This article is the second in a series of my article about Knative. After publishing the first of them, Spring Boot on Knative, you were asking me about a long application startup time after scaling to zero. That’s why I resolved this Spring Boot issue by compiling it to a native image with GraalVM. The problem with startup time seems to be an important thing in a serverless approach.

On Knative you can run any type of application – not only a function. In this article, when I’m writing “microservices”, in fact, I’m thinking about service to service communication.

Source Code

If you would like to try it by yourself, you may always take a look at my source code. In order to do that you need to clone my GitHub repository. Then you should just follow my instructions

As the example of microservices in this article, I used two applications callme-service and caller-service. Both of them exposes a single endpoint, which prints a name of the application pod. The caller-service application also calls the endpoint exposed by the callme-service application.

On Kubernetes, both these applications will be deployed as Knative services in multiple revisions. We will also distribute traffic across those revisions using Knative routes. The picture visible below illustrates the architecture of our sample system.

1. Prepare Spring Boot microservices

We have two simple Spring Boot applications that expose a single REST endpoint, health checks, and run an in-memory H2 database. We use Hibernate and Lombok. Therefore, we need to include the following list of dependencies in Maven pom.xml.

<dependency>
    <groupId>org.springframework.boot</groupId>
    <artifactId>spring-boot-starter-web</artifactId>
</dependency>
<dependency>
    <groupId>org.springframework.boot</groupId>
    <artifactId>spring-boot-starter-actuator</artifactId>
</dependency>
<dependency>
    <groupId>org.springframework.boot</groupId>
    <artifactId>spring-boot-starter-data-jpa</artifactId>
</dependency>
<dependency>
    <groupId>com.h2database</groupId>
    <artifactId>h2</artifactId>
    <scope>runtime</scope>
</dependency>
<dependency>
    <groupId>org.projectlombok</groupId>
    <artifactId>lombok</artifactId>
    <version>1.18.16</version>
</dependency>

Each time we call the ping endpoint it creates an event and stores it in the H2 database. The REST endpoint returns the name of a pod and namespace inside Kubernetes and the id of the event. That method will be useful in our manual tests on the cluster.

@RestController
@RequestMapping("/callme")
public class CallmeController {

    @Value("${spring.application.name}")
    private String appName;
    @Value("${POD_NAME}")
    private String podName;
    @Value("${POD_NAMESPACE}")
    private String podNamespace;
    @Autowired
    private CallmeRepository repository;

    @GetMapping("/ping")
    public String ping() {
        Callme c = repository.save(new Callme(new Date(), podName));
        return appName + "(id=" + c.getId() + "): " + podName + " in " + podNamespace;
    }

}

Here’s our model class – Callme. The model class inside the caller-service application is pretty similar.

@Entity
@Getter
@Setter
@NoArgsConstructor
@RequiredArgsConstructor
public class Callme {

    @Id
    @GeneratedValue
    private Integer id;
    @Temporal(TemporalType.TIMESTAMP)
    @NonNull
    private Date addDate;
    @NonNull
    private String podName;

}

Also, let’s take a look at the first version of the ping method in CallerController. We will modify it later when we will discussing communication and tracing. For now, it is important to understand that this method also calls the ping method exposed by callme-service and returns the whole response.

@GetMapping("/ping")
public String ping() {
    Caller c = repository.save(new Caller(new Date(), podName));
    String callme = callme();
    return appName + "(id=" + c.getId() + "): " + podName + " in " + podNamespace
            + " is calling " + callme;
}

2. Prepare Spring Boot native image with GraalVM

Spring Native provides support for compiling Spring applications to native executables using the GraalVM native compiler. For more details about this project, you may refer to its documentation. Here’s the main class of our application.

@SpringBootApplication
public class CallmeApplication {

   public static void main(String[] args) {
      SpringApplication.run(CallmeApplication.class, args);
   }

}

Hibernate does a lot of dynamic things at runtime. So we need to get Hibernate to enhance the entities in our application at build time. We need to add the following Maven plugin to our build.

