A message is late if its timestamp is after the watermark.

A message is late if its timestamp is before the clock of the worker where it is processed.

This is a runner-specific value. It depends on the runner.

Exactly one.

As many as we set.

One or none.

You cannot do anything to recover the order of the messages.

You can recover the order of the messages with a window using event time.

You can recover the order of the messages with a window using processing time.

To mark the end of a window and trigger window computations.

To mark the beginning of a window and start window computations.

To indicate the progress of event time and handle late data.

By automatically balancing the data distribution across workers.

By discarding skewed data to maintain processing efficiency.

By using key grouping and partitioning to evenly distribute data.

By dropping late data to maintain processing efficiency.

By reordering late data based on event time.

By using windowing to process late data.

To define the time boundaries for data processing.

To filter out irrelevant data from the pipeline.

To control the parallelism of data processing.

By discarding out-of-order data to prevent processing errors.

By buffering out-of-order data until it can be correctly ordered.

By using timestamps to reorder out-of-order data.

To define when the pipeline should be restarted.

To measure the completeness of data.

To filter out irrelevant data.

To control the parallelism of data processing.

The pipeline will discard all out-of-order data.

The trigger will fire based on the watermark.

The data will be processed immediately without waiting for a trigger.

The window will close without producing any results.

By setting a custom watermark.

By using triggers based on event time, processing time, or data.

By filtering the input data before processing.

By adjusting the window size.

Previous results are discarded after each trigger.

The same calculation is repeated, including all previous and new messages.

Only new data is processed, ignoring previous messages.

The window automatically closes after the first trigger.

It increases the accuracy of calculations.

It ensures that all data is processed multiple times.

It reduces the storage and processing burden by discarding old messages after each trigger.

It allows for the combination of multiple triggers.

To handle late-arriving data more effectively.

To immediately discard incomplete data.

To prioritize certain windows over others.

To avoid using watermarks in data processing.

Accumulate mode.

Discard mode.

Aggregate mode.

Windowed mode.

Event time triggers ensure results are emitted based on the arrival time of messages, while processing time triggers ensure results are emitted based on the system clock. Combining them can result in more frequent triggers or delayed results depending on the configuration.

Event time triggers override processing time triggers, resulting in output only based on event time. Combining them is not supported.

Processing time triggers override event time triggers, ensuring that results are emitted only based on the system clock.

Combining event time and processing time triggers will automatically discard out-of-order messages.

It ensures that the pipeline handles high throughput efficiently by emitting results frequently, but it may increase the load on the system due to frequent trigger evaluations.

It decreases the pipeline's efficiency by causing the pipeline to wait for the watermark to pass before emitting results.

It reduces the memory usage of the pipeline by keeping fewer messages in the window.

It automatically combines results from multiple windows to reduce the number of triggers.

Composite triggers allow late data to be included in subsequent trigger evaluations, based on the defined combination of event time, processing time, and data-driven triggers.

Late data is discarded by composite triggers as they do not support handling late data.

Composite triggers automatically extend the window size to accommodate late data.

Composite triggers stop processing once the watermark has passed, regardless of late data.

When the calculation involves associative and commutative operations that can be safely updated incrementally.

When the window is very narrow, and frequent recalculation of results is required.

When the data needs to be retained for post-processing or further analysis beyond the current trigger.

When minimal latency is critical, and frequent results are needed without concern for resource usage.

The composite trigger will emit results based on the count and event time conditions, but the final results may be delayed due to high latency.

The event time-based trigger will override the count-based trigger, resulting in output only based on the event time.

The count-based trigger will override the event time-based trigger, resulting in output only based on the count of messages.

The composite trigger will automatically adjust the latency based on the system's performance.