). Similarly, the API will rely on MongoDB Atlas to provide a fully-managed, MongoDB-based Database-as-a-service (DBaaS).

Background

In a previous two-part post, Using Eventual Consistency and Spring for Kafka to Manage a Distributed Data Model: Part 1 and Part 2, we examined the role of Apache Kafka in an event-driven, eventually consistent, distributed system architecture. The system, an online storefront RESTful API simulation, was composed of multiple, Java Spring Boot microservices, each with their own MongoDB database. The microservices used a publish/subscribe model to communicate with each other using Kafka-based messaging. The Spring services were built using the Spring for Apache Kafka and Spring Data MongoDB projects.

Given the use case of placing an order through the Storefront API, we examined the interactions of three microservices, the Accounts, Fulfillment, and Orders service. We examined how the three services used Kafka to communicate state changes to each other, in a fully-decoupled manner.

The Storefront API’s microservices were managed behind an API Gateway, Netflix’s Zuul. Service discovery and load balancing were handled by Netflix’s Eureka. Both Zuul and Eureka are part of the Spring Cloud Netflix project. In that post, the entire containerized system was deployed to Docker Swarm.

Developing the services, not operationalizing the platform, was the primary objective of the previous post.

Featured Technologies

The following technologies are featured prominently in this post.

Confluent Cloud

confluent_cloud_apache-300x228

In May 2018, Google announced a partnership with Confluence to provide Confluent Cloud on GCP, a managed Apache Kafka solution for the Google Cloud Platform. Confluent, founded by the creators of Kafka, Jay Kreps, Neha Narkhede, and Jun Rao, is known for their commercial, Kafka-based streaming platform for the Enterprise.

Confluent Cloud is a fully-managed, cloud-based streaming service based on Apache Kafka. Confluent Cloud delivers a low-latency, resilient, scalable streaming service, deployable in minutes. Confluent deploys, upgrades, and maintains your Kafka clusters. Confluent Cloud is currently available on both AWS and GCP.

Confluent Cloud offers two plans, Professional and Enterprise. The Professional plan is optimized for projects under development, and for smaller organizations and applications. Professional plan rates for Confluent Cloud start at $0.55/hour. The Enterprise plan adds full enterprise capabilities such as service-level agreements (SLAs) with a 99.95% uptime and virtual private cloud (VPC) peering. The limitations and supported features of both plans are detailed, here.

MongoDB Atlas

mongodb

Similar to Confluent Cloud, MongoDB Atlas is a fully-managed MongoDB-as-a-Service, available on AWS, Azure, and GCP. Atlas, a mature SaaS product, offers high-availability, uptime SLAs, elastic scalability, cross-region replication, enterprise-grade security, LDAP integration, BI Connector, and much more.

MongoDB Atlas currently offers four pricing plans, Free, Basic, Pro, and Enterprise. Plans range from the smallest, M0-sized MongoDB cluster, with shared RAM and 512 MB storage, up to the massive M400 MongoDB cluster, with 488 GB of RAM and 3 TB of storage.

MongoDB Atlas has been featured in several past posts, including Deploying and Configuring Istio on Google Kubernetes Engine (GKE) and Developing Applications for the Cloud with Azure App Services and MongoDB Atlas.

Kubernetes Engine

gke According to Google, Google Kubernetes Engine (GKE) provides a fully-managed, production-ready Kubernetes environment for deploying, managing, and scaling your containerized applications using Google infrastructure. GKE consists of multiple Google Compute Engine instances, grouped together to form a cluster.

A forerunner to other managed Kubernetes platforms, like EKS (AWS), AKS (Azure), PKS (Pivotal), and IBM Cloud Kubernetes Service, GKE launched publicly in 2015. GKE was built on Google’s experience of running hyper-scale services like Gmail and YouTube in containers for over 12 years.

