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ballerina-guides/open-api-based-service

Name: open-api-based-service

Owner: ballerina-guides

Description: null

Created: 2018-03-01 09:16:24.0

Updated: 2018-04-30 13:28:44.0

Pushed: 2018-04-30 13:28:42.0

Homepage: null

Size: 344

Language: Ballerina

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README

Swagger / OpenAPI

OpenAPI Specification (formerly called the Swagger Specification) is a specification that creates RESTful contract for APIs, detailing all of its resources and operations in a human and machine-readable format for easy development, discovery, and integration. The Swagger to Ballerina Code Generator can take existing Swagger definition files and generate Ballerina services from them.

This guide walks you through building a RESTful Ballerina web service using Swagger / OpenAPI Specification.

The following are the sections available in this guide.

What you'll build
Prerequisites
Implementation
Testing
Deployment
Observability

What you'll build

You'll build an RESTful web service using an OpenAPI / Swagger specification. This guide will walk you through building a pet store server from an OpenAPI / Swagger definition. The pet store service will have RESTful POST,PUT,GET and DELETE methods to handle pet data.

alt text

Prerequisites

Ballerina Distribution
A Text Editor or an IDE

Optional requirements

Ballerina IDE plugins (IntelliJ IDEA, VSCode, Atom)
Docker
Kubernetes

Implementation

If you want to skip the basics, you can download the git repo and directly move to the “Testing” section by skipping “Implementation” section.

Understand the Swagger / OpenAPI specification

The scenario that we use throughout this guide will base on a petstore.json swagger specification. The OpenAPI / Swagger specification contains all the required details about the pet store RESTful API. Additionally, You can use the Swagger view in Ballerina Composer to create and edit OpenAPI / Swagger files.

Basic structure of petstore Swagger/OpenAPI specification


wagger": "2.0",
nfo": {
"title": "Ballerina Petstore",
"description": "This is a sample Petstore server.",
"version": "1.0.0"

ost": "localhost:9090",
asePath": "/v1",
chemes": [
"http"

aths": {
"/pet": {
  "post": {
    "summary": "Add a new pet to the store",
    "description": "Optional extended description in Markdown.",
    "produces": [
      "application/json"
    ],
    "responses": {
      "200": {
        "description": "OK"
      }
    }
  }
}

NOTE : The above OpenAPI / Swagger definition is only the basic structure. You can find the complete OpenAPI / Swagger definition in petstore.json file.

Create the project structure

Ballerina is a complete programming language that can have any custom project structure that you wish. Although the language allows you to have any package structure, use the following project structure for this project to follow this guide.

-api-based-service
? guide
 ??? petstore.json

Create the above directories in your local machine and also copy the petstore.json file to the open-api-based-service directory.
Then open the terminal and navigate to open-api-based-service/guide and run Ballerina project initializing toolkit.
```
llerina init
```

Genarate the web service from the Swagger / OpenAPI definition

Ballerina language is capable of understanding the Swagger / OpenAPI specifications. You can easily generate the web service just by typing the following command structure in the terminal.

erina swagger mock <swaggerFile> [-o <output directory name>] [-p <package name>]

For our pet store service you need to run the following command from the /guide in sample root directory(location where you have the petstore.json file) to generate the Ballerina service from the OpenAPI / Swagger definition

llerina swagger mock petstore.json -p petstore

The -p flag indicates the package name and -o flag indicates the file destination for the web service. These parameters are optional and can be used to have a customized package name and file location for the project.

Generated ballerina packages

After running the above command, the pet store web service will be auto-generated. You should now see a package structure similar to the following,

open-api-based-service
??? guide
 �� ??? petstore
    ?�� ??? ballerina_petstore_impl.bal
 �� ?�� ??? gen
 �� ?�� ?�� ??? ballerina_petstore.bal
 �� ?�� ?�� ??? schema.bal
 �� ?�� ??? tests
 �� ?��     ??? ballerina_petstore_test.bal
 �� ??? petstore.json

ballerina_petstore.bal is the generated Ballerina code of the pet store service and ballerina_petstore_impl.bal is the generated mock implementation for the pet store functions.

