Use CRaC to accelerate the startup of Java applications - Container Compute Service

Java applications usually occupy large amounts of resources to load classes and perform JIT compiling during startups. If a CrashBackOff event occurs after the application starts up, the application needs to restart and reload data again. This can cause business interruptions in a production environment. Container Compute Service (ACS) not only allows you to accelerate the startup of Java applications based on in-place scaling but also supports Coordinated Restore at Checkpoint (CRaC). This topic describes how CRaC is used to accelerate the startup of Java applications and the use scenarios.

Background information

CRaC is an open source technology developed to accelerate the startup of Java applications. It is suitable for large applications or microservices applications. This technology saves and restores the data and states of programs at certain checkpoints to reduce the amount of time required for reloading and recreating applications.

How CRaC works

Checkpoints are introduced in CRaC to restore applications. CRaC creates a process snapshot for each JVM by persisting the current status of the JVM, including the context and memory status. The snapshot is known as a checkpoint. When the current business process exceptionally exits, it can be restored to the previous state from the snapshot. Restoring applications to checkpoints is much faster than cold start.

CRaC creates checkpoints and restores applications based on Checkpoint and Restore in Userspace (CRIU), which is suitable for Linux. CRaC is adjusted and enhanced based on CRIU and can be used with container in-place scaling to further accelerate the startup of Java applications. The following figure shows the architecture.

Benefits

Using CRaC in ACS provides the following benefits:

Simplicity: CRaC greatly simplifies the procedure for launching ACS applications.
Standardization: CRaC ensure the compatibility and extensibility of ACS applications.
Elasticity: Intelligent scheduling and resource optimization are used to ensure the stability and performance of applications with different loads.

Usage notes

JDK version limit: You need to replace the JDK with the latest version of Alibaba Dragonwell 11 or a JDK that supports CRaC.
Checkpoint setting: Make sure that your development team has deep knowledge about CRaC because they need to set checkpoints in the application code or script. For more information, see CRaC library.
- No operation is needed if the application states earlier than the specified checkpoint are reusable.
- If the application states earlier than the specified checkpoint are not reusable, you need to use callbacks to restore the state data or business logic.
To work with in-place scaling, you can configure JVM parameters for the application to maximize startup acceleration. For more information, see Configure JVM parameters to accelerate the startup of Java applications.

Scenarios

Note

To perform this operation, you must submit a ticket to enable the privileged mode for the ACS pod.

Scenario 1: Launch applications with and without CRaC

In this scenario, the Java application named Spring-Petclinic is launched without in-place scaling. This example compares the results of application startups before and after CRaC is used.

Create a file named spring-petclinic-crac.yaml based on the following content.

Show the YAML file content

apiVersion: apps/v1
kind: Deployment
metadata:
  name: spring-petclinic-crac
  namespace: default
spec:
  progressDeadlineSeconds: 600
  replicas: 1
  revisionHistoryLimit: 10
  selector:
    matchLabels:
      app: spring-petclinic-crac
  template:
    metadata:
      # annotations:  Disable in-place scaling.
      #   scaling.alibabacloud.com/enable-inplace-resource-resize: "true"
      creationTimestamp: null
      labels:
        alibabacloud.com/compute-class: general-purpose
        alibabacloud.com/compute-qos: default
        app: spring-petclinic-crac
    spec:
      containers:
      - env:
        # Enable CRaC checkpoints.
        - name: DO_CRAC_CHECKPOINT
          value: "true"
        # Specify the checkpoint path. We recommend that you specify an emptyDir if you use a container environment.
        - name: CRAC_IMAGE_DIR
          value: /home/crac
        image: registry-cn-hangzhou.ack.aliyuncs.com/ack-demo/quickstart-acs-petclinic-demo:alpha.3
        imagePullPolicy: IfNotPresent
        name: crac-container
        securityContext:
          privileged: true
        terminationMessagePath: /dev/termination-log
        terminationMessagePolicy: File
        resources:
          requests:
            cpu: "500m"          
            memory: "1Gi"        
          limits:
            cpu: "500m"          
            memory: "1Gi"
        volumeMounts:
        - mountPath: /home/crac
          name: crac-cache-volume
      restartPolicy: Always
      schedulerName: default-scheduler
      volumes:
      - emptyDir: {}
        name: crac-cache-volume

Create a file named spring-petclinic.yaml based on the following content.

