Archive for category Client-Side Development

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

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).

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Technologies

Image result for grpc logogRPC

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.

Image result for google developerProtocol 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.

Image result for envoy proxyEnvoy 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.

Golang Service Diagram with Proxy v2

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

screen_shot_2019-04-15_at_10_23_47_pm

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.

Golang-Service-Diagram-with-gRPC

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.

grpc_gateway.png

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.

screen_shot_2019-04-15_at_9_05_23_pm

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.

screen_shot_2019-04-15_at_11_10_50_pm

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.

  1. Create the external MongoDB Atlas database and CloudAMQP RabbitMQ clusters;
  2. Modify the Kubernetes resource files and bash scripts for your own environments;
  3. Create the managed GKE or AKS cluster on GCP or Azure;
  4. Configure and deploy Istio to the managed Kubernetes cluster, using Helm;
  5. Create DNS records for the platform’s exposed resources;
  6. Deploy the Go-based microservices, gRPC Gateway reverse proxy, Angular UI, and associated resources to Kubernetes cluster;
  7. Test and troubleshoot the platform deployment;
  8. 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.

screen_shot_2019-04-15_at_11_10_36_pm

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.

screen_shot_2019-04-15_at_11_10_50_pm

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.

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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.

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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 abhey supports HTTP/2. The use of hey was detailed in the previous post.

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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.

screen_shot_2019-04-15_at_11_06_13_pm

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.

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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.

screen_shot_2019-04-15_at_11_08_22_pm

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.

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

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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.

 

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Kubernetes-based Microservice Observability with Istio Service Mesh: Part 1

In this two-part post, we will explore the set of observability tools which are part of the Istio Service Mesh. These tools include Jaeger, Kiali, Prometheus, and Grafana. To assist in our exploration, we will deploy a Go-based, microservices reference platform to Google Kubernetes Engine, on the Google Cloud Platform.

Golang Service Diagram with Proxy v2

What is Observability?

Similar to blockchain, serverless, AI and ML, chatbots, cybersecurity, and service meshes, Observability is a hot buzz word in the IT industry right now. According to Wikipedia, observability is a measure of how well internal states of a system can be inferred from knowledge of its external outputs. 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.

The O’Reilly book, Distributed Systems Observability, by Cindy Sridharan, does an excellent job of detailing ‘The Three Pillars of Observability’, in Chapter 4. I recommend reading this free online excerpt, before continuing. A second great resource for information on observability is honeycomb.io, a developer of observability tools for production systems, led by well-known industry thought-leader, Charity Majors. The honeycomb.io site includes articles, blog posts, whitepapers, and podcasts on observability.

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 Prometheus and Grafana for metric collection, monitoring, and alerting. Combined with cloud platform-native monitoring and logging services, such as Stackdriver for Google Kubernetes Engine (GKE) on Google Cloud Platform (GCP), we have a complete observability platform for modern, distributed applications.

A Reference Microservices Platform

To demonstrate the observability tools integrated with the latest version of Istio Service Mesh, we will deploy a reference microservices platform, written in Go, to GKE on GCP. I developed the reference platform to demonstrate concepts such as API management, Service Meshes, Observability, DevOps, and Chaos Engineering. The platform is comprised of (14) components, including (8) Go-based microservices, labeled generically as Service A – Service H, (1) Angular 7, TypeScript-based front-end, (4) MongoDB databases, and (1) RabbitMQ queue for event queue-based communications. The platform and all its source code is free and open source.

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

Service Responses

On the reference platform, each upstream service responds to requests from downstream services by returning a small informational JSON payload (termed a greeting in the source code).

Golang Service Diagram with Proxy v2 res

The responses are aggregated across the service call chain, resulting in an array of service responses being returned to the edge service and on to the Angular-based UI, running in the end user’s web browser. The response aggregation feature is simply used to confirm that the service-to-service communications, Istio components, and the telemetry tools are working properly.

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Each Go microservice contains a /ping and /health endpoint. The /health endpoint can be used to configure Kubernetes Liveness and Readiness Probes. Additionally, the edge service, Service A, is configured for Cross-Origin Resource Sharing (CORS) using the access-control-allow-origin: * response header. CORS allows the Angular UI, running in end user’s web browser, to call the Service A /ping endpoint, which resides in a different subdomain from UI. Shown below is the Go source code for Service A.

For this demonstration, the MongoDB databases will be hosted, external to the services on GCP, on MongoDB Atlas, a MongoDB-as-a-Service, cloud-based platform. Similarly, the RabbitMQ queues will be hosted on CloudAMQP, a RabbitMQ-as-a-Service, cloud-based platform. I have used both of these SaaS providers in several previous posts. Using external services will help us understand how Istio and its observability tools collect telemetry for communications between the Kubernetes cluster and external systems.

Shown below is the Go source code for Service F, This service consumers messages from the RabbitMQ queue, placed there by Service D, and writes the messages to MongoDB.

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

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

Docker images referenced in the Kubernetes Deployment resource files, for the Go services and UI, are all available on Docker Hub. The Go microservice Docker images were built using the official Golang Alpine base image on DockerHub, containing Go version 1.12.0. Using the Alpine image to compile the Go source code ensures the containers will be as small as possible and contain a minimal attack surface.

System Requirements

To follow along with the post, you will need the latest version of gcloud CLI (min. ver. 239.0.0), part of the Google Cloud SDK, Helm, and the just releases Istio 1.1.0, installed and configured locally or on your build machine.
screen_shot_2019-03-19_at_9_23_17_pm.png

Set-up and Installation

To deploy the microservices platform to GKE, we will proceed in the following order.

  1. Create the MongoDB Atlas database cluster;
  2. Create the CloudAMQP RabbitMQ cluster;
  3. Modify the Kubernetes resources and scripts for your own environments;
  4. Create the GKE cluster on GCP;
  5. Deploy Istio 1.1.0 to the GKE cluster, using Helm;
  6. Create DNS records for the platform’s exposed resources;
  7. Deploy the Go-based microservices, Angular UI, and associated resources to GKE;
  8. Test and troubleshoot the platform;
  9. Observe the results in part two!

MongoDB Atlas Cluster

MongoDB Atlas is a fully-managed MongoDB-as-a-Service, available on AWS, Azure, and GCP. Atlas, a mature SaaS product, offers high-availability, guaranteed uptime SLAs, elastic scalability, cross-region replication, enterprise-grade security, LDAP integration, a 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.

For this post, I have created an M2-sized MongoDB cluster in GCP’s us-central1 (Iowa) region, with a single user database account for this demo. The account will be used to connect from four of the eight microservices, running on GKE.

screen_shot_2019-03-09_at_7_48_00_pm

Originally, I started with an M0-sized cluster, but the compute resources were insufficient to support the volume of calls from the Go-based microservices. I suggest at least an M2-sized cluster or larger.

CloudAMQP RabbitMQ Cluster

CloudAMQP provides full-managed RabbitMQ clusters on all major cloud and application platforms. RabbitMQ will support a decoupled, eventually consistent, message-based architecture for a portion of our Go-based microservices. For this post, I have created a RabbitMQ cluster in GCP’s us-central1 (Iowa) region, the same as our GKE cluster and MongoDB Atlas cluster. I chose a minimally-configured free version of RabbitMQ. CloudAMQP also offers robust, multi-node RabbitMQ clusters for Production use.

Modify Configurations

There are a few configuration settings you will need to change in the GitHub project’s Kubernetes resource files and Bash deployment scripts.

Istio ServiceEntry for MongoDB Atlas

Modify the Istio ServiceEntry, external-mesh-mongodb-atlas.yaml file, adding you MongoDB Atlas host address. This file allows egress traffic from four of the microservices on GKE to the external MongoDB Atlas cluster.

apiVersion: networking.istio.io/v1alpha3
kind: ServiceEntry
metadata:
  name: mongodb-atlas-external-mesh
spec:
  hosts:
  - {{ your_host_goes_here }}
  ports:
  - name: mongo
    number: 27017
    protocol: MONGO
  location: MESH_EXTERNAL
  resolution: NONE

Istio ServiceEntry for CloudAMQP RabbitMQ

Modify the Istio ServiceEntry, external-mesh-cloudamqp.yaml file, adding you CloudAMQP host address. This file allows egress traffic from two of the microservices to the CloudAMQP cluster.

apiVersion: networking.istio.io/v1alpha3
kind: ServiceEntry
metadata:
  name: cloudamqp-external-mesh
spec:
  hosts:
  - {{ your_host_goes_here }}
  ports:
  - name: rabbitmq
    number: 5672
    protocol: TCP
  location: MESH_EXTERNAL
  resolution: NONE

Istio Gateway and VirtualService Resources

There are numerous strategies you may use to route traffic into the GKE cluster, via Istio. I am using a single domain for the post, example-api.com, and four subdomains. One set of subdomains is for the Angular UI, in the dev Namespace (ui.dev.example-api.com) and the test Namespace (ui.test.example-api.com). The other set of subdomains is for the edge API microservice, Service A, which the UI calls (api.dev.example-api.com and api.test.example-api.com). Traffic is routed to specific Kubernetes Service resources, based on the URL.

According to Istio, the Gateway describes a load balancer operating at the edge of the mesh, receiving incoming or outgoing HTTP/TCP connections. Modify the Istio ingress Gateway,  inserting your own domains or subdomains in the hosts section. These are the hosts on port 80 that will be allowed into the mesh.

apiVersion: networking.istio.io/v1alpha3
kind: Gateway
metadata:
  name: demo-gateway
spec:
  selector:
    istio: ingressgateway
  servers:
  - port:
      number: 80
      name: http
      protocol: HTTP
    hosts:
    - ui.dev.example-api.com
    - ui.test.example-api.com
    - api.dev.example-api.com
    - api.test.example-api.com

According to Istio, a VirtualService defines a set of traffic routing rules to apply when a host is addressed. A VirtualService is bound to a Gateway to control the forwarding of traffic arriving at a particular host and port. Modify the project’s four Istio VirtualServices, inserting your own domains or subdomains. Here is an example of one of the four VirtualServices, in the istio-gateway.yaml file.

apiVersion: networking.istio.io/v1alpha3
kind: VirtualService
metadata:
  name: angular-ui-dev
spec:
  hosts:
  - ui.dev.example-api.com
  gateways:
  - demo-gateway
  http:
  - match:
    - uri:
        prefix: /
    route:
    - destination:
        port:
          number: 80
        host: angular-ui.dev.svc.cluster.local

Kubernetes Secret

The project contains a Kubernetes Secret, go-srv-demo.yaml, with two values. One is for the MongoDB Atlas connection string and one is for the CloudAMQP connections string. Remember Kubernetes Secret values need to be base64 encoded.

apiVersion: v1
kind: Secret
metadata:
  name: go-srv-config
type: Opaque
data:
  mongodb.conn: {{ your_base64_encoded_secret }}
  rabbitmq.conn: {{ your_base64_encoded_secret }}

On Linux and Mac, you can use the base64 program to encode the connection strings.