<plugin>
   <groupId>org.hibernate.orm.tooling</groupId>
   <artifactId>hibernate-enhance-maven-plugin</artifactId>
   <version>${hibernate.version}</version>
   <executions>
      <execution>
         <configuration>
            <failOnError>true</failOnError>
            <enableLazyInitialization>true</enableLazyInitialization>
            <enableDirtyTracking>true</enableDirtyTracking>
            <enableExtendedEnhancement>false</enableExtendedEnhancement>
         </configuration>
         <goals>
            <goal>enhance</goal>
         </goals>
      </execution>
   </executions>
</plugin>

In this article, I’m using the latest version of Spring Native – 0.9.0. Since Spring Native is actively developed, there are significant changes between subsequent versions. If you compare it to some other articles based on the earlier versions, we don’t have to disable proxyBeansMethods, exclude SpringDataWebAutoConfiguration, add spring-context-indexer into dependencies or create hibernate.properties. Cool! I can also use Buildpacks for building a native image.

So, now we just need to add the following dependency.

<dependency>
   <groupId>org.springframework.experimental</groupId>
   <artifactId>spring-native</artifactId>
   <version>0.9.0</version>
</dependency>

The Spring AOT plugin performs ahead-of-time transformations required to improve native image compatibility and footprint.

<plugin>
    <groupId>org.springframework.experimental</groupId>
    <artifactId>spring-aot-maven-plugin</artifactId>
    <version>${spring.native.version}</version>
    <executions>
        <execution>
            <id>test-generate</id>
            <goals>
                <goal>test-generate</goal>
            </goals>
        </execution>
        <execution>
            <id>generate</id>
            <goals>
                <goal>generate</goal>
            </goals>
        </execution>
    </executions>
</plugin>

3. Run native image on Knative with Buildpacks

Using Builpacks for creating a native image is our primary option. Although it requires a Docker daemon it works properly on every OS. However, we need to use the latest stable version of Spring Boot. In that case, it is 2.4.3. You can configure Buildpacks as well inside Maven pom.xml with the spring-boot-maven-plugin. Since we need to build and deploy the application on Kubernetes in one step, I prefer configuration in Skaffold. We use paketobuildpacks/builder:tiny as a builder image. It is also required to enable the native build option with the BP_BOOT_NATIVE_IMAGE environment variable.

apiVersion: skaffold/v2beta11
kind: Config
metadata:
  name: callme-service
build:
  artifacts:
  - image: piomin/callme-service
    buildpacks:
      builder: paketobuildpacks/builder:tiny
      env:
        - BP_BOOT_NATIVE_IMAGE=true
deploy:
  kubectl:
    manifests:
      - k8s/ksvc.yaml

Skaffold configuration refers to our Knative Service manifest. It is quite non-typical since we need to inject a pod and namespace names into the container. We also allow a maximum of 10 concurrent requests per single pod. If it is exceeded Knative scale up a number of running instances.

apiVersion: serving.knative.dev/v1
kind: Service
metadata:
  name: callme-service
spec:
  template:
    spec:
      containerConcurrency: 10
      containers:
      - name: callme
        image: piomin/callme-service
        ports:
          - containerPort: 8080
        env:
          - name: POD_NAME
            valueFrom:
              fieldRef:
                fieldPath: metadata.name
          - name: POD_NAMESPACE
            valueFrom:
              fieldRef:
                fieldPath: metadata.namespace

By default, Knative doesn’t allow to use Kubernetes fieldRef feature. In order to enable it, we need to update the knative-features ConfigMap in the knative-serving namespace. The required property name is kubernetes.podspec-fieldref.

kind: ConfigMap
apiVersion: v1
metadata:
  annotations:
  namespace: knative-serving
  labels:
    serving.knative.dev/release: v0.16.0
data:
  kubernetes.podspec-fieldref: enabled

Finally, we may build and deploy our Spring Boot microservices on Knative with the following command.

$ skaffold run

4. Run native image on Knative with Jib

The same as in my previous article about Knative we will build and run our applications on Kubernetes with Skaffold and Jib. Fortunately, Jib Maven Plugin has already introduced support for GraalVM “native images”. The Jib GraalVM Native Image Extension expects the native-image-maven-plugin to do the heavy lifting of generating a “native image” (with the native-image:native-image goal). Then the extension just simply copies the binary into a container image and sets it as executable.

Of course, unlike Java bytecode, a native image is not portable but platform-specific. The Native Image Maven Plugin doesn’t support cross-compilation, so the native-image should be built on the same OS as the runtime architecture. Since I build a GraalVM image of my applications on Ubuntu 20.10, I should use the same base Docker image for running containerized microservices. In that case, I chose image ubuntu:20.10 as shown below.