GKE’s pricing is based on a pay-as-you-go, per-second-billing plan, with no up-front or termination fees, similar to Confluent Cloud and MongoDB Atlas. Cluster sizes range from 1 – 1,000 nodes. Node machine types may be optimized for standard workloads, CPU, memory, GPU, or high-availability. Compute power ranges from 1 – 96 vCPUs and memory from 1 – 624 GB of RAM.

Demonstration

In this post, we will deploy the three Storefront API microservices to a GKE cluster on GCP. Confluent Cloud on GCP will replace the previous Docker-based Kafka implementation. Similarly, MongoDB Atlas will replace the previous Docker-based MongoDB implementation.

Kubernetes and Istio 1.0 will replace Netflix’s Zuul and Eureka for API management, load-balancing, routing, and service discovery. Google Stackdriver will provide logging and monitoring. Docker Images for the services will be stored in Google Container Registry. Although not fully operationalized, the Storefront API will be closer to a Production-like platform, than previously demonstrated on Docker Swarm.

For brevity, we will not enable standard API security features like HTTPS, OAuth for authentication, and request quotas and throttling, all of which are essential in Production. Nor, will we integrate a full lifecycle API management tool, like Google Apigee.

Source Code

The source code for this demonstration is contained in four separate GitHub repositories, storefront-kafka-docker, storefront-demo-accounts, storefront-demo-orders, and, storefront-demo-fulfillment. However, since the Docker Images for the three storefront services are available on Docker Hub, it is only necessary to clone the storefront-kafka-docker project. This project contains all the code to deploy and configure the GKE cluster and Kubernetes resources (gist).

Source code samples in this post are displayed as GitHub Gists, which may not display correctly on all mobile and social media browsers.

Setup Process

The setup of the Storefront API platform is divided into a few logical steps:

Create the MongoDB Atlas cluster;
Create the Confluent Cloud Kafka cluster;
Create Kafka topics;
Modify the Kubernetes resources;
Modify the microservices to support Confluent Cloud configuration;
Create the GKE cluster with Istio on GCP;
Apply the Kubernetes resources to the GKE cluster;
Test the Storefront API, Kafka, and MongoDB are functioning properly;

MongoDB Atlas Cluster

This post assumes you already have a MongoDB Atlas account and an existing project created. MongoDB Atlas accounts are free to set up if you do not already have one. Account creation does require the use of a Credit Card.

For minimal latency, we will be creating the MongoDB Atlas, Confluent Cloud Kafka, and GKE clusters, all on the Google Cloud Platform’s us-central1 Region. Available GCP Regions and Zones for MongoDB Atlas, Confluent Cloud, and GKE, vary, based on multiple factors.

For this demo, I suggest creating a free, M0-sized MongoDB cluster. The M0-sized 3-data node cluster, with shared RAM and 512 MB of storage, and currently running MongoDB 4.0.4, is fine for individual development. The us-central1 Region is the only available US Region for the free-tier M0-cluster on GCP. An M0-sized Atlas cluster may take between 7-10 minutes to provision.

MongoDB Atlas’ Web-based management console provides convenient links to cluster details, metrics, alerts, and documentation.

Once the cluster is ready, you can review details about the cluster and each individual cluster node.

In addition to the account owner, create a demo_user account. This account will be used to authenticate and connect with the MongoDB databases from the storefront services. For this demo, we will use the same, single user account for all three services. In Production, you would most likely have individual users for each service.

Again, for security purposes, Atlas requires you to whitelist the IP address or CIDR block from which the storefront services will connect to the cluster. For now, open the access to your specific IP address using whatsmyip.com, or much less-securely, to all IP addresses (0.0.0.0/0). Once the GKE cluster and external static IP addresses are created, make sure to come back and update this value; do not leave this wide open to the Internet.

The Java Spring Boot storefront services use a Spring Profile, gke. According to Spring, Spring Profiles provide a way to segregate parts of your application configuration and make it available only in certain environments. The gke Spring Profile’s configuration values may be set in a number of ways. For this demo, the majority of the values will be set using Kubernetes Deployment, ConfigMap and Secret resources, shown later.