> echo -n "mongodb+srv://username:password@atlas-cluster.gcp.mongodb.net/test?retryWrites=true" | base64
bW9uZ29kYitzcnY6Ly91c2VybmFtZTpwYXNzd29yZEBhdGxhcy1jbHVzdGVyLmdjcC5tb25nb2RiLm5ldC90ZXN0P3JldHJ5V3JpdGVzPXRydWU=

> echo -n "amqp://username:password@rmq.cloudamqp.com/cluster" | base64
YW1xcDovL3VzZXJuYW1lOnBhc3N3b3JkQHJtcS5jbG91ZGFtcXAuY29tL2NsdXN0ZXI=

Bash Scripts Variables

The bash script, part3_create_gke_cluster.sh, contains a series of environment variables. At a minimum, you will need to change the PROJECT variable in all scripts to match your GCP project name.

# Constants - CHANGE ME!
readonly PROJECT='{{ your_gcp_project_goes_here }}'
readonly CLUSTER='go-srv-demo-cluster'
readonly REGION='us-central1'
readonly MASTER_AUTH_NETS='72.231.208.0/24'
readonly GKE_VERSION='1.12.5-gke.5'
readonly MACHINE_TYPE='n1-standard-2'

The bash script, part4_install_istio.sh, includes the ISTIO_HOME variable. The value should correspond to your local path to Istio 1.1.0. On my local Mac, this value is shown below.

readonly ISTIO_HOME='/Applications/istio-1.1.0'

Deploy GKE Cluster

Next, deploy the GKE cluster using the included bash script, part3_create_gke_cluster.sh. This will create a Regional, multi-zone, 3-node GKE cluster, using the latest version of GKE at the time of this post, 1.12.5-gke.5. The cluster will be deployed to the same region as the MongoDB Atlas and CloudAMQP clusters, GCP’s us-central1 (Iowa) region. Planning where your Cloud resources will reside, for both SaaS providers and primary Cloud providers can be critical to minimizing latency for network I/O intensive applications.

screen_shot_2019-03-09_at_5_44_33_pm

Deploy Istio using Helm

With the GKE cluster and associated infrastructure in place, deploy Istio. For this post, I have chosen to install Istio using Helm, as recommended my Istio. To deploy Istio using Helm, use the included bash script, part4_install_istio.sh.

screen_shot_2019-03-09_at_5_47_57_pm

The script installs Istio, using the Helm Chart in the local Istio 1.1.0 install/kubernetes/helm/istio directory, which you installed as a requirement for this demonstration. The Istio install script overrides several default values in the Istio Helm Chart using the --set, flag. The list of available configuration values is detailed in the Istio Chart’s GitHub project. The options enable Istio’s observability features, which we will explore in part two. Features include Kiali, Grafana, Prometheus, and Jaeger.

helm install ${ISTIO_HOME}/install/kubernetes/helm/istio-init \
  --name istio-init \
  --namespace istio-system

helm install ${ISTIO_HOME}/install/kubernetes/helm/istio \
  --name istio \
  --namespace istio-system \
  --set prometheus.enabled=true \
  --set grafana.enabled=true \
  --set kiali.enabled=true \
  --set tracing.enabled=true

kubectl apply --namespace istio-system \
  -f ./resources/secrets/kiali.yaml

Below, we see the Istio-related Workloads running on the cluster, including the observability tools.

screen_shot_2019-03-09_at_5_58_35_pm

Below, we see the corresponding Istio-related Service resources running on the cluster.

screen_shot_2019-03-09_at_5_59_14_pm

Modify DNS Records

Instead of using IP addresses to route traffic the GKE cluster and its applications, we will use DNS. As explained earlier, I have chosen a single domain for the post, example-api.com, and four subdomains. One set of subdomains is for the Angular UI, in the dev Namespace and the test Namespace. The other set of subdomains is for the edge microservice, Service A, which the API calls. Traffic is routed to specific Kubernetes Service resources, based on the URL.

Deploying the GKE cluster and Istio triggers the creation of a Google Load Balancer, four IP addresses, and all required firewall rules. One of the four IP addresses, the one shown below, associated with the Forwarding rule, will be associated with the front-end of the load balancer.screen_shot_2019-03-09_at_5_49_37_pm

Below, we see the new load balancer, with the front-end IP address and the backend VM pool of three GKE cluster’s worker nodes. Each node is assigned one of the IP addresses, as shown above.

screen_shot_2019-03-09_at_5_57_20_pm

As shown below, using Google Cloud DNS, I have created the four subdomains and assigned the IP address of the load balancer’s front-end to all four subdomains. Ingress traffic to these addresses will be routed through the Istio ingress Gateway and the four Istio VirtualServices, to the appropriate Kubernetes Service resources. Use your choice of DNS management tools to create the four A Type DNS records.

screen_shot_2019-03-09_at_5_56_29_pm

Deploy the Reference Platform

Next, deploy the eight Go-based microservices, the Angular UI, and the associated Kubernetes and Istio resources to the GKE cluster. To deploy the platform, use the included bash deploy script, part5a_deploy_resources.sh. If anything fails and you want to remove the existing resources and re-deploy, without destroying the GKE cluster or Istio, you can use the part5b_delete_resources.sh delete script.

screen_shot_2019-03-09_at_6_01_29_pm

The deploy script deploys all the resources two Kubernetes Namespaces, dev and test. This will allow us to see how we can differentiate between Namespaces when using the observability tools.

Below, we see the Istio-related resources, which we just deployed. They include the Istio Gateway, four Istio VirtualService, and two Istio ServiceEntry resources.

screen_shot_2019-03-10_at_10_48_49_pm

Below, we see the platform’s Workloads (Kubernetes Deployment resources), running on the cluster. Here we see two Pods for each Workload, a total of 18 Pods, running in the dev Namespace. Each Pod contains both the deployed microservice or UI component, as well as a copy of Istio’s Envoy Proxy.

screen_shot_2019-03-09_at_6_12_59_pm

Below, we see the corresponding Kubernetes Service resources running in the dev Namespace.

screen_shot_2019-03-09_at_6_03_02_pm

Below, a similar view of the Deployment resources running in the test Namespace. Again, we have two Pods for each deployment with each Pod contains both the deployed microservice or UI component, as well as a copy of Istio’s Envoy Proxy.

screen_shot_2019-03-09_at_6_13_16_pm

Test the Platform

We do want to ensure the platform’s eight Go-based microservices and Angular UI are working properly, communicating with each other, and communicating with the external MongoDB Atlas and CloudAMQP RabbitMQ clusters. The easiest way to test the cluster is by viewing the Angular UI in a web browser.

screen_shot_2019-03-19_at_8_43_10_pm

The UI requires you to input the host domain of the Service A, the API’s edge service. Since you cannot use my subdomain, and the JavaScript code is running locally to your web browser, this option allows you to provide your own host domain. This is the same domain or domains you inserted into the two Istio VirtualService for the UI. This domain route your API calls to either the FQDN (fully qualified domain name) of the Service A Kubernetes Service running in the dev namespace, service-a.dev.svc.cluster.local, or the test Namespace, service-a.test.svc.cluster.local.

screen_shot_2019-03-17_at_12_02_22_pm.png

You can also use performance testing tools to load-test the platform. Many issues will not show up until the platform is under load. I recently starting using hey, a modern load generator tool, as a replacement for Apache Bench (ab), Unlike ab, hey supports HTTP/2 endpoints, which is required to test the platform on GKE with Istio. Below, I am running hey directly from Google Cloud Shell. The tool is simulating 25 concurrent users, generating a total of 1,000 HTTP/2-based GET requests to Service A.

screen_shot_2019-03-19_at_8_53_47_pm

Troubleshooting

If for some reason the UI fails to display, or the call from the UI to the API fails, and assuming all Kubernetes and Istio resources are running on the GKE cluster (all green), the most common explanation is usually a misconfiguration of the following resources:

  1. Your four Cloud DNS records are not correct. They are not pointing to the load balancer’s front-end IP address;
  2. You did not configure the four Kubernetes VirtualService resources with the correct subdomains;
  3. The GKE-based microservices cannot reach the external MongoDB Atlas and CloudAMQP RabbitMQ clusters. Likely, the Kubernetes Secret is constructed incorrectly, or the two ServiceEntry resources contain the wrong host information for those external clusters;

I suggest starting the troubleshooting by calling Service A, the API’s edge service, directly, using cURL or Postman. You should see a JSON response payload, similar to the following. This suggests the issue is with the UI, not the API.

screen_shot_2019-03-17_at_12_06_27_pm.png

Next, confirm that the four MongoDB databases were created for Service D, Service, F, Service, G, and Service H. Also, confirm that new documents are being written to the database’s collections.

screen_shot_2019-03-17_at_11_55_19_am

Next, confirm new the RabbitMQ queue was created, using the CloudAMQP RabbitMQ Management Console. Service D produces messages, which Service F consumes from the queue.

screen_shot_2019-03-09_at_6_22_08_pm

Lastly, review the Stackdriver logs to see if there are any obvious errors.

screen-shot-2019-03-08-at-4_44_03-pm

Part Two

In part two of this post, we will explore each observability tool, and see how they can help us manage our GKE cluster and the reference platform running in the cluster.

screen_shot_2019-03-09_at_11_38_34_pm

Since the cluster only takes minutes to fully create and deploy resources to, if you want to tear down the GKE cluster, run the part6_tear_down.sh script.

screen_shot_2019-03-10_at_10_58_55_pm.png

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

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3 Comments

Scaffolding Modern Web Applications

Scaffold Three Full-Stack Modern Web Application Examples using Yeoman with npm, Bower, and Gruntarticle background

Introduction

The capabilities of modern web applications have quickly matched and surpassed those of most traditional desktop applications. However, with the increase in capabilities, comes an increase in architectural complexity of web applications. To help deal with the increase in complexity, developers have benefitted from a plethora of popular support libraries, frameworks, API’s, and similar tooling. Examples of these include AngularJSReact.js, Play!Node.js, Express, npmYeoman, Bower, Grunt, and Gulp.

With so many choices, selecting an optimal toolset to construct a modern web application can be overwhelming. In this post, we will examine three distinct modern web application architectures, predominantly JavaScript-based. The post will discuss how to select and install the required tools, and how to use those tools to scaffold the applications. By the end of the post, we will have three working web applications, ready for further development.

