How Apache Flink is able to recover from failures and resume processing without data loss or inconsistency?
The central part of Apache Flink's fault tolerance mechanism is indeed based on creating consistent snapshots of the distributed data stream and operator state
Couple of key factors that support Flink’s fault tolerant mechanism.
Flink periodically takes consistent snapshots of the entire data stream and the state of all operators. These snapshots serve as checkpoints that the system can revert to in the event of a failure.
These snapshots capture the state of the system at a specific point in time, ensuring that all operators and data sources are consistent with each other.
The snapshot which Flink takes is light in weight and async in nature,thereby not blocking ongoing data processing while taking snapshot.
Because of above nature Flink is able to minimize the latency and have impact on throughput.
How does Flink ensure that snapshots that has been taken is consistent across distributed Systems?
The snapshot mechanism in Flink is inspired by the Chandy-Lamport algorithm, which is a well-known algorithm for distributed snapshots in distributed systems
The algorithm ensures that the snapshot is consistent across distributed components by carefully controlling the flow of messages and recording the state of each component.
Flink has adapted the Chandy-Lamport algorithm to suit its data stream processing model. This includes handling the continuous flow of data and the need for high throughput and low-latency processing.
Flink's snapshotting mechanism works in conjunction with its event-driven processing, allowing it to capture the state at precise moments without interrupting the data flow.
How fault tolerant and recovery happens incase of Flink?
In case of a failure, Flink can restore the state of the system using the most recent consistent snapshot. This allows the system to resume processing from the last checkpoint with minimal data loss.
Flink’s fault tolerance mechanism ensures that the data stream processing is both reliable and resilient, even in the face of failures.
From above points it can be concluded that
Flink’s fault tolerance mechanism is a robust solution that leverages consistent snapshots, asynchronous processing, and adaptations of classical distributed systems algorithms to ensure reliable and efficient stream processing.