The first two Spring configuration values will need are the MongoDB Atlas cluster’s connection string and the demo_user account password. Note these both for later use.

Confluent Cloud Kafka Cluster

Similar to MongoDB Atlas, this post assumes you already have a Confluent Cloud account and an existing project. It is free to set up a Professional account and a new project if you do not already have one. Atlas account creation does require the use of a Credit Card.

The Confluent Cloud web-based management console is shown below. Experienced users of other SaaS platforms may find the Confluent Cloud web-based console a bit sparse on features. In my opinion, the console lacks some necessary features, like cluster observability, individual Kafka topic management, detailed billing history (always says $0?), and persistent history of cluster activities, which survives cluster deletion. It seems like Confluent prefers users to download and configure their Confluent Control Center to get the functionality you might normally expect from a web-based Saas management tool.

As explained earlier, for minimal latency, I suggest creating the MongoDB Atlas cluster, Confluent Cloud Kafka cluster, and the GKE cluster, all on the Google Cloud Platform’s us-central1 Region. For this demo, choose the smallest cluster size available on GCP, in the us-central1 Region, with 1 MB/s R/W throughput and 500 MB of storage. As shown below, the cost will be approximately $0.55/hour. Don’t forget to delete this cluster when you are done with the demonstration, or you will continue to be charged.

Cluster creation of the minimally-sized Confluent Cloud cluster is pretty quick.

Once the cluster is ready, Confluent provides instructions on how to interact with the cluster via the Confluent Cloud CLI. Install the Confluent Cloud CLI, locally, for use later.

As explained earlier, the Java Spring Boot storefront services use a Spring Profile, gke. Like MongoDB Atlas, the Confluent Cloud Kafka cluster configuration values will be set using Kubernetes ConfigMap and Secret resources, shown later. There are several Confluent Cloud Java configuration values shown in the Client Config Java tab; we will need these for later use.

SASL and JAAS

Some users may not be familiar with the terms, SASL and JAAS. According to Wikipedia, Simple Authentication and Security Layer (SASL) is a framework for authentication and data security in Internet protocols. According to Confluent, Kafka brokers support client authentication via SASL. SASL authentication can be enabled concurrently with SSL encryption (SSL client authentication will be disabled).

There are numerous SASL mechanisms. The PLAIN SASL mechanism (SASL/PLAIN), used by Confluent, is a simple username/password authentication mechanism that is typically used with TLS for encryption to implement secure authentication. Kafka supports a default implementation for SASL/PLAIN which can be extended for production use. The SASL/PLAIN mechanism should only be used with SSL as a transport layer to ensure that clear passwords are not transmitted on the wire without encryption.

According to Wikipedia, Java Authentication and Authorization Service (JAAS) is the Java implementation of the standard Pluggable Authentication Module (PAM) information security framework. According to Confluent, Kafka uses the JAAS for SASL configuration. You must provide JAAS configurations for all SASL authentication mechanisms.

Cluster Authentication

Similar to MongoDB Atlas, we need to authenticate with the Confluent Cloud cluster from the storefront services. The authentication to Confluent Cloud is done with an API Key. Create a new API Key, and note the Key and Secret; these two additional pieces of configuration will be needed later.

Confluent Cloud API Keys can be created and deleted as necessary. For security in Production, API Keys should be created for each service and regularly rotated.

Kafka Topics

With the cluster created, create the storefront service’s three Kafka topics manually, using the Confluent Cloud’s ccloud CLI tool. First, configure the Confluent Cloud CLI using the ccloud init command, using your new cluster’s Bootstrap Servers address, API Key, and API Secret. The instructions are shown above Clusters Client Config tab of the Confluent Cloud web-based management interface.