Generators

The post’s examples use Yeoman generators. What is a generator? According to Wikipedia, Yeoman’s generator concept was inspired by Ruby on Rails. Using a generator’s blueprint, Yeoman scaffolds an application’s project directory and file structure, and installs required vendor libraries and dependencies. Yeoman generators usually run interactively, guiding the developer through a series of configuration questions. The configuration choices determine the project’s physical structure and components installed by Yeoman.

Generators are inherently opinionated. They dictate a particular application architecture they feel represents current best practices. However, good generators also allow developers to select from a range of architectural choices to meet the requirements of a developer’s environment. For example, a generator might allow Grunt or Gulp for task automation, or allow either Less or Sass for styling the UI. Similar to npm, RubyGems, Bower, Docker Hub, and Puppet Forge, Yeoman provides a searchable public repository. This allows developers to choose from a variety of generators to meet their specific needs.

Preparing the Development Environment

The examples in this post were built on Mac OS X. However, all the tools discussed in the post are available on the three major platforms, Linux, Mac, and Windows. Installation and configuration will vary.

An IDE is not required to scaffold the post’s application examples. However, for further development of the applications, I strongly recommend JetBrain’s WebStorm. According to Slant, WebStorm is a popular, highly rated IDE for building modern web applications. WebStorm is available on all three major platforms. A paid license is required, but well worth the reasonable investment based on the IDE’s rich feature set. WebStorm is integrated with many popular JavaScript frameworks. Additionally, there are hundreds of plug-ins available to extend WebStorm’s functionality.

Each of the post’s examples varies, architecturally. However, each also shares several common components, which we will install. They include:

npm
We will use npm (aka Node Package Manager), a leading server-side package manager, to manage the application’s server-side JavaScript dependencies.

Node.js
We will use Node.js, a JavaScript runtime, to power the JavaScript-based web application examples.

Bower
Similar to npm, Bower, is a popular client-side package manager. We will use Bower to manage the application’s client-side JavaScript dependencies.

Yeoman
We will use Yeoman, a leading web application scaffolding tool, to quickly build the frameworks for the example applications based on best practices and tooling.

Grunt
We will use Grunt, a leading JavaScript task runner, to automate common tasks such as minification, compilation, unit-testing, linting, and packaging of applications for deployment. At least two of the three examples also offer Gulp as an alternative.

Express
We will use Express, a Node.js web application framework that provides a robust set of features for web applications development, to support our example applications.

MongoDB
We will use MongoDB, a leading open-source NoSQL document database, for all three examples. For two of the examples, you can easily substitute alternate databases, such as MySQL, when configuring the application with Yeoman. The choice of database is of secondary importance in this post.

First install Node.js, which comes packaged with npm. Then, use npm to install Bower, Yeoman, and Grunt. Make sure you run the command to fix the permissions for npm. If permissions are set correctly, you should not have to use sudo with your npm commands.

The global mode option (-g) installs packages globally. Packages are usually installed globally, only if they are used as a command line tool, such as with Bower, Yeoman, and Grunt.

The easiest way to install Node.js and npm on OS X, is with Homebrew:

Alternatively, install Node and npm by downloading the package installer for Mac OS X (x64) from nodejs.org. If you are not a currently a Homebrew user, I suggest this route. At the time of this post, you have the choice of Node.js version v4.2.3 LTS or v5.1.1 Stable.

Fix the potential permission problem with npm, so sudo is not required:

Then, install Yeoman, Bower, and Grunt, globally:

Lastly, install MongoDB. Similar to Node, we can use Homebrew, or download and install MongoDB from the MongoDB website. Review this page for detailed installation and configuration instructions. To install MongoDB with Homebrew, we would issue the following commands:

Example #1: Server-Centric Express Application

For our first example, we will scaffold a server-centric JavaScript web application using Pete Cooper’s Express Generator (v2.9.2). According to the project’s GitHub site, the Express Generator is ‘An Expressjs generator for Yeoman, based on the express command line tool.’ I suggest reading the project’s documentation before continuing; it describes the generator’s functionality in greater detail.

To install, download the Express Generator with npm, and install with Yeoman, as follows:

As part of the Express Generator’s configuration process, Yeoman will ask a series of configuration questions. The Express generator offers several choices for scaffolding the application. For this example, we will choose the following options: MVC, Marko, Sass, MongoDB, and Grunt.

Express-Generator with Yeoman'

Using Express-Generator with Yeoman

For those not as familiar with developing full-stack JavaScript applications, some of the generator’s choices may be unfamiliar, such as view engines, css preprocessors, and build tools. For this example, we will select Marko, a highly regarded JavaScript templating engine (aka view engine), for the first application. You can compare different engines on Slant. For CSS preprocessors, you can also refer to Slant for a comparison of leading candidates. We will choose Sass.

Lastly, for a build tool (aka task runner) we will choose Grunt. Grunt and Gulp are the two most popular choices. Either is a proven tool for automation tasks such as minification, compilation, unit-testing, linting, and packaging applications for deployment.

As shown below, Yeoman creates a series of files and directories and installs JavaScript libraries with npm and Bower. Choices are based on best practices, as prescribed by the generator.

Default Express-Generator File Structure

Default Express-Generator File Structure

npm

Yeoman uses npm and bower to install the generator’s required packages. Based on our five configuration choices for the Express Generator, npm installed over 225 packages in the project’s local node_module directory. This includes primary and secondary npm package dependencies. For example, Marko, one of the choices, which npm installed, has 24 dependencies it requires. In turn, each of those packages may have more dependencies. You quickly see why npm, and other similar package managers, are invaluable to building and managing a modern web application. The npm dependencies are declared in the package.json file, in the project’s root directory.

Partial List of npm Packages Installed

Partial List of npm Packages Installed

We will still need to install a few more items. We chose Sass as an Express generator option. Sass requires Ruby, which comes preinstalled on Mac OS X. If you wish, you can upgrade your pre-installed version of Ruby with Homebrew, but it is not required. Sass is installed with RubyGems, a package manager for Ruby. To automate the Sass-related tasks with Grunt, we also need to install the Grunt plug-in for Sass, grunt-contrib-sass, using npm:

The Express Generator’s test are written in Mocha. Mocha is an asynchronous JavaScript test framework running on Node.js. The website suggests installing Mocha globally with npm. Mocha can be run from the command line:

Up and Running

Simply running the grunt command will start the default Generator-Express MVC application. While in development, I prefer to run Grunt with the debug option (grunt -d) and/or with the verbose option (grunt -v or grunt -dv). These options offer enhanced feedback, especially on which tasks are run by Grunt. Review the terminal output to make sure the application started properly.

Starting the Application with Grunt

Starting the Application with Grunt

To confirm the Express application started correctly, in a second terminal window, curl the application using curl -I localhost:3000. Easier yet, point your web browser to localhost:3000. You should see the following default web page.

Default Express-Generator Application

Default Express-Generator Application

Example #2: Full-Stack MEAN Application

In our second example, we will scaffold a true full-stack JavaScript web application using Tyler Henkel’s popular AngularJS Full-Stack Generator for Yeoman (v3.0.2). According to the project’s GitHub site, the generator is a ‘Yeoman generator for creating MEAN stack applications, using MongoDB, Express, AngularJS, and Node – lets you quickly set up a project following best practices.’ As with the earlier example, I suggest you read the project’s documentation before continuing.

To install theAngularJS Full-Stack Generator, download the with npm and install with Yeoman:

Similar to the Express example, Yeoman will ask a series of configuration questions. We will choose the following options: Jade, Less, ngRoute, Bootstrap, UI Bootstrap, and MongoDB with Mongoose. AngularJS, Express, and Grunt are installed by the generator, automatically. For the sake of brevity, we will not include other available options, including Babel for ES6, OAuth authentication, or socket.io.

AngularJS Full-Stack Generator Config Options

AngularJS Full-Stack Generator Config Options

PhantomJS

After generating the AngularJS Full-Stack project, I received errors regarding PhantomJS. According to several sources, this is not uncommon. The AngularJS Full-Stack project uses PhantomJS as the default browser for Karma, the popular test runner, designed by the AngularJS team. Although npm installed PhantomJS locally, as part of the project, Karma complained about missing the path to the PhantomJS binary. To eliminate the issue, I installed PhantomJS globally with npm. I then manually added the PhantomJS binary path to the $PATH environment variable:

To test Karma, with PhantomJS, run the grunt test command. This should result in error-free output, similar to the following.

Running "karma:unit" (karma) task

Running “karma:unit” (karma) task

Client/Server Architecture

Similar to the previous example, Yeoman creates a series of files and directories, and installs JavaScript packages on the server and client sides with npm and Bower.

AngularJS Full-Stack Generator File Structure

AngularJS Full-Stack Generator File Structure

Both the Express and AngularJS examples share several common files and directories. However, one major difference between the two is the client/server oriented directory structure of the AngularJS generator. Unlike the Express example, the AngularJS example has both a client and a server directory. The server-side of the application (aka back-end) is driven primarily by Express and Node. Mongoose serves as an interface between our application’s domain model and MongoDB, on the server-side. Also, on the server-side, Jade is used for HTML templating. The client-side of the application (aka front-end) is driven primarily by AngularJS. Twitter’s Bootstrap and Bootstrap UI offer a responsive web interface for our example application.

Full-Stack Server/Client File Structure

Full-Stack Server/Client File Structure

Up and Running

Running the grunt serve command will eventually start the default AngularJS Full-Stack application, after running a series of pre-defined Grunt tasks.

AngularJS Full-Stack App Starting with Grunt

AngularJS Full-Stack App Starting with Grunt

Review the terminal output to make sure the application started properly. You may see some warnings, suggesting the installation of several dependencies globally. You may also see warnings about dependency versions being outdated. Outdated versions are one of the challenges with generators that are not constantly kept refreshed and tested with the latest package dependencies. Warnings shouldn’t prevent the application from starting, only Errors.

AngularJS Full-Stack App Started with Grunt

AngularJS Full-Stack App Started with Grunt

To confirm the application started, in a second terminal window, curl the application using curl -I localhost:9000. Easier yet, point your web browser at localhost:9000. The default web page for the AngularJS Full-Stack web application is much more elaborate than the previous Express example. This is thanks to the Bootstrap and AngularJS client-side components.

AngularJS Full-Stack App Running in Browser

AngularJS Full-Stack App Running in Browser

Additional Generator Features

The AngularJS Full-Stack generator is capable of generating more than just the default application project. The AngularJS Full-Stack generator contains a set of generators. Beyond generating the basic application framework, you may use the generator to create boilerplate code for AngularJS and Node.js components for endpoints, services, routes, models, controllers, directives, and filters. The generators also provide the ability to prepare your application for deployment to OpenStack and Heroku.

