 component is running in Voidbox Client, and other components are in the cluster. Users can see much more detailed logs about the application’s status. When Voidbox Client quits, the application in cluster will exit too. So it’s more convenient for debugging.
As shown in figure 3, Voidbox Master and Voidbox Driver are both running in the cluster. Voidbox Driver is responsible for controlling the logic and Voidbox Master takes care of application resource management.
As shown in figure 4, Voidbox Master is running in the cluster, and Voidbox Driver is running in Voidbox Client. Users can submit Voidbox application in IDE for debugging.
After receiving Voidbox application, Resource Manager will allocate resources for Voidbox Master, then launch it.
Voidbox Master starts Voidbox Driver, the latter will decompose Voidbox application into several Docker jobs(a job contains one or more Docker tasks). Voidbox Driver calls Voidbox Master interface to launch the Docker tasks in compute nodes.
Voidbox Master requests resources from Resource Manager, and Resource Manager allocates some YARN containers according to the YARN cluster status. Voidbox Master launches Voidbox Proxy in YARN container, and the latter is responsible for communication with Docker engine to start the Docker container.
User’s Docker task is running in Docker container, and the log output to a local file. User can see real-time application logs through YARN Web Portal.
After all Docker tasks are done, the logs will be aggregate to HDFS, so user still can get the application logs by history server.
YARN acts as a uniform resource manager in the cluster, and is responsible for resource management on all machines. Docker as a container engine also has the function of resource management. So how to integrate their resource management function is particularly important.
In YARN, the user task can only run in the YARN container, while Docker container can only be handled by Docker engine. This case would get out of the management of YARN and damage the unified management and scheduling principle of YARN, which could produce resource leaks risk issue. In order to enable YARN to manage and schedule Docker container, we need to build a proxy layer between YARN and Docker engine. This is why Docker Proxy is introduced. Through Voidbox Proxy, YARN can manage the container lifecycle including start, stop, etc.
In order to understand Voidbox Proxy more clearly, we take stopping Voidbox application as an example. When a user needs to kill Voidbox application, YARN will recycle all the resources of the application. At this point, YARN will send a kill signal to the related machines. The corresponding Voidbox Proxy will catch the kill signal, then stop Docker container in Docker engine to do the resource recycling. So with the help of Voidbox Proxy, it can not only stop YARN container, but also stop the Docker container to avoid resources leaks issue(This is the problem existing in open source version, see YARN-1964).
Although Docker has some stable releases, the enterprise production environment has a variety versions of operating system or kernel, so it brings unstable factors. We consider multiple levels in Voidbox fault-tolerant design to ensure Voidbox’s high availability.
If Resource Manager finds Voidbox Master crashes, it will notify NodeManager to recycle all the YARN containers belonging to this Voidbox application, then restart Voidbox Master.
If Voidbox Master finds Voidbox Proxy crashes, it will recycle Docker containers on behalf of Voidbox Proxy.
Each Voidbox application can configure the maximum retry times on failure, when the Docker container crashes, Voidbox Master will do some work according to the exit code of Docker container.
As shown in figure 5, there are four jobs in this Voidbox application, and each job can configure its requirements of CPU, Memory, Docker image, parallelism and so on. Job3 will start when job1 and job2 both complete. Job1, job2 and job3 make a stage, so user can insert their codes after this stage is done, and finally start running job4.
The shell script above will submit a task to run “echo Hello Voidbox” in a docker image named ‘centos’, and the resource requirement is 1000Mb memory, 2 cpu virtual cores.
At present we can run Docker, MapReduce, Spark and other applications in YARN cluster. There has been lots of short tasks using Voidbox within HULU.
Cooperating with Jenkins, GitLab and private Docker registry, when the application codes update, Jenkins will complete automatic test, package program, regenerate Docker image and push it to the private Docker Registry. It’s a process of development, testing and automatically release.
Test Framework is used to do some testings to detect the availability of some components. The project is implemented by Ruby/Java and has complex dependencies. So we maintain two layers of Docker image, the first layer is the system software as a base image, and the second layer is the business level. We publish a test framework Docker image and use some timing scheduling software to start Voidbox application regularly. Thanks to Voidbox, we solve the issues such as the complex dependencies and the multitasking parallelism.
Facematch(link:http://tech.hulu.com/blog/2014/05/03/face-match-system-overview/) is a video analysis application. It’s implemented by C and has lots of graphics libraries. That can be optimized by Voidbox: first of all we need to package all face match program into a Docker image, then write Voidbox application to handle the multiple videos. Voidbox solves the complex machine environment and the parallelism control problem.
Some tasks have a dependent with each other, such as it needs to load user behaviors first, then do the analysis of user behaviors. These two steps have successively dependencies. We use Voidbox container-based programming model to handle this case easily.
DockerContainerExecutor(link:https://issues.apache.org/jira/browse/YARN-1964) is released in YARN 2.6.0 and it’s alpha version. Not mature enough, and it is only an encapsulation layer above the default executor.
DockerContainerExecutor is difficult to coexist with other ContainerExecutor in one YARN cluster.
Voidbox would like to support more versions in the future besides YARN 2.6.0.
Currently, if a Voidbox Master crashes, YARN will recycle resources belonging to this Voidbox application and restart Voidbox Master to do some tasks from the very beginning. It’s not necessary to impact tasks which are already done or running. We might keep some metadatas in the State Server to reduce the cost in case of Voidbox Master on-failure.
Voidbox will support long running Voidbox Master to receive streaming tasks.
Voidbox will support long running service if Voidbox Master’s downtime doesn’t influence running task.
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