Create the storefront service’s three Kafka topics using the ccloud topic create command. Use the list command to confirm they are created.

# manually create kafka topics
ccloud topic create accounts.customer.change
ccloud topic create fulfillment.order.change
ccloud topic create orders.order.fulfill
  
# list kafka topics
ccloud topic list
  
accounts.customer.change
fulfillment.order.change
orders.order.fulfill

Another useful ccloud command, topic describe, displays topic replication details. The new topics will have a replication factor of 3 and a partition count of 12.

Adding the --verbose flag to the command, ccloud --verbose topic describe, displays low-level topic and cluster configuration details, as well as a log of all topic-related activities.

Kubernetes Resources

The deployment of the three storefront microservices to the dev Namespace will minimally require the following Kubernetes configuration resources.

(1) Kubernetes Namespace;
(3) Kubernetes Deployments;
(3) Kubernetes Services;
(1) Kubernetes ConfigMap;
(2) Kubernetes Secrets;
(1) Istio 1.0 Gateway;
(1) Istio 1.0 VirtualService;
(2) Istio 1.0 ServiceEntry;

The Istio networking.istio.io v1alpha3 API introduced the last three configuration resources in the list, to control traffic routing into, within, and out of the mesh. There are a total of four new io networking.istio.io v1alpha3 API routing resources: Gateway, VirtualService, DestinationRule, and ServiceEntry.

Creating and managing such a large number of resources is a common complaint regarding the complexity of Kubernetes. Imagine the resource sprawl when you have dozens of microservices replicated across several namespaces. Fortunately, all resource files for this post are included in the storefront-kafka-docker project’s gke directory.

To follow along with the demo, you will need to make minor modifications to a few of these resources, including the Istio Gateway, Istio VirtualService, two Istio ServiceEntry resources, and two Kubernetes Secret resources.

Istio Gateway & VirtualService

Both the Istio Gateway and VirtualService configuration resources are contained in a single file, istio-gateway.yaml. For the demo, I am using a personal domain, storefront-demo.com, along with the sub-domain, api.dev, to host the Storefront API. The domain’s primary A record (‘@’) and sub-domain A record are both associated with the external IP address on the frontend of the load balancer. In the file, this host is configured for the Gateway and VirtualService resources. You can choose to replace the host with your own domain, or simply remove the host block altogether on lines 13–14 and 21–22. Removing the host blocks, you would then use the external IP address on the frontend of the load balancer (explained later in the post) to access the Storefront API (gist).

Istio ServiceEntry

There are two Istio ServiceEntry configuration resources. Both ServiceEntry resources control egress traffic from the Storefront API services, both of their ServiceEntry Location items are set to MESH_INTERNAL. The first ServiceEntry, mongodb-atlas-external-mesh.yaml, defines MongoDB Atlas cluster egress traffic from the Storefront API (gist).

The other ServiceEntry, confluent-cloud-external-mesh.yaml, defines Confluent Cloud Kafka cluster egress traffic from the Storefront API (gist).

Both need to have their host items replaced with the appropriate Atlas and Confluent URLs.

Inspecting Istio Resources

The easiest way to view Istio resources is from the command line using the istioctl and kubectl CLI tools.

istioctl get gateway
istioctl get virtualservices
istioctl get serviceentry
  
kubectl describe gateway
kubectl describe virtualservices
kubectl describe serviceentry

Multiple Namespaces

In this demo, we are only deploying to a single Kubernetes Namespace, dev. However, Istio will also support routing traffic to multiple namespaces. For example, a typical non-prod Kubernetes cluster might support dev, test, and uat, each associated with a different sub-domain. One way to support multiple Namespaces with Istio 1.0 is to add each host to the Istio Gateway (lines 14–16, below), then create a separate Istio VirtualService for each Namespace. All the VirtualServices are associated with the single Gateway. In the VirtualService, each service’s host address is the fully qualified domain name (FQDN) of the service. Part of the FQDN is the Namespace, which we change for each for each VirtualService (gist).