The best place to review available options for the generators is on the GitHub sites. You can display a high-level list of the generator’s features using the yo --help command. Below are the three generators used in this post.

Below, is an example of generating additional application components using the AngularJS Full-Stack generator. First scaffold a server-side Express RESTful API endpoint, called ‘user’. The single command generates a server-side directory structure and several boilerplate files, including an Express model, controller, and router, and Mocha tests.

List of Installed Yeoman Generators

List of Installed Yeoman Generators

Next, generate a client-side AngularJS service, which connects to the server-side, Express RESTful ‘user’ endpoint above. The command creates a boilerplate AngularJS service and Mocha test. Lastly, create an AngularJS route. This generator command creates a boilerplate AngularJS route and controller, Mocha test, Jade view template, and less file.

AngularJS Full-Stack Component Generators

AngularJS Full-Stack Component Generators

Example #3: Java Hipster Application

In the third and last example, we will scaffold another full-stack web application. However, this time, we will use a generator that relies on Java EE as the primary development platform on the server-side, as opposed to JavaScript. JavaScript will be relegated largely to the client-side.

Again, we will use a Yeoman generator, JHipster, built by Julien Dubois and team, to scaffold the application (v2.25.0). According to the project’s GitHub site, JHipster uses a robust server-side Java EE stack with Spring Boot and Maven. JHipster’s mobile-first front-end is enabled with AngularJS and Bootstrap. Being the most complex of the three examples, it’s important to review the project documentation.

JHipster offers three ways to install the application, which are 1) locally, 2) a Docker container, or 3) a Vagrant VM. We will install the application framework locally as not to introduce additional complexity. To install the generator, download the JHipster generator with npm, and install with Yeoman:

Again, Yeoman will ask a series of configuration questions on behalf of JHipster. For this example, we will choose the following options: token-based authentication, MongoDB, Maven, Grunt, LibSass (Sass), and Gatling for testing. AngularJS and Bootstrap are installed automatically. We have chosen not to include other configuration options in this example, such as Angular Translate, WebSockets, and clustered HTTP sessions.

Default JHipster Generator Options

Default JHipster Generator Options

Once Yeoman finishes scaffolding the application, you should see the following output.

JHipster Generator Install Complete

JHipster Generator Install Complete

Maven Project Structure

The file and directory structure of JHipster is very different from the previous two examples. The first two example’s project structure is typical of a JavaScript project. In contrast, the JHipster example’s structure is more typical of a Maven-based Java project. In the JHipster project, the client-side JavaScript files are in the /src/main/webapp/ directory. The presence of the webapp directory is based on part of the project’s reliance on the Spring MVC web framework. Additionally, npm has loaded required server-side JavaScript packages into the node_modules directory, in the project’s root directory.

Default JHipster File Structure

Default JHipster File Structure

Up and Running

Running the mvn command will start the default JHipster application. The URL for the JHipster application is included in the terminal output.

JHipster Application Running with Maven

JHipster Application Running with Maven

To confirm the application has started, curl the application in a second terminal window, using curl -I localhost:8080. Easier yet, point your web browser to localhost:8080. Again, thanks to Bootstrap and AngularJS, the application presents a rich client UI.

Default JHipster Application Running

Default JHipster Application Running

Conclusion

The post’s examples represent a narrow sampling of available modern web application stacks, which can be easily scaffolded with generators. The JavaScript space continues to evolve rapidly. Even within the realm of JavaScript-based solutions, we didn’t examine several other popular frameworks, such as Meteor, FaceBook’s ReactJS, Ember, Backbone, and Polymer. They are all worth exploring, along with the hundreds of popular supporting frameworks, libraries, and API’s.

Useful Links

  • Post: JavaScript Frameworks: The Best 10 for Modern Web Apps (link)
  • Post: Best Web Frameworks (link)
  • Post: State Of Web Development 2014 (link)
  • YouTube: Modern Front-end Engineering (link)
  • YouTube: WebStorm – Things You Probably Didn’t Know (link)
  • Website: MEAN Stack (link)
  • Website: Full Stack Python (link)
  • Post: Best Full Stack Web Framework (link)

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Calling Third-Party HTTP-based RESTful APIs from the MEAN Stack

Example of calling Google’s Custom Search http-based RESTful API, using Node.js with Express and Request, from a MEAN.io-generated MEAN stack application. CustomSearchExample

Introduction

Most MEAN stack articles and tutorials demonstrate how AngularJS, on the client-side, calls Node.js with Express on the server-side, via a http-based RESTful API. In turn, on the server-side, Node.js with Express, and often a ODM like Mongoose, calls MongoDB. Below is a simple, high-level sequence diagram of a typical MEAN stack request/response data flow from the client to the server to the database, and back.

Typical MEAN Stack Request/Response Data Flow

Typical MEAN Stack Request/Response Data Flow

However in many situations, applications don’t only call into their own application stack. Applications often call third-party http-based RESTful APIs, including social networks, cloud providers, e-commerce, and news aggregators. Twitter’s REST API and Facebook Graph API are two popular social network examples. Within larger enterprise environments, applications call multiple internal applications. For example, an online retailer’s storefront application accesses their own inventory control system via RESTful URIs. This is the same RESTful API the retailer’s authorized resellers use to interact with the retailer’s own inventory control system.

Calling APIs from the MEAN Stack

From the Client-Side
There are two ways to call third-party http-based APIs from a MEAN stack application. The first approach is calling directly from the client-side. AngularJS calls the third-party API, directly. All logic is on the client-side, instead of on the server-side. Node.js and Express are not involved in the process. The approach requires less moving parts than the next approach, but is less secure and places more demand on the client to handle the application’s business logic. Below is a simple, high-level sequence diagram demonstrating a request/response data flow from AngularJS on the client-side to a third-party API, and back.

Example AngularJS/Third-Party API Request/Response Data Flow

Example AngularJS/Third-Party API Request/Response Data Flow

From the Server-Side
The second approach, using Node.js and Express, on the servers-side, is slightly more complex. However, this approach is also more architecturally sound, scalable, secure, and performant. AngularJS, on the client side, calls Node.js with Express, on the server-side. Node.js with Express then calls the service and pass the response back to the client-side, to AngularJS. Below is a simple, high-level sequence diagram demonstrating a request/response data flow from the client-side to the server-side, to a third-party API, and back.

Example Node.js/Third-Party API Request/Response Data Flow

Example Node.js/Third-Party API Request/Response Data Flow

Example

MEAN.io
Using the MEAN.io ‘FullStack JS Development’ framework, I have created a basic example of calling Google’s Custom Search http-based RESTful API, from Node.js with Express and Request. MEAN.io provides an ready-made MEAN stack boilerplate framework/generator, saving a lot of coding time. Irregardless of the generator or framework you choose, you would architect this example the same.

Google Custom Search API
Google provides the Custom Search API as part of their Custom Search, one of many API’s, available through the Google Developers portal. According to Google, “the JSON/Atom Custom Search API lets you develop websites and applications to retrieve and display search results from Google Custom Search programmatically. With this API, you can use RESTful requests to get either web search or image search results in JSON or Atom format.

Google APIs Explorer - Exploring Custom Search API

Google APIs Explorer – Exploring Custom Search API

In order to use the Custom Search API, you will need to first create a Google account, API project, API keyCustom Search Engine (CSE), and CSE ID, through Google’s Developers Console. If you have previously worked with Google, FaceBook, or Twitter APIs, creating an API project, CSE, API key, and CSE ID, if very similar.

Google Custom Search - Your Search Engine ID

Google Custom Search – Your Search Engine ID

Like most of Google’s APIs, the Custom Search API pricing and quotas depend on the engine’s edition. You have a choice of two engines. According to Google, the free Custom Search Engine provides 100 search queries per day for free. If you need more, you may sign up for billing in the Developers Console. Additional requests cost $5 per 1000 queries, up to 10k queries per day. The limit of 100 is more than enough for this demonstration.

Installing and Configuring the Project

All the code for this project is available on GitHub at /meanio-custom-search. Before continuing, make sure you have the prerequisite software installed – GitNode.js with npm, and MongoDB. To install the GitHub project, follow these commands:

git clone https://github.com/garystafford/meanio-custom-search.git
cd meanio-custom-search
npm install

Alternatively, if you want to code the project yourself, these are the commands I used to set up the base MEAN.io framework, and create ‘search‘ package:

sudo npm install -g mean-cli
mean init meanio-custom-search
cd meanio-custom-search
npm install
mean package search

After creating your own CSE ID and API key, create two environmental variables, GOOGLE_CSE_ID and GOOGLE_API_KEY, to hold the values.

echo "export GOOGLE_API_KEY=<YOUR_API_KEY_HERE>" >> ~/.bashrc
echo "export GOOGLE_CSE_ID=<YOUR_CSE_ID_HERE>"   >> ~/.bashrc

The code is run from a terminal prompt with the grunt command. Then, in the browser, go to http://localhost:3000. Once on the main home page, you can navigate to the ‘Search Example’ page, and input a search term, such as ‘MEAN Stack’. All the instructions on the MEAN.io Github site, apply to this project.

The Project’s Architecture

According to MEAN.io, everything in mean.io is a ‘package’. When extending mean with custom functionality, you create a new ‘package’. In this case, I have created a ‘search’ package, with the command above, ‘mean package search‘. Below is the basic file structure of the ‘search‘ package, within the overall MEAN.io project framework. The ‘public‘ folder contains all the client-side, AngularJS code. The ‘server‘ folder contains all the server-side, Node.js/Express/Request code. Note that each ‘package’ also has its own ‘package.json‘ npm file and ‘bower.json‘ Bower file.

Folder Structure of Search Package with Callouts

The simple, high-level sequence diagram below shows the flow of the custom search request from the ‘Search Example’ view to the Google Custom Search API. The diagram also shows the response from the Google Custom Search API all the way back up the MEAN stack to the client-side view.

High-Level Custom Search API Request/Response Data Flow

High-Level Custom Search API Request/Response Data Flow

Client-Side Request/Response
If you view the network traffic in your web browser, you will see a RESTful URI call is made between AngularJS’ service factory, on the client-side, and Node.js with Express, on the server-side. The RESTful endpoint, called with $http.jsonp(), will be similar to: http://localhost:3000/customsearch/MEAN.io/10?callback=angular.callbacks._0. In actuality, the callback parameter name, the AngularJS service factory, is ‘JSON_CALLBACK‘. This is replaced by AngularJS with an incremented ‘angular.callbacks._X‘ parameter name, making the response callback name incremental and unique.

The response returned to AngularJS from Node.js is a sub-set of full response from Google’s Custom Search API. Only the search results items and a ‘200’ status code are returned to AngularJS as JavaScript, JSONP wrapped in a callback. Below is a sample response, truncated to just a single search result. I have highlighted the four fields that are displayed in the ‘Search Example’ view, using AngularJS’ ng-repeat directive.

