class_docstr: The checkpointing mode defines what consistency guarantees the system gives in the presence of
  failures.
  
  When checkpointing is activated, the data streams are replayed such that lost parts of the
  processing are repeated. For stateful operations and functions, the checkpointing mode defines
  whether the system draws checkpoints such that a recovery behaves as if the operators/functions
  see each record "exactly once" (:data:`CheckpointingMode.EXACTLY_ONCE`), or whether the
  checkpoints are drawn in a simpler fashion that typically encounters some duplicates upon
  recovery (:data:`CheckpointingMode.AT_LEAST_ONCE`)
  
  :data:`EXACTLY_ONCE`:
  
  Sets the checkpointing mode to "exactly once". This mode means that the system will
  checkpoint the operator and user function state in such a way that, upon recovery,
  every record will be reflected exactly once in the operator state.
  
  For example, if a user function counts the number of elements in a stream,
  this number will consistently be equal to the number of actual elements in the stream,
  regardless of failures and recovery.
  
  Note that this does not mean that each record flows through the streaming data flow
  only once. It means that upon recovery, the state of operators/functions is restored such
  that the resumed data streams pick up exactly at after the last modification to the state.
  
  Note that this mode does not guarantee exactly-once behavior in the interaction with
  external systems (only state in Flink's operators and user functions). The reason for that
  is that a certain level of "collaboration" is required between two systems to achieve
  exactly-once guarantees. However, for certain systems, connectors can be written that
  facilitate this collaboration.
  
  This mode sustains high throughput. Depending on the data flow graph and operations,
  this mode may increase the record latency, because operators need to align their input
  streams, in order to create a consistent snapshot point. The latency increase for simple
  dataflows (no repartitioning) is negligible. For simple dataflows with repartitioning, the
  average latency remains small, but the slowest records typically have an increased latency.
  
  :data:`AT_LEAST_ONCE`:
  
  Sets the checkpointing mode to "at least once". This mode means that the system will
  checkpoint the operator and user function state in a simpler way. Upon failure and recovery,
  some records may be reflected multiple times in the operator state.
  
  For example, if a user function counts the number of elements in a stream,
  this number will equal to, or larger, than the actual number of elements in the stream,
  in the presence of failure and recovery.
  
  This mode has minimal impact on latency and may be preferable in very-low latency
  scenarios, where a sustained very-low latency (such as few milliseconds) is needed,
  and where occasional duplicate messages (on recovery) do not matter.
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code: def _from_j_checkpointing_mode(
      j_checkpointing_mode,
  ) -> "CheckpointingMode":
      return CheckpointingMode[j_checkpointing_mode.name()]
  
imports: from enum import Enum
  from pyflink.java_gateway import get_gateway
label_desc: Use image node_id 1 for calling the CheckpointingMode obj's underlying member method code with example usage: obj._from_j_checkpointing_mode(j_checkpointing_mode) and returns: CheckpointingMode