MongoDB Atlas Secret

There is one Kubernetes Secret for the sensitive MongoDB configuration and one Secret for the sensitive Confluent Cloud configuration. The Kubernetes Secret object type is intended to hold sensitive information, such as passwords, OAuth tokens, and SSH keys.

The mongodb-atlas-secret.yaml file contains the MongoDB Atlas cluster connection string, with the demo_user username and password, one for each of the storefront service’s databases (gist).

Kubernetes Secrets are Base64 encoded. The easiest way to encode the secret values is using the Linux base64 program. The base64 program encodes and decodes Base64 data, as specified in RFC 4648. Pass each MongoDB URI string to the base64 program using echo -n.

MONGODB_URI=mongodb+srv://demo_user:your_password@your_cluster_address/accounts?retryWrites=true
echo -n $MONGODB_URI | base64

bW9uZ29kYitzcnY6Ly9kZW1vX3VzZXI6eW91cl9wYXNzd29yZEB5b3VyX2NsdXN0ZXJfYWRkcmVzcy9hY2NvdW50cz9yZXRyeVdyaXRlcz10cnVl

Repeat this process for the three MongoDB connection strings.

Confluent Cloud Secret

The confluent-cloud-kafka-secret.yaml file contains two data fields in the Secret’s data map, bootstrap.servers and sasl.jaas.config. These configuration items were both listed in the Client Config Java tab of the Confluent Cloud web-based management console, as shown previously. The sasl.jaas.config data field requires the Confluent Cloud cluster API Key and Secret you created earlier. Again, use the base64 encoding process for these two data fields (gist).

Confluent Cloud ConfigMap

The remaining five Confluent Cloud Kafka cluster configuration values are not sensitive, and therefore, may be placed in a Kubernetes ConfigMap, confluent-cloud-kafka-configmap.yaml (gist).

Accounts Deployment Resource

To see how the services consume the ConfigMap and Secret values, review the Accounts Deployment resource, shown below. Note the environment variables section, on lines 44–90, are a mix of hard-coded values and values referenced from the ConfigMap and two Secrets, shown above (gist).

Modify Microservices for Confluent Cloud

As explained earlier, Confluent Cloud’s Kafka cluster requires some very specific configuration, based largely on the security features of Confluent Cloud. Connecting to Confluent Cloud requires some minor modifications to the existing storefront service source code. The changes are identical for all three services. To understand the service’s code, I suggest reviewing the previous post, Using Eventual Consistency and Spring for Kafka to Manage a Distributed Data Model: Part 1. Note the following changes are already made to the source code in the gke git branch, and not necessary for this demo.

The previous Kafka SenderConfig and ReceiverConfig Java classes have been converted to Java interfaces. There are four new SenderConfigConfluent, SenderConfigNonConfluent, ReceiverConfigConfluent, and ReceiverConfigNonConfluent classes, which implement one of the new interfaces. The new classes contain the Spring Boot Profile class-level annotation. One set of Sender and Receiver classes are assigned the @Profile("gke") annotation, and the others, the @Profile("!gke") annotation. When the services start, one of the two class implementations are is loaded, depending on the Active Spring Profile, gke or not gke. To understand the changes better, examine the Account service’s SenderConfigConfluent.java file (gist).

Line 20: Designates this class as belonging to the gke Spring Profile.

Line 23: The class now implements an interface.

Lines 25–44: Reference the Confluent Cloud Kafka cluster configuration. The values for these variables will come from the Kubernetes ConfigMap and Secret, described previously, when the services are deployed to GKE.

Lines 55–59: Additional properties that have been added to the Kafka Sender configuration properties, specifically for Confluent Cloud.

Once code changes were completed and tested, the Docker Image for each service was rebuilt and uploaded to Docker Hub for public access. When recreating the images, the version of the Java Docker base image was upgraded from the previous post to Alpine OpenJDK 12 (openjdk:12-jdk-alpine).