/**/
typeof angular.callbacks._0 === 'function' && angular.callbacks._0({
    "statusCode": 200,
    "items"     : [{
        "kind"            : "customsearch#result",
        "title"           : "MEAN.IO - MongoDB, Express, Angularjs Node.js powered fullstack ...",
        "htmlTitle"       : "<b>MEAN</b>.<b>IO</b> - MongoDB, Express, Angularjs Node.js powered fullstack <b>...</b>",
        "link"            : "http://mean.io/",
        "displayLink"     : "mean.io",
        "snippet"         : "MEAN - MongoDB, ExpressJS, AngularJS, NodeJS. based fullstack js framework.",
        "htmlSnippet"     : "<b>MEAN</b> - MongoDB, ExpressJS, AngularJS, NodeJS. based fullstack js framework.",
        "cacheId"         : "_CZQNNP6VMEJ",
        "formattedUrl"    : "mean.io/",
        "htmlFormattedUrl": "<b>mean</b>.<b>io</b>/",
        "pagemap"         : {
            "cse_image"    : [{"src": "http://i.ytimg.com/vi/oUtWtSF_VNY/hqdefault.jpg"}],
            "cse_thumbnail": [{
                "width" : "259",
                "height": "194",
                "src"   : "https://encrypted-tbn2.gstatic.com/images?q=tbn:ANd9GcSIVwPo7OcW9u_b3P3DGxv8M7rKifGZITi1Bhmpy10_I2tlUqjRUVVUBKNG"
            }],
            "metatags"     : [{
                "viewport"      : "width=1024",
                "fb:app_id"     : "APP_ID",
                "og:title"      : "MEAN.IO - MongoDB, Express, Angularjs Node.js powered fullstack web framework - MEAN.IO - MongoDB, Express, Angularjs Node.js powered fullstack web framework",
                "og:description": "MEAN  MongoDB, ExpressJS, AngularJS, NodeJS.",
                "og:type"       : "website",
                "og:url"        : "APP_URL",
                "og:image"      : "APP_LOGO",
                "og:site_name"  : "MEAN.IO",
                "fb:admins"     : "APP_ADMIN"
            }]
        }
    }]
});

Server-Side Request/Response
On the server-side, Node.js with Express and Request, calls the Google Custom Search API via a RESTful URI. The RESTful URI, called with request.get(), will be similar to: https://www.googleapis.com/customsearch/v1?cx=ed026i714398r53510g2ja1ru6741h:73&q=MEAN.io&num=10&key=jtHeNjIAtSa1NaWJzmVvBC7qoubrRSyIAmVJjpQu. Note the URI contains both the your CSE ID and API key (not my real ones, of course). The JSON response from Google’s Custom Search API has other data, which is not necessary to display the results.

Shown below is a sample response with a single search result. Like the URI above, the response from Google has your Custom Search Engine ID. Your CSE ID and API key should both be considered confidential and not visible to the client. The CSE ID could be easily intercepted in both the URI and the response object, and used without your authorization. Google has a page that suggests methods to keep your keys secure.

{
  kind: "customsearch#search",
  url: {
    type: "application/json",
    template: "https://www.googleapis.com/customsearch/v1?q={searchTerms}&num={count?}&start={startIndex?}&lr={language?}&safe={safe?}&cx={cx?}&cref={cref?}&sort={sort?}&filter={filter?}&gl={gl?}&cr={cr?}&googlehost={googleHost?}&c2coff={disableCnTwTranslation?}&hq={hq?}&hl={hl?}&siteSearch={siteSearch?}&siteSearchFilter={siteSearchFilter?}&exactTerms={exactTerms?}&excludeTerms={excludeTerms?}&linkSite={linkSite?}&orTerms={orTerms?}&relatedSite={relatedSite?}&dateRestrict={dateRestrict?}&lowRange={lowRange?}&highRange={highRange?}&searchType={searchType}&fileType={fileType?}&rights={rights?}&imgSize={imgSize?}&imgType={imgType?}&imgColorType={imgColorType?}&imgDominantColor={imgDominantColor?}&alt=json"
  },
  queries: {
    nextPage: [
      {
        title: "Google Custom Search - MEAN.io",
        totalResults: "12100000",
        searchTerms: "MEAN.io",
        count: 10,
        startIndex: 11,
        inputEncoding: "utf8",
        outputEncoding: "utf8",
        safe: "off",
        cx: "ed026i714398r53510g2ja1ru6741h:73"
      }
    ],
    request: [
      {
        title: "Google Custom Search - MEAN.io",
        totalResults: "12100000",
        searchTerms: "MEAN.io",
        count: 10,
        startIndex: 1,
        inputEncoding: "utf8",
        outputEncoding: "utf8",
        safe: "off",
        cx: "ed026i714398r53510g2ja1ru6741h:73"
      }
    ]
  },
  context: {
    title: "my_search_engine"
  },
  searchInformation: {
    searchTime: 0.237431,
    formattedSearchTime: "0.24",
    totalResults: "12100000",
    formattedTotalResults: "12,100,000"
  },
  items: [
    {
      kind: "customsearch#result",
      title: "MEAN.IO - MongoDB, Express, Angularjs Node.js powered fullstack ...",
      htmlTitle: "<b>MEAN</b>.<b>IO</b> - MongoDB, Express, Angularjs Node.js powered fullstack <b>...</b>",
      link: "http://mean.io/",
      displayLink: "mean.io",
      snippet: "MEAN - MongoDB, ExpressJS, AngularJS, NodeJS. based fullstack js framework.",
      htmlSnippet: "<b>MEAN</b> - MongoDB, ExpressJS, AngularJS, NodeJS. based fullstack js framework.",
      cacheId: "_CZQNNP6VMEJ",
      formattedUrl: "mean.io/",
      htmlFormattedUrl: "<b>mean</b>.<b>io</b>/",
      pagemap: {
        cse_image: [
          {
            src: "http://i.ytimg.com/vi/oUtWtSF_VNY/mqdefault.jpg"
          }
        ],
        cse_thumbnail: [
          {
            width: "256",
            height: "144",
            src: "https://encrypted-tbn1.gstatic.com/images?q=tbn:ANd9GcTXm3rYwGdWs9Cx3s5VvooATKlgtrVZoP83hxfAOjGvsRMqLpMKuycVl_sF"
          }
        ],
        metatags: [
          {
            viewport: "width=1024",
            fb:app_id: "APP_ID",
            og:title: "MEAN.IO - MongoDB, Express, Angularjs Node.js powered fullstack web framework - MEAN.IO - MongoDB, Express, Angularjs Node.js powered fullstack web framework",
            og:description: "MEAN  MongoDB, ExpressJS, AngularJS, NodeJS.",
            og:type: "website",
            og:url: "APP_URL",
            og:image: "APP_LOGO",
            og:site_name: "MEAN.IO",
            fb:admins: "APP_ADMIN"
          }
        ]
      }
    }
  ]
}

The best way to understand the project’s sample code is to clone the GitHub repo, and explore the files directly associated with the search, starting in the ‘packages/custom/search‘ subdirectory.

Helpful Links

Learn REST: A RESTful Tutorial
Using an AngularJS Factory to Interact with a RESTful Service
Google APIs Client Library for JavaScript (Beta)
REST-ful URI design
Creating a CRUD App in Minutes with Angular’s $resource

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Preventing Race Conditions Between Containers in ‘Dockerized’ MEAN Applications

Eliminate potential race conditions between the MongoDB data Docker container and the Node.js web-application container in a ‘Dockerized’ MEAN application.

MEAN.JS Dockerized

Introduction

The MEAN stack is a has gained enormous popularity as a reliable and scalable full-stack JavaScript solution. MEAN web application’s have four main components, MongoDB, Express, AngularJS, and Node.js. MEAN web-applications often includes other components, such as Mongoose, Passport, Twitter Bootstrap, Yoeman, Grunt or Gulp, and Bower. The two most popular ready-made MEAN application templates are MEAN.io from Linnovate, and MEAN.JS. Both of these offer a ready-made application framework for building MEAN applications.

Docker has also gained enormous popularity. According to Docker, Docker is an open platform, which enables developers and sysadmins apps to be quickly assembled from components. ‘Dockerized’ apps are completely portable and can run anywhere.

Docker is an ideal solution for MEAN applications. Being a full-stack JavaScript solution, MEAN applications are based on a multi-tier architecture. The MEAN application’s data tier contains the MongoDB noSQL database. The application tier (logic tier) contains Node.js and Express. The application tier can also contain other components, such as Mongoose, a Node.js Object Document Mapper (ODM) for MongoDB, and Passport, an authentication middleware for Node.js. Lastly, the presentation tier (front end) has client-side tools, such as AngularJS and Twitter Bootstrap.

Using Docker, we can ‘Dockerize’ or containerize each tier of a MEAN application, mirroring the physical architecture we would deploy a MEAN application to, in a Production environment. Just as we would always run a separate database server or servers for MongoDB, we can isolate MongoDB into a Docker container. Likewise, we can isolate the Node.js web server, along with the rest of the components (Mongoose, Express, Passport) on the application and presentation tiers, into a Docker container. We can easily add more containers, for more functionality, such as load-balancing and reverse-proxies (nginx), and caching (Redis and Memcached).

The MEAN.JS project has been very progressive in implementing Docker, to offer a more realistic environment for development and testing. An additional tool that the MEAN.JS project has implemented, to automate the creation of multiple Docker containers, is Fig. The tool, Fig, provides quick, automated creation of multiple, linked Docker containers.

Using Docker and Fig, a Developer can pull down ready-made base containers from Docker Hub, configure the containers as part of a multi-tier application environment, deploy our MEAN application components to the containers, and start the applications, all with a short list of commands.

MEAN.JS Dockerized
Note, I said development and test, not production. To extend Docker and Fig to production, you can use tools such as Flocker. Flocker, by ClusterHQ, can scale the single-host Fig environment to multiple containers on multiple machines (hosts).

MEAN Dockerized

Race Conditions

Docker containers have a very fast start-up time, compared to other technologies, such as VMs (virtual machines). However, based on their contents, containers take varying amounts of time to fully start-up. In most multi-tier applications, there is a required start-up sequence for components (tiers, servers, applications). For example, in a database-driven application, like a MEAN application, you should make sure the MongoDB database server is up and running, before starting the application. Although this is obvious, it becomes harder to guarantee the order in which components will start-up, when you leverage an asynchronous, automated, continuous delivery solution like Docker with Fig.

