- #Mac os docker compose cpu memory update#
- #Mac os docker compose cpu memory code#
- #Mac os docker compose cpu memory zip#
- #Mac os docker compose cpu memory download#
The new one is a hadoop cluster with master and n slaves, and one spark master and jupyter-lab is added on. Then a new cluster contains hadoop service is needed. If using yarn scheduler, the hadoop cluster is necessary.
You could create spark session by using the yarn scheduler of hadoop.
#Mac os docker compose cpu memory code#
(2) The sample data and code is able to use after unzipping the attached file and uploading jupyter lab.
#Mac os docker compose cpu memory zip#
(1) Using the sample code in this zip file, you can communicate with the livy server through REST API. Then the adress of livy web ui is localhost:8998. (3) The REST communication port of livy server is 8998, and have same of web ui. (2) If the result of docker exec -it master bash -c “livy-server status” command, however, is “livy server is not running”, restart the livy with. (1) Livy is executed when the container created, no execution command is needed to run livy. Livy is a REST API server made for ease spark-job control at outside of the cluster. (1) Using the sample code in this zip file, you can test the data pre-processing and clustering with spark. (3) When created, spark session would be seen and managed in the spark web ui. Token is changing value every time you run. Copy and Paste the address started with 127.0.0.1 to your web browser. (2) Copy the URL in the console and access the jupyter lab service. (1) Jupyter lab is also executed when the container created, no execution command is needed to run jupyter lab. (2) If no errors found in the process above, you can access the spark web ui. (1) Spark master and workers are run when the container created, you could save the time to enter spark execution command. So if you want to change the number of hadoop slaves, you’re recommended to run. compose-up.sh command may mis-correct the generated docker-compose.yml file constructing another containers without compose-down command. compose-down.sh command if you want to destroy all containers. The host paths you’ll use are be made by yourself, preparing when they may not be automatically generated on the host.
Host path must be set with compatibility of your test environment, through some parameters of. This volumes are used for keep the data when docker containers are broken. (5) There are volumes mounted on host path in each container for logs or data backup. Each container uses host resource flexibly. (4) Cause no resource limit exists when creating containers, there’s no problem when the sum of each worker’s core and memory may be exceed the host machine’s. After running spark in background process, livy server also be executed in background. (3) If you create spark containers, initialization and execution of spark will be done automatically. compose-up.sh 3.1.1 3 4 8 /Users/Shared/workspace/docker-ws/spark-notebook /tmp/spark_logs
#Mac os docker compose cpu memory update#
The version of image maybe changed up, with update of the open-source version. (7) If you have any problems, questions or bugs when using codes, just contact me.
#Mac os docker compose cpu memory download#
You can download them with docker pull command. (6) The docker images are in my docker hub. docker-compose.yml file also be generated automatically in same path, and deleted when the docker-compose is down.
This shell files get some parameter from user, and then constructs cluster and executes some files needed. (5) This practice uses shell script files in folder named by docker-script, where are sub-folder of each folder in github. Sub-folders related on: jupyter-lab, spark If you’re used to Dockerfile, you can revise the image with Dockerfile in github. (4) All files related to this practice are in my github. (3) The reason of using jdk 1.8.0 is because of the compatibility with livy, The latest version is used in other things. (2) Docker images are based on RedHat linux (CentOS 8) with bash shell, which is similar to real-world servers. Also, you need linux shell environment with docker and docker-compose installed. (1) You’re recommended to use machine with 16GB memory or above. Get experience of pyspark session and spark-livy service.Construct Spark cluster composed of 1 master, n of slaves, and jupyter-lab using docker-compose.If it's helpful, here's my docker version and docker compose version. Is there any other way to enforce this limit?. It is a 4cpu virtual machine hosted with Oracle, running Ubuntu. No matter what limit I set, the container shows up to 400% cpu usage in docker stats. As you can see in the commented out portions, i'v also tried using v3. I'm fairly new to docker and I'm trying to run some minecraft containers, however they don't seem to be honoring the cpu limits I'm setting.
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