apiVersion: apps/v1
kind: Deployment
metadata:
  name: spring-petclinic
  namespace: default
spec:
  progressDeadlineSeconds: 600
  replicas: 1
  revisionHistoryLimit: 10
  selector:
    matchLabels:
      app: spring-petclinic
  template:
    metadata:
      # annotations:  Disable in-place scaling.
      #   scaling.alibabacloud.com/enable-inplace-resource-resize: "true"
      creationTimestamp: null
      labels:
        alibabacloud.com/compute-class: general-purpose
        alibabacloud.com/compute-qos: default
        app: spring-petclinic
    spec:
      containers:
      - name: crac-container
        image: registry-cn-hangzhou.ack.aliyuncs.com/ack-demo/quickstart-acs-petclinic-demo:alpha.3
        imagePullPolicy: IfNotPresent
        securityContext:
          privileged: true
        terminationMessagePath: /dev/termination-log
        terminationMessagePolicy: File
        resources:
          requests:
            cpu: "500m"          
            memory: "1Gi"        
          limits:
            cpu: "500m"          
            memory: "1Gi"
      restartPolicy: Always
      schedulerName: default-scheduler

Deploy the application.

kubectl apply -f spring-petclinic-crac.yaml && kubectl apply -f spring-petclinic.yaml

Verify the startup speed.

View the status of the Deployments.

kubectl get pod | grep spring-petclinic

Expected results:

spring-petclinic-64cb7xxxxx-xxxxx        1/1     Running   0   110m
spring-petclinic-crac-574cdxxxxx-xxxxx   1/1     Running   0   47m

View the startup logs of the Deployments.

kubectl logs spring-petclinic-64cb7xxxxx-xxxxx --tail=5 && \
echo -e "\033[31m↑↑↑ No crac ↑↑↑\033[0m-------------------\033[32m↓↓↓ With crac ↓↓↓\033[0m" && \
kubectl logs spring-petclinic-crac-574cdxxxxx-xxxxx --tail=5

Expected results:

2025-01-21 06:50:34.521  INFO 9 --- [           main] o.s.b.a.e.web.EndpointLinksResolver      : Exposing 13 endpoint(s) beneath base path '/actuator'
2025-01-21 06:50:35.035  INFO 9 --- [           main] o.s.b.w.embedded.tomcat.TomcatWebServer  : Tomcat started on port(s): 8080 (http) with context path ''
2025-01-21 06:50:35.036  INFO 9 --- [           main] DeferredRepositoryInitializationListener : Triggering deferred initialization of Spring Data repositories…
2025-01-21 06:50:37.022  INFO 9 --- [           main] DeferredRepositoryInitializationListener : Spring Data repositories initialized!
2025-01-21 06:50:37.098  INFO 9 --- [           main] o.s.s.petclinic.PetClinicApplication     : Started PetClinicApplication in 26.587 seconds (JVM running for 28.57)
↑↑↑ No crac ↑↑↑-------------------↓↓↓ With crac ↓↓↓
2025-01-21 06:50:38.312  INFO 109 --- [           main] o.s.b.a.e.web.EndpointLinksResolver      : Exposing 13 endpoint(s) beneath base path '/actuator'
2025-01-21 06:50:38.628  INFO 109 --- [           main] o.s.b.w.embedded.tomcat.TomcatWebServer  : Tomcat started on port(s): 8080 (http) with context path ''
2025-01-21 06:50:38.629  INFO 109 --- [           main] DeferredRepositoryInitializationListener : Triggering deferred initialization of Spring Data repositories…
2025-01-21 06:50:40.700  INFO 109 --- [           main] DeferredRepositoryInitializationListener : Spring Data repositories initialized!
2025-01-21 06:50:40.792  INFO 109 --- [           main] o.s.s.petclinic.PetClinicApplication     : Started PetClinicApplication in 27.941 seconds (JVM running for 31.305)

Create a checkpoint for the Deployment that uses CRaC.
```
kubectl exec -it spring-petclinic-crac-574cdxxxxx-xxxxx  -- sh -c './checkpoint.sh'
```
Note
The operation for creating checkpoints is listed separately only for demonstration. In the actual production environment, you need to automate checkpoint creation for your application pods.