When component dependencies are not met because another container is not fully started, we can refer to this as race condition. I have found with most multi-container MEAN application, the slower starting MongoDB data container prevents the quicker-starting Node.js web-application container from properly starting the MEAN application. In other words, the application crashes.

Fixing Race Conditions with MEAN.JS Applications

In order to eliminate race conditions, we need to script our start-up sequence to guarantee the order in which components will start, ensuring the overall application starts correctly. Specifically in this post, we will eliminate the potential race condition between the MongoDB data container (db_1) and the Node.js web-application container (web_1). At the same time, we will fix a small error with the existing MEAN.JS project, that prevents proper start-up of the ‘dockerized’ container MEAN.JS application.

Race Condition with Docker

 

Download and Build MEAN.JS App

Clone the meanjs/mean repository, and install npm and bower packages.

git clone https://github.com/meanjs/mean.git
cd mean
npm install
bower install

Modify MEAN.JS App

  1. Add fig_start.sh start-up script to root of mean project.
  2. Modify the Dockerfile, replace CMD["grunt"] with CMD /bin/sh /home/mean/wait_mongo_start.sh
  3. Optional, add wait_mongo_start.sh clean-up script to root of mean project.

Fix Existing Issue with MEAN.JS App When Using Docker and Fig

The existing MEAN.JS application references localhost in the development configuration (config/env/development.js). The development configuration is the one used by the MEAN.JS application, at start-up. The MongoDB data container (db_1) is not running on localhost, it is running on a IP address, assigned my Docker. To discover the IP address, we must reference an environment variable (DB_1_PORT_27017_TCP_ADDR), created by Docker, within the Node.js web-application container (web_1).

  1. Modify the config/env/development.js file, add var DB_HOST = process.env.DB_1_PORT_27017_TCP_ADDR || 'localhost';
  2. Modify the config/env/development.js file, change db: 'mongodb://localhost/mean-dev', to db: 'mongodb://' + DB_HOST + '/mean-dev',

Start the Application

Start the application using Fig commands or using the clean-up/start-up script (sh fig_start.sh).

  1. Run fig build && fig up
  2. Alternately, run sh fig_start.sh

The Details…

The CMD command is the last step in the Dockerfile.The CMD command sets the wait_mongo_start.sh script to execute in the Node.js web-application container (web_1) when the container starts. This script prevents the grunt command from running, until nc (or netcat) succeeds at connecting to the IP address and port of mongod, the primary daemon process for the MongoDB system, on the MongoDB data container (db_1). The script uses a 3-second polling interval, which can be modified if necessary.

#!/bin/sh

polling_interval=3

# optional, view db_1 container-related env vars
#env | grep DB_1 | sort

echo "wait for mongo to start first..."

# wait until mongo is running in db_1 container
until nc -z $DB_1_PORT_27017_TCP_ADDR $DB_1_PORT_27017_TCP_PORT
do
 echo "waiting for $polling_interval seconds..."
 sleep $polling_interval
done

# start node app
grunt

The environment variables referenced in the script are created in the Node.js web-application container (web_1), automatically, by Docker. They are shown in the screen grab, below. You can discover these variables by uncommenting the env | grep DB_1 | sort line, above.

Docker Environment Variables Relating to DB_1

Docker Environment Variables Relating to DB_1

The Dockerfile modification is highlighted below.

#FROM dockerfile/nodejs

MAINTAINER Matthias Luebken, matthias@catalyst-zero.com

WORKDIR /home/mean

# Install Mean.JS Prerequisites
RUN npm install -g grunt-cli
RUN npm install -g bower

# Install Mean.JS packages
ADD package.json /home/mean/package.json
RUN npm install

# Manually trigger bower. Why doesn't this work via npm install?
ADD .bowerrc /home/mean/.bowerrc
ADD bower.json /home/mean/bower.json
RUN bower install --config.interactive=false --allow-root

# Make everything available for start
ADD . /home/mean

# Currently only works for development
ENV NODE_ENV development

# Port 3000 for server
# Port 35729 for livereload
EXPOSE 3000 35729

CMD /bin/sh /home/mean/wait_mongo_start.sh

The config/env/development.js modifications are highlighted below (abridged code).

'use strict';

// used when building application using fig and Docker
var DB_HOST = process.env.DB_1_PORT_27017_TCP_ADDR || 'localhost';

module.exports = {
	db: 'mongodb://' + DB_HOST + '/mean-dev',
	log: {
		// Can specify one of 'combined', 'common', 'dev', 'short', 'tiny'
		format: 'dev',
		// Stream defaults to process.stdout
		// Uncomment to enable logging to a log on the file system
		options: {
			//stream: 'access.log'
		}
	},
        ...

The fig_start.sh file is optional and not part of the solution for the race condition. Instead of repeating multiple commands, I prefer running a single script, which can execute the commands, consistently. Note, commands in this script remove ALL ‘Exited’ containers and untagged (<none>) images.

#!/bin/sh

# remove all exited containers
echo "Removing all 'Exited' containers..."
docker rm -f $(docker ps --filter 'status=Exited' -a) > /dev/null 2>&1

# remove all  images
echo "Removing all untagged images..."
docker rmi $(docker images | grep "^" | awk "{print $3}") > /dev/null 2>&1

# build and start containers with fig
fig build && fig up

MEAN Application Start-Up Screen Grabs

Below are screen grabs showing the MEAN.JS application starting up, both before and after the changes were implemented.

Start Script Cleaning Up Docker Images and Containers and Running Fig

Start Script Cleaning Up Docker Images and Containers and Running Fig

MongoDB Cannot Connect on localhost

MongoDB Cannot Connect on localhost

MEAN Application Waiting for MongoDB to Start, Currently at 70%...

MEAN Application Waiting for MongoDB to Start, Currently at 70%…

Connected to MongoDB on Correct IP Address and Grunt Running

Connected to MongoDB on Correct IP Address and Grunt Running

MEAN.JS Docker Containers Created

MEAN.JS Docker Containers Created

MEAN Application Successfully Running in Docker Containers

MEAN Application Successfully Running in Docker Containers

New Article Created with MEAN Application

New Article Created with MEAN Application

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Install Latest Node.js and npm in a Docker Container

Install the latest versions of Node.js and npm, into a Docker container, with or without the need for root access. Easily update both applications to the latest versions.

Install and Confirm Node and npm

Ubuntu and Node

Recently, I was setting up a new development laptop with Ubuntu 14.10 (Utopic Unicorn). As part of the setup, I needed to install all the several development tools, including Node.js and npm. Researching the current recommendations for installing Node.js and npm on Ubuntu, I found using the traditional ‘apt-get‘ command does not always install the latest versions of either application. Additionally, ‘apt-get’ makes updating those versions difficult.

After a lot of investigation, I created three different snippets of code to install the latest copies of Node.js and npm. Some of my code came from Isaac Z. Schlueter‘s series of installations Gists, and a post on StackOverflow by Pascal Hartig. Joyant and others recommended Isaac’s Gists for installing earlier versions of Node.js and npm. Other code was found in posts by DigitalOcean. Versions are as follows:

  • Version 1: using ‘apt-get install’
  • Version 2: using curl, make, and npmjs.org’s install script
  • Version 3: version 2 without requiring ‘sudo’ to use npm*

*There is some debate on the use of ‘sudo’ with some earlier versions of npm. It appears not to be recommended with the latest versions of npm.

Docker

Docker containers and virtual machines (VM) are ideal platforms for developing and testing applications, locally. I often create a Docker container or VirtualBox VM, to install and test new scripts, before running them within our software environments. To test this code, I created three separate Docker containers, based on the official 14.04 Ubuntu base image, located on Docker Hub. I then executed each version of code within a container. After installation testing, I chose version 2 for my laptop.

Displaying Docker Ubuntu Image and Containers

Docker Ubuntu Image and Containers

GitHub Gists

The three versions of install scripts on gist.github.com, perform the following tasks:

  • Creates Docker container
  • Updates Ubuntu system packages within container
  • Creates new ‘testuser’ account within container (‘testuser’)
  • Installs required software to install Node.js, if necessary (curl, make, etc.)
  • Installs Node.js and npm
  • Installs some common full-stack JavaScript npm packages
  • Verifies installation locations and contents correct

Running Code

Installing Node, npm, and New User Account

Installing Node, npm, and New User Account

Installing and Verifying npm Packages

Installing and Verifying npm Packages

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Data-Driven Forms with AngularJS’s Two-Way Data Binding and Custom Directives

Use the two-way data binding and custom directives features of AngularJS to develop data-driven, interactive forms.

Introduction

AngularJS has exploded on to the web-application development scene. Since being introduced in 2009, AngularJS’s use has grown exponentially. Its wide range of features and ease of use make it an ideal tool for rapidly developing modern web-applications. Combined with other modern JavaScript tools, such as Node, Express, Twitter Bootstrap, Yeoman, and NoSQL databases such as MongoDB, AngularJS developers can create robust, full-stack JavaScript applications.

A primary feature of AngularJS is two-way data binding. According to AngularJS’s website, ‘data-binding is the automatic synchronization of data between the model and view. The way that Angular implements data-binding lets you treat the model as the single-source-of-truth in your application. The view is a projection of the model at all times. When the model changes, the view reflects the change, and vice versa.‘ In the past, developers spent much of their coding time wiring up UI components to the application’s data model. AngularJS has greatly simplified this process.

Another key feature of AngularJS are directives. At a high level, according to AngularJS’ site, ‘directives are markers on a DOM element (such as an attribute, element name, comment or CSS class) that tell AngularJS’s HTML compiler to attach a specified behavior to that DOM element or even transform the DOM element and its children.‘ AngularJS provides many built-in directives, including ngModel, ngBind, ngInclude, ngRepeat, and ngChange. These directives are the building blocks of an AngularJS application. We will use many of these built-in directives in this post.

In addition to built-in directives, AngularJS allows us to create custom directives. Custom directives are a powerful feature, allowing us to encapsulate our own reusable DOM manipulation functionality.

The Sample Project

There is an infinite variety of web-based forms (‘electronic forms’). We interact with web-based forms at work, at home, and at school. Forms serve the primary purpose of collecting data user. Web-based forms allow us to order products and services over the internet, file our taxes, manage our benefits at work, track our time, and take online classes.

Tests or quizzes are a perfect example of web-based forms to demonstrate AngularJS’s many strengths, including data-binding and custom directives. In this post, we will create a series of interactive quizzes on the theme of AngularJS – sort of a learning opportunity inside a learning opportunity. Quizzes often contain several common types of question/answer formats, including true-false, multiple-choice, and multiple-correct, ordering, matching, short-answer, essay, and so forth. These question/answer formats take advantage of all the HTML form elements, including radio buttons, check-boxes, text fields, drop-down lists, list boxes, and text areas. We will build the quizzes from static JSON data files, and using AngularJS’s services, controllers, routes, views, templates, directives, and custom directives.