<plugin>
   <groupId>com.google.cloud.tools</groupId>
   <artifactId>jib-maven-plugin</artifactId>
   <version>2.8.0</version>
   <dependencies>
      <dependency>
         <groupId>com.google.cloud.tools</groupId>
         <artifactId>jib-native-image-extension-maven</artifactId>
         <version>0.1.0</version>
      </dependency>
   </dependencies>
   <configuration>
      <from>
         <image>ubuntu:20.10</image>
      </from>
      <pluginExtensions>
         <pluginExtension>
            <implementation>com.google.cloud.tools.jib.maven.extension.nativeimage.JibNativeImageExtension</implementation>
         </pluginExtension>
      </pluginExtensions>
   </configuration>
</plugin>

If you use Jib Maven Plugin you first need to build a native image. In order to build a native image of the application we also need to include a native-image-maven-plugin. Of you need to build our application using GraalVM JDK.

<plugin>
   <groupId>org.graalvm.nativeimage</groupId>
   <artifactId>native-image-maven-plugin</artifactId>
   <version>21.0.0.2</version>
   <executions>
      <execution>
         <goals>
            <goal>native-image</goal>
         </goals>
         <phase>package</phase>
      </execution>
   </executions>
</plugin>

So, the last in this section is just to run the Maven build. In my configuration, a native-image-maven-plugin needs to be activated under the native-image profile.

$ mvn clean package -Pnative-image

After the build native image of callme-service is visible inside the target directory.

The configuration of Skaffold is typical. We just need to enable Jib as a build tool.

apiVersion: skaffold/v2beta11
kind: Config
metadata:
  name: callme-service
build:
  artifacts:
  - image: piomin/callme-service
    jib: {}
deploy:
  kubectl:
    manifests:
      - k8s/ksvc.yaml

Finally, we may build and deploy our Spring Boot microservices on Knative with the following command.

$ skaffold run

5. Communication between microservices on Knative

I deployed two revisions of each application on Knative. Just for comparison, the first version of deployed applications is compiled with OpenJDK. Only the latest version is basing on the GraalVM native image. Thanks to that we may compare startup time for both revisions.

Let’s take a look at a list of revisions after deploying both versions of our applications. The traffic is splitted 60% to the latest version, and 40% to the previous version of each application.

Under the hood, Knative creates Kubernetes Services and multiple Deployments. There is always a single Deployment per each Knative Revision. Also, there are multiple services, but always one of them is per all revisions. That Service is an ExternalName service type. Assuming you still want to split traffic across multiple revisions you should use exactly that service in your communication. The name of the service is callme-service. However, we should use FQDN name with a namespace name and svc.cluster.local suffix.

We can use Spring RestTemplate for calling endpoint exposed by the callme-service. In order to guarantee tracing for the whole request path, we need to propagate Zipkin headers between the subsequent calls. For communication, we will use a service with a fully qualified internal domain name (callme-service.serverless.svc.cluster.local) as mentioned before.

@RestController
@RequestMapping("/caller")
public class CallerController {

   private RestTemplate restTemplate;

   CallerController(RestTemplate restTemplate) {
      this.restTemplate = restTemplate;
   }

   @Value("${spring.application.name}")
   private String appName;
   @Value("${POD_NAME}")
   private String podName;
   @Value("${POD_NAMESPACE}")
   private String podNamespace;
   @Autowired
   private CallerRepository repository;

   @GetMapping("/ping")
   public String ping(@RequestHeader HttpHeaders headers) {
      Caller c = repository.save(new Caller(new Date(), podName));
      String callme = callme(headers);
      return appName + "(id=" + c.getId() + "): " + podName + " in " + podNamespace
                     + " is calling " + callme;
   }

   private String callme(HttpHeaders headers) {
      MultiValueMap<String, String> map = new LinkedMultiValueMap<>();
      Set<String> headerNames = headers.keySet();
      headerNames.forEach(it -> map.put(it, headers.get(it)));
      HttpEntity httpEntity = new HttpEntity(map);
      ResponseEntity<String> entity = restTemplate
         .exchange("http://callme-service.serverless.svc.cluster.local/callme/ping",
                  HttpMethod.GET, httpEntity, String.class);
      return entity.getBody();
   }

}

In order to test the communication between our microservices we just need to invoke caller-service via Knative Route.