Generated ballerina_petstore.bal file

rt ballerina/log;
rt ballerina/http;
rt ballerina/swagger;

oint http:Listener ep0 { 
host: "localhost",
port: 9090


gger:ServiceInfo { 
title: "Ballerina Petstore",
description: "This is a sample Petstore server.",
serviceVersion: "1.0.0",
termsOfService: "http://ballerina.io/terms/",
contact: {name: "", email: "samples@ballerina.io", url: ""},
license: {name: "Apache 2.0", url: "http://www.apache.org/licenses/LICENSE-2.0.html"},
tags: [
    {name: "pet", description: "Everything about your Pets", externalDocs:
    { description: "Find out more", url: "http://ballerina.io" } }
],
externalDocs: { description: "Find out about Ballerina", url: "http://ballerina.io" },
security: [
]

p:ServiceConfig {
basePath: "/v1"

ice BallerinaPetstore bind ep0 {

@swagger:ResourceInfo {
    tags: ["pet"],
    summary: "Update an existing pet",
    description: "",
    externalDocs: {  },
    parameters: [
    ]
}
@http:ResourceConfig { 
    methods:["PUT"],
    path:"/pet"
}
updatePet (endpoint outboundEp, http:Request req) {
    http:Response res = updatePet(req);
    outboundEp->respond(res) but { error e => log:printError("Error while responding",
        err = e) };
}

@swagger:ResourceInfo {
    tags: ["pet"],
    summary: "Add a new pet to the store",
    description: "",
    externalDocs: {  },
    parameters: [
    ]
}
@http:ResourceConfig { 
    methods:["POST"],
    path:"/pet"
}
addPet (endpoint outboundEp, http:Request req) {
    http:Response res = addPet(req);
    outboundEp->respond(res) but { error e => log:printError("Error while responding",
        err = e) };
}

@swagger:ResourceInfo {
    tags: ["pet"],
    summary: "Find pet by ID",
    description: "Returns a single pet",
    externalDocs: {  },
    parameters: [
        {
            name: "petId",
            inInfo: "path",
            description: "ID of pet to return", 
            required: true, 
            allowEmptyValue: ""
        }
    ]
}
@http:ResourceConfig { 
    methods:["GET"],
    path:"/pet/{petId}"
}
getPetById (endpoint outboundEp, http:Request req, int petId) {
    http:Response res = getPetById(req, petId);
    outboundEp->respond(res) but { error e => log:printError("Error while responding",
        err = e) };
}

@swagger:ResourceInfo {
    tags: ["pet"],
    summary: "Deletes a pet",
    description: "",
    externalDocs: {  },
    parameters: [
        {
            name: "petId",
            inInfo: "path",
            description: "Pet id to delete", 
            required: true, 
            allowEmptyValue: ""
        }
    ]
}
@http:ResourceConfig { 
    methods:["DELETE"],
    path:"/pet/{petId}"
}
deletePet (endpoint outboundEp, http:Request req, int petId) {
    http:Response res = deletePet(req, petId);
    outboundEp->respond(res) but { error e => log:printError("Error while responding",
        err = e) };
}

Next we need to implement the business logic in the ballerina_petstore_impl.bal file.

Implement the business logic for petstore

Now you have the Ballerina web service for the give petstore.json Swagger file. Then you need to implement the business logic for functionality of each resource. The Ballerina Swagger generator has generated ballerina_petstore_impl.bal file inside the open-api-based-service/guide/petstore. You need to fill the ballerina_petstore_impl.bal as per your requirement. For simplicity, we will use an in-memory map to store the pet data. The following code is the completed pet store web service implementation.