In the first example, we will use AngularJS’s factory service, controller, partial templates, view, routing, and built-in directive features to read JSON data from a file, and display and validate a basic true-false quiz. In the second example, we will expand our true-false quiz to contain additional types of questions, including multiple-choice and multiple-correct. For the advanced quiz, we will make use of use custom directives and partial view templates. These two new features will allow us to increase the quizzes complexity without substantially increasing the complexity of code we need to write.

Installing and Configuring the Project

This post’s project is available on GitHub. The easiest way to obtain all the source code, is to clone the project with Git. Once you have cloned the project, don’t forget to install the npm and bower packages. All commands are shown below. The minimum requirements for the project, are to have Bower, Grunt, npm, and Git installed.

git clone https://github.com/garystafford/angular-quiz.git
cd angular-quiz
npm install
bower install

Alternately, if you are experienced building JavaScript applications with the scaffolding tool, yo, you can create a new project and recreate the code yourself. To use generator-angular’s code generators, you will need yo installed, in addition to Bower, Grunt, npm, and Git. Since this post’s project is based on the Yeoman’s generator-angular, you can use npm to install Yeoman’s generator-angular. Afterwards, using generator-angular’s available code generators, you can easily reproduce the post’s basic project structure.

npm install -g generator-angular

# Use generator-angular code generators to create project components
# Instructions here: https://github.com/yeoman/generator-angular
mkdir quiz-app && cd $_
yo angular quiz
yo angular:route quizAdvanced
yo angular:factory quizAdvancedFactory
yo angular:directive quizTrueFalseDirective
Using yo with generator-angular to Set-up a New Application

Using yo with generator-angular to Set-up a New Application

Using yo with generator-angular to Create New Components

Using yo with generator-angular to Create New Components

If you used the generator-angular code generator to create the project yourself, using the above instructions, your module will be called ‘quizApp’. The application name, found in the ‘package.json’ and ‘bower.json’ files, will be ‘quiz’. I changed my project’s module and app names to be more descriptive, along with the names of the routes, factories, directives, and other components. They will also vary slightly using the code generators.

Also, if you used the generator-angular code generator to create the project yourself, you may need to install a few additional npm and bower packages, not part of generator-angular project, to reproduce this post’s project, exactly.

Project Structure

The project structure follows the generator-angular format. Most core application files are kept in the ‘app’ folder. This post’s project has added the ‘app/data’ folder, which holds the quiz data, and the ‘app/scripts/partials’, which holds the partial view templates for the custom directives (explained later).

Project View from WebStorm 8

Project View from WebStorm 8

Starting the Project

The project is started using the ‘grunt serve‘ command. Using the grunt server, the project be hosted on ‘localhost’, port 9000, by default. This can be changed to a specific hostname or IP address by editing the ‘Gruntfile.js’ file’s ‘connect‘ task.

Testing the Project

There are some basic tests created using the Karma, Test Runner for JavaScript. These tests are run using the ‘grunt test‘ command. Test are set to run on port 8092, using the PhantomJS web browser. PhantomJS, if you’re not familiar, is a headless WebKit scriptable with a JavaScript API. PhantomJS is ideal for use with Continuous Integration Servers, such as TravisCI. If you do not have PhantomJS installed, and plan to run the tests, change the ‘browser‘ property in the ‘karma.conf.js’ file, located in the project’s root directory. Chrome is a good alternative for local testing. Test results for this GitHub project can be reviewed on TravisCI.

Creating a complete set of unit tests for the advanced quiz proved challenging based on its nested, partial view templates, described in the Advanced Quiz section. I may add a more complete set of unit test in the future.

Basic Quiz

The first quiz is a six-question, basic true-false format form. The user answers all six questions, and then pushes a button to display the results.

Basic Quiz Before User Input

Basic Quiz Before User Input

Basic Quiz With User Input

Basic Quiz With User Input

The basic quiz uses a single controller (quizBasicController.js), single factory service (quizBasicFactory.js), single route (apps.js – ‘/quizBasic’), and a single partial view template (quiz-basic.html), in addition to the main layout (index.html). All these components are part the ‘quizModule’ AngularJS module. I’ve attempted to illustrate these relationships in the diagram, below.

The factory service (quizBasicFactory.js) uses $resource, a service in AngularJS’s ngResource module, to load the contents of a local JSON-format file (quiz-basic.json).

angular.module('quizModule')
 .factory('quizBasicFactory', function ($resource) {
 return $resource('./data/quiz-basic.json');
 });
{
  "name":      "Basic Quiz Example",
  "questions": [
    {
      "_id":      1,
      "question": "AngularJS is a declarative programming language.",
      "answer":   true
    },
    {
      "_id":      2,
      "question": "The acronym 'SPA' stands for Single-Page Application.",
      "answer":   true
    },
    {
      "_id":      3,
      "question": "AngularJS is written in C++.",
      "answer":   false
    }
    ...
  ]
}

The controller (quizBasicController.js), calls the factory service (quizBasicFactory.js), which returns the ‘data’ object.

angular.module('quizModule')
  .controller('QuizBasicController',
  function ($scope, quizBasicFactory) {
    var createResults;
    $scope.title = null; // quiz title
    $scope.quiz = {}; // quiz questions
    $scope.results = []; // user results

    quizBasicFactory.get(function (data) {
      $scope.title = data.name;
      $scope.quiz = data.questions;
      createResults();
    });

    // prepare array of result objects
    createResults = function () {
      var len = $scope.quiz.length;
      for (var i = 0; i < len; i++) {
        $scope.results.push({
          _id:        $scope.quiz[i]._id,
          answer:     $scope.quiz[i].answer,
          userChoice: null,
          correct:    null
        });
      }
    };

    // assign and check user's choice
    $scope.checkUserChoice = function (question, userChoice) {
      // assign the user's choice to userChoice
      $scope.results.userChoice = userChoice;

      // check the user's choice against the answer
      if ($scope.results.answer === userChoice) {
        $scope.results.correct = 'Correct';
      } else {
        $scope.results.correct = 'Incorrect';
      }
    };

    // only show results if all questions are answered
    $scope.checkQuizCompleted = function () {
      var len = $scope.results.length;
      for (var i = 0; i < len; i++) {
        if ($scope.results[i].userChoice === null) {
          return true;
        }
      }
      return false;
    };
  });
The 'data' Object Returned from Factory Service containing  JSON Data

The ‘data’ Object Returned from Factory Service containing JSON Data

Contents of the ‘data’ object are used to populate ‘$scope.quiz[]’, ‘$scope.title’, and ‘$scope.results[]’ properties. The $scope holds the quiz data ($scope.quiz[]), the quiz title ($scope.title), and the results ($scope.results[]). The ‘$scope.checkUserChoice()’ method stores the user’s answer in ‘$scope.results[].answer’ property, and evaluates if the answer is correct ($scope.results[].correct). The ‘$scope.checkQuizCompleted()’ method checks to make sure all questions have been answered before showing the results, when the user clicks the ‘Show Results’ button.

The $scope Containing Quiz, Title, and Results Properties

The $scope Containing Quiz, Title, and Results Properties

AngularJS bootstraps the application. Through AngularJS’s compiling and linking process, the partial view template (quiz-basic.html), shown below, the controller (quizBasicController.js), and the main layout (index.html), form the ‘\quizBasic’ View, which is presented to the user. Blogger, Dag-Inge Aas does a nice job of explaining this process in his post, Understanding template compiling in AngularJS.

<h4 class="title">{{title}}
<br/>

<!--quiz section-->
<form name="quiz">
  <div ng-repeat="question in quiz">
    <strong>{{question._id}}. {{question.question}}</strong>

    <div class="radio">
      <input required
             name="_id{{question._id}}"
             type="radio"
             value="true"
             ng-model="question.userChoice"
             ng-change="$parent.checkUserChoice(question._id, true)"/>
      <label for="_id{{question._id}}">True</label>
      <br/>
      <input required
             name="_id{{question._id}}"
             type="radio"
             ng-value="false"
             ng-model="question.userChoice"
             ng-change="$parent.checkUserChoice(question._id, false)"/>
      <label for="_id{{question._id}}">False</label>
    </div>
  </div>
</form>

<hr/>

<!--results section-->
<div ng-init="showAnswers=true">
  btn-sm"
          ng-click="showAnswers=checkQuizCompleted()">
    Show Results
  </button>
  <br/>
  <br/>

  <div ng-hide="showAnswers">
    <strong>Results</strong>

    <div ng-repeat="result in results">
      {{result._id}}. <span
        ng-class="result.correct == 'Correct' ? 'correct' : 'incorrect'">
        {{result.correct}}
      </span>
    </div>
  </div>

We load all the contents of the JSON data file into $scope and use the ‘ng-repeat‘ directive to iterate over the questions ($scope.quiz[]) and the results ($scope.results[]). Because of this, modifying existing questions and adding new ones is easy. This requires no additional coding, just a change to the JSON data file.

 Advanced Quiz

Using all the same basic building blocks as the basic quiz, with the addition of custom-directives, we can add complexity to our quiz, without a lot of additional coding. This advanced quiz has nine questions, including three true-false format, three multiple-choice format, and three multiple-correct format. As the user answers each questions, they are presented with the results, either ‘Correct’ or ‘Incorrect’.

Advanced Quiz Before User Input

Advanced Quiz Before User Input

Advanced Quiz With User Input

Advanced Quiz With User Input

Similar to the basic quiz, the advanced quiz uses a single controller (quizAdvancedController.js), factory service (quizAdvancedFactory.js), route (apps.js – ‘/quizAdvanced’), partial view template (quiz-advanced.html), and the main layout (index.html). Additionally, the advanced quiz uses a filter, three custom directives, and four partial view templates. The fourth partial view template, ‘quiz-choice-response.html’, is called by the first three partial view templates. It contains common DOM elements. Like the basic quiz, all these components are part the ‘quizModule’ module. I’ve attempted to illustrate these relationships in the diagram, below.

