google-cloud-dataproc

问题 I'm following this tutorial and I'm stuck when I want to create a new Jupyter Notebook (Python 3). The cluster is created using this command: gcloud beta dataproc clusters create ${CLUSTER_NAME} \ --region=${REGION} \ --image-version=1.4 \ --master-machine-type=n1-standard-4 \ --worker-machine-type=n1-standard-4 \ --bucket=${BUCKET_NAME} \ --optional-components=ANACONDA,JUPYTER \ --enable-component-gateway When I accessing the JupyterLab and try to create a new notebook I can see: and then

来源： https://stackoverflow.com/questions/58832044/dataproc-import-python-module-stored-in-google-cloud-storage-gcs-bucket

来源： https://stackoverflow.com/questions/58832044/dataproc-import-python-module-stored-in-google-cloud-storage-gcs-bucket

来源： https://stackoverflow.com/questions/55907767/spark-set-to-read-from-earliest-offset-throws-error-on-attempting-to-consumer

来源： https://stackoverflow.com/questions/55907767/spark-set-to-read-from-earliest-offset-throws-error-on-attempting-to-consumer

来源： https://stackoverflow.com/questions/55907767/spark-set-to-read-from-earliest-offset-throws-error-on-attempting-to-consumer

来源： https://stackoverflow.com/questions/63062308/problem-to-load-plugin-githubnotebookrepo-and-gcsnotebookrepo-on-zeppelin-using

来源： https://stackoverflow.com/questions/63062308/problem-to-load-plugin-githubnotebookrepo-and-gcsnotebookrepo-on-zeppelin-using

问题 Just starting to get familiar with GCP dataproc. I've noticed when I use gcloud dataproc jobs submit pyspark that jobs are submitted with spark.submit.deployMode=client . Is spark.submit.deployMode=cluster an option for us? 回答1: Yes, you can, by specifying --properties spark.submit.deployMode=cluster . Just note that driver output will be in yarn userlogs (you can access them in Stackdriver Logging from the Console). We run in client mode by default to stream driver output to you. 来源： https:/

问题 I want to run python map reduce job in Google Cloud Dataproc using hadoop streaming method. My map reduce python script, input file and job result output are located in Google Cloud Storage. I tried to run this command hadoop jar /usr/lib/hadoop-mapreduce/hadoop-streaming.jar -file gs://bucket-name/intro_to_mapreduce/mapper_prod_cat.py -mapper gs://bucket-name/intro_to_mapreduce/mapper_prod_cat.py -file gs://bucket-name/intro_to_mapreduce/reducer_prod_cat.py -reducer gs://bucket-name/intro_to