Google Kubernetes Engine (GKE) with Istio

Having created the MongoDB Atlas and Confluent Cloud clusters, built the Kubernetes and Istio resources, modified the service’s source code, and pushed the new Docker Images to Docker Hub, the GKE cluster may now be built.

For the sake of brevity, we will manually create the cluster and deploy the resources, using the Google Cloud SDK gcloud and Kubernetes kubectl CLI tools, as opposed to automating with CI/CD tools, like Jenkins or Spinnaker. For this demonstration, I suggest a minimally-sized two-node GKE cluster using n1-standard-2 machine-type instances. The latest available release of Kubernetes on GKE at the time of this post was 1.11.5-gke.5 and Istio 1.03 (Istio on GKE still considered beta). Note Kubernetes and Istio are evolving rapidly, thus the configuration flags often change with newer versions. Check the GKE Clusters tab for the latest clusters create command format (gist).

Executing these commands successfully will build the cluster and the dev Namespace, into which all the resources will be deployed. The two-node cluster creation process takes about three minutes on average.

We can also observe the new GKE cluster from the GKE Clusters Details tab.

Creating the GKE cluster also creates several other GCP resources, including a TCP load balancer and three external IP addresses. Shown below in the VPC network External IP addresses tab, there is one IP address associated with each of the two GKE cluster’s VM instances, and one IP address associated with the frontend of the load balancer.

While the TCP load balancer’s frontend is associated with the external IP address, the load balancer’s backend is a target pool, containing the two GKE cluster node machine instances.

A forwarding rule associates the load balancer’s frontend IP address with the backend target pool. External requests to the frontend IP address will be routed to the GKE cluster. From there, requests will be routed by Kubernetes and Istio to the individual storefront service Pods, and through the Istio sidecar (Envoy) proxies. There is an Istio sidecar proxy deployed to each Storefront service Pod.

Below, we see the details of the load balancer’s target pool, containing the two GKE cluster’s VMs.

As shown at the start of the post, a simplified view of the GCP/GKE network routing looks as follows. For brevity, firewall rules and routes are not illustrated in the diagram.

ConfluentCloudRouting

Apply Kubernetes Resources

Again, using kubectl, deploy the three services and associated Kubernetes and Istio resources. Note the Istio Gateway and VirtualService(s) are not deployed to the dev Namespace since their role is to control ingress and route traffic to the dev Namespace and the services within it (gist).

Once these commands complete successfully, on the Workloads tab, we should observe two Pods of each of the three storefront service Kubernetes Deployments deployed to the dev Namespace, all six Pods with a Status of ‘OK’. A Deployment controller provides declarative updates for Pods and ReplicaSets.

On the Services tab, we should observe the three storefront service’s Kubernetes Services. A Service in Kubernetes is a REST object.

On the Configuration Tab, we should observe the Kubernetes ConfigMap and two Secrets also deployed to the dev Environment.

Below, we see the confluent-cloud-kafka ConfigMap resource with its data map of Confluent Cloud configuration.

Below, we see the confluent-cloud-kafka Secret with its data map of sensitive Confluent Cloud configuration.

Test the Storefront API

If you recall from part two of the previous post, there are a set of seven Storefront API endpoints that can be called to create sample data and test the API. The HTTP GET Requests hit each service, generate test data, populate the three MongoDB databases, and produce and consume Kafka messages across all three topics. Making these requests is the easiest way to confirm the Storefront API is working properly.

Sample Customer: accounts/customers/sample
Sample Orders: orders/customers/sample/orders
Sample Fulfillment Requests: orders/customers/sample/fulfill
Sample Processed Order Event: fulfillment/fulfillment/sample/process
Sample Shipped Order Event: fulfillment/fulfillment/sample/ship
Sample In-Transit Order Event: fulfillment/fulfillment/sample/in-transit
Sample Received Order Event: fulfillment/fulfillment/sample/receive

Thee are a wide variety of tools to interact with the Storefront API. The project includes a simple Python script, sample_data.py, which will make HTTP GET requests to each of the above endpoints, after confirming their health, and return a success message.

