class_docstr: Configuration that captures all checkpointing related settings.
  
  :data:`DEFAULT_MODE`:
  
  The default checkpoint mode: exactly once.
  
  :data:`DEFAULT_TIMEOUT`:
  
  The default timeout of a checkpoint attempt: 10 minutes.
  
  :data:`DEFAULT_MIN_PAUSE_BETWEEN_CHECKPOINTS`:
  
  The default minimum pause to be made between checkpoints: none.
  
  :data:`DEFAULT_MAX_CONCURRENT_CHECKPOINTS`:
  
  The default limit of concurrently happening checkpoints: one.
class_docstr_tok: ["Configuration","that","captures","all","checkpointing","related","settings",".",":","data",":","`","DEFAULT_MODE","`",":","The","default","checkpoint","mode",":","exactly","once",".",":","data",":","`","DEFAULT_TIMEOUT","`",":","The","default","timeout","of","a","checkpoint","attempt",":","10","minutes",".",":","data",":","`","DEFAULT_MIN_PAUSE_BETWEEN_CHECKPOINTS","`",":","The","default","minimum","pause","to","be","made","between","checkpoints",":","none",".",":","data",":","`","DEFAULT_MAX_CONCURRENT_CHECKPOINTS","`",":","The","default","limit","of","concurrently","happening","checkpoints",":","one","."]
docstr: Checkpoint storage defines how stat backends checkpoint their state for fault
  tolerance in streaming applications. Various implementations store their checkpoints
  in different fashions and have different requirements and availability guarantees.
  
  For example, `JobManagerCheckpointStorage` stores checkpoints in the memory of the
  JobManager. It is lightweight and without additional dependencies but is not highly
  available and only supports small state sizes. This checkpoint storage policy is convenient
  for local testing and development.
  
  The `FileSystemCheckpointStorage` stores checkpoints in a filesystem. For systems like
  HDFS, NFS Drivs, S3, and GCS, this storage policy supports large state size, in the
  magnitude of many terabytes while providing a highly available foundation for stateful
  applications. This checkpoint storage policy is recommended for most production deployments.
  
  .. note:: Deprecated since version 1.19: This method is deprecated and will be removed in
            future FLINK major version. Use `stream_execution_environment.configure` method
            instead to set the checkpoint storage.
code: def set_checkpoint_storage(
      self, storage: CheckpointStorage
  ) -> "CheckpointConfig":
      """
      Checkpoint storage defines how stat backends checkpoint their state for fault
      tolerance in streaming applications. Various implementations store their checkpoints
      in different fashions and have different requirements and availability guarantees.
  
      For example, `JobManagerCheckpointStorage` stores checkpoints in the memory of the
      JobManager. It is lightweight and without additional dependencies but is not highly
      available and only supports small state sizes. This checkpoint storage policy is convenient
      for local testing and development.
  
      The `FileSystemCheckpointStorage` stores checkpoints in a filesystem. For systems like
      HDFS, NFS Drivs, S3, and GCS, this storage policy supports large state size, in the
      magnitude of many terabytes while providing a highly available foundation for stateful
      applications. This checkpoint storage policy is recommended for most production deployments.
  
      .. note:: Deprecated since version 1.19: This method is deprecated and will be removed in
                future FLINK major version. Use `stream_execution_environment.configure` method
                instead to set the checkpoint storage.
      """
      warnings.warn(
          "Deprecated since version 1.19: This method is deprecated and will be removed"
          " in future FLINK major version. Use `stream_execution_environment.configure`"
          " method instead to set the checkpoint storage.",
          DeprecationWarning,
      )
      self._j_checkpoint_config.setCheckpointStorage(
          storage._j_checkpoint_storage
      )
      return self
  
imports: import warnings
  from enum import Enum
  from typing import Optional
  from pyflink.common import Duration
  from pyflink.datastream.checkpoint_storage import CheckpointStorage, _from_j_checkpoint_storage
  from pyflink.datastream.checkpointing_mode import CheckpointingMode
  from pyflink.java_gateway import get_gateway
label_desc: Use image node_id 28 for calling the CheckpointConfig obj's underlying member method code with example usage: obj.set_checkpoint_storage(storage) and returns: self