Just like with the basic quiz, the factory service (quizAdvancedFactory.js) uses $resource to load the contents of a local JSON-format file (quiz-advanced.json). This time however, the JSON file contains three types of questions, each with a slightly different schema. The three different question types are shown in the code snippet below. The true-false questions have a boolean value as the answer, the multiple choice questions, an integer as an answer, and the multiple correct questions, an array of integers as an answer.

angular.module('quizModule')
  .factory('quizAdvancedFactory', function ($resource) {
    return $resource('./data/quiz-advanced.json');
  });
{
  "name":      "Advanced Quiz Example",
  "questions": [
    {
      "_id":      1,
      "question": "AngularJS is written completely in JavaScript.",
      "type":     "True-false",
      "answer":   true
    },
    {
      "_id":      4,
      "question": "What does the acronym 'MVC' stand for?",
      "type":     "Multiple choice",
      "choices":  [
        {
          "_id":    1,
          "choice": "Method, Variable, Constant"
        },
        {
          "_id":    2,
          "choice": "Module, View, Constraint"
        },
        {
          "_id":    3,
          "choice": "Model, View, Controller"
        },
        {
          "_id":    4,
          "choice": "None of the above"
        }
      ],
      "answer":   3
    },
    {
      "_id":      7,
      "question": "Which of the following are associated with AngularJS?",
      "type":     "Multiple correct",
      "choices":  [
        {
          "_id":    1,
          "choice": "Controller"
        },
        {
          "_id":    2,
          "choice": "Interface"
        },
        {
          "_id":    3,
          "choice": "Route"
        },
        {
          "_id":    4,
          "choice": "View"
        },
        {
          "_id":    5,
          "choice": "Model"
        },
        {
          "_id":    6,
          "choice": "Generator"
        },
        {
          "_id":    7,
          "choice": "Service"
        },
        {
          "_id":    8,
          "choice": "Node"
        }
      ],
      "answer":   [1, 3, 4, 5, 7]
    }
    ...
  ]
}

The controller (quizAdvancedController.js), calls the factory service (quizAdvancedFactory.js), which returns the ‘data’ object, just like in the basic quiz example.

angular.module('quizModule')
  .controller('QuizAdvancedController',
  function ($scope, quizAdvancedFactory, filterFilter) {
    var createResults;
    $scope.title = null; // quiz title
    $scope.quiz = {}; // quiz questions
    $scope.results = []; // user results

    quizAdvancedFactory.get(function (data) {
      $scope.title = data.name;
      $scope.quiz = data.questions;
      createResults();
    });

    // prepare array of result objects
    createResults = function () {
      var len = $scope.quiz.length;
      for (var i = 0; i < len; i++) {
        $scope.results.push({
          _id:        $scope.quiz[i]._id,
          answer:     $scope.quiz[i].answer,
          userChoice: null,
          correct:    null
        });
      }
    };

    // used for multiple correct type questions
    $scope.checkUserMultiCorrectChoice = function (question, userChoice) {
      // create blank array
      if ($scope.results.userChoice === null) {
        $scope.results.userChoice = [];
      }

      // find choice, if not there the add or if there remove
      var pos = $scope.results.userChoice.indexOf(userChoice);
      if (pos < 0) {
        $scope.results.userChoice.push(userChoice);
      } else {
        $scope.results.userChoice.slice(pos, 1);
      }

      // check the user's choice against the answer
      var answer = JSON.stringify($scope.quiz.answer.sort());
      var choice = JSON.stringify($scope.results.userChoice.sort());

      if (answer === choice) {
        $scope.results.correct = true;
      } else {
        $scope.results.correct = false;
      }
    };

    // used for multiple choice and true-false type questions
    $scope.checkUserChoice = function (question, userChoice) {
      // assign the user's choice to userChoice
      $scope.results.userChoice = userChoice;

      // check the user's choice against the answer
      if ($scope.results.answer === userChoice) {
        $scope.results.correct = true;
      } else {
        $scope.results.correct = false;
      }
    };

    // find a specific question
    $scope.filteredQuestion = function (questionId) {
      return filterFilter($scope.quiz, {_id: questionId});
    };
  });

For true-false and multiple-choice questions, the ‘$scope.checkUserChoice()’ method stores the user’s answer in the ‘$scope.results[].answer’ property. The method also evaluates if the answer is correct, and stores that value in the ‘$scope.results[].correct’ property. The method takes two input parameters, question id and user’s choice.

For multiple correct questions, the ‘$scope.checkUserMultiCorrectChoice()’ method does the same. The difference, for multiple-correct questions, the method stores both the multiple answers and multiple user choices in a pair of arrays, ‘$scope.results[].answer[]’ and ‘$scope.results[].userChoice[]’ object arrays. In addition to storing the user’s choices, the method removes user choices if they are deselected by the user, in the view.

Lastly, the ‘$scope.checkUserMultiCorrectChoice()’ method evaluates the user’s choices array against the correct answers array. In the example below, note the ‘$scope.results[6].answer[]’ array and the ‘$scope.results[6].userChoice[]’ array. They were determined to be equal by the ‘$scope.checkUserMultiCorrectChoice()’, and reflected in the ‘true’ value of the ‘$scope.results[6].correct’ property.

Advanced Quiz Results for Multiple-Correct Question

Advanced Quiz Results for Multiple-Correct Question

Filter

In the ‘quizAdvancedController.js’ controller, note the ‘filterFilter’ object injected into the controller’s main function. At the end of the controller, also note the ‘$scope.filterQuestion(questionId)’ method.

angular.module('quizModule')
  .controller('QuizAdvancedController',
  function ($scope, quizAdvancedFactory, filterFilter) {
    ...
    // find a specific question
    $scope.filteredQuestion = function (questionId) {
      return filterFilter($scope.quiz, {_id: questionId});
    };
  });

The ‘$scope.filterQuestion(questionId)’ method takes a question id as an input parameter, and returns that single question. The ‘$scope.filterQuestion(questionId)’ method actually returns a call to the angular.filter‘s filterFilter. It takes two parameters,  an array containing the entire set of questions (‘$scope.quiz’ array), and a ‘pattern object’ containing the specific ‘id’ to filter on (‘{_id: questionId}’).

The filter method is called by the three question-type partial view templates, for example ‘quiz-multi-choice.html’. For example, the partial view template, ‘quiz-advanced.html’, uses the ‘quiz-multichoice’ element to call the custom directive, ‘quizMultiChoiceDirective.js’, passing it a request for question id 4.

<h4 class="title">{{title}}</h4>
<br/>
<form name="quiz">
  <!--true-false-->
  <quiz-truefalse filter-by="1"></quiz-truefalse>
  <quiz-truefalse filter-by="2"></quiz-truefalse>
  <quiz-truefalse filter-by="3"></quiz-truefalse>

  <!--multi-choice-->
  <quiz-multichoice filter-by="4"></quiz-multichoice>
  <quiz-multichoice filter-by="5"></quiz-multichoice>
  <quiz-multichoice filter-by="6"></quiz-multichoice>

  <!--multi-correct-->
  <quiz-multicorrect filter-by="7"></quiz-multicorrect>
  <quiz-multicorrect filter-by="8"></quiz-multicorrect>
  <quiz-multicorrect filter-by="9"></quiz-multicorrect>
</form>

The custom directive, ‘quizMultiChoiceDirective.js’, loads the partial view template, ‘quiz-multi-choice.html’, using the ‘templateUrl’ argument. The ‘templateUrl’ argument uses ajax to load the template. The template, ‘quiz-multi-choice.html’, uses the ‘ng-repeat‘ directive to populate its section of the advanced quiz with question id 4 (div ng-repeat="question in $parent.filteredQuestion(filterBy)). It does so by calling filteredQuestion(4), in the ‘quizAdvancedController.js’ controller.

<div ng-repeat="question in $parent.filteredQuestion(filterBy)">
  <strong>{{question._id}}. {{question.question}}</strong>
  <div class="radio" ng-repeat="choice in question.choices">
    <input
        name="_id{{question._id}}"
        type="radio"
        value="{{choice._id}}"
        ng-model="question.userChoice"
        ng-change="$parent.$parent.$parent.checkUserChoice(question._id, choice._id)"/>
    <label for="_id{{question._id}}">{{choice.choice}}</label>
  </div>
  <div ng-include src="'/scripts/partials/quiz-choice-response.html'"></div>
</div>
<br/>

The ‘quiz-multi-choice.html’ template also loads the contents of the ‘choice-response.html’ template. This template contains DOM elements, common to all three question-type templates.

<div ng-if="$parent.$parent.$parent.results.correct"
     class="result correct">
  <span class="glyphicon glyphicon-thumbs-up"></span>
  Correct!
</div>
<!--specify 'false' because not true (!) would include null (blank)-->
<div ng-if="$parent.$parent.$parent.results.correct === false"
     class="result incorrect">
  <span class="glyphicon glyphicon-thumbs-down"></span>
  Incorrect
</div>

I have attempted to illustrate the filter in the diagram, below. I intentionally left out a few non-essential components to simplify the diagram, such as the main layout, config, route, service, other custom directives, and the JSON data file.

Using these techniques, we can easily extend the quiz, adding new answer types, such as ordering, matching, short-answer, and so forth.

Managing Scope

Being familiar with AngularJS, you should understand how scope works. You should know there is more than one scope, and that scope is normally inherited from the parent scope. Directives such as ng-repeat, ng-switch, ng-view, and ng-include, all create their own child scopes. Said better by AngularJS’s team, ‘in AngularJS, a child scope normally prototypically inherits from its parent scope. One exception to this rule is a directive that uses scope: { … } — this creates an isolate scope that does not prototypically inherit.‘ We use a number of directives. We also use ‘scope:’ within our custom directives for the advanced quiz example, which breaks the chain of inheritance.

In some of the code examples in this post, you will notice the use of ‘$parent‘, ‘$parent.$parent‘, or even ‘$parent.$parent.$parent‘, instead of simply ‘$scope‘. Sometimes, it necessary to reach outside the current scope, to a parent’s scope (‘$parent‘), or that parent’s parent’s scope (‘$parent.$parent‘). A simple example of this, in the partial view template, ‘quiz-multi-choice.htm’, we call ‘$parent.filteredQuestion(filterBy)‘. The ‘filteredQuestion(filterBy)’ method we need to use is in the parent scope of the template’s scope, so we call ‘$parent’ instead of ‘$scope’.

So how can you determine which scope contains the method or properties you are seeking? Batarang, the AngularJS WebInspector Extension for Chrome. Batarang adds an additional ‘AngularJS’ tab to Developer tools for Chrome. Previously, we were using the example of question id 4 with the AngularJS’s filter. Using the Batarang, below, we can see the question id 4 in the final View. Each question returned using the filter is contained within its own separate scope.

Question #4 in Batarang Models Tab

Question #4 in Batarang Models Tab

This example also shows how complex working with AngularJS’s scope(s) can be. Starting with a particular scope, using Batarang, you can visually move up (parent scope) or down (child scope) within the scope hierarchy. The contents of each scope, the Model, is displayed on the right. Batarang also offers several other feature, seen below, including AngularJS application performance and dependency visualization.

Links

Quiz Question Types (presentation)

Understanding Service Types (article)

Understanding Scopes (article)

Build custom directives with AngularJS (article)

Google I/O 2012 – Better Web App Development Through Tooling (YouTube video)

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