Postman

Postman, my personal favorite, is also an excellent tool to explore the Storefront API resources. I have the above set of the HTTP GET requests saved in a Postman Collection. Using Postman, below, we see the response from an HTTP GET request to the /accounts/customers endpoint.

Postman also allows us to create integration tests and run Collections of Requests in batches using Postman’s Collection Runner. To test the Storefront API, below, I used Collection Runner to run a single series of integration tests, intended to confirm the API’s functionality, by checking for expected HTTP response codes and expected values in the response payloads. Postman also shows the response times from the Storefront API. Since this platform was not built to meet Production SLAs, measuring response times is less critical in the Development environment.

Google Stackdriver

If you recall, the GKE cluster had the Stackdriver Kubernetes option enabled, which gives us, amongst other observability features, access to all cluster, node, pod, and container logs. To confirm data is flowing to the MongoDB databases and Kafka topics, we can check the logs from any of the containers. Below we see the logs from the two Accounts Pod containers. Observe the AfterSaveListener handler firing on an onAfterSave event, which sends a CustomerChangeEvent payload to the accounts.customer.change Kafka topic, without error. These entries confirm that both Atlas and Confluent Cloud are reachable by the GKE-based workloads, and appear to be functioning properly.

MongoDB Atlas Collection View

Review the MongoDB Atlas Clusters Collections tab. In this Development environment, the MongoDB databases and collections are created the first time a service tries to connects to them. In Production, the databases would be created and secured in advance of deploying resources. Once the sample data requests are completed successfully, you should now observe the three Storefront API databases, each with collections of documents.

MongoDB Compass

In addition to the Atlas web-based management console, MongoDB Compass is an excellent desktop tool to explore and manage MongoDB databases. Compass is available for Mac, Linux, and Windows. One of the many great features of Compass is the ability to visualize collection schemas and interactively filter documents. Below we see the fulfillment.requests collection schema.

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Confluent Control Center

Confluent Control Center is a downloadable, web browser-based tool for managing and monitoring Apache Kafka, including your Confluent Cloud clusters. Confluent Control Center provides rich functionality for building and monitoring production data pipelines and streaming applications. Confluent offers a free 30-day trial of Confluent Control Center. Since the Control Center is provided at an additional fee, and I found difficult to configure for Confluent Cloud clusters based on Confluent’s documentation, I chose not to cover it in detail, for this post.

Tear Down Cluster

Delete your Confluent Cloud and MongoDB clusters using their web-based management consoles. To delete the GKE cluster and all deployed Kubernetes resources, use the cluster delete command. Also, double-check that the external IP addresses and load balancer, associated with the cluster, were also deleted as part of the cluster deletion (gist).

Conclusion

In this post, we have seen how easy it is to integrate Cloud-based DBaaS and MaaS products with the managed Kubernetes services from GCP, AWS, and Azure. As this post demonstrated, leading SaaS providers have sufficiently matured the integration capabilities of their product offerings to a point where it is now reasonable for enterprises to architect multi-vendor, single- and multi-cloud Production platforms, without re-engineering existing cloud-native applications.

In future posts, we will revisit this Storefront API example, further demonstrating how to enable HTTPS (Securing Your Istio Ingress Gateway with HTTPS) and end-user authentication (Istio End-User Authentication for Kubernetes using JSON Web Tokens (JWT) and Auth0)

All opinions expressed in this post are my own and not necessarily the views of my current or past employers or their clients.

Atlas, Confluent, DBaaS, GCP, GKE, Google Cloud Platform, Google Kubernetes Engine, Kafka, Kubernetes, MaaS, MongoDB, SaaS, Spring Boot

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