org.apache.flink.streaming.api.CheckpointingMode Maven / Gradle / Ivy
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*
* http://www.apache.org/licenses/LICENSE-2.0
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package org.apache.flink.streaming.api;
import org.apache.flink.annotation.Public;
/**
* 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" ({@link #EXACTLY_ONCE}), or whether the checkpoints are drawn
* in a simpler fashion that typically encounters some duplicates upon recovery
* ({@link #AT_LEAST_ONCE})
*/
@Public
public enum CheckpointingMode {
/**
* 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.
*/
EXACTLY_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.
*/
AT_LEAST_ONCE
}