Let’s perform some test calls of the caller-service GET /caller/ping endpoint. We should use the URL http://caller-service-serverless.apps.cluster-d556.d556.sandbox262.opentlc.com/caller/ping.

In the first to requests caller-service calls the latest version of callme-service (compiled with GraalVM). In the third request it communicates with the older version callme-service (compiled with OpenJDK). Let’s compare the time of start for those two versions of the same application.

With GraalVM we have 0.3s instead of 5.9s. We should also keep in mind that our applications start an in-memory, embedded H2 database.

6. Configure tracing with Jaeger

In order to enable tracing for Knative, we need to update the knative-tracing ConfigMap in the knative-serving namespace. Of course, we first need to install Jaeger in our cluster.

apiVersion: operator.knative.dev/v1alpha1
kind: KnativeServing
metadata:
  name: knative-tracing
  namespace: knative-serving
spec:
  sample-rate: "1" 
  backend: zipkin 
  zipkin-endpoint: http://jaeger-collector.knative-serving.svc.cluster.local:9411/api/v2/spans 
  debug: "false"

You can also use Helm chart to install Jaeger. With this option, you need to execute the following Helm commands.

$ helm repo add jaegertracing https://jaegertracing.github.io/helm-charts
$ helm install jaeger jaegertracing/jaeger

Knative automatically creates Zipkin span headers. The only single goal for us is to propagate HTTP headers between the caller-service and callme-service applications. In my configuration, Knative sends 100% traces to Jaeger. Let’s take a look at some traces for GET /caller/ping endpoint within Knative microservices mesh.

We can also take a look on the detailed view for every single request.

Conclusion

There are several important things you need to consider when you are running microservices on Knative. I focused on the aspects related to communication and tracing. I also showed that Spring Boot doesn’t have to start in a few seconds. With GraalVM it can start in milliseconds, so you can definitely consider it as a serverless framework. You may expect more articles about Knative soon!

The post Microservices on Knative with Spring Boot and GraalVM appeared first on Piotr's TechBlog.

Knative introduces a new way of managing your applications on Kubernetes. It extends Kubernetes to add some new key features. One of the most significant of them is a “Scale to zero”. If Knative detects that a service is not used, it scales down the number of running instances to zero. Consequently, it provides a built-in autoscaling feature based on a concurrency or a number of requests per second. We may also take advantage of revision tracking, which is responsible for switching from one version of your application to another. With Knative you just have to focus on your core logic.

All the features I described above are provided by the component called “Knative Serving”. There are also two other components: “Eventing” and “Build”. The Build component is deprecated and has been replaced by Tekton. The Eventing component requires attention. However, I’ll discuss it in more detail in the separated article.

Source Code

If you would like to try it by yourself, you may always take a look at my source code. In order to do that you need to clone my GitHub repository. Then you should just follow my instructions

I used the same application as the example in some of my previous articles about Spring Boot and Kubernetes. I just wanted to focus that you don’t have to change anything in the source code to run it also on Knative. The only required change will be in the YAML manifest.

Since Knative provides built-in autoscaling you may want to compare it with the horizontal pod autoscaler (HPA) on Kubernetes. To do that you may read the article Spring Boot Autoscaling on Kubernetes. If you are interested in how to easily deploy applications on Kubernetes read the following article about the Okteto platform.

Install Knative on Kubernetes

Of course, before we start Spring Boot development we need to install Knative on Kubernetes. We can do it using the kubectl CLI or an operator. You can find the detailed installation instruction here. I decided to try it on OpenShift. It is obviously the fastest way. I could do it with one click using the OpenShift Serverless Operator. No matter which type of installation you choose, the further steps will apply everywhere.

Using Knative CLI

This step is optional. You can deploy and manage applications on Knative with CLI. To download CLI do to the site https://knative.dev/docs/install/install-kn/. Then you can deploy the application using the Docker image.

$ kn service create sample-spring-boot-on-kubernetes \
   --image piomin/sample-spring-boot-on-kubernetes:latest

We can also verify a list of running services with the following command.

$ kn service list

For more advanced deployments it will be more suitable to use the YAML manifest. We will start the build from the source code build with Skaffold and Jib. Firstly, let’s take a brief look at our Spring Boot application.

Spring Boot application for Knative

As I mentioned before, we are going to create a typical Spring Boot REST-based application that connects to a Mongo database. The database is deployed on Kubernetes. Our model class uses the person collection in MongoDB. Let’s take a look at it.