rt ballerina/http;
rt ballerina/mime;

petData;

ic function addPet(http:Request req) returns http:Response {

// Initialize the http response message
http:Response resp;
// Retrieve the data about pets from the json payload of the request
var reqesetPayloadData = req.getJsonPayload();
// Match the json payload with json and errors
match reqesetPayloadData {
    // If the req.getJsonPayload() returns JSON
    json petDataJson => {
        // Transform into Pet data structure
        Pet petDetails = check <Pet>petDataJson;
        if (petDetails.id == "") {
            // Send bad request message if request doesn't contain valid pet id
            resp.setTextPayload("Error : Please provide the json payload with `id`,
            `catogery` and `name`");
            // set the response code as 400 to indicate a bad request
            resp.statusCode = 400;
        }
        else {
            // Add the pet details into the in memory map
            petData[petDetails.id] = petDetails;
            // Send back the status message back to the client
            string payload = "Pet added successfully : Pet ID = " + petDetails.id;
            resp.setTextPayload(payload);
        }
    }
    error => {
        // Send bad request message if request doesn't contain valid pet data
        resp.setTextPayload("Error : Please provide the json payload with `id`,
        `catogery` and `name`");
        // set the response code as 400 to indicate a bad request
        resp.statusCode = 400;
    }
}
return resp;


ic function updatePet(http:Request req) returns http:Response {

// Initialize the http response message
http:Response resp;
// Retrieve the data about pets from the json payload of the request
var reqesetPayloadData = req.getJsonPayload();
// Match the json payload with json and errors
match reqesetPayloadData {
    // If the req.getJsonPayload() returns JSON
    json petDataJson => {
        // Transform into Pet data structure
        Pet petDetails = check <Pet>petDataJson;
        if (petDetails.id == "" || !petData.hasKey(petDetails.id)) {
            // Send bad request message if request doesn't contain valid pet id
            resp.setTextPayload("Error : provide the json payload with valid `id``");
            // set the response code as 400 to indicate a bad request
            resp.statusCode = 400;
        }
        else {
            // Update the pet details in the map
            petData[petDetails.id] = petDetails;
            // Send back the status message back to the client
            string payload = "Pet updated successfully : Pet ID = " + petDetails.id;
            resp.setTextPayload(payload);
        }
    }

    error => {
        // Send bad request message if request doesn't contain valid pet data
        resp.setTextPayload("Error : Please provide the json payload with `id`,
        `catogery` and `name`");
        // set the response code as 400 to indicate a bad request
        resp.statusCode = 400;
    }
}
return resp;



ic function getPetById(http:Request req, int petId) returns http:Response {
// Initialize http response message to send back to the client
http:Response resp;
// Send bad request message to client if pet ID cannot found in petData map
if (!petData.hasKey(<string>petId)) {
    resp.setTextPayload("Error : Invalid Pet ID");
    // set the response code as 400 to indicate a bad request
    resp.statusCode = 400;
}
else {
    // Set the pet data as the payload and send back the response
    var payload = <string>petData[<string>petId];
    resp.setTextPayload(payload);
}
return resp;


ic function deletePet(http:Request req, int petId) returns http:Response {
// Initialize http response message
http:Response resp;
// Send bad request message to client if pet ID cannot found in petData map
if (!petData.hasKey(<string>petId)) {
    resp.setTextPayload("Error : Invalid Pet ID");
    // set the response code as 400 to indicate a bad request
    resp.statusCode = 400;
}
else {
    // Remove the pet data from the petData map
    _ = petData.remove(<string>petId);
    // Send the status back to the client
    string payload = "Deleted pet data successfully : Pet ID = " + petId;
    resp.setTextPayload(payload);
}
return resp;

With that, we have completed the implementation of the pet store web service.

Testing

Invoking the petstore service

You can run the RESTful service that you developed above, in your local environment. Open your terminal and navigate to open-api-based-service/guide, and execute the following command.

llerina run petstore

You can test the functionality of the pet store RESTFul service by sending HTTP request for each operation. For example, we have used the curl commands to test each operation of pet store as follows.

Add a new pet

 -X POST -d '{"id":1, "catogery":"dog", "name":"doggie"}' 
p://localhost:9090/v1/pet/" -H "Content-Type:application/json"

ut :  
added successfully : Pet ID = 1

Retrieve pet data

 "http://localhost:9090/v1/pet/1"

ut:
":"1","catogery":"dog","name":"Updated"}

Update pet data

 -X PUT -d '{"id":1, "catogery":"dog-updated", "name":"Updated-doggie"}' 
p://localhost:9090/v1/pet/" -H "Content-Type:application/json"

ut: 
details updated successfully : id = 1

Delete pet data

 -X DELETE  "http://localhost:9090/v1/pet/1"

ut:
ted pet data successfully: Pet ID = 1

Writing Unit Tests

In Ballerina, the unit test cases should be in the same package inside a folder named as 'tests'. When writing the test functions the below convention should be followed.