Simulate an exceptional exit.

kubectl exec -it spring-petclinic-64cb7xxxxx-xxxxx -- sh -c 'pid=`ps -ef | pgrep java` && kill -9 $pid' 
kubectl exec -it spring-petclinic-crac-574cdxxxxx-xxxxx -- sh -c 'pid=`ps -ef | pgrep java` && kill -9 $pid'

View the logs of the Deployments.

kubectl logs spring-petclinic-64cb7xxxxx-xxxxx --tail=5 && \
echo -e "\033[31m↑↑↑ No crac ↑↑↑\033[0m-------------------\033[32m↓↓↓ With crac ↓↓↓\033[0m" && \
kubectl exec -it  spring-petclinic-crac-574cdxxxxx-xxxxx -- sh -c 'cat /home/app/app_start.log'

Note

No log is generated for the application that is restored from the checkpoint. Therefore, a built-in function is provided in the container image to calculate the restore time. In this example, the log that stores the output of the function is printed.

Expected results:

2025-01-21 02:32:36.254  INFO 9 --- [           main] o.s.b.a.e.web.EndpointLinksResolver      : Exposing 13 endpoint(s) beneath base path '/actuator'
2025-01-21 02:32:36.821  INFO 9 --- [           main] o.s.b.w.embedded.tomcat.TomcatWebServer  : Tomcat started on port(s): 8080 (http) with context path ''
2025-01-21 02:32:36.822  INFO 9 --- [           main] DeferredRepositoryInitializationListener : Triggering deferred initialization of Spring Data repositories…
2025-01-21 02:32:38.858  INFO 9 --- [           main] DeferredRepositoryInitializationListener : Spring Data repositories initialized!
2025-01-21 02:32:38.950  INFO 9 --- [           main] o.s.s.petclinic.PetClinicApplication     : Started PetClinicApplication in 26.558 seconds (JVM running for 28.644)
↑↑↑ No crac ↑↑↑-------------------↓↓↓ With crac ↓↓↓
Checking application start at Thu Jan 21 02:32:54 UTC 2025
Start PetClinic Cost : 417 ms
Application started successfully at Thu Jan 21 02:32:54 UTC 2025
===========================================

The first-time startups of the two Deployments cost almost the same amount of time. However, the restart of the Deployment that uses CRaC is much faster than the other Deployment.

Scenario 2: Use CRaC to launch application with and without in-place scaling

In this scenario, CRaC is used to accelerate the startup of a Java application. This example compares the results of application startups before and after in-place scaling is used.

Create a file named spring-petclinic-crac-resize.yaml based on the following content.

Show the YAML file content

apiVersion: apps/v1
kind: Deployment
metadata:
  name: spring-petclinic-crac-resize
  namespace: default
spec:
  progressDeadlineSeconds: 600
  replicas: 1
  revisionHistoryLimit: 10
  selector:
    matchLabels:
      app: spring-petclinic-crac-resize
  template:
    metadata:
      annotations:  
        scaling.alibabacloud.com/enable-inplace-resource-resize: "true" # Enable in-place scaling.
        alibabacloud.com/startup-cpu-burst-factor: '2' #Set the CPU Burst factor to 2.
        alibabacloud.com/startup-cpu-burst-duration-seconds: "30" #If you do not specify this annotation, the pod is scaled down 30 seconds after the pod is ready.
      creationTimestamp: null
      labels:
        alibabacloud.com/compute-class: general-purpose
        alibabacloud.com/compute-qos: default
        app: spring-petclinic-crac-resize
    spec:
      containers:
      - env:
        # Enable CRaC checkpoints.
        - name: DO_CRAC_CHECKPOINT
          value: "true"
        # Specify the checkpoint path. We recommend that you specify an emptyDir if you use a container environment.
        - name: CRAC_IMAGE_DIR
          value: /home/crac
        image: registry-cn-hangzhou.ack.aliyuncs.com/ack-demo/quickstart-acs-petclinic-demo:alpha.3
        imagePullPolicy: IfNotPresent
        name: crac-container
        securityContext:
          privileged: true
        terminationMessagePath: /dev/termination-log
        terminationMessagePolicy: File
        resources:
          requests:
            cpu: "500m"          
            memory: "1Gi"        
          limits:
            cpu: "500m"          
            memory: "1Gi"
        volumeMounts:
        - mountPath: /home/crac
          name: crac-cache-volume
        readinessProbe:
            tcpSocket:
              port: 8080
            initialDelaySeconds: 20
            periodSeconds: 10
      restartPolicy: Always
      schedulerName: default-scheduler
      volumes:
      - emptyDir: {}
        name: crac-cache-volume

The spring-petclinic-crac.yaml file in Scenario 1 is used in this example for comparison.

Deploy the application.

kubectl apply -f spring-petclinic-crac-resize.yaml

Verify the startup speed.