@Document(collection = "person")
@Getter
@Setter
@AllArgsConstructor
@NoArgsConstructor
public class Person {

   @Id
   private String id;
   private String firstName;
   private String lastName;
   private int age;
   private Gender gender;
}

We use Spring Data MongoDB to integrate our application with the database. In order to simplify this integration we take advantage of its “repositories” feature.

public interface PersonRepository extends CrudRepository<Person, String> {
   Set<Person> findByFirstNameAndLastName(String firstName, String lastName);
   Set<Person> findByAge(int age);
   Set<Person> findByAgeGreaterThan(int age);
}

Our application exposes several REST endpoints for adding, searching and updating data. Here’s the controller class implementation.

@RestController
@RequestMapping("/persons")
public class PersonController {

   private PersonRepository repository;
   private PersonService service;

   PersonController(PersonRepository repository, PersonService service) {
      this.repository = repository;
      this.service = service;
   }

   @PostMapping
   public Person add(@RequestBody Person person) {
      return repository.save(person);
   }

   @PutMapping
   public Person update(@RequestBody Person person) {
      return repository.save(person);
   }

   @DeleteMapping("/{id}")
   public void delete(@PathVariable("id") String id) {
      repository.deleteById(id);
   }

   @GetMapping
   public Iterable<Person> findAll() {
      return repository.findAll();
   }

   @GetMapping("/{id}")
   public Optional<Person> findById(@PathVariable("id") String id) {
      return repository.findById(id);
   }

   @GetMapping("/first-name/{firstName}/last-name/{lastName}")
   public Set<Person> findByFirstNameAndLastName(@PathVariable("firstName") String firstName,
			@PathVariable("lastName") String lastName) {
      return repository.findByFirstNameAndLastName(firstName, lastName);
   }

   @GetMapping("/age-greater-than/{age}")
   public Set<Person> findByAgeGreaterThan(@PathVariable("age") int age) {
      return repository.findByAgeGreaterThan(age);
   }

   @GetMapping("/age/{age}")
   public Set<Person> findByAge(@PathVariable("age") int age) {
      return repository.findByAge(age);
   }

}

We inject database connection settings and credentials using environment variables. Our application exposes endpoints for liveness and readiness health checks. The readiness endpoint verifies a connection with the Mongo database. Of course, we use the built-in feature from Spring Boot Actuator for that.

spring:
  application:
    name: sample-spring-boot-on-kubernetes
  data:
    mongodb:
      uri: mongodb://${MONGO_USERNAME}:${MONGO_PASSWORD}@mongodb/${MONGO_DATABASE}

management:
  endpoints:
    web:
      exposure:
        include: "*"
  endpoint.health:
      show-details: always
      group:
        readiness:
          include: mongo
      probes:
        enabled: true

Defining Knative Service in YAML

Firstly, we need to define a YAML manifest with a Knative service definition. It sets an autoscaling strategy using the Knative Pod Autoscaler (KPA). In order to do that we have to add annotation autoscaling.knative.dev/target with the number of simultaneous requests that can be processed by each instance of the application. By default, it is 100. We decrease that limit to 20 requests. Of course, we need to set liveness and readiness probes for the container. Also, we refer to the Secret and ConfigMap to inject MongoDB settings.

apiVersion: serving.knative.dev/v1
kind: Service
metadata:
  name: sample-spring-boot-on-kubernetes
spec:
  template:
    metadata:
      annotations:
        autoscaling.knative.dev/target: "20"
    spec:
      containers:
        - image: piomin/sample-spring-boot-on-kubernetes
          livenessProbe:
            httpGet:
              path: /actuator/health/liveness
          readinessProbe:
            httpGet:
              path: /actuator/health/readiness
          env:
            - name: MONGO_DATABASE
              valueFrom:
                secretKeyRef:
                  name: mongodb
                  key: database-name
            - name: MONGO_USERNAME
              valueFrom:
                secretKeyRef:
                  name: mongodb
                  key: database-user
            - name: MONGO_PASSWORD
              valueFrom:
                secretKeyRef:
                  name: mongodb
                  key: database-password