Test functions should be annotated with @test:Config. See the below example.
```
t:Config
tion testPetStore() {
```

This guide contains unit test cases for each method available in the 'petstore service' implemented above.

To run the unit tests, open your terminal and navigate to open-api-based-service/guide, and run the following command.

llerina test

To check the implementation of the test file, refer to the ballerina_petstore_test.bal.

Deployment

Once you are done with the development, you can deploy the service using any of the methods that we listed below.

Deploying locally

As the first step you can build a Ballerina executable archive (.balx) of the service that we developed above, using the following command. It points to the directory in which the service we developed above located and it will create an executable binary out of that. Navigate to open-api-based-service/guide and run the following command.
```
llerina build petstore
```
Once the restful_service.balx is created inside the target folder, you can run that with the following command.
```
llerina run target/petstore.balx
```

The successful execution of the service will show us the following output.

erina: initiating service(s) in 'target/petstore.balx'
erina: started HTTP/WS endpoint 0.0.0.0:9090

Deploying on Docker

You can run the service that we developed above as a docker container. As Ballerina platform offers native support for running ballerina programs on containers, you just need to put the corresponding docker annotations on your service code.

In our ballerina_petstore, we need to import import ballerinax/docker; and use the annotation @docker:Config as shown below to enable docker image generation during the build time.

BallerinaPetstore.bal

ther imports
rt ballerinax/docker;

ker:Config {
registry:"ballerina.guides.io",
name:"petstore",
tag:"v1.0"


ker:Expose{}
oint http:ServiceEndpoint ep0 {
host:"localhost",
port:9090


petData' Map definition

@swagger:ServiceInfo' annotation

p:ServiceConfig {
basePath:"/v1"

ice<http:Service> BallerinaPetstore bind ep0 {

Now you can build a Ballerina executable archive (.balx) of the service that we developed above, using the following command. It points to the service file that we developed above and it will create an executable binary out of that. This will also create the corresponding docker image using the docker annotations that you have configured above. Navigate to the <SAMPLE_ROOT>/guide/ folder and run the following command.
```
lerina build petstore

following command to start docker container: 
er run -d -p 9090:9090 ballerina.guides.io/petstore:v1.0
```
Once you successfully build the docker image, you can run it with the docker run command that is shown in the previous step.

docker run -d -p 9090:9090 ballerina.guides.io/petstore:v1.0

Here we run the docker image with flag`` -p <host_port>:<container_port>`` so that we  use  the host port 9090 and the container port 9090. Therefore you can access the service through the host port.

Verify docker container is running with the use of $ docker ps. The status of the docker container should be shown as 'Up'.
You can access the service using the same curl commands that we've used above.

curl -X POST -d '{"id":1, "catogery":"dog", "name":"doggie"}' \
"http://localhost:9090/v1/pet/" -H "Content-Type:application/json"

Deploying on Kubernetes

You can run the service that we developed above, on Kubernetes. The Ballerina language offers native support for running a ballerina programs on Kubernetes, with the use of Kubernetes annotations that you can include as part of your service code. Also, it will take care of the creation of the docker images. So you don't need to explicitly create docker images prior to deploying it on Kubernetes.
We need to import import ballerinax/kubernetes; and use @kubernetes annotations as shown below to enable kubernetes deployment for the service we developed above.