View the status of the Deployments.

kubectl get pod | grep spring-petclinic-crac

Expected results:

spring-petclinic-crac-574cdxxxxx-xxxxx          1/1     Running   0          29m
spring-petclinic-crac-resize-6474cxxxxx-xxxxx   1/1     Running   0          32m

View the startup logs of the Deployments.

kubectl logs spring-petclinic-crac-574cdxxxxx-xxxxx --tail=5 && \
echo -e "\033[31m↑↑↑ No resize ↑↑↑\033[0m-------------------\033[32m↓↓↓ With resize ↓↓↓\033[0m" && \
kubectl logs spring-petclinic-crac-resize-6474cxxxxx-xxxxx --tail=5

Expected results:

2025-01-23 05:50:16.564  INFO 109 --- [           main] o.s.b.a.e.web.EndpointLinksResolver      : Exposing 13 endpoint(s) beneath base path '/actuator'
2025-01-23 05:50:17.346  INFO 109 --- [           main] o.s.b.w.embedded.tomcat.TomcatWebServer  : Tomcat started on port(s): 8080 (http) with context path ''
2025-01-23 05:50:17.347  INFO 109 --- [           main] DeferredRepositoryInitializationListener : Triggering deferred initialization of Spring Data repositories…
2025-01-23 05:50:19.848  INFO 109 --- [           main] DeferredRepositoryInitializationListener : Spring Data repositories initialized!
2025-01-23 05:50:19.936  INFO 109 --- [           main] o.s.s.petclinic.PetClinicApplication     : Started PetClinicApplication in 38.912 seconds (JVM running for 43.614)
↑↑↑ No resize ↑↑↑-------------------↓↓↓ With resize ↓↓↓
2025-01-23 05:48:28.334  INFO 108 --- [           main] o.s.b.a.e.web.EndpointLinksResolver      : Exposing 13 endpoint(s) beneath base path '/actuator'
2025-01-23 05:48:28.793  INFO 108 --- [           main] o.s.b.w.embedded.tomcat.TomcatWebServer  : Tomcat started on port(s): 8080 (http) with context path ''
2025-01-23 05:48:28.794  INFO 108 --- [           main] DeferredRepositoryInitializationListener : Triggering deferred initialization of Spring Data repositories…
2025-01-23 05:48:29.940  INFO 108 --- [           main] DeferredRepositoryInitializationListener : Spring Data repositories initialized!
2025-01-23 05:48:29.981  INFO 108 --- [           main] o.s.s.petclinic.PetClinicApplication     : Started PetClinicApplication in 19.449 seconds (JVM running for 22.339)

The output indicates that in-place scaling greatly reduces the first-time startup time of the application.

Create a checkpoint for the Deployments.

kubectl exec -it spring-petclinic-crac-574cdxxxxx-xxxxx  -- sh -c './checkpoint.sh' 
kubectl exec -it spring-petclinic-crac-resize-6474cxxxxx-xxxxx  -- sh -c './checkpoint.sh'

Simulate an exceptional exit.

kubectl exec -it spring-petclinic-crac-574cdxxxxx-xxxxx -- sh -c 'pid=`ps -ef | pgrep java` && kill -9 $pid' 
kubectl exec -it spring-petclinic-crac-resize-6474cxxxxx-xxxxx -- sh -c 'pid=`ps -ef | pgrep java` && kill -9 $pid'

View the logs of the Deployments.

kubectl exec -it spring-petclinic-crac-574cdxxxxx-xxxxx -- sh -c 'cat /home/app/app_start.log' && \
echo -e "\033[31m↑↑↑ No crac ↑↑↑\033[0m-------------------\033[32m↓↓↓ With crac ↓↓↓\033[0m" && \
kubectl exec -it spring-petclinic-crac-resize-6474cxxxxx-xxxxx -- sh -c 'cat /home/app/app_start.log'

Expected results:

Checking application start at Thu Jan 23 05:56:34 UTC 2025
Start PetClinic Cost : 440 ms
Application started successfully at Thu Jan 23 05:56:34 UTC 2025
===========================================
↑↑↑ No resize ↑↑↑-------------------↓↓↓ With resize ↓↓↓
Checking application start at Thu Jan 23 05:56:45 UTC 2025
Start PetClinic Cost : 349 ms
Application started successfully at Thu Jan 23 05:56:46 UTC 2025
===========================================

The restart of the Deployment that has in-place scaling enabled is much faster than the other Deployment.