Source code for apache_beam.runners.interactive.dataproc.types

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# pytype: skip-file

import uuid
from dataclasses import dataclass
from dataclasses import field
from typing import Optional
from typing import Union

from apache_beam.pipeline import Pipeline

def _generate_unique_cluster_name():
  return f'interactive-beam-{uuid.uuid4().hex}'

[docs]@dataclass class ClusterMetadata: """Metadata of a provisioned worker cluster that executes Beam pipelines. Apache Beam supports running Beam pipelines on different runners provisioned in different setups based on the runner and pipeline options associated with each pipeline. To provide similar portability features, Interactive Beam automatically extracts such ClusterMetadata information from pipeline options of a pipeline in the REPL context and provision suitable clusters to execute the pipeline. The lifecyle of the clusters is managed by Interactive Beam and the user doesn not need to interact with it. It's not recommended to build this ClusterMetadata from raw values nor use it to interact with the cluster management logic directly. Interactive Beam now supports:: 1. Runner: FlinkRunner; Setup: on Google Cloud with Flink on Dataproc. """ project_id: Optional[str] = None region: Optional[str] = 'us-central1' cluster_name: Optional[str] = field( default_factory=_generate_unique_cluster_name) # From WorkerOptions. subnetwork: Optional[str] = None num_workers: Optional[int] = None machine_type: Optional[int] = None # Derivative fields do not affect hash or comparison. master_url: Optional[str] = None dashboard: Optional[str] = None def __key(self): return ( self.project_id, self.region, self.cluster_name, self.subnetwork, self.num_workers, self.machine_type) def __hash__(self): return hash(self.__key()) def __eq__(self, other): if isinstance(other, ClusterMetadata): return self.__key() == other.__key() return False
[docs] def reset_name(self): self.cluster_name = _generate_unique_cluster_name()
ClusterIdentifier = Union[str, Pipeline, ClusterMetadata]