BallerinaPetstore.bal

ther imports
rt ballerinax/kubernetes;

ernetes:Ingress {
stname:"ballerina.guides.io",
me:"ballerina-guides-petstore",
th:"/"


ernetes:Service {
rviceType:"NodePort",
me:"ballerina-guides-petstore"


ernetes:Deployment {
age:"ballerina.guides.io/petstore:v1.0",
me:"ballerina-guides-petstore"


oint http:ServiceEndpoint ep0 {
host:"localhost",
port:9090


petData' Map definition

@swagger:ServiceInfo' annotation

p:ServiceConfig {
basePath:"/v1"

ice<http:Service> BallerinaPetstore bind ep0 {

Here we have used @kubernetes:Deployment to specify the docker image name which will be created as part of building this service.
We have also specified @kubernetes:Service so that it will create a Kubernetes service which will expose the Ballerina service that is running on a Pod.
In addition we have used @kubernetes:Ingress which is the external interface to access your service (with path / and host name ballerina.guides.io)
Now you can build a Ballerina executable archive (.balx) of the service that we developed above, using the following command. It points to the service file that we developed above and it will create an executable binary out of that. This will also create the corresponding docker image and the Kubernetes artifacts using the Kubernetes annotations that you have configured above.

allerina build petstore

n following command to deploy kubernetes artifacts:  
bectl apply -f ./target/petstore/kubernetes

You can verify that the docker image that we specified in @kubernetes:Deployment is created, by using docker ps images.
Also the Kubernetes artifacts related our service, will be generated in ./target/petstore/kubernetes.
Now you can create the Kubernetes deployment using:

ubectl apply -f ./target/petstore/kubernetes 
eployment.extensions "ballerina-guides-petstore" created
ngress.extensions "ballerina-guides-petstore" created
ervice "ballerina-guides-petstore" created

You can verify Kubernetes deployment, service and ingress are running properly, by using following Kubernetes commands.
```
ectl get service
ectl get deploy
ectl get pods
ectl get ingress
```

 everything is successfully deployed, you can invoke the service either via Node port or ingress. 

 Port:

curl -X POST -d '{“id”:1, “catogery”:“dog”, “name”:“doggie”}' \ “http://:/v1/pet/” -H “Content-Type:application/json”

ess:

`/etc/hosts` entry to match hostname.

127.0.0.1 ballerina.guides.io

ss the service

curl -X POST -d '{“id”:1, “catogery”:“dog”, “name”:“doggie”}' \ “http://ballerina.guides.io/v1/pet/” -H “Content-Type:application/json”

bservability 
erina is by default observable. Meaning you can easily observe your services, resources, etc.
ver, observability is disabled by default via configuration. Observability can be enabled by adding following configurations to `ballerina.conf` file in `open-api-based-service/guide/`.

[b7a.observability]

[b7a.observability.metrics]

Flag to enable Metrics

enabled=true

[b7a.observability.tracing]

Flag to enable Tracing

enabled=true

: The above configuration is the minimum configuration needed to enable tracing and metrics. With these configurations default values are load as the other configuration parameters of metrics and tracing.

Tracing 

can monitor ballerina services using in built tracing capabilities of Ballerina. We'll use [Jaeger](https://github.com/jaegertracing/jaeger) as the distributed tracing system.
ow the following steps to use tracing with Ballerina.

u can add the following configurations for tracing. Note that these configurations are optional if you already have the basic configuration in `ballerina.conf` as described above.

[b7a.observability]

[b7a.observability.tracing] enabled=true name=“jaeger”

[b7a.observability.tracing.jaeger] reporter.hostname=“localhost” reporter.port=5775 sampler.param=1.0 sampler.type=“const” reporter.flush.interval.ms=2000 reporter.log.spans=true reporter.max.buffer.spans=1000

n Jaeger docker image using the following command

$ docker run -d -p5775:5775/udp -p6831:6831/udp -p6832:6832/udp -p5778:5778 -p16686:16686 \ -p14268:14268 jaegertracing/all-in-one:latest

vigate to `open-api-based-service/guide` and run the restful-service using following command

$ ballerina run petstore

serve the tracing using Jaeger UI using following URL

http://localhost:16686

Metrics
ics and alarts are built-in with ballerina. We will use Prometheus as the monitoring tool.
ow the below steps to set up Prometheus and view metrics for Ballerina restful service.

u can add the following configurations for metrics. Note that these configurations are optional if you already have the basic configuration in `ballerina.conf` as described under `Observability` section.