Configure Skaffold and Jib for Knative deployment

We will use Skaffold to automate the deployment of our application on Knative. Skaffold is a command-line tool that allows running the application on Kubernetes using a single command. You may read more about it in the article Local Java Development on Kubernetes. It may be easily integrated with the Jib Maven plugin. We just need to set jib as a default option in the build section of the Skaffold configuration. We can also define a list of YAML scripts executed during the deploy phase. The skaffold.yaml file should be placed in the project root directory. Here’s a current Skaffold configuration. As you see, it runs the script with the Knative Service definition.

apiVersion: skaffold/v2beta5
kind: Config
metadata:
  name: sample-spring-boot-on-kubernetes
build:
  artifacts:
    - image: piomin/sample-spring-boot-on-kubernetes
      jib:
        args:
          - -Pjib
  tagPolicy:
    gitCommit: {}
deploy:
  kubectl:
    manifests:
      - k8s/mongodb-deployment.yaml
      - k8s/knative-service.yaml

Skaffold activates the jib profile during the build. Within this profile, we will place a jib-maven-plugin. Jib is useful for building images in dockerless mode.

<profile>
   <id>jib</id>
   <activation>
      <activeByDefault>false</activeByDefault>
   </activation>
   <build>
      <plugins>
         <plugin>
            <groupId>com.google.cloud.tools</groupId>
            <artifactId>jib-maven-plugin</artifactId>
            <version>2.8.0</version>
         </plugin>
      </plugins>
   </build>
</profile>

Finally, all we need to do is to run the following command. It builds our application, creates and pushes a Docker image, and run it on Knative using knative-service.yaml.

$ skaffold run

Verify Spring Boot deployment on Knative

Now, we can verify our deployment on Knative. To do that let’s execute the command kn service list as shown below. We have a single Knative Service with the name sample-spring-boot-on-kubernetes.

Then, let’s imagine we deploy three versions (revisions) of our application. To do that let’s just provide some changes in the source and redeploy our service using skaffold run. It creates new revisions of our Knative Service. However, the whole traffic is forwarded to the latest revision (with -vlskg suffix).

With Knative we can easily split traffic between multiple revisions of the single service. To do that we need to add a traffic section in the Knative Service YAML configuration. We define a percent of the whole traffic per a single revision as shown below.

apiVersion: serving.knative.dev/v1
kind: Service
metadata:
  name: sample-spring-boot-on-kubernetes
  spec:
    template:
      ...
    traffic:
      - latestRevision: true
        percent: 60
        revisionName: sample-spring-boot-on-kubernetes-vlskg
      - latestRevision: false
        percent: 20
        revisionName: sample-spring-boot-on-kubernetes-t9zrd
      - latestRevision: false
        percent: 20
        revisionName: sample-spring-boot-on-kubernetes-9xhbw

Let’s take a look at the graphical representation of our current architecture. 60% of traffic is forwarded to the latest revision, while both previous revisions receive 20% of traffic.

Autoscaling and scale to zero

By default, Knative supports autoscaling. We may choose between two types of targets: concurrency and requests-per-second (RPS). The default target is concurrency. As you probably remember, I have overridden this default value to 20 with the autoscaling.knative.dev/target annotation. So, our goal now is to verify autoscaling. To do that we need to send many simultaneous requests to the application. Of course, the incoming traffic is distributed across three different revisions of Knative Service.

Fortunately, we may easily simulate a large traffic with the siege tool. We will call the GET /persons endpoint that returns all available persons. We are sending 150 concurrent requests with the command visible below.

$ siege http://sample-spring-boot-on-kubernetes-pminkows-serverless.apps.cluster-7260.7260.sandbox1734.opentlc.com/persons \
   -i -v -r 1000  -c 150 --no-parser

Under the hood, Knative still creates a Deployment and scales down or scales up the number of running pods. So, if you have three revisions of a single Service, there are three different deployments created. Finally, I have 10 running pods for the latest deployment that receives 60% of traffic. There are also 3 and 2 running pods for the previous revisions.

What will happen if there is no traffic coming to the service? Knative will scale down the number of running pods for all the deployments to zero.

Conclusion

In this article, you learned how to deploy the Spring Boot application as a Knative service using Skaffold and Jib. I explained with the examples how to create a new revision of the Service, and distribute traffic across those revisions. We also test the scenario with autoscaling based on concurrent requests and scale to zero in case of no incoming traffic. You may expect more articles about Knative soon! Not only with Spring Boot

The post Spring Boot on Knative appeared first on Piotr's TechBlog.