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https://issues.apache.org/jira/browse/BEAM-13812?focusedWorklogId=727940&page=com.atlassian.jira.plugin.system.issuetabpanels:worklog-tabpanel#worklog-727940
]
ASF GitHub Bot logged work on BEAM-13812:
-----------------------------------------
Author: ASF GitHub Bot
Created on: 16/Feb/22 00:10
Start Date: 16/Feb/22 00:10
Worklog Time Spent: 10m
Work Description: VictorPlusC commented on a change in pull request
#16741:
URL: https://github.com/apache/beam/pull/16741#discussion_r807423696
##########
File path:
sdks/python/apache_beam/runners/interactive/dataproc/dataproc_cluster_manager.py
##########
@@ -124,10 +131,23 @@ def create_flink_cluster(self) -> None:
'config': {
'software_config': {
'optional_components': ['DOCKER', 'FLINK']
+ },
+ 'gce_cluster_config': {
+ 'metadata': {
+ 'flink-start-yarn-session': 'true'
+ },
+ 'service_account_scopes': [
+ 'https://www.googleapis.com/auth/cloud-platform'
+ ]
+ },
+ 'endpoint_config': {
+ 'enable_http_port_access': True
}
}
}
self.create_cluster(cluster)
+ self.master_url = self.get_master_url(
+ self.master_url_identifier, default=False)
Review comment:
Quite a few things have changed with the PR, so here's the updated
answer:
The master_url and identifier (which we're calling 'cluster_metadata') are
used for the following use cases:
- During pipeline runtime, when we use the FlinkRunner, we'll detect some
information (cluster default name from the Interactive Environment, project and
region from the pipeline options). We can use this information to build the
identifier and check if it corresponds to any existing master_urls via the
bidirectional mapping. If it does, then we can simply skip the cluster creation
stage and begin using the cluster.
- We now instantiate instances of DataprocClusterManager with the
identifier, and if the cluster_metadata corresponds to some existing
master_url, we can assign the value to the DataprocClusterManager immediately
during instantiation without having to search for it again. We have this
functionality in place since we want to have each pipeline mapped to a separate
instance of DataprocClusterManager.
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Issue Time Tracking
-------------------
Worklog Id: (was: 727940)
Time Spent: 11h 40m (was: 11.5h)
> Integrate DataprocClusterManager into Interactive Environment
> -------------------------------------------------------------
>
> Key: BEAM-13812
> URL: https://issues.apache.org/jira/browse/BEAM-13812
> Project: Beam
> Issue Type: New Feature
> Components: runner-py-interactive
> Reporter: Victor Chen
> Assignee: Victor Chen
> Priority: P2
> Time Spent: 11h 40m
> Remaining Estimate: 0h
>
> * Adds code to automatically create a Dataproc cluster configured with Docker
> and Flink if a user instantiates an InteractiveRunner using
> underlying_runner='FlinkRunner'
> * By default, the created Dataproc cluster will be called
> 'interactive-beam-cluster' and will be cleaned up when the Interactive
> Environment is interrupted
> * Users will be able to toggle an automatic deletion of the cluster with the
> default name, 'interactive-beam-cluster' when the kernel is interrupted
> * Introduces the Clusters class in interactive_beam.py, which acts as a user
> interface that provides the user with the following capabilities:
> ** Changing the default name of the clusters created by Interactive Beam
> ** Cleaning up a cluster by providing a corresponding pipeline, an option to
> force the deletion is provided, in the case where more than one pipeline use
> the same cluster
> ** Getting all pipelines that use the provided master_url
> ** Receiving a description back for either the cluster corresponding to a
> specific pipeline, or the descriptions of all pipelines
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