[b7a.observability.metrics] enabled=true provider=“micrometer”

[b7a.observability.metrics.micrometer] registry.name=“prometheus”

[b7a.observability.metrics.prometheus] port=9700 hostname=“0.0.0.0” descriptions=false step=“PT1M”

eate a file `prometheus.yml` inside `/tmp/` location. Add the below configurations to the `prometheus.yml` file.

global:

 scrape_interval:     15s
 evaluation_interval: 15s

scrape_configs:

 - job_name: prometheus
   static_configs:
     - targets: ['172.17.0.1:9797']

OTE : Replace `172.17.0.1` if your local docker IP differs from `172.17.0.1`

n the Prometheus docker image using the following command

$ docker run -p 19090:9090 -v /tmp/prometheus.yml:/etc/prometheus/prometheus.yml \ prom/prometheus

u can access Prometheus at the following URL

http://localhost:19090/

:  Ballerina will by default have following metrics for HTTP server connector. You can enter following expression in Prometheus UI
ttp_requests_total
ttp_response_time


Logging

erina has a log package for logging to the console. You can import ballerina/log package and start logging. The following section will describe how to search, analyze, and visualize logs in real time using Elastic Stack.

art the Ballerina Service with the following command from `open-api-based-service/guide`

$ nohup ballerina run petstore &» ballerina.log&

OTE: This will write the console log to the `ballerina.log` file in the `open-api-based-service/guide` directory

art Elasticsearch using the following command

art Elasticsearch using the following command

$ docker run -p 9200:9200 -p 9300:9300 -it -h elasticsearch –name \ elasticsearch docker.elastic.co/elasticsearch/elasticsearch:6.2.2

OTE: Linux users might need to run `sudo sysctl -w vm.max_map_count=262144` to increase `vm.max_map_count` 

art Kibana plugin for data visualization with Elasticsearch

$ docker run -p 5601:5601 -h kibana –name kibana –link \ elasticsearch:elasticsearch docker.elastic.co/kibana/kibana:6.2.2

nfigure logstash to format the ballerina logs

reate a file named `logstash.conf` with the following content

input {
beats{

 port => 5044

}
}

filter {
grok{

 match => { 
 "message" => "%{TIMESTAMP_ISO8601:date}%{SPACE}%{WORD:logLevel}%{SPACE}
 \[%{GREEDYDATA:package}\]%{SPACE}\-%{SPACE}%{GREEDYDATA:logMessage}"
 }

}
}

output {
elasticsearch{

 hosts => "elasticsearch:9200"  
 index => "store"  
 document_type => "store_logs"

}
}

Save the above `logstash.conf` inside a directory named as `{SAMPLE_ROOT}\pipeline`

 Start the logstash container, replace the `{SAMPLE_ROOT}` with your directory name

$ docker run -h logstash –name logstash –link elasticsearch:elasticsearch \ -it –rm -v ~/{SAMPLE_ROOT}/pipeline:/usr/share/logstash/pipeline/ \ -p 5044:5044 docker.elastic.co/logstash/logstash:6.2.2

onfigure filebeat to ship the ballerina logs

reate a file named `filebeat.yml` with the following content

filebeat.prospectors:

type: log paths:
- /usr/share/filebeat/ballerina.log output.logstash: hosts: [“logstash:5044”]
```
 : Modify the ownership of filebeat.yml file using `$chmod go-w filebeat.yml` 
```

ii) Save the above filebeat.yml inside a directory named as {SAMPLE_ROOT}\filebeat

iii) Start the logstash container, replace the {SAMPLE_ROOT} with your directory name

cker run -v {SAMPLE_ROOT}/filebeat/filebeat.yml:/usr/share/filebeat/filebeat.yml \
SAMPLE_ROOT}/guide.restful_service/restful_service/ballerina.log:/usr/share\
ebeat/ballerina.log --link logstash:logstash docker.elastic.co/beats/filebeat:6.2.2

Access Kibana to visualize the logs using following URL
```
://localhost:5601 
```

This work is supported by the National Institutes of Health's National Center for Advancing Translational Sciences, Grant Number U24TR002306. This work is solely the responsibility of the creators and does not necessarily represent the official views of